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Nagin, Daniel S., Bobby L. Jones, Valéria Lima Passos, and Richard E. Tremblay. "Group-based multi-trajectory modeling." Statistical Methods in Medical Research 27, no. 7 (October 17, 2016): 2015–23. http://dx.doi.org/10.1177/0962280216673085.
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Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.
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Brice, Sandrine, Aude Jabouley, Sonia Reyes, Carla Machado, Christina Rogan, Nathalie Dias-Gastellier, Hugues Chabriat, and Sophie Tezenas du Montcel. "Modeling the Cognitive Trajectory in CADASIL." Journal of Alzheimer's Disease 77, no. 1 (September 1, 2020): 291–300. http://dx.doi.org/10.3233/jad-200310.
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Background: For developing future clinical trials in Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), it seems crucial to study the long-term changes of cognition. Objective: We aimed to study the global trajectory of cognition, measured by the Mini-Mental State Examination (MMSE) and the Mattis Dementia Rating Scale (MDRS), along the course of CADASIL. Methods: Follow-up data of 185 CADASIL patients, investigated at the French National Referral center CERVCO from 2003, were considered for analysis based on strict inclusion criteria. Assuming that the MMSE and the MDRS provide imprecise measures of cognition, the trajectory of a common cognitive latent process during follow-up was delineated using a multivariate latent process mixed model. After adjustment of this model for sex and education, the sensitivities of the two scales to cognitive change were compared. Results: Analysis of the cognitive trajectory over a time frame of 60 years of age showed a decrease of performances with aging, especially after age of 50 years. This decline was not altered by sex or education but patients who graduated from high school had a higher mean cognitive level at baseline. The sensitivities of MMSE and MDRS scales were similar and the two scales suffered from a ceiling effect and curvilinearity. Conclusion: These data support that cognitive decline is not linear and mainly occurs after the age of 50 years during the course of CADASIL. They also showed that MMSE and MDRS scales are hampered by major limitations for longitudinal studies.
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Budhwani, Suman, Rahim Moineddin, Walter P. Wodchis, Camilla Zimmermann, and Doris Howell. "Longitudinal Symptom Burden Trajectories in a Population-Based Cohort of Women with Metastatic Breast Cancer: A Group-Based Trajectory Modeling Analysis." Current Oncology 28, no. 1 (February 14, 2021): 879–97. http://dx.doi.org/10.3390/curroncol28010087.
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Understanding the symptom burden trajectory for metastatic breast cancer patients can enable the provision of appropriate supportive care for symptom management. The aim of this study was to describe the longitudinal trajectories of symptom burden for metastatic breast cancer patients at the population-level. A cohort of 995 metastatic breast cancer patients with 16,146 Edmonton Symptom Assessment System (ESAS) assessments was constructed using linked population-level health administrative databases. The patient-reported ESAS total symptom distress score (TSDS) was studied over time using group-based trajectory modeling, and covariate influences on trajectory patterns were examined. Cohort patients experienced symptom burden that could be divided into six distinct trajectories. Patients experiencing a higher baseline TSDS were likely to be classified into trajectory groups with high, uncontrolled TSDS within the study follow-up period (χ2 (1, N = 995) = 136.25, p < 0.001). Compared to patients classified in the group trajectory with the highest relative TSDS (Group 6), patients classified in the lowest relative TSDS trajectory group (Group 1) were more likely to not have comorbidities (97.34% (for Groups 1–3) vs. 91.82% (for Group 6); p < 0.05), more likely to receive chemotherapy (86.52% vs. 80.50%; p < 0.05), and less likely to receive palliative care (52.81% vs. 79.25%; p < 0.0001). Receiving radiotherapy was a significant predictor of how symptom burden was experienced in all identified groups. Overall, metastatic breast cancer patients follow heterogeneous symptom burden trajectories over time, with some experiencing a higher, uncontrolled symptom burden. Understanding trajectories can assist in establishing risk-stratified care pathways for patients.
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Balasubramanian, Ishwarya, Eric Finkelstein, Rahul Malhotra, Semra Ozdemir, Chetna Malhotra, and _. _. "Healthcare Cost Trajectories in the Last 2 Years of Life Among Patients With a Solid Metastatic Cancer: A Prospective Cohort Study." Journal of the National Comprehensive Cancer Network 20, no. 9 (September 2022): 997–1004. http://dx.doi.org/10.6004/jnccn.2022.7038.
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Background: Most studies describe the “average healthcare cost trend” among patients with cancer. We aimed to delineate heterogeneous trajectories of healthcare cost during the last 2 years of life of patients with a metastatic cancer and to assess the associated sociodemographic and clinical characteristics and healthcare use. Patients and Methods: We analyzed a sample of 353 deceased patients from a cohort of 600 with a solid metastatic cancer in Singapore, and we used group-based trajectory modeling to identify trajectories of total healthcare cost during the last 2 years of life. Results: The average cost trend showed that mean monthly healthcare cost increased from SGD $3,997 during the last 2 years of life to SGD $7,516 during the last month of life (USD $1 = SGD $1.35). Group-based trajectory modeling identified 4 distinct trajectories: (1) low and steadily decreasing cost (13%); (2) steeply increasing cost in the last year of life (14%); (3) high and steadily increasing cost (57%); and (4) steeply increasing cost before the last year of life (16%). Compared with the low and steadily decreasing cost trajectory, patients with private health insurance (β [SE], 0.75 [0.37]; P=.04) and a greater preference for life extension (β [SE], −0.14 [0.07]; P=.06) were more likely to follow the high and steadily increasing cost trajectory. Patients in the low and steadily decreasing cost trajectory were most likely to have used palliative care (62%) and to die in a hospice (27%), whereas those in the steeply increasing cost before the last year of life trajectory were least likely to have used palliative care (14%) and most likely to die in a hospital (75%). Conclusions: The study quantifies healthcare cost and shows the variability in healthcare cost trajectories during the last 2 years of life. Policymakers, clinicians, patients, and families can use this information to better anticipate, budget, and manage healthcare costs.
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M’bailara, K., O. Cosnefroy, A. Desage, S. Gard, L. Zanouy, I. Minois, and C. Henry. "Group-based trajectory modeling a good model to explore sub-groups of recovery during acute bipolar mood episodes." European Psychiatry 26, S2 (March 2011): 233. http://dx.doi.org/10.1016/s0924-9338(11)71943-5.
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Group-based trajectory modeling (GBTM) is a statistical method created to explore the heterogeneity of clinical groups based on their longitudinal outcomes by identifying distinct trajectories of change. This model can be applied to assess heterogeneity in responses to treatment. This pilot study explored the relevance of the GBTM associated with the dimensional evaluation of mood (MATHYS) to define trajectory of recovery in acute bipolar mood episodes on a short period of time during a naturalistic study.MethodThe sample consisted in 118 bipolar patients and all patients were recruited during an acute phase: 56% had a major depressive episode, 26% a manic or hypomanic episode, and 18% a mixed state using the DSM-IV criteria. Patients were assessed four times with MATHYS during a three weeks follow-up period. It is an observational study and treatment was prescribed as usual. We applied the GBTM method and MATHYS total score to define trajectories of recovery.ResultsThis method allows identifying 4 trajectories of recovery. At Baseline, two of them started with a score of inhibition but with quite different evolutive profiles (stable inhibition versus improvement). The two others trajectories started with a score of activation (mild versus moderate) and showed a linear improvement of symptoms but with a more rapid recovery for the patients with the higher activation at baseline.ConclusionWhen considering the diagnosis of patients belonging in each trajectory, there model seems particular relevant to explore the high heterogeneity in response to treatment in bipolar patients during an acute depressive episode.
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Senay, Andréa, Julio C. Fernandes, Josée Delisle, Suzanne N. Morin, Daniel Nagin, and Sylvie Perreault. "Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis." Health Services Research and Managerial Epidemiology 8 (January 2021): 233339282110470. http://dx.doi.org/10.1177/23333928211047024.
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Introduction/Objectives Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. Methods Using clinical and administrative data of a prospective cohort study aiming to improve the secondary prevention of osteoporosis-related fractures with a Fracture Liaison Service (FLS), trajectory groups for visit compliance over time (2-year follow-up) were predicted using group-based trajectory modeling. Predictors of trajectory groups were identified using multinomial logistic regressions. Results Among 532 participants (86% women, mean age 63 years), three trajectories were identified and interpreted as high followers, intermediate followers, and low followers. The predicted probability for group-membership was: 48.4% high followers, 28.1% intermediate followers, 23.5% low followers. A lower femoral bone mineral density and polypharmacy were predictors of being in the high followers compared to the low followers group; predictors for being in the intermediate followers group were polypharmacy and referral to a bone specialist at baseline. Conclusions Results provided information on visit compliance patterns and predictors for the patients undergoing the intervention. This information has important implications when implementing such health services and determining their effectiveness.
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Chen, Kezhou, Xu Lu, Rongjun Chen, and Jun Liu. "Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue." Mathematical Biosciences and Engineering 19, no. 2 (2021): 1496–514. http://dx.doi.org/10.3934/mbe.2022069.
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<abstract> <p>Most existing physiological testing systems broadly classify monitored physiological data into three categories: normal, abnormal, and highly abnormal, but do not consider differences in the importance of data within the same category, which may result in the loss of data of higher importance. In addition, the purpose of physiological monitoring is to detect health abnormalities in patients earlier and faster, thus enabling risk avoidance and real-time rescue. Therefore, we designed a system called the adaptive physiological monitoring and rescue system (APMRS) that innovatively incorporates emergency rescue functions into traditional physiological monitoring systems using the rescue of modified-MAC (RM-MAC) protocol. The relay selection (RS) algorithm of APMRS can select the appropriate relay to forward based on the importance of the physiological data, thus ensuring priority transmission of more important monitoring data. In addition, we apply deep learning target trajectory prediction technology to the indoor rescue module (IRM) of APMRS to provide high-performance scheduling of location tracking nodes in advance by trajectory prediction. It reduces network energy consumption and ensures perceptual tracking accuracy. When APMRS monitors abnormal physiological data that may endanger a patient's life, IRM can implement effective and fast location rescue to avoid risks.</p> </abstract>
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Müller, Ebba Gløersen, Trine Holt Edwin, Bjørn Heine Strand, Caroline Stokke, Mona Elisabeth Revheim, and Anne-Brita Knapskog. "Is Amyloid Burden Measured by 18F-Flutemetamol PET Associated with Progression in Clinical Alzheimer’s Disease?" Journal of Alzheimer's Disease 85, no. 1 (January 4, 2022): 197–205. http://dx.doi.org/10.3233/jad-215046.
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Background: Patients with Alzheimer’s disease (AD) show heterogeneity in clinical progression rate, and we have limited tools to predict prognosis. Amyloid burden from 18F-Flutemetamol positron emission tomography (PET), as measured by standardized uptake value ratios (SUVR), might provide prognostic information. Objective: We investigate whether 18F-Flutemetamol PET composite or regional SUVRs are associated with trajectories of clinical progression. Methods: This observational longitudinal study included 94 patients with clinical AD. PET images were semi-quantified with normalization to pons. Group-based trajectory modeling was applied to identify trajectory groups according to change in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) over time. Multinomial logistic regression models assessed the association of SUVRs with trajectory group membership. Results: Three trajectory groups were identified. In the regression models, neither composite nor regional SUVRs were associated with trajectory group membership. Conclusion: There were no associations between CDR progression and 18F-Flutemetamol PET-derived composite SUVRs or regional SUVRs in clinical AD.
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Mara, Constance A., and Adam C. Carle. "Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling." Journal of Pediatric Psychology 46, no. 2 (February 18, 2021): 179–88. http://dx.doi.org/10.1093/jpepsy/jsab010.
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Abstract Objective This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models. Methods Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software. Results The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models. Conclusions Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or “classes”) of individuals with shared trajectories that differ from the average trajectory.
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Fan, Yen-Chun, Sheng-Feng Lin, Chia-Chi Chou, and Chyi-Huey Bai. "Developmental Trajectories and Predictors of Incident Dementia among Elderly Taiwanese People: A 14-Year Longitudinal Study." International Journal of Environmental Research and Public Health 20, no. 4 (February 9, 2023): 3065. http://dx.doi.org/10.3390/ijerph20043065.
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The aim of this study was to identify dementia trajectories and their associated predictors among elderly Taiwanese people over a 14-year period using a nationwide representative longitudinal study. This retrospective cohort study was performed using the National Health Insurance Research Database. Group-based trajectory modeling (GBTM) was used to distinguish the specific trajectory groups of incident dementia during 2000–2013. All 42,407 patients were classified by GBTM to identify the trajectory of incident dementia, which included high- (n = 11,637, 29.0%), moderate- (n = 19,036, 44.9%), and low-incidence (n = 11,734, 26.1%) groups. Those diagnosed with hypertension (adjusted odds ratio [aOR] = 1.43; 95% confidence interval [CI] = 1.35–1.52), stroke (aOR = 1.45, 95% CI = 1.31–1.60), coronary heart disease (aOR = 1.29, 95% CI = 1.19–1.39), heart failure (aOR = 1.62, 95% CI = 1.36–1.93), and chronic obstructive pulmonary disease (aOR = 1.10, 95% CI = 1.02–1.18) at baseline revealed tendencies to be classified into high-incidence groups in dementia risk. The results from a 14-year longitudinal study identified three distinct trajectories of incident dementia among elderly Taiwanese people: patients with cardiovascular disease risk factors and cardiovascular disease events tended to be classified into high-incidence dementia groups. Early detection and management of these associated risk factors in the elderly may prevent or delay the deterioration of cognitive decline.
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Binswanger, Ingrid A., Susan M. Shetterly, Stanley Xu, Komal J. Narwaney, David L. McClure, Deborah J. Rinehart, Anh P. Nguyen, and Jason M. Glanz. "Opioid Dose Trajectories and Associations With Mortality, Opioid Use Disorder, Continued Opioid Therapy, and Health Plan Disenrollment." JAMA Network Open 5, no. 10 (October 5, 2022): e2234671. http://dx.doi.org/10.1001/jamanetworkopen.2022.34671.
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ImportanceUncertainty remains about the longer-term benefits and harms of different opioid management strategies, such as tapering and dose escalation. For instance, opioid tapering could help patients reduce opioid exposure to prevent opioid use disorder, but patients may also seek care elsewhere and engage in nonprescribed opioid use.ObjectiveTo evaluate the association between opioid dose trajectories observed in practice and patient outcomes.Design, Setting, and ParticipantsThis retrospective cohort study was conducted in 3 health systems in Colorado and Wisconsin. The study population included patients receiving long-term opioid therapy between 50 and 200 morphine milligram equivalents between August 1, 2014, and July 31, 2017. Follow-up ended on December 31, 2019. Data were analyzed from January 2020 to August 2022.ExposuresGroup-based trajectory modeling identified 5 dosing trajectories over 1 year: 1 decreasing, 1 high-dose increasing, and 3 stable.Main Outcomes and MeasuresPrimary outcomes assessed after the trajectory period were 1-year all-cause mortality, incident opioid use disorder, continued opioid therapy at 1 year, and health plan disenrollment. Associations were tested using Cox proportional hazards regression and log-binomial models, adjusting for baseline covariates.ResultsA total of 3913 patients (mean [SD] age, 59.2 [14.4] years; 2767 White non-Hispanic [70.7%]; 2237 female patients [57.2%]) were included in the study. Compared with stable trajectories, the decreasing dose trajectory was negatively associated with opioid use disorder (adjusted hazard ratio [aHR], 0.40; 95% CI, 0.29-0.55) and continued opioid therapy (site 1: adjusted relative risk [aRR], 0.39; 95% CI, 0.34-0.44), but was positively associated with health plan disenrollment (aHR, 1.66; 95% CI, 1.24-2.22). The decreasing trajectory was not associated with mortality (aHR, 1.28; 95% CI, 0.87-1.86). In contrast, the high-dose increasing trajectory was positively associated with mortality (aHR, 2.19; 95% CI, 1.44-3.32) and opioid use disorder (aHR, 1.81; 95% CI, 1.39-2.37) but was not associated with disenrollment (aHR, 0.90; 95% CI, 0.56-1.42) or continued opioid therapy (site 1: aRR, 0.98; 95% CI, 0.94-1.03).Conclusions and RelevanceIn this cohort study, decreasing opioid dose was associated with reduced risk of opioid use disorder and continued opioid therapy but increased risk of disenrollment compared with stable dosing, whereas the high-dose increasing trajectory was associated with an increased risk of mortality and opioid use disorder. These findings can inform opioid management decision-making.
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Chien, Ting-Ying, Mei-Lien Lee, Wan-Ling Wu, and Hsien-Wei Ting. "Exploration of Medical Trajectories of Stroke Patients Based on Group-Based Trajectory Modeling." International Journal of Environmental Research and Public Health 16, no. 18 (September 18, 2019): 3472. http://dx.doi.org/10.3390/ijerph16183472.
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A high mortality rate is an issue with acute cerebrovascular disease (ACVD), as it often leads to a high medical expenditure, and in particular to high costs of treatment for emergency medical conditions and critical care. In this study, we used group-based trajectory modeling (GBTM) to study the characteristics of various groups of patients hospitalized with ACVD. In this research, the patient data were derived from the 1 million sampled cases in the National Health Insurance Research Database (NHIRD) in Taiwan. Cases who had been admitted to hospitals fewer than four times or more than eight times were excluded. Characteristics of the ACVD patients were collected, including age, mortality rate, medical expenditure, and length of hospital stay for each admission. We then performed GBTM to examine hospitalization patterns in patients who had been hospitalized more than four times and fewer than or equal to eight times. The patients were divided into three groups according to medical expenditure: high, medium, and low groups, split at the 33rd and 66th percentiles. After exclusion of unqualified patients, a total of 27,264 cases (male/female = 15,972/11,392) were included. Analysis of the characteristics of the ACVD patients showed that there were significant differences between the two gender groups in terms of age, mortality rate, medical expenditure, and total length of hospital stay. In addition, the data were compared between two admissions, which included interval, outpatient department (OPD) visit after discharge, OPD visit after hospital discharge, and OPD cost. Finally, the differences in medical expenditure between genders and between patients with different types of stroke—ischemic stroke, spontaneous intracerebral hemorrhage (sICH), and subarachnoid hemorrhage (SAH)—were examined using GBTM. Overall, this study employed GBTM to examine the trends in medical expenditure for different groups of stroke patients at different admissions, and some important results were obtained. Our results demonstrated that the time interval between subsequent hospitalizations decreased in the ACVD patients, and there were significant differences between genders and between patients with different types of stroke. It is often difficult to decide when the time has been reached at which further treatment will not improve the condition of ACVD patients, and the findings of our study may be used as a reference for assessing outcomes and quality of care for stroke patients. Because of the characteristics of NHIRD, this study had some limitations; for example, the number of cases for some diseases was not sufficient for effective statistical analysis.
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Mucherino, Sara, Alexandra Lelia Dima, Enrico Coscioni, Maria Giovanna Vassallo, Valentina Orlando, and Enrica Menditto. "Longitudinal Trajectory Modeling to Assess Adherence to Sacubitril/Valsartan among Patients with Heart Failure." Pharmaceutics 15, no. 11 (November 1, 2023): 2568. http://dx.doi.org/10.3390/pharmaceutics15112568.
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Medication adherence in chronic conditions is a long-term process. Modeling longitudinal trajectories using routinely collected prescription data is a promising method for describing adherence patterns and identifying at-risk groups. The study aimed to characterize distinct long-term sacubitril/valsartan adherence trajectories and factors associated with them in patients with heart failure (HF). Subjects with incident HF starting sac/val in 2017–2018 were identified from the Campania Regional Database for Medication Consumption. We estimated patients’ continuous medication availability (CMA9; R package AdhereR) during a 12-month period. We selected groups with similar CMA9 trajectories (Calinski-Harabasz criterion; R package kml). We performed multinomial regression analysis, assessing the relationship between demographic and clinical factors and adherence trajectory groups. The cohort included 4455 subjects, 70% male. Group-based trajectory modeling identified four distinct adherence trajectories: high adherence (42.6% of subjects; CMA mean 0.91 ± 0.08), partial drop-off (19.6%; CMA 0.63 ± 0.13), moderate adherence (19.3%; CMA 0.54 ± 0.11), and low adherence (18.4%; CMA 0.17 ± 0.12). Polypharmacy was associated with partial drop-off adherence (OR 1.194, 95%CI 1.175–1.214), while the occurrence of ≥1 HF hospitalization (OR 1.165, 95%CI 1.151–1.179) or other hospitalizations (OR 1.481, 95%CI 1.459–1.503) were associated with low adherence. This study found that tailoring patient education, providing support, and ongoing monitoring can boost adherence within different groups, potentially improving health outcomes.
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Hu, Rui, Bruno Michel, Dario Russo, Niccolò Mora, Guido Matrella, Paolo Ciampolini, Francesca Cocchi, Enrico Montanari, Stefano Nunziata, and Thomas Brunschwiler. "An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care." Future Internet 13, no. 1 (December 30, 2020): 6. http://dx.doi.org/10.3390/fi13010006.
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Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort.
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Fatima, B., A. Mohan, H. Chen, A. A. Deshmukh, M. Wanat, E. J. Essien, R. Paranjpe, and S. M. Abughosh. "RWD3 Group-Based Trajectory Modeling to Evaluate Adherence Patterns for Direct Oral Anticoagulant Among Patients with Atrial Fibrillation." Value in Health 26, no. 6 (June 2023): S360. http://dx.doi.org/10.1016/j.jval.2023.03.2033.
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Ye, Ting, Pei Zhang, Zhaolian Ouyang, Jiajuan Yang, Chengzhong Xu, Zijing Pan, Zhouzhi Wu, Liang Zhang, and Boyang Li. "Multi-trajectory modeling of home blood pressure telemonitoring utilization among hypertensive patients in China: A latent class growth analysis." International Journal of Medical Informatics 119 (November 2018): 70–74. http://dx.doi.org/10.1016/j.ijmedinf.2018.09.005.
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Ornstein, Katherine, and Claire Ankuda. "Trajectories of Emergency Department Use After Incident Functional Disability." Innovation in Aging 5, Supplement_1 (December 1, 2021): 118. http://dx.doi.org/10.1093/geroni/igab046.452.
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Abstract Emergency department (ED) visits for older adults with functional disability may represent unmet needs and are often burdensome to patients and families. While it is known that older adults with functional disability use the ED at high rates, this does not capture the heterogeneity of experience after the onset of disability. Using NHATS, we identified a cohort of older adults with incident disability, or who reported they began to receive help with self-care and/or mobility in the prior year. Using the month that they report first receiving help, we linked to Medicare data to assess quarterly patterns of ED use. We used Group Based Trajectory Modeling to assess the trajectories of ED use after disability. We identified three distinct trajectories of ED use: persistently high, declining, and persistently low. We describe the clinical, household, and sociodemographic characteristics associated with likely membership in each trajectory group.
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Yuan, Min, Xu Steven Xu, Yaning Yang, Jinfeng Xu, Xiaohui Huang, Fangbiao Tao, Liang Zhao, Liping Zhang, and Jose Pinheiro. "A quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage." Statistical Methods in Medical Research 28, no. 12 (November 9, 2018): 3568–78. http://dx.doi.org/10.1177/0962280218812595.
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Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling.
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Cisternas, M. G., D. Rajagopalan, R. Halpern, M. Leszko, K. Andrade, and A. Phillips. "PND91 Group-Based Trajectory Modeling for the Characterization of Adherence Patterns in Patients with Multiple Sclerosis: MODEL Evaluation and Selection." Value in Health 23 (December 2020): S638—S639. http://dx.doi.org/10.1016/j.jval.2020.08.1416.
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He, Jin-Yu, Jia-Ni Xue, Pei-En Chen, Tao-Hsin Tung, and Ching-Wen Chien. "The association between continuity of care and the severity of diabetes-related complications." Journal of Epidemiological Research 5, no. 1 (November 29, 2018): 20. http://dx.doi.org/10.5430/jer.v5n1p20.
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Purpose: To assess the association between continuity of care and severity of diabetes-related complications for geriatric diabetic patients.Methods: A retrospective study using 2009-2013 Taiwan’s National Health Insurance Research Database one million beneficiary version were conducted. Utilization data of 3,885 geriatric patients who newly had type 2 diabetes were studied. Five-year Continuity of Care Index (COCI) and the adapted Diabetes Complications Severity Index (aDCSI) of each subject were calculated. A Group-based trajectory modeling (GBTM) was used to distinguish patients with similar five-year COCI into different trajectories. A general estimating equation(GEE) was used to assess the association between COCI trajectories and aDCSI. Results: The ratio of male to female in this study was 2:3. At the time of inclusion, the average age was 71.79 (4.65) years old. GBTM subjects can be divided into four different trajectories according to their COCI: low-level continuity of care trajectories, increasing continuity of care trajectories, decreasing continuity of care trajectory, and high-level continuity of care trajectory. After GEE analysis, the high continuity of care trajectories were associated with a significant decrease in aDCSI score.Conclusion:The results of this study suggested better continuity of care was associated with less severity of diabetes-related complications for geriatric patients.
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De Vera, M., E. C. Sayre, and N. Rebić. "POS0372 GROUP-BASED TRAJECTORIES OF ADHERENCE TO ANTI-TUMOUR NECROSIS FACTOR (TNF) AGENTS: A POPULATION-BASED LONGITUDINAL STUDY." Annals of the Rheumatic Diseases 82, Suppl 1 (May 30, 2023): 438.2–439. http://dx.doi.org/10.1136/annrheumdis-2023-eular.4098.
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BackgroundStudies of adherence to biologic disease modifying anti-rheumatic drugs (bDMARDs), namely anti-TNFs, have been largely limited to short durations or used traditional methods that do not capture the dynamic nature of medication taking.ObjectivesOur objective was to characterize long-term trajectories of adherence to anti-TNFs and evaluate associated factors.MethodsWe linked population-based health data on all physician visits, hospital admissions, and all dispensed medications, regardless of payer in British Columbia from 01/01/1996 to 3/31/2021. We identified prescriptions for anti-TNFs (including infliximab, etanercept, adalimumab) using drug identification numbers among indicated individuals (e.g., rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis) with at ≥6 years of continuous data following initiation. We used group-based trajectory models (GBTMs), a semi-parametric application of finite mixture modeling which detects longitudinal patterns in a repeatedly measured outcome, to identify and group individuals with similar patterns of bDMARD use (i.e., ‘adherence trajectory group’) over 6 years of follow-up. We then evaluated factors associated with each adherence trajectory group using multinomial logistic regression.ResultsWe identified 1,593 patients prescribed anti-TNFs, of which 59.7% were female with a mean age of 45.2 ± 13.2 years. Group-based trajectory modeling identified 4 distinct adherence trajectories for anti-TNFs overall (Figure 1a): “moderate then high adherence” (Group 1; n = 814, 51.1% of the cohort), “moderate then low adherence” (Group 2; n = 314, 19.7%), “low adherence, declining to discontinuation” (Group 3; n = 291, 18.3%), and “low then high adherence” (Group 4; n = 174, 10.9%).Specific group-based trajectories for adalimumab, etanercept, and infliximab are presented inFigure 1b-d. Among anti-TNFs, number of prior hospitalizations was significantly associated with initial low adherence increasing to high adherence (Group 4) compared to initial moderate adherence increasing to high adherence (Group 1)(odds ratio 1.41; 95% confidence interval: 1.19, 1.68).ConclusionThis population-based study demonstrates the heterogeneity in real-world patterns of anti-TNFs use. Findings also suggest the inadequacy of clinical and demographic characteristics in predicting patients’ adherence trajectories.Figure 1.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
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Cole, John A., Joseph R. Peterson, Tyler M. Earnest, Michael J. Hallock, Daniel J. Cook, Eduardo Braun, Anu Antony, John R. Pfeiffer, and Tushar Pandey. "Abstract P1-08-31: Simbiosys tumorscope: Biophysical modeling of patient-specific response to chemotherapy." Cancer Research 82, no. 4_Supplement (February 15, 2022): P1–08–31—P1–08–31. http://dx.doi.org/10.1158/1538-7445.sabcs21-p1-08-31.
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Abstract Background: Breast Cancer (BC) patients exhibit a wide variety of responses to neoadjuvant chemotherapy (NACT). This is driven by factors both intrinsic (e.g., mutations, dysregulation, metabolic reprogramming) and extrinsic (e.g., nutrient/drug perfusion, interactions with surrounding healthy tissues and the tumor microenvironment (TME)) to the cells that make up each tumor. The SimBioSys TumorScope is a platform for making individualized predictions of the response of each patient's tumor to NACT. It employs 3D biophysical simulations that explicitly model the dynamics of cellular response to the ever-changing chemical milieu of drugs and nutrients that perfuse the TME during treatment, in order to predict when and where different regions of the tumor are growing, dying, and ultimately how a given patient will respond to treatment. Methods: The SimBioSys TumorScope constructs 3D in silico models of each patient's tumor directly from pretreatment DCE-MRIs. It combines this spatial model with personalized genome-scale models of tumor and tissue metabolism, pharmacokinetics, and pharmacodynamics, and vascular perfusion (based on DCE-MRI timeseries). The combined model is then simulated using a custom high-performance reaction-diffusion-material mechanics simulation engine which produces a spatio-temporal trajectory of tumor size, morphology, intra- and extracellular biochemistry. We evaluated the ability of the TumorScope software to predict volumetric response to NACT. A validation set comprising the pretreatment records (including MRIs) of 780 BC patients that underwent NACT was used. These patients spanned a wide range of tumor sizes, molecular subtypes, and NCCN-recommended treatment regimens. Simulations were initialized using each patient’s pretreatment MRI and pathology data and run from the start of therapy to the specified surgical date. Simulated tumor volumetric percent response (calculated as the ratio of change in tumor volume to initial volume) at the time of surgery was then compared with actual tumor volumetric percent response extracted from presurgical MRIs. Among patients for which event free survival data was available (n = 480), we performed a Cox proportional hazard analysis. Results: The SimBioSys TumorScope predicted pre-surgical tumor volumetric response with a median error of 0.03% and median absolute deviation of 8.2%. Among the patients for which EFS data was available, we found a hazard ratio of 1.8 associated with having a final simulated volume greater than 0.01 cc (p = 0.00048). Conclusions: The SimBioSys TumorScope produces accurate patient specific predictions of response to NACT using only standard-of-care pre-treatment data. Such predictions can aid in decision making, enabling physicians to select less-toxic regimens for patients in which a robust response is predicted, and more aggressive treatments and/or clinical trial enrollment when response is likely to be poor. Citation Format: John A Cole, Jr., Joseph R Peterson, Tyler M Earnest, Michael J Hallock, Daniel J Cook, Eduardo Braun, Anu Antony, John R Pfeiffer, Tushar Pandey. Simbiosys tumorscope: Biophysical modeling of patient-specific response to chemotherapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-31.
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Fosdahl, Merete Aarsland, Bjørnar Berg, May Arna Risberg, Britt Elin Øiestad, and Inger Holm. "Body Mass Index, Quality of Life and Activity Limitation Trajectories over 2 Years in Patients with Knee or Hip Osteoarthritis: A Dual Trajectory Approach Based on 4265 Patients Included in the AktivA Quality Register." Journal of Clinical Medicine 12, no. 22 (November 14, 2023): 7094. http://dx.doi.org/10.3390/jcm12227094.
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(1) Background: Recent studies claim that weight-neutral approaches emphasizing physical activity might be as effective as weight-loss-centered approaches for improving pain and physical function in patients with knee and hip osteoarthritis. The objectives were to identify distinctive groups of individuals with similar BMI, quality of life and activity limitation trajectories over two years, to compare the overall differences between BMI trajectory groups for baseline variables and to explore the probabilities of the quality of life and activity limitation trajectory groups conditional on the BMI group. (2) Methods: Baseline data for age, gender, BMI, quality of life, activity limitations, pain, general health, knee or hip osteoarthritis and follow-up data on BMI, quality of life and activity limitations at 3, 12 and 24 months were retrieved from the “Active with osteoarthritis” (AktivA) electronic quality register. Group-based trajectory modeling was used to identify distinct trajectories for BMI, quality of life and activity limitations. (3) Results: 4265 patients were included in the study. Four distinct BMI trajectories were identified, normal weight (31%), slightly overweight (43%), overweight (20%) and obese (6%). At baseline, there were highly significant differences between all BMI groups, pain increased and age and general health decreased with higher BMI. Irrespective of weight category, minimal changes in BMI were found over the two-year follow-up period. Over 80% of the participants showed moderate-to-considerable improvements both in quality of life and activity limitations. (4) Conclusions: Almost 70% of the participants belonged to the overweight trajectories. Despite no significant weight reduction over the two years, eight in every 10 participants improved their quality of life and reduced their activity limitations after participating in the AktivA program.
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Borza, Tom, Knut Engedal, Sverre Bergh, Jūratė Šaltytė Benth, and Geir Selbæk. "Trajectories of Depression in Late Life: A 1-Year Follow-Up Study." Dementia and Geriatric Cognitive Disorders 43, no. 3-4 (2017): 180–92. http://dx.doi.org/10.1159/000458148.
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Aims: To investigate the prognosis of depression in late life (DLL) in terms of the course of depression over 1 year and assess clinical factors related to the prognosis. Methods: We performed an observational, multicenter, longitudinal study of 160 patients aged ≥60 years who were admitted to inward treatment of DLL. The patients were followed with 3 assessments: at inclusion (T0), at discharge from the hospital (T1), and after 1 year (T2). Growth mixture modeling was applied to identify patient classes following distinct trajectories of the Montgomery-Åsberg Depression Rating Scale (MADRS) score. Two regression models were estimated to assess clinical factors for the trajectories and for a clinical assessment of the depression course between T1 and T2. Results: Two trajectory classes were identified: one with higher and one with lower MADRS scores. Not being in remission at T1 and a longer hospital stay were associated with higher odds of being in the trajectory class with more severe depression. Early-onset depression (EOD) was associated with higher odds of being in a group with a poorer clinical course between T1 and T2. Conclusion: EOD and not being in remission at discharge were important negative prognostic factors for DLL.
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Shi, Qiuling, Tito R. Mendoza, G. Brandon Gunn, Xin Shelley Wang, David I. Rosenthal, and Charles S. Cleeland. "Using group-based trajectory modeling to examine heterogeneity of symptom burden in patients with head and neck cancer undergoing aggressive non-surgical therapy." Quality of Life Research 22, no. 9 (March 9, 2013): 2331–39. http://dx.doi.org/10.1007/s11136-013-0380-2.
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Wen, Fur-Hsing, Chia-Hsun Hsieh, Wen-Chi Shen, Ming-Mo Hou, Po-Jung Su, Wen-Chi Chou, Jen-Shi Chen, Wen-Cheng Chang, and Siew Tzuh Tang. "Associations Between Surrogates’ Decisional Regret Trajectories and Bereavement Outcomes." Journal of the National Comprehensive Cancer Network 21, no. 11 (November 2023): 1141–48. http://dx.doi.org/10.6004/jnccn.2023.7053.
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Background: Family surrogates experience heterogeneous decisional regret and negative long-lasting postdecision impacts. Cross-sectional findings on the associations between decisional regret and surrogates’ bereavement outcomes are conflicting and cannot illustrate the directional and dynamic evolution of these associations. In this study, we sought to longitudinally examine the associations between 4 previously identified decisional-regret trajectories and bereavement outcomes among family surrogates of terminally ill patients with cancer. Patients and Methods: This prospective, longitudinal, observational study included 377 family surrogates. Decisional regret was measured using the 5-item Decision Regret Scale, and 4 decisional regret trajectories were identified: resilient, delayed-recovery, late-emerging, and increasing-prolonged. Associations between bereavement outcomes (depressive symptoms, prolonged grief symptoms, and physical and mental health-related quality of life [HRQoL]) and decisional-regret trajectories were examined simultaneously by multivariate hierarchical linear modeling using the resilient trajectory as a reference. Results: Surrogates in the delayed-recovery, late-emerging, and increasing-prolonged trajectories experienced significantly higher symptoms of prolonged grief (β [95% CI], 1.815 [0.782 to 2.848]; 2.312 [0.834 to 3.790]; and 7.806 [2.681 to 12.931], respectively) and poorer physical HRQoL (−1.615 [−2.844 to −0.386]; −1.634 [−3.226 to −0.042]; and −4.749 [−9.380 to −0.118], respectively) compared with those in the resilient trajectory. Membership in the late-emerging and increasing-prolonged trajectories was associated with higher symptoms of depression (β [95% CI], 2.942 [1.045 to 4.839] and 8.766 [2.864 to 14.668], respectively), whereas only surrogates in the increasing-prolonged decisional-regret trajectory reported significantly worse mental HRQoL (−4.823 [−8.216 to −1.430]) than those in the resilient trajectory. Conclusions: Surrogates who experienced delayed-recovery, unresolved, or late-emerging decisional regret may carry ceaseless doubt, guilt, or self-blame for patient suffering, leading to profound symptoms of prolonged grief, depressive symptoms, and worse HRQoL over their first 2 bereavement years.
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Lee, Eunkyung, Sushantti Rupesh, Katia Ferdowsi, Robert Hines, and Victoria Loerzel. "Abstract PD6-02: PD6-02 Health-Related Quality of Life Trajectories and Predictors among Older Breast Cancer Survivors: A SEER-MHOS analysis." Cancer Research 83, no. 5_Supplement (March 1, 2023): PD6–02—PD6–02. http://dx.doi.org/10.1158/1538-7445.sabcs22-pd6-02.
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Abstract Health-related quality of life (HRQOL) is an important issue for breast cancer patients and clinicians in the treatment decision process. In addition, older women are more vulnerable due to pre-existing comorbidities and socioeconomic status. This study explored 10-year trajectories of HRQoL in older breast cancer survivors and their predictors using patient-reported outcomes data queried from the Surveillance, Epidemiology and End Results - Medicare Health Outcomes Survey (SEER-MHOS) data resources among Medicare beneficiaries. Older women diagnosed with breast cancer in 1998-2012 and who participated in the surveys before and at least once after diagnosis were included in the analysis. HRQOL was measured using SF-36/VR-12 questionnaire and summarized as Physical Component Summary (PCS) Score and Mental Component Summary (MCS) Score. Latent Class Growth Mixture Modeling was conducted to identify distinct groups of women with a similar trajectory of HRQOL. A total of 1089 women with breast cancer completed surveys an average of 2.6 times (range 2 - 19). The results showed that there were three latent classes of PCS trajectory: high-declining (46% of the sample), mid-declining (37%), and low-improving (17%). Two latent classes of MCS trajectory were identified: high-stable (76%) and low-declining (24%). The univariate analysis showed that age at diagnosis, body mass index, level of education, geographic region, tumor grade, tumor size, and the number of comorbidities were related to PCS and MCS scores. Multivariable multinomial logistic regression analysis identified the number of comorbidities as the most significant predictor for PCS score and level of education as the most significant predictor for MCS score. Future research needs to identify the most common comorbidities that influence HRQOL deterioration in older breast cancer survivors to develop interventions that better the physical HRQOL in patients. Also, interventions that target less educated, underserved patients to improve mental HRQOL. Citation Format: Eunkyung Lee, Sushantti Rupesh, Katia Ferdowsi, Robert Hines, Victoria Loerzel. PD6-02 Health-Related Quality of Life Trajectories and Predictors among Older Breast Cancer Survivors: A SEER-MHOS analysis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD6-02.
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Lin, Lingmin, Kailai Liu, Huan Feng, Jing Li, Hengle Chen, Tao Zhang, Boyun Xue, and Jiarui Si. "Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying." Mathematical Biosciences and Engineering 19, no. 10 (2022): 10096–107. http://dx.doi.org/10.3934/mbe.2022472.
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<abstract> <p>Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently and economically, an intelligent prediction of glucose is demanding. A glucose trajectory prediction system based on subcutaneous interstitial continuous glucose monitoring data and deep learning models for ensuing glucose trajectory was constructed, followed by the application of personalised prediction models on one participant with type 2 diabetes in a community. The predictive accuracy was then assessed by RMSE (root mean square error) using blood glucose data. Changes in glycaemic parameters of the participant before and after model intervention were also compared to examine the efficacy of this intelligence-aided health care. Individual Recurrent Neural Network model was developed on glucose data, with an average daily RMSE of 1.59 mmol/L in the application segment. In terms of the glucose variation, the mean glucose decreased by 0.66 mmol/L, and HBGI dropped from 12.99 × 10<sup>2</sup> to 9.17 × 10<sup>2</sup>. However, the participant also had increased stress, especially in eating and social support. Our research presented a personalised care system for people with diabetes based on deep learning. The intelligence-aided health management system is promising to enhance the outcome of diabetic patients, but further research is also necessary to decrease stress in the intelligence-aided health management and investigate the stress impacts on diabetic patients.</p> </abstract>
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Belcher, Sarah M., Susan M. Sereika, Jacqueline Dunbar-Jacob, Katherine Yeager, Margaret Q. Rosenzweig, Mounzer E. Agha, Benyam Muluneh, et al. "Patterns and predictors of electronically measured oral anticancer medication (OAM) adherence among patients with multiple myeloma (MM)." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 7547. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.7547.
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7547 Background: Adherence to costly, long-term OAM is a mainstay of life-extending therapy for patients with MM and can dramatically affect cancer outcomes, but little is known about adherence in patients with MM. The purpose was to describe temporal patterns and predictors of OAM adherence among patients with MM via electronic event monitoring (EEM) adherence data. Methods: This was a six-month prospective study of OAM adherence, symptoms, and quality of life among n=70 patients prescribed OAM maintenance therapy for MM who used EEM. Patient reported measures of symptoms (Edmonton Symptom Assessment Scale; Patient Health Questionnaire-9; PROMIS Fatigue; Brief Pain Inventory; Comprehensive Score for financial Toxicity), sociodemographic data, and medical record clinical data were collected at enrollment and 3 and 6 months. Group-based trajectory modeling (GBTM) was applied to EEM data based on AARDEX MEMS smart pill bottles, aggregated monthly (i.e., 30-day intervals) over 6 months of monitoring. Adherence indices were % of prescribed dosestaken and % of days with correct intake. Predictors of adherence trajectory group membership were explored and summarized as bivariate correlations as effect sizes. Results: Participants were on average 63.9 y/o (SD=10.6) and predominantly male (55.9%) and non-Hispanic white (86.8%) or Black (10.3%). At enrollment, participants had been prescribed lenalidomide (68.6%) or pomalidomide (31.4%) for a median 11.5 (IQR: 22, range: 0-100) months. For mean dose adherence, GBTM revealed 3 distinct trajectories: 62.9% were in the high (~97% adherence) and slightly linear decreasing adherence group (π3=.627); 27.1% were in the high/moderate and curvilinear decreasing group (π2=.272), representing 85% adherence at start, dropping to <70% by 6 months; and 10% had a low and curvilinear (π1=.100) pattern, representing only ~40% adherence over time. For mean days adherence, 2 distinct trajectories were identified: high and linear decreasing (81.4%, π2=.801), representing adherence starting at 90%, dropping to 85%; and low and stable (18.6%, π1=.199), representing ~40% adherence over time. Effect sizes for baseline predictors of the low trajectory group ranged from .02 to .37 (median r = .20, small), In particular, participants who self-identified as non-Hispanic Black or Hispanic “other” race being more likely to be in the low trajectory groups for both dose (p=.009) and days (p=.010) adherence. Conclusions: OAM adherence measured with EEM data was dynamic and suggests potential mechanisms of health inequities by race and ethnicity and a need for interventions to monitor for and address disparate adherence. Larger studies with longer observation and more frequent assessments with in-depth social determinants of health are needed to better understand OAM adherence patterns and correlates over time.
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Chua, Alicia S., and Yorghos Tripodis. "A state-space approach for longitudinal outcomes: An application to neuropsychological outcomes." Statistical Methods in Medical Research 31, no. 3 (December 13, 2021): 520–33. http://dx.doi.org/10.1177/09622802211055858.
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Longitudinal assessments are crucial in evaluating the disease state and trajectory in patients with neurodegenerative diseases. Neuropsychological outcomes measured over time often have a non-linear trajectory with autocorrelated residuals and a skewed distribution. We propose the adjusted local linear trend model, an extended state-space model in lieu of the commonly used linear mixed-effects model in modeling longitudinal neuropsychological outcomes. Our contributed model has the capability to utilize information from the stochasticity of the data while accounting for subject-specific trajectories with the inclusion of covariates and unequally spaced time intervals. The first step of model fitting involves a likelihood maximization step to estimate the unknown variances in the model before parsing these values into the Kalman filter and Kalman smoother recursive algorithms. Results from simulation studies showed that the adjusted local linear trend model is able to attain lower bias, lower standard errors, and high power, particularly in short longitudinal studies with equally spaced time intervals, as compared to the linear mixed-effects model. The adjusted local linear trend model also outperforms the linear mixed-effects model when data is missing completely at random, missing at random, and, in certain cases, even in data with missing not at random.
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Petersen, Nils H., Sreeja Kodali, Can Meng, Fangyong Li, Cindy Khanh Nguyen, Krithika U. Peshwe, Sumita Strander, et al. "Blood Pressure Trajectory Groups and Outcome After Endovascular Thrombectomy: A Multicenter Study." Stroke 53, no. 4 (April 2022): 1216–25. http://dx.doi.org/10.1161/strokeaha.121.034408.
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Background: Elevated blood pressure after endovascular thrombectomy (EVT) has been associated with an increased risk of hemorrhagic transformation and poor functional outcomes. However, the optimal hemodynamic management after EVT remains unknown, and the blood pressure course in the acute phase of ischemic stroke has not been well characterized. This study aimed to identify patient subgroups with distinct blood pressure trajectories after EVT and study their association with radiographic and functional outcomes. Methods: This multicenter retrospective cohort study included consecutive patients with anterior circulation large-vessel occlusion ischemic stroke who underwent EVT. Repeated time-stamped blood pressure data were recorded for the first 72 hours after thrombectomy. Latent variable mixture modeling was used to separate subjects into five groups with distinct postprocedural systolic blood pressure (SBP) trajectories. The primary outcome was functional status, measured on the modified Rankin Scale 90 days after stroke. Secondary outcomes included hemorrhagic transformation, symptomatic intracranial hemorrhage, and death. Results: Two thousand two hundred sixty-eight patients (mean age [±SD] 69±15, mean National Institutes of Health Stroke Scale 15±7) were included in the analysis. Five distinct SBP trajectories were observed: low (18%), moderate (37%), moderate-to-high (20%), high-to-moderate (18%), and high (6%). SBP trajectory group was independently associated with functional outcome at 90 days ( P <0.0001) after adjusting for potential confounders. Patients with high and high-to-moderate SBP trajectories had significantly greater odds of an unfavorable outcome (adjusted odds ratio, 3.5 [95% CI, 1.8–6.7], P =0.0003 and adjusted odds ratio, 2.2 [95% CI, 1.5–3.2], P <0.0001, respectively). Subjects in the high-to-moderate group had an increased risk of symptomatic intracranial hemorrhage (adjusted odds ratio, 1.82 [95% CI, 1–3.2]; P =0.04). No significant association was found between trajectory group and hemorrhagic transformation. Conclusions: Patients with acute ischemic stroke demonstrate distinct SBP trajectories during the first 72 hours after EVT that have differing associations with functional outcome. These findings may help identify potential candidates for future blood pressure modulation trials.
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Khong, Thi Minh Thu, Thi Tra Bui, Hee-Yeon Kang, Jinhee Lee, Eunjung Park, and Jin-Kyoung Oh. "Cancer risk according to fasting blood glucose trajectories: a population-based cohort study." BMJ Open Diabetes Research & Care 12, no. 1 (February 2024): e003696. http://dx.doi.org/10.1136/bmjdrc-2023-003696.
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IntroductionDiabetes mellitus is known to increase the risk of cancer. Fasting blood glucose (FBG) levels can be changed over time. However, the association between FBG trajectory and cancer risk has been insufficiently studied. This research aims to examine the relationship between FBG trajectories and cancer risk in the Korean population.Research design and methodsWe analyzed data from the National Health Insurance Service–National Health Screening Cohort collected between 2002 and 2015. Group-based trajectory modeling was performed on 256,271 Koreans aged 40–79 years who had participated in health examinations at least three times from 2002 to 2007. After excluding patients with cancer history before 2008, we constructed a cancer-free cohort. The Cox proportional hazards model was applied to examine the association between FBG trajectories and cancer incidence by cancer type, after adjustments for covariates. Cancer case was defined as a person who was an outpatient thrice or was hospitalized once or more with a cancer diagnosis code within the first year of the claim.ResultsDuring the follow-up time (2008–2015), 18,991 cancer cases were identified. Four glucose trajectories were found: low-stable (mean of FBG at each wave <100 mg/dL), elevated-stable (113–124 mg/dL), elevated-high (104–166 mg/dL), and high-stable (>177 mg/dL). The high-stable group had a higher risk of multiple myeloma, liver cancer and gastrointestinal cancer than the low-stable group, with HR 4.09 (95% CI 1.40 to 11.95), HR 1.68 (95% CI 1.25 to 2.26) and HR 1.27 (95% CI 1.11 to 1.45), respectively. In elevated-stable trajectory, the risk increased for all cancer (HR 1.08, 95% CI 1.02 to 1.16) and stomach cancer (HR 1.24, 95% CI 1.07 to 1.43). Significant associations were also found in the elevated-high group with oral (HR 2.13, 95% CI 1.01 to 4.47), liver (HR 1.50, 95% CI 1.08 to 2.08) and pancreatic cancer (HR 1.99, 95% CI 1.20 to 3.30).ConclusionsOur study highlights that the uncontrolled high glucose level for many years may increase the risk of cancer.
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Castaneda-Avila, Maira, Kate Lapane, and Mara Epstein. "THE ROLE OF A MONOCLONAL GAMMOPATHY OF UNDETERMINED SIGNIFICANCE DIAGNOSIS IN HEALTHCARE UTILIZATION." Innovation in Aging 6, Supplement_1 (November 1, 2022): 148. http://dx.doi.org/10.1093/geroni/igac059.590.
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Abstract Monoclonal Gammopathy of Undetermined Significance (MGUS) is an understudied precursor of multiple myeloma (MM), the second most prevalent hematologic malignancy in the US. MGUS is incidentally diagnosed, and its significance is unclear as only 1% per year transition to MM. MGUS is highly prevalent among adults aged ≥ 50 years. In this presentation, we will review mixed-method approaches. Using healthcare claims and electronic health records from patients in central Massachusetts, we applied group-based trajectory modeling and conditional Poisson regression. These analyses were complemented by a qualitative analysis of in-depth interviews with providers and MGUS patients. Together, these methodologies provided a comprehensive evaluation of the impact of MGUS on healthcare utilization in older adults. The qualitative analysis provided a better understanding of the patient and provider factors influencing healthcare utilization after an MGUS diagnosis. The presentation will highlight how the use of these methodologies provide different perspectives among understudied premalignant conditions.
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Malakhova, Zh L., A. M. Tynterova, S. V. Korenev, O. A. Tikhonova, L. A. Perminova, and S. A. Botman. "Modeling neurophysiological and psychomotor relevant criteria in children with motor dysfunction." Russian Journal of Child Neurology 18, no. 4 (January 3, 2024): 26–35. http://dx.doi.org/10.17650/2073-8803-2023-18-4-26-35.
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Background. Cerebral palsy is the most common cause of childhood disability among diseases of the nervous system, the prevalence of which averages 2.5 cases per 1,000 children.Aim. To identify relevant indicators in relation to the prediction of delayed rates of neuropsychic development in children and the formation of cerebral palsy in children 5–8 years old using mathematical modeling.Materials and methods. The study included 100 patients aged 5–8 years: 79 patients with neurological disorders and deviations in psycho-speech development, 21 patients – comparison group. The examination of children in both groups included: analysis of the child’s life history, analysis of psychomotor development in the first year of life, analysis of morbidity in the first year of life, assessment of psychomotor development at the time of the study, electroneuromyography using stimulation and superficial electroneuromyography, as well as ultrasound examination of the lower leg muscles with assessment of the functional state and muscle density using fibroscan.For statistical processing of the obtained data, descriptive statistics were performed. Determination of the statistical significance of indicators was carried out using the Kolmogorov–Smirnov test for continuously distributed values and Fisher’s exact test for discrete values. One-hot coding was used to represent categorical features. The analysis of the obtained data was carried out using a Python program using the pandas, numpy, scikit-learn, and boruta libraries.Results and conclusion. For children with deviations in neuropsychic development, significant early diagnostic markers are parameters of psychomotor development and neurological status (age when the child began to hold his head, decreased strength of the flexors of the foot and hip, level of walking) and instrumental examination (ultrasound of muscles thighs, electroneuromyography) – changes in the density and parameters of electrical excitability of muscles, which can serve as an early diagnostic sign of the development of motor dysfunction and an indicator for the formation of a trajectory of rehabilitation therapy.
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Itoshima, Hisashi, Jung-ho Shin, Noriko Sasaki, Etsu Goto, Susumu Kunisawa, and Yuichi Imanaka. "Regional variations in primary percutaneous coronary intervention for acute myocardial infarction patients: A trajectory analysis using the national claims database in Japan." PLOS ONE 19, no. 10 (October 22, 2024): e0312248. http://dx.doi.org/10.1371/journal.pone.0312248.
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Background Previous studies have demonstrated geographical disparities regarding the quality of care for acute myocardial infarction (AMI). The aim of this study was two-fold: first, to calculate the proportion of patients with AMI who received primary percutaneous coronary interventions (pPCIs) by secondary medical areas (SMAs), which provide general inpatient care, as a quality indicator (QI) of the process of AMI practice. Second, to identify patterns in their trajectories and to investigate the factors related to regional differences in their trajectories. Methods We included patients hospitalized with AMI between April 2014 and March 2020 from the national health insurance claims database in Japan and calculated the proportion of pPCIs across 335 SMAs and fiscal years. Using these proportions, we conducted group-based trajectory modeling to identify groups that shared similar trajectories of the proportions. In addition, we investigated area-level factors that were associated with the different trajectories. Results The median (interquartile range) proportions of pPCIs by SMAs were 63.5% (52.9% to 70.5%) in FY 2014 and 69.6% (63.3% to 74.2%) in FY 2020. Four groups, named low to low (LL; n = 48), low to middle (LM; n = 16), middle to middle (MM; n = 68), and high to high (HH; n = 208), were identified from our trajectory analysis. The HH and MM groups had higher population densities and higher numbers of physicians and cardiologists per capita than the LL and LM groups. The LL and LM groups had similar numbers of physicians per capita, but the number of cardiologists per capita in the LM group increased over the years of the study compared with the LL group. Conclusion The trajectory of the proportion of pPCIs for AMI patients identified groups of SMAs. Among the four groups, the LM group showed an increasing trend in the proportions of pPCIs, whereas the three other groups showed relatively stable trends.
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Nicholas, J., N. C. Edwards, R. A. Edwards, A. Dellarole, L. Manca, D. E. Harlow, and A. Phillips. "ND3 USE OF GROUP-BASED TRAJECTORY MODELING TO IDENTIFY ADHERENCE CLUSTERS IN PATIENTS WITH MULTIPLE SCLEROSIS NEWLY-INITIATING ONCE- OR TWICE-DAILY ORAL DISEASE-MODIFYING DRUGS." Value in Health 22 (May 2019): S43. http://dx.doi.org/10.1016/j.jval.2019.04.061.
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Bellera, Carine, Cécile Proust-Lima, Lawrence Joseph, Pierre Richaud, Jeremy Taylor, Howard Sandler, James Hanley, and Simone Mathoulin-Pélissier. "A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication." Statistical Methods in Medical Research 27, no. 4 (September 1, 2016): 1271–81. http://dx.doi.org/10.1177/0962280216660127.
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Background Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure. Objective Our aim was to highlight the flexibility of a two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework. For this purpose, we monitored the prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy. In the presence of rising prostate-specific antigens after external beam radiation therapy, salvage hormone therapy can be prescribed to reduce both the prostate-specific antigens concentration and the risk of clinical failure, an illustration of confounding by indication. We focused on the assessment of the prognostic value of hormone therapy and prostate-specific antigens trajectory on the risk of failure. Methods We used a two-stage model within a Bayesian framework to assess the role of the prostate-specific antigens profile on clinical failure while accounting for a secondary treatment prescribed by indication. We modeled prostate-specific antigens using a hierarchical piecewise linear trajectory with a random changepoint. Residual prostate-specific antigens variability was expressed as a function of prostate-specific antigens concentration. Covariates in the survival model included hormone therapy, baseline characteristics, and individual predictions of the prostate-specific antigens nadir and timing and prostate-specific antigens slopes before and after the nadir as provided by the longitudinal process. Results We showed positive associations between an increased prostate-specific antigens nadir, an earlier changepoint and a steeper post-nadir slope with an increased risk of failure. Importantly, we highlighted a significant benefit of hormone therapy, an effect that was not observed when the prostate-specific antigens trajectory was not accounted for in the survival model. Conclusion Our modeling strategy was particularly flexible and accounted for multiple complex features of longitudinal and survival data, including the presence of a random changepoint and a time-dependent covariate.
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Pan, Bo-Lin, Chia-Pei Chou, Kun-Siang Huang, Pin-Jie Bin, Kuei-Hau Luo, and Hung-Yi Chuang. "The Pattern of Hemoglobin A1C Trajectories and Risk of Herpes Zoster Infection: A Follow-Up Study." International Journal of Environmental Research and Public Health 19, no. 5 (February 24, 2022): 2646. http://dx.doi.org/10.3390/ijerph19052646.
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To investigate the risks of herpes zoster (HZ) infection among heterogeneous HbA1C trajectories of patients with newly diagnosed type 2 diabetes, this cohort study used data from the Chang Gung Research Database (CGRD), from the 10-year period of 1 January 2007 to 31 December 2017. We applied group-based trajectory modeling (GBTM) to identify the patterns of HbA1C trajectories, and multiple Cox proportional hazards regressions were used to estimate the hazard ratio (HR) for the risk of HZ infection with adjustment of age, sex, and comorbidities. This study enrolled 121,999 subjects to perform the analysis. The GBTM identified four HbA1C trajectories: ‘good control’ (58.4%), ‘high decreasing’ (8.9%), ‘moderate control’ (25.1%), and ‘poor control’ (7.6%) with the mean HbA1C of 6.7% (50 mmol/mol), 7.9% (63 mmol/mol), 8.4% (68 mmol/mol), and 10.7% (93 mmol/mol) respectively. The risk of HZ was significantly higher in the poor control with an HR = 1.44 (95% CI 1.26–1.64) after adjustment for confounders and comorbidities. The risk of HZ infection for the high decreasing group (initially poor then rapidly reaching optimal control) was nonsignificant compared to the good control group. In conclusion, the patients with poor glycemic control (mean HbA1C = 10.7%) had the highest risk of HZ infection. The patients with initial hyperglycemia then reaching optimal control could have a lower risk of HZ infection.
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Cipkar, Christopher, Srishti Kumar, Kednapa Thavorn, and Natasha Kekre. "Decision Analysis of Allogeneic Stem Cell Transplantation for Primary Myelofibrosis." Blood 138, Supplement 1 (November 5, 2021): 2923. http://dx.doi.org/10.1182/blood-2021-147376.
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Abstract Introduction: Primary myelofibrosis (PMF) is a chronic myeloproliferative neoplasm characterized by cytopenias, splenomegaly and a risk of leukemic transformation. In light of newer therapies such as ruxolitinib that are not curative but can improve quality of life, the timing of transplant needs more in-depth analysis to determine which patients would benefit from an early versus delayed transplant strategy. Methods: We developed a Markov cohort model to simulate the long-term disease trajectory in patients with PMF and predict the optimal timing of transplant stratified by a Dynamic International Prognostic Scoring System (DIPSS) risk. Our model consisted of five health states including alive with PMF, alive after leukemic transformation, alive after transplant, alive after relapse and death. Transition probabilities between health states were acquired from published literature on the natural history of the disease and outcomes following transplantation. The model was run over a patient's lifetime until all patients transitioned to the death state. We used a cycle length of one-month to represent the natural progression of PMF. The structure of the Markov model is delineated in Figure 1. In this decision model, a hypothetical cohort of patients begins in the Alive-PMF state and can transition after each monthly cycle to other health states. Patients could remain in an alive state for any number of cycles without transitioning to another health state, indicated by the arrow wheels. We performed probabilistic analyses by jointly varying all model parameters over 1000 simulations and calculated 95% confidence intervals (CI) for the model outcome. Results: Regardless of DIPSS risk, all patients with PMF benefited from a transplant with respect to life expectancy gained (Figure 2). Life expectancy gains from a transplant among patients with high-risk disease peak at 9.7 months (95% CI: 9.5-9.9) from diagnosis, while patients with intermediate-2 disease have a peak gain in life expectancy at 16.6 months (95% CI: 16.4-16.8). Intermediate-1 DIPSS risk patients have a more delayed time frame where the net gain in life expectancy from transplant begins to slow at 20.5 months (95% CI: 20.2-20.7). Patients with low risk DIPSS had greater net gain in life expectancy the longer transplant was delayed; this trend plateaued at 29 to 45 months, when thereafter net gain in life expectancy begins to be lost (Figure 1). Conclusion: Our modeling suggests that transplant processes including donor selection and pre-transplant work-up are indicated upfront for patients diagnosed with intermediate-2 and high risk PMF, while this can be delayed for patients with low or intermediate-1 risk disease. This model should provide clinicians with guidance on when to refer eligible patients with PMF for transplantation. Figure 1 Figure 1. Disclosures Kekre: Novartis: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Celgene: Consultancy, Honoraria.
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Rebić, N., E. C. Sayre, M. Law, and M. De Vera. "POS0272 IMPACT OF THE COVID-19 PANDEMIC ON ADHERENCE TO DISEASE MODIFYING DRUGS AMONG PATIENTS WITH RHEUMATIC DISEASES: A POPULATION-BASED, INTERRUPTED TIME SERIES ANALYSIS." Annals of the Rheumatic Diseases 82, Suppl 1 (May 30, 2023): 374.2–375. http://dx.doi.org/10.1136/annrheumdis-2023-eular.4191.
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BackgroundAlthough studies have quantified adherence to medications among patients with rheumatic diseases (RD) during the COVID-19, lack of direct pre-pandemic comparison precludes understanding of impact of the pandemic.ObjectivesOur objective was to evaluate the effect of the COVID-19 pandemic on adherence to disease modifying drugs (DMARDs) including conventional synthetic (csDMARDs) and targeted synthetic (tsDMARDs).MethodsWe linked population-based health data on all physician visits, hospital admissions, and all dispensed medications, regardless of payer in British Columbia from 01/01/1996 to 3/31/2021. We identified prescriptions for csDMARDs (including methotrexate, hydroxychloroquine) and tsDMARDs, namely anti-TNFs (including infliximab, etanercept, adalimumab) and rituximab using drug identification numbers among indicated individuals with RD. We defined March 11, 2020, as the ‘index date’ which corresponded to the date that mitigation measures for the COVID-19 pandemic were first introduced. We assessed adherence as proportion days covered (PDC), calculated monthly in the 12 months before and 12 months after the index date. We used interrupted time-series models, namely segmented regression to estimate changes and trends in adherence before and after the index date.ResultsOur analysis showed that the mean PDCs for all included DMARDs stayed relatively steady in the 12 months before and after mitigation measures were introduced (seeTable 1). Adherence was highest among anti-TNFs, methotrexate, and azathioprine. Anti-TNFs were on a downward trajectory 12 months prior to the index date. Interrupted time-series modeling demonstrated statistically significant differences in the trends in PDCs post- vs. pre-mitigation measures for all anti-TNFS (slope [∂]: 1.38, standard error [SE]: 0.23), infliximab (∂: 1.35, SE: 0.23), adalimumab (∂: 0.82, SE: 0.25), and etanercept (∂: 1.07, SE: 0.25) (seeFigure 1a). Conversely, the csDMARDs were on a flatter trajectory, and methotrexate (∂: -0.53, SE: 0.16), leflunomide (∂: 0.43, SE: 0.08), mycophenolate (∂: -1.26, SE: 0.48), cyclophosphamide (∂: 0.29, SE: 0.05), minocycline (∂: 0.04, SE: 0.02), chloroquine (∂: 0.02, SE: 0.00) showed statistically significant changes in estimated PDC trajectory after mitigation measures were introduced (seeFigure 1b).ConclusionThis population-based study demonstrates that messaging and pandemic mitigation measures did not affect adherence to DMARDs.Table 1.Mean PDC 1 year before and after mitigation measures for the COVID-19 pandemic were introduced.MedicationMean PDC (%) 12 months before index dateMean PDC (%) 12 months after index datecsDMARDsmethotrexate28.926.8azathioprine21.819.5sulfasalazine16.214.9leflunomide14.313.0cyclosporine13.711.5hydroxychloroquine10.59.6mycophenolate4.52.9antimalarials4.43.9penicillamine3.53.4cyclophosphamide1.50.7chlorambucil1.20.4minocycline1.10.9gold0.50.2chloroquine0.10.0tsDMARDsanti-TNFs52.149.2infliximab41.838.3adalimumab40.336.8etanercept31.828.9rituximab3.42.9REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
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Phadnis, Milind A., James B. Wetmore, Theresa I. Shireman, Edward F. Ellerbeck, and Jonathan D. Mahnken. "An ensemble survival model for estimating relative residual longevity following stroke: Application to mortality data in the chronic dialysis population." Statistical Methods in Medical Research 26, no. 6 (September 24, 2015): 2667–80. http://dx.doi.org/10.1177/0962280215605107.
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Time-dependent covariates can be modeled within the Cox regression framework and can allow both proportional and nonproportional hazards for the risk factor of research interest. However, in many areas of health services research, interest centers on being able to estimate residual longevity after the occurrence of a particular event such as stroke. The survival trajectory of patients experiencing a stroke can be potentially influenced by stroke type (hemorrhagic or ischemic), time of the stroke (relative to time zero), time since the stroke occurred, or a combination of these factors. In such situations, researchers are more interested in estimating lifetime lost due to stroke rather than merely estimating the relative hazard due to stroke. To achieve this, we propose an ensemble approach using the generalized gamma distribution by means of a semi-Markov type model with an additive hazards extension. Our modeling framework allows stroke as a time-dependent covariate to affect all three parameters (location, scale, and shape) of the generalized gamma distribution. Using the concept of relative times, we answer the research question by estimating residual life lost due to ischemic and hemorrhagic stroke in the chronic dialysis population.
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Rahman, Mohammad Habibur, Thierry Kittel-Ouimet, Maarouf Saad, Jean-Pierre Kenné, and Philippe S. Archambault. "Development and Control of a Robotic Exoskeleton for Shoulder, Elbow and Forearm Movement Assistance." Applied Bionics and Biomechanics 9, no. 3 (2012): 275–92. http://dx.doi.org/10.1155/2012/956310.
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World health organization reports, annually more than 15 million people worldwide suffer a stroke and cardiovascular disease, among which 85% of stroke patients incur acute arm impairment, and 40% of victims are chronically impaired or permanently disabled. This results a burden on the families, communities and to the country as well. Rehabilitation programs are the main way to promote functional recovery in these individuals. Since the number of such cases is constantly growing and that the duration of treatment is long, an intelligent robot could significantly contribute to the success of these programs. We therefore developed a new 5DoFs robotic exoskeleton namedMARSE-5 (motion assistive robotic-exoskeleton for superior extremity) that supposed to be worn on the lateral side of upper arm to rehabilitate and ease the shoulder, elbow and forearm movements. This paper focused on the design, modeling, development and control of the proposedMARSE-5. To control the exoskeleton, a nonlinear sliding mode control (SMC) technique was employed. In experiments, trajectory tracking that corresponds to typical passive rehabilitation exercises was carried out. Experimental results reveal that the controller is able to maneuver theMARSE-5 efficiently to track the desired trajectories.
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Pinaire, Jessica, Etienne Chabert, Jérôme Azé, Sandra Bringay, and Paul Landais. "Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome." Journal of Healthcare Engineering 2021 (May 25, 2021): 1–12. http://dx.doi.org/10.1155/2021/5531807.
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Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events’ prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients’ patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.
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Ajrouche, Aya, Candice Estellat, Yann De Rycke, and Florence Tubach. "Trajectories of Adherence to Low-Dose Aspirin Treatment Among the French Population." Journal of Cardiovascular Pharmacology and Therapeutics 25, no. 1 (July 24, 2019): 37–46. http://dx.doi.org/10.1177/1074248419865287.
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Background: Previous studies have shown that adherence to low-dose aspirin (LDA) is suboptimal. However, these studies were based on an average measure of adherence during follow-up, ignoring its dynamic process over time. We described the trajectories of adherence to LDA treatment among the French population over 3 years of follow-up. Methods: We identified a cohort of 11 793 new LDA users, aged ≥50 years in 2010, by using the French national health-care database. Patients included had at least 3 years of history in the database before study entry to exclude prevalent aspirin users and to assess baseline comorbidities. They were followed from the first date of LDA supply (the index date) until the first date among death, exit from the database, or 3 years after the index date. Adherence to LDA was assessed every 3 months by using the proportion of days covered (PDC) and dichotomized with a cutoff of PDC of 0.8. We used group-based trajectory modeling to identify trajectories of LDA adherence. Predictors of LDA adherence trajectory membership were identified by multinomial logistics regression. Results: We identified 4 trajectories of adherence among new LDA users: the not-adherents (4737 [40.2%]), the delayed not-adherents (gradual decrease in adherence probability, 1601 [13.6%]), the delayed adherents (gradual increase in adherence probability, 1137 [9.6%]), and the persistent adherents (4318 [36.6%]). The probability of belonging to the not-adherent group was increased with female sex, low socioeconomic status, and polymedication and was reduced with a secondary indication for LDA use, such as diabetes, hypertension, and dementia, at least 4 consultations in the previous year, or 1 hospitalization or a cardiologist consultation in the 3 months before the index date. Conclusion: This study provides a dynamic picture of adherence behaviors among new LDA users and underlines the presence of critical trajectories that intervention could target to improve adherence.
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Miedema, Harald S., Anita Feleus, Sita M. A. Bierma-Zeinstra, Trynke Hoekstra, Alex Burdorf, and Bart W. Koes. "Disability Trajectories in Patients With Complaints of Arm, Neck, and Shoulder (CANS) in Primary Care: Prospective Cohort Study." Physical Therapy 96, no. 7 (July 1, 2016): 972–84. http://dx.doi.org/10.2522/ptj.20150226.
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Abstract Background Nontraumatic complaints of arm, neck, and shoulder (CANS) represent an important health issue, with a high prevalence in the general working age population and huge economic impact. Nevertheless, only few prospective cohort studies for the outcome of CANS are available. Objectives The purpose of this study was to identify disability trajectories and associated prognostic factors during a 2-year follow-up of patients with a new episode of CANS in primary care. Design This was a prospective cohort study. Methods Data of 682 participants were collected through questionnaires at baseline and every 6 months thereafter. Disability was measured with the Disabilities of the Arm, Shoulder and Hand questionnaire (DASH). Latent class growth mixture (LCGM) modeling was used to identify clinically meaningful groups of patients who were similar in their disability trajectory during follow-up. Multivariate multinomial regression analysis was used to evaluate associations between sociodemographic, complaint-related, physical, and psychosocial variables and the identified disability trajectories. Results Three disability trajectories were identified: fast recovery (67.6%), modest recovery (23.6%), and continuous high disability (8.8%). A high level of somatization was the most important baseline predictor of continuous high disability. Furthermore, poor general health, widespread complaints, and medium level of somatization were associated with this trajectory and >3 months complaint duration, musculoskeletal comorbidity, female sex, history of trauma, low educational level, low social support, and high complaint severity were associated with both continuous high disability and modest recovery. Age, kinesiophobia, and catastrophizing showed significant associations only with modest recovery. Limitations Loss to follow-up ranged from 10% to 22% at each follow-up measurement. Disabilities were assessed only with the DASH and not with physical tests. Misclassification by general practitioners regarding specific or nonspecific diagnostic category might have occurred. The decision for optimal LCGM model, resulting in the disability trajectories, remains arbitrary to some extent. Conclusions Three trajectories described the course of disabilities due to CANS. Several prognostic indicators were identified that can easily be recognized in primary care. As some of these prognostic indicators may be amenable for change, their presence in the early stages of CANS may lead to more intensive or additional interventions (eg, psychological or multidisciplinary therapy). Further research focusing on the use of these prognostic indicators in treatment decisions is needed to further substantiate their predictive value.
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Bansal, Aasthaa, Patrick J. Heagerty, Lurdes Y. T. Inoue, David L. Veenstra, Charles J. Wolock, and Anirban Basu. "A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing." Medical Decision Making 42, no. 4 (November 7, 2021): 474–86. http://dx.doi.org/10.1177/0272989x211049213.
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Background Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients’ earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. Methods We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient’s longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. Results When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an “always test” strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. Conclusions A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. Highlights In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk. We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)–based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs. A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.
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Wang, Xin Shelley, Lisa M. Hess, Fang-Yu Lin, Tsun Hsuan Chen, Elizabeth Gonzalez, Araceli Garcia-Gonzalez, Anindya Chatterjee, Astra M. Liepa, Loretta A. Williams, and Charles S. Cleeland. "Patient-reported symptom burden and functioning in patients with advanced esophageal, gastroesophageal junction (GEJ), and gastric cancer undergoing chemotherapy." Journal of Clinical Oncology 39, no. 3_suppl (January 20, 2021): 183. http://dx.doi.org/10.1200/jco.2021.39.3_suppl.183.
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183 Background: For advanced upper gastrointestinal (GI) cancers, patient-reported symptoms are driven by both disease and treatment toxicities. This real-world longitudinal study was designed to evaluate patient-reported outcomes (PROs) of symptom burden and functioning among patients (pts) treated with anti-cancer therapy. Methods: Adult pts with advanced esophageal, gastroesophageal junction (GEJ), or gastric cancer were invited to participate in a prospective, longitudinal study. PRO assessment included pre-treatment and weekly completion of the M.D. Anderson Symptom Inventory GI module (MDASI-GI) as pts initiated a new line of therapy. Clinical and disease characteristics and treatment details were obtained from electronic health records. Up to 12 weeks longitudinal PRO data were analyzed using mixed-effect modeling with random intercept. Pts were classified into high or low symptom groups by group-based trajectory modeling. Time to deterioration in functioning (defined as a ≥2-point increase from baseline on 0-10 scale) was examined with Kaplan-Meier method. Results: At the interim analysis, 76 pts had enrolled: 33 (43%) were chemotherapy naïve for advanced disease, 18 (24%) had received 1 line of therapy, 14 (18%) had received > 1 line of therapy, and 11 (14%) were excluded from this analysis due to study discontinuation without follow-up assessment. Among the 65 eligible pts in this analysis, 46 (70.8%) were male, most were white (n = 56, 86.2%), and 44 (67.7%) had an ECOG performance status score of 0-1. Pain, fatigue, disturbed sleep, lack of appetite and inability to eat were the most common severe symptoms reported prior to and during therapy. Pts who had received one line of therapy reported worsening symptoms of pain, fatigue, lack of appetite, difficulty swallowing, and reduced general activity, work, and enjoyment of life over time (group and time interaction P<.05). Pts with > 1 line of prior therapy had increased neuropathy symptoms (numbness/tingling) than those who were treatment-naïve ( P= 0.022). The median time to functioning deterioration of ≥2 points from baseline on the MDASI-“General activity” was 7 weeks. Up to 57% of pts were classified into high symptom trajectory group of pain, fatigue, disturbed sleep, and drowsiness with moderate to severe symptoms (≥4 points out of possible 10) over time; group membership was related to moderate to severe symptoms at baseline, after adjusting for age, sex, ECOG performance status, cancer site and prior therapies. Conclusions: This real-world observational study suggests that pts with advanced stage upper GI cancer undergoing standard therapy report high symptom burden. These preliminary findings support the use of a targeted symptom monitoring using well-defined PROs during active therapy, thereby adding the knowledge obtained from PROs to improve routine patient care.
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Halpin, Sean. "METHODOLOGICAL APPROACHES TO GERONTOLOGICAL CANCER RESEARCH." Innovation in Aging 6, Supplement_1 (November 1, 2022): 147. http://dx.doi.org/10.1093/geroni/igac059.586.
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Abstract The wide range of gerontological cancer research necessitates a variety of methodological approaches. In our symposium, we bring together researchers who represent varied approaches to studying multiple cancer types—with a focus on demonstrating how to apply different methods. First, Ye, will discuss the use of a unique cross-sequential design to facilitate comparison between health change in long-term older cancer survivors and demographically-matched older adults with no history of cancer. Zanwar, will present disparities in cancer screening using secondary nationally representative complex survey data, provide examples of survey data that can be utilized in aging and cancer prevention and control research, and present challenges and opportunities for using survey data. Von Ah, will discuss research methods pertaining to a series of non-pharmacological clinical trials and offer insight to reducing barriers and improving acceptability to technology-based intervention programs in older breast cancer survivors. Next, Castaneda will present quantitative (i.e., group trajectory modeling, conditional Poisson regression) and qualitative approaches to understand the role of monoclonal gammopathy of undetermined significance in healthcare utilization and progression to multiple myeloma. Last, Halpin will discuss the use of naturally occurring data such as participant observation and audio recordings to evaluate education for patients with multiple myeloma preparing for autologous stem cell transplant. Understanding how a variety of methodological approaches are applied to gerontological cancer research will help facilitate a broader understanding of the tools available for these studies.
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Wang, Shikun, Jing Ning, Ying Xu, Ya-Chen Tina Shih, Yu Shen, and Liang Li. "Longitudinal varying coefficient single-index model with censored covariates." Biometrics 80, no. 1 (January 29, 2024). http://dx.doi.org/10.1093/biomtc/ujad006.
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ABSTRACT It is of interest to health policy research to estimate the population-averaged longitudinal medical cost trajectory from initial cancer diagnosis to death, and understand how the trajectory curve is affected by patient characteristics. This research question leads to a number of statistical challenges because the longitudinal cost data are often non-normally distributed with skewness, zero-inflation, and heteroscedasticity. The trajectory is nonlinear, and its length and shape depend on survival, which are subject to censoring. Modeling the association between multiple patient characteristics and nonlinear cost trajectory curves of varying lengths should take into consideration parsimony, flexibility, and interpretation. We propose a novel longitudinal varying coefficient single-index model. Multiple patient characteristics are summarized in a single-index, representing a patient's overall propensity for healthcare use. The effects of this index on various segments of the cost trajectory depend on both time and survival, which is flexibly modeled by a bivariate varying coefficient function. The model is estimated by generalized estimating equations with an extended marginal mean structure to accommodate censored survival time as a covariate. We established the pointwise confidence interval of the varying coefficient and a test for the covariate effect. The numerical performance was extensively studied in simulations. We applied the proposed methodology to medical cost data of prostate cancer patients from the Surveillance, Epidemiology, and End Results-Medicare-Linked Database.
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Skelton, Megan, Ewan Carr, Joshua E. J. Buckman, Molly R. Davies, Kimberley A. Goldsmith, Colette R. Hirsch, Alicia J. Peel, et al. "Trajectories of depression and anxiety symptom severity during psychological therapy for common mental health problems." Psychological Medicine, December 13, 2022, 1–11. http://dx.doi.org/10.1017/s0033291722003403.
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Abstract Background There is substantial variation in patient symptoms following psychological therapy for depression and anxiety. However, reliance on endpoint outcomes ignores additional interindividual variation during therapy. Knowing a patient's likely symptom trajectories could guide clinical decisions. We aimed to identify latent classes of patients with similar symptom trajectories over the course of psychological therapy and explore associations between baseline variables and trajectory class. Methods Patients received high-intensity psychological treatment for common mental health problems at National Health Service Improving Access to Psychological Therapies services in South London (N = 16 258). To identify trajectories, we performed growth mixture modelling of depression and anxiety symptoms over 11 sessions. We then ran multinomial regressions to identify baseline variables associated with trajectory class membership. Results Trajectories of depression and anxiety symptoms were highly similar and best modelled by four classes. Three classes started with moderate-severe symptoms and showed (1) no change, (2) gradual improvement, and (3) fast improvement. A final class (4) showed initially mild symptoms and minimal improvement. Within the moderate-severe baseline symptom classes, patients in the two showing improvement as opposed to no change tended not to be prescribed psychotropic medication or report a disability and were in employment. Patients showing fast improvement additionally reported lower baseline functional impairment on average. Conclusions Multiple trajectory classes of depression and anxiety symptoms were associated with baseline characteristics. Identifying the most likely trajectory for a patient at the start of treatment could inform decisions about the suitability and continuation of therapy, ultimately improving patient outcomes.