Academic literature on the topic 'Clinical risk prediction'
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Journal articles on the topic "Clinical risk prediction"
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Dolan, M., and M. Doyle. "Violence risk prediction." British Journal of Psychiatry 177, no. 4 (October 2000): 303–11. http://dx.doi.org/10.1192/bjp.177.4.303.
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BackgroundViolence risk prediction is a priority issue for clinicians working with mentally disordered offenders.AimsTo review the current status of violence risk prediction research.MethodLiterature search (Medline). Key words: violence, risk prediction, mental disorder.ResultsSystematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings.ConclusionsViolence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.
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Halabi, Susan, Cai Li, and Sheng Luo. "Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.19.00068.
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The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators often are interested in examining the relationship among host, tumor-related, and environmental variables in predicting clinical outcomes. We distinguish between static and dynamic prediction models. In static prediction modeling, variables collected at baseline typically are used in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates collected during treatment or follow-up and hence provide accurate predictions of patients’ prognoses. To date, most risk assessment models in oncology have been based on static models. In this article, we cover topics related to the analysis of prognostic factors, centering on factors that are both relevant at the time of diagnosis or initial treatment and during treatment. We describe the types of risk prediction and then provide a brief description of the penalized regression methods. We then review the state-of-the art methods for dynamic prediction and compare the strengths and limitations of these methods. Although static models will continue to play an important role in oncology, developing and validating dynamic models of clinical outcomes need to take a higher priority. A framework for developing and validating dynamic tools in oncology seems to still be needed. One of the limitations in oncology that may constrain modelers is the lack of access to longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider longitudinal biomarker data and outcomes so that prediction can be continually updated.
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Lawrie, Stephen M. "Clinical risk prediction in schizophrenia." Lancet Psychiatry 1, no. 6 (November 2014): 406–8. http://dx.doi.org/10.1016/s2215-0366(14)70310-4.
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Fonarow, Gregg C., Deborah B. Diercks, and W. Franklin Peacock. "Assessing Clinical Risk Prediction Tools." Annals of Emergency Medicine 50, no. 6 (December 2007): 741–42. http://dx.doi.org/10.1016/j.annemergmed.2007.05.028.
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Nguyen, A. Tuan, Hyewon Jeong, Eunho Yang, and Sung Ju Hwang. "Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9081–91. http://dx.doi.org/10.1609/aaai.v35i10.17097.
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Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since low loss could result from overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on the feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model.
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Lambert, Samuel A., Gad Abraham, and Michael Inouye. "Towards clinical utility of polygenic risk scores." Human Molecular Genetics 28, R2 (July 31, 2019): R133—R142. http://dx.doi.org/10.1093/hmg/ddz187.
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Abstract Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.
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Ky, Bonnie, Carla L. Warneke, Daniel John Lenihan, Puneet S. Cheema, Dennis Frederic Moore, Mark G. Campbell, Chilakamarri Yeshwant, et al. "Clinical risk prediction in anthracycline cardiotoxicity." Journal of Clinical Oncology 32, no. 15_suppl (May 20, 2014): 9624. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.9624.
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van Geel, Tineke, Geert-Jan Dinant, Piet Geusens, and Joop van den Bergh. "Fracture risk prediction in clinical practice." Maturitas 81, no. 1 (May 2015): 112. http://dx.doi.org/10.1016/j.maturitas.2015.02.035.
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Magee, L. A., and P. v. Dadelszen. "Clinical risk prediction of pre-eclampsia." BMJ 342, apr07 4 (April 7, 2011): d1863. http://dx.doi.org/10.1136/bmj.d1863.
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Li, Juan, Mingyao Lai, and Linbo Cai. "MEDB-58. Risk factors and risk prediction models for medulloblastoma recurrence." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i119—i120. http://dx.doi.org/10.1093/neuonc/noac079.432.
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Abstract BACKGROUND: There is a clear need for systematic appraisal of models/factors predicting medulloblastoma recurrence because clinical decisions about adjuvant treatment are taken on the basis of such variables. METHODS： A total of 273 patients diagnosed with medulloblastoma were retrospectively analyzed. The pre-rediotherapy neutrophile-lymphocyte ratio (NLR) was calculated, and other clinical characteristics were collected such as genetic type , whether with dissemination, degree with excision. The Kaplan-Meier method was used for survival analysis. Cox regression models was used to identify independent prognostic factors. R software was used to develop a nomogram with all the independent prognostic factors included. The prognostic predictive ability of the nomogram was evaluated by Concordance-index (C-index), area under the curve (AUC), and calibration curve. RESULTS: The median median progression-free survival time was 63.8 months in overall cohort. Univariate and multivariate cox hazards regression analysis identified independent prognostic factors associated with the PFS of patients with medulloblastoma to include age, residual tumor volume ＞1.5cm3 after excision, NLR ＞4.5, whether with dissemination before RT, and whether the genetic type is group 3,which were integrated to establish a nomogram. The C-indexes of nomogram were 0.696 and 0.676 in the training and validation cohort, respectively. The AUC of predicting 3-years PFS showed satisfactory accuracy as well (Training cohort: AUC=0.696; Validation cohort: AUC=0.676). The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the PFS showed significant differences between nomogram predictive low-, and high groups (P < 0.001). CONCLUSIONS: We found that pre-treatment NLR was an independent prognostic factor for recurrence or metastasis of medulloblastoma after treatment. In combination with NRL and clinical factors, nomogram has a good prediction of PFS in patients with medulloblastoma after radiotherapy. It has the potential to facilitate more precise risk stratification to guide personalized treatment of medulloblastoma.
Dissertations / Theses on the topic "Clinical risk prediction"
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Townsend, Daphne. "Clinical trial of estimated risk stratification prediction tool." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27926.
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This work presents doctors with a model of the estimated degree of risk of rare and important neonatal outcomes to aid in better decisions and improved allocation of equipment and resources. An extensive list of admission day parameters is reduced to minimum variable sets to create models for outcomes that are relevant to decision-making in the neonatal intensive care unit. Models are applied to a special collection of cases and compared to neonatologists' risk estimates. A comparative analysis of physician's predictions and the models' discrimination abilities highlights areas of success and areas that can be improved for future trials. Doctors responded positively to the prediction interface concept and to the estimated risk stratification models. Physicians' strengths identified outcomes that could benefit from increased sensitivity.
A substantial effort was made to conduct the usability and performance evaluations within the ethical standards that are especially important for engineering healthcare management applications.
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Jackson, Rebecca L. "Contextualized Risk Assessment in Clinical Practice: Utility of Actuarial, Clinical, and Structured Clinical Approaches to Predictions of Violence." Thesis, University of North Texas, 2004. https://digital.library.unt.edu/ark:/67531/metadc4603/.
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Assessing offenders' risk of future violent behavior continues to be an important yet controversial role of forensic psychologists. A key debate is the relative effectiveness of assessment methods. Specifically, actuarial methods (see Quinsey et al., 1998 for a review) have been compared and contrasted to clinical and structured clinical methods (see e.g. Hart, 1998; Webster et al., 1997). Proponents of each approach argue for its superiority, yet validity studies have made few formal comparisons. In advancing the available research, the present study examines systematically the type of forensic case (i.e., sexual violence versus nonsexual violence) and type of assessment method (i.e., actuarial, structured clinical, and unstructured clinical). As observed by Borum, Otto, and Golding (1993), forensic decision making can also be influenced by the presence of certain extraneous clinical data. To address these issues, psychologists and doctoral students attending the American Psychology Law Society conference were asked to make several ratings regarding the likelihood of future sexual and nonsexual violence based on data derived from actual defendants with known outcomes. Using a mixed factorial design, each of these assessment methods were investigated for its influence on decision-makers regarding likelihood of future violence and sexually violent predator commitments. Finally, the potentially biasing effects of victim impact statements on resultant decisions were also explored.
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Grant, Stuart William. "Risk prediction models in cardiovascular surgery." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/risk-prediction-models-in-cardiovascular-surgery(1befbc5d-2aa6-4d24-8c32-e635cf55e339).html.
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Objectives: Cardiovascular disease is the leading cause of mortality and morbidity in the developed world. Surgery can improve prognosis and relieve symptoms. Risk prediction models are increasingly being used to inform clinicians and patients about the risks of surgery, to facilitate clinical decision making and for the risk-adjustment of surgical outcome data. The importance of risk prediction models in cardiovascular surgery has been highlighted by the publication of cardiovascular surgery outcome data and the need for risk-adjustment. The overall objective of this thesis is to advance risk prediction modelling in cardiovascular surgery with a focus on the development of models for elective AAA repair and assessment of models for cardiac surgery. Methods: Three large clinical databases (two elective AAA repair and one cardiac surgery) were utilised. Each database was cleaned prior to analysis. Logistic regression was used to develop both regional and national risk prediction models for mortality following elective AAA repair. A regional model to identify the risk of developing renal failure following elective AAA repair was also developed. The performance of a widely used cardiac surgery risk prediction model (the logistic EuroSCORE) over time was evaluated using a national cardiac database. In addition an updated model version (EuroSCORE II) was validated and both models’ performance in emergency cardiac surgery was evaluated. Results: Regional risk models for mortality following elective AAA repair (VGNW model) and a model to predict post-operative renal failure were developed. Validation of the model for mortality using a national dataset demonstrated good performance compared to other available risk models. To improve generalisability a national model (the BAR score) with better discriminatory ability was developed. In a prospective validation of both models using regional data, the BAR score demonstrated excellent discrimination overall and good discrimination in procedural sub-groups. The EuroSCORE was found to have lost calibration over time due to a fall in observed mortality despite an increase in the predicted mortality of patients undergoing cardiac surgery. The EuroSCORE II demonstrated good performance for contemporary cardiac surgery. Both EuroSCORE models demonstrated inadequate performance for emergency cardiac surgery. Conclusions: Risk prediction models play an important role in cardiovascular surgery. Two accurate risk prediction models for mortality following elective AAA repair have been developed and can be used to risk-adjust surgical outcomes and facilitate clinical decision making. As surgical practice changes over time risk prediction models may lose accuracy which has implications for their application. Cardiac risk models may not be sufficiently accurate for high-risk patient groups such as those undergoing emergency surgery and specific emergency models may be required. Continuing research into new risk factors and model outcomes is needed and risk prediction models may play an increasing role in clinical decision making in the future.
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Donovan, Brittney Marie. "Early risk prediction tools for gestational diabetes mellitus." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6408.
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Gestational diabetes mellitus (GDM) is the most common metabolic complication in pregnancy and is associated with substantial maternal and neonatal morbidity. The standard of care for GDM in most developed countries is universal mid- to late- pregnancy (24-28 weeks gestation) glucose testing. While earlier diagnosis and treatment could improve pregnancy outcomes, tools for early identification of risk for GDM are not commonly used in practice. Existing models for predicting GDM risk within the first trimester of pregnancy based on maternal risk factors perform only modestly in the clinical setting. Heavy reliance on history of GDM to predict GDM development in the current pregnancy prevents these tools from being applicable to nulliparous women (i.e., women who have never given birth). In order to offer timely preventive intervention and enhanced antenatal care to nulliparous women, we need to be able to accurately identify those at high risk for GDM early in pregnancy.
Data from the California Office of Statewide Health Planning and Development Linked Birth File was used to address three aims: 1) improve early pregnancy prediction of GDM risk in nulliparous women through development of a risk factor-based model, 2) conduct a systematic review and meta-analysis assessing the relationship between first trimester prenatal screening biomarker levels and development of GDM, and 3) determine if the addition of first and second trimester prenatal screening biomarkers to risk factor-based models will improve early prediction of GDM in nulliparous women.
We developed a clinical prediction model including five well-established risk factors for GDM (race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension). Our model had moderate predictive performance among all nulliparous women, and performed particularly well among Hispanic and Black women when assessed within specific racial/ethnic groups. Our risk prediction model also showed superior performance over the commonly used American College of Obstetricians and Gynecologists (ACOG) screening guidelines, encouraging the prompt incorporation of this tool into preconception and prenatal care.
Biomarkers commonly assessed in prenatal screening have been associated with a number of adverse perinatal and birth outcomes. However, reports on the relationship between first trimester measurements of prenatal screening biomarkers and GDM development are inconsistent. Our meta-analysis demonstrated that women who are diagnosed with GDM have lower first trimester multiple of the median (MoM) levels of both pregnancy associated plasma protein-A (PAPP-A) and free β-human chorionic gonadotropin (free β-hCG) than women who remain normoglycemic throughout pregnancy.
Findings from our meta-analysis suggested that incorporation of prenatal screening biomarkers in clinical risk prediction models could aid in earlier identification of women at risk of developing GDM. Upon linkage of California Office of Statewide Health Planning and Development Linked Birth File and California Prenatal Screening Program records, we found that decreased levels of first trimester PAPP-A, increased second trimester unconjugated estriol, and increased second trimester dimeric inhibin A were associated with GDM development in nulliparous women. However, the addition of these biomarkers in clinical models did not offer improvements to the clinical utility (i.e., risk stratification) of models including maternal risk factors alone.
Our findings demonstrate that incorporation of maternal risk factors in a clinical risk prediction model can more accurately identify nulliparous women at high risk for GDM early in pregnancy compared to current standard practice. The maternal characteristics model we developed is based on clinical history and demographic variables that are already routinely collected by clinicians in the United States so that it may be easily adapted into existing prenatal care practice and screening programs. Future work should focus on evaluating the clinical impact of model implementation on maternal and infant outcomes as well as financial costs to the health care system.
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Ghassemi, Marzyeh. "Representation learning in multi-dimensional clinical timeseries for risk and event prediction." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112389.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 99-108).
There are major practical and technical barriers to understanding human health, and therefore a need for methods that thrive on large, complex, noisy data. In this work, we present machine learning methods that distill large amounts of heterogeneous health data into latent state representations. These representations are then used to estimate risks of poor outcomes, and response to intervention in multivariate physiological signals. We evaluate the reduced latent representations by 1) establishing their predictive value in important clinical tasks and 2) showing that the latent space representations themselves provide useful insight into underlying systems. In particular, we focus on case studies that can provide evidence-based risk assessment and forecasting in settings with guidelines that have not traditionally been data-driven. In this thesis we evaluate several methods to create patient representations, and use these features to predict important outcomes. Representation learning can be thought of as a form of phenotype discovery, where we attempt to discover spaces in the new representation that are markers of important events. We argue that these latent representations are useful markers when they 1) create better prediction results on outcomes of interest, and 2) do not duplicate features that are currently known bio-markers. We present four case studies of learning representations, and evaluate the representations on real predictive tasks. First, we create forward-facing prediction models using baseline clinical features, and those from a Latent Dirichlet Allocation (LDA) model trained with clinical progress notes. We then evaluate the per-patient latent state membership to predict mortality in an intensive care setting as time moves forward. Second, we use non-parametric Multi-task Gaussian Process (MTGP) hyper-parameters as latent features to estimate correlations within and between signals in sparse, heterogeneous time series data. We evaluate the hyper-parameters for forecasting missing signals in traumatic brain injury patients, and predicting mortality in intensive care unit patients. Third, we train switching-state autoregressive models (SSAMs) to model the underlying states that emit patient vital signs over time. We evaluate the time-specific latent state distributions as features to predict vasopressor onset and weaning in intensive care unit patients. Finally, we use statistical and symbolic features extracted from wearable ambulatory accelerometers (ACC) mounted to the neck to classify patient pathology, and stratify patients' risk of voice misuse. We evaluate the utility of both statistically generated features and symbolic representations of glottal pulses towards patient classification.
by Marzyeh Ghassemi.
Ph. D.
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Olsson, Thomas. "Risk Prediction at the Emergency Department." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4632.
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Payne, Beth. "Development, validation and pilot implementation of the miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) clinical risk prediction model." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51460.
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The hypertensive disorders of pregnancy (HDPs) are one of the leading causes of maternal death and morbidity in low-resourced countries due to delays in case identification and a shortage of health workers trained to manage these disorders. The objective of this thesis was to develop an evidence-based tool that could aid community-based health workers in decision making around the care of women with the HDPs.
This objective was achieved using a prospective cohort of data collected in five low and middle income countries (LMICs) to: (1) develop a clinical risk predication model using logistic regression (the “miniPIERS” model); (2) validate the miniPIERS model through bootstrapping and by applying the model to a second cohort of women with HDP; (3) extend and recalibrate the model to include the novel biomarker, pulse oximetry (SpO₂); and (4) translate the miniPIERS model into a decision rule for final creation of the PIERS on the Move decision algorithm. All stages of development of the PIERS on the Move tool included input from stakeholders in low-resourced countries.
The miniPIERS model, based on demographics, symptoms and clinical signs, accurately identified women who were at greatest risk of complications from the HDP (AUC ROC 0.77 [95% CI 0.74 – 0.80]). Internal validation demonstrated minimal overfitting with an average optimism of 0.037. Addition of SpO2 to the miniPIERS model resulted in a 20% increase in classification accuracy of high-risk women. Using an iterative review and feedback process including stakeholders from our partner low-resourced countries, decision points defined by the miniPIERS model were combined with the WHO recommendations for treatment of women with HDP to create a novel decision algorithm for population level risk screening. This decision algorithm identified high-risk women in the miniPIERS cohort with a sensitivity of 74.1% and specificity of 51.4%. Pilot testing of this tool in South Africa demonstrated potential impact but the true impact of use of the PIERS on the Move tool on maternal outcome rates requires assessment through an implementation study.Medicine, Faculty of
Obstetrics and Gynaecology, Department of
Graduate
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Yan, Jia. "Using Genetic Information in Risk Prediction for Alcohol Dependence." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2878.
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Family-based and genome-wide association studies (GWAS) of alcohol dependence (AD) have reported numerous associated variants. The clinical validity of these variants for predicting AD compared to family history has not yet been reported. These studies aim to explore the aggregate impact of multiple genetic variants with small effect sizes on risk prediction in order to provide a clinical interpretation of genetic contributions to AD. Data simulations showed that given AD’s prevalence and heritability, a risk prediction model incorporating all genetic contributions would have an area under the receiver operating characteristic curve (AUC) approaching 0.80, which is often a target AUC for screening. Adding additional environmental factors could increase the AUC to 0.95. Using the Collaborative Study on the Genetics of Alcoholism (COGA) and the Study of Addiction: Genes and Environment (SAGE) GWAS samples, we used several different sources to capture genetic information associated with AD in discovery samples, and then tested genetic sum scores created based on this information for predictive accuracy in validation samples. Scores were assessed separately for single nucleotide polymorphisms (SNPs) associated in candidate gene studies and in GWAS analyses. Candidate gene sum scores did not exhibit significant predictive accuracy, but SNPs meeting less stringent p-value thresholds in GWAS analyses did, ranging from mean estimates of 0.549 for SNPs meeting p<0.01 to 0.565 for SNPs meeting p<0.50. Variants associated with subtypes of AD showed that there is similarly modest and significant predictive ability for an externalizing subtype. Scores created based on all individual SNP effects in aggregate across the entire genome accounted for 0.46%-0.57% of the variance in AD symptom count, and have AUCs of 0.527 to 0.549. Additional covariates and environmental factors that are correlated with AD increased the AUC to 0.865. Family history was a better classifier of case-control status than genetic sum scores, with an AUC of 0.686 in COGA and 0.614 in SAGE. This project suggests that SNPs from candidate gene studies and genome-wide association studies currently have limited clinical validity, but there is potential for enhanced predictive ability with better detection of genetic factors contributing to AD.
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Martínez, Millana Antonio. "ASSESSMENT OF RISK SCORES FOR THE PREDICTION AND DETECTION OF TYPE 2 DIABETES MELLITUS IN CLINICAL SETTINGS." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/86209.
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Health and sociological indicators confirm that life expectancy is increasing, and so, the years that patients have to live with chronic diseases and co-morbidities.
Type 2 Diabetes is one of the most common chronic diseases, specially linked to overweight and ages over sixty. As a metabolic disease, Type 2 Diabetes affects
multiple organs by causing damage in blood vessels and nervous system at micro and macro scale. Mortality of subjects with diabetes is three times higher than the
mortality for subjects with other chronic diseases.
On the one hand, the management of diabetes is focused on the maintenance of the blood glucose levels under a threshold by the prescription of anti-diabetic drugs and
a combination of healthy food habits and moderate physical activity. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the
onset of Type 2 Diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. On the other hand, prospective research has been driven
on large groups of population to build risk scores which aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently
there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and, to date, none of them has been
tested on a population based study.
The research study presented in this doctoral thesis strives to use externally validated risk scores for the prediction and detection of Type 2 Diabetes on a population
data base in Hospital La Fe (Valencia, Spain). The study hypothesis is that the integration of existing prediction and detection risk scores on Electronic Health Records
increases the early-detection of high risk cases. To evaluate this hypothesis three studies on the clinical, user and technology dimensions have been driven to evaluate
the extent to which the models and the hospital is ready to exploit such models to identify high risk groups and drive efficient preventive strategies. The findings
presented in this thesis suggest that Electronic Health Records are not prepared to massively feed risk models. Some of the evaluated models have shown a good classification
performance, which accompanied to the well-acceptance of web-based tools and the acceptable technical performance of the information and communication technology system, suggests that after some work these models can effectively drive
a new paradigm of active screening for Type 2 Diabetes.Los indicadores de salud y sociológicos confirman que la esperanza de vida está aumentando, y por lo tanto, los años que los pacientes tienen que vivir con enfermedades crónicas y comorbilidades. Diabetes tipo 2 es una de las enfermedades crónicas más comunes, especialmente relacionadas con el sobrepeso y edades superiores a los sesenta años. Como enfermedad metabólica, la diabetes tipo 2 afecta a múltiples órganos causando daño en los vasos sanguíneos y el sistema nervioso a escala micro y macro. La mortalidad de sujetos con diabetes es tres veces mayor que la mortalidad de sujetos con otras enfermedades crónicas. Por un lado, la estrategia de manejo se centra en el mantenimiento de los niveles de glucosa en sangre bajo un umbral mediante la prescripción de fármacos antidiabéticos y una combinación de hábitos alimentarios saludables y actividad física moderada. Estudios recientes han demostrado la eficacia de nuevas estrategias para retrasar e incluso prevenir la aparición de la diabetes tipo 2 mediante una combinación de estilo de vida activo y saludable en cohortes de sujetos de riesgo medio a alto. Por otro lado, la investigación prospectiva se ha dirigido a grupos de la población para construir modelos de riesgo que pretenden obtener una regla para la clasificación de las personas según las probabilidades de desarrollar la enfermedad. Actualmente hay más de doscientos modelos de riesgo para hacer esta identificación, no obstante la inmensa mayoría no han sido debidamente evaluados en grupos externos y, hasta la fecha, ninguno de ellos ha sido probado en un estudio poblacional. El estudio de investigación presentado en esta tesis doctoral pretende utilizar modelos riesgo validados externamente para la predicción y detección de la Diabetes Tipo 2 en una base de datos poblacional del Hospital La Fe de Valencia (España). La hipótesis del estudio es que la integración de los modelos de riesgo de predicción y detección existentes la práctica clínica aumenta la detección temprana de casos de alto riesgo. Para evaluar esta hipótesis, se han realizado tres estudios sobre las dimensiones clínicas, del usuario y de la tecnología para evaluar hasta qué punto los modelos y el hospital están dispuestos a explotar dichos modelos para identificar grupos de alto riesgo y conducir estrategias preventivas eficaces. Los hallazgos presentados en esta tesis sugieren que los registros de salud electrónicos no están preparados para alimentar masivamente modelos de riesgo. Algunos de los modelos evaluados han demostrado un buen desempeño de clasificación, lo que acompañó a la buena aceptación de herramientas basadas en la web y el desempeño técnico aceptable del sistema de tecnología de información y comunicación, sugiere que después de algún trabajo estos modelos pueden conducir un nuevo paradigma de la detección activa de la Diabetes Tipo 2.
Els indicadors sociològics i de salut confirmen un augment en l'esperança de vida, i per tant, dels anys que les persones han de viure amb malalties cròniques i comorbiditats. la diabetis de tipus 2 és una de les malalties cròniques més comunes, especialment relacionades amb l'excés de pes i edats superiors als seixanta anys. Com a malaltia metabòlica, la diabetis de tipus 2 afecta múltiples òrgans causant dany als vasos sanguinis i el sistema nerviós a escala micro i macro. La mortalitat de subjectes amb diabetis és tres vegades superior a la mortalitat de subjectes amb altres malalties cròniques. D'una banda, l'estratègia de maneig se centra en el manteniment dels nivells de glucosa en sang sota un llindar mitjançant la prescripció de fàrmacs antidiabètics i una combinació d'hàbits alimentaris saludables i activitat física moderada. Estudis recents han demostrat l'eficàcia de noves estratègies per a retardar i fins i tot prevenir l'aparició de la diabetis de tipus 2 mitjançant una combinació d'estil de vida actiu i saludable en cohorts de subjectes de risc mitjà a alt. D'altra banda, la investigació prospectiva s'ha dirigit a grups específics de la població per construir models de risc que pretenen obtenir una regla per a la classificació de les persones segons les probabilitats de desenvolupar la malaltia. Actualment hi ha més de dos-cents models de risc per fer aquesta identificació, però la immensa majoria no han estat degudament avaluats en grups externs i, fins ara, cap d'ells ha estat provat en un estudi poblacional. L'estudi d'investigació presentat en aquesta tesi doctoral utilitza models de risc validats externament per a la predicció i detecció de diabetis de tipus 2 en una base de dades poblacional de l'Hospital La Fe de València (Espanya). La hipòtesi de l'estudi és que la integració dels models de risc de predicció i detecció existents la pràctica clínica augmenta la detecció de casos d'alt risc. Per avaluar aquesta hipòtesi, s'han realitzat tres estudis sobre les dimensions clíniques, de l'usuari i de la tecnologia per avaluar fins a quin punt els models i l'hospital estan disposats a explotar aquests models per identificar grups d'alt risc i conduir estratègies preventives. Les troballes presentades sugereixen que els registres de salut electrònics no estan preparats per alimentar massivament models de risc. Alguns dels models avaluats han demostrat una bona classificació, el que va acompanyar a la bona acceptació d'eines basades en el web i el rendiment tècnic acceptable del sistema de tecnologia d'informació i comunicacions implementat. La conclusió es que encara es necesari treball per que aquests models poden conduir un nou paradigma de la detecció activa de la diabetis de tipus 2.
Martínez Millana, A. (2017). ASSESSMENT OF RISK SCORES FOR THE PREDICTION AND DETECTION OF TYPE 2 DIABETES MELLITUS IN CLINICAL SETTINGS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86209
TESIS
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Bello, Ghalib. "Application and Extension of Weighted Quantile Sum Regression for the Development of a Clinical Risk Prediction Tool." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/608.
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In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient’s health and the overall function of different organs. In this dissertation, we develop and validate a weighted composite index that aggregates the information from a variety of health biomarkers covering multiple organ systems. The index can be used for predicting all-cause mortality and could also be used as a holistic measure of overall physiological health status. We refer to it as the Health Status Metric (HSM). Validation analysis shows that the HSM is predictive of long-term mortality risk and exhibits a robust association with concurrent chronic conditions, recent hospital utilization, and self-rated health. We develop the HSM using Weighted Quantile Sum (WQS) regression (Gennings et al., 2013; Carrico, 2013), a novel penalized regression technique that imposes nonnegativity and unit-sum constraints on the coefficients used to weight index components. In this dissertation, we develop a number of extensions to the WQS regression technique and apply them to the construction of the HSM. We introduce a new guided approach for the standardization of index components which accounts for potential nonlinear relationships with the outcome of interest. An extended version of the WQS that accommodates interaction effects among index components is also developed and implemented. In addition, we demonstrate that ensemble learning methods borrowed from the field of machine learning can be used to improve the predictive power of the WQS index. Specifically, we show that the use of techniques such as weighted bagging, the random subspace method and stacked generalization in conjunction with the WQS model can produce an index with substantially enhanced predictive accuracy. Finally, practical applications of the HSM are explored. A comparative study is performed to evaluate the feasibility and effectiveness of a number of ‘real-time’ imputation strategies in potential software applications for computing the HSM. In addition, the efficacy of the HSM as a predictor of hospital readmission is assessed in a cohort of emergency department patients.
Books on the topic "Clinical risk prediction"
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International Workshop on Epileptic Seizure Prediction (3rd 2007 Freiburg im Breisgau, Germany). Seizure prediction in epilepsy: From basic mechanisms to clinical applications. Weinheim: Wiley-VCH, 2008.
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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.
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The purpose of the monograph, which contains a modern view of the problem of adaptation of
children with extremely low body weight, is to provide a wide range of doctors with basic information
about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria
of postnatal pathology, based on their own research. The specific features of the immunological
reactivity of premature infants of various gestational ages who have developed bronchopulmonary
dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching
postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation
with the participation of factors of innate and adaptive immunity are considered in detail. Methods
for early prediction of BPD and RN with the determination of an integral indicator and an algorithm
for the management of premature infants with a high risk of postnatal complications at the stage
of early rehabilitation are proposed. The information provided makes it possible to personify the
treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for
obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other
specialties, residents, students of the system of continuing medical education.
This work was done with financial support from the Ministry of Education and Science, grant of
the President of the Russian Federation No. MK-1140.2020.7.
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Hochman, Michael E. Identifying Children with Low-Risk Head Injuries Who Do Not Require Computed Tomography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0002.
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This chapter, found in the headache section of the book, provides a succinct synopsis of a key study examining the use of computed tomography (CT) scans for children with low-risk head injuries. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. This study derived and validated prediction rules that can accurately identify children at very low risk for ci-TBI; the authors provide several guidelines for applying these rules depending on the severity of predictive features, patient history, and clinician’s judgment. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Langton, Calvin M. Contrasting approaches to risk assessment with adult male sexual offenders: An evaluation of recidivism prediction schemes and the utility of supplementary clinical information for enhancing predictive accuracy. 2003.
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Lee, Christoph I. Repeat Bone Mineral Density Screening and Osteoporotic Fracture Prediction. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0035.
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This chapter, found in the bone, joint, and extremity pain section of the book, provides a succinct synopsis of a key study examining the need for repeat bone densitometry screening and prediction of fractures from osteoporosis. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. The study showed that a repeat bone mineral density test within 4 years adds little additional value beyond the baseline test when assessing hip fracture risk. Moreover, a repeat test within 4 years may not improve fracture risk stratification used for clinical management of osteoporosis. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Seshadri, Sudha, and Stéphanie Debette, eds. Risk Factors for Cerebrovascular Disease and Stroke. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199895847.001.0001.
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Risk Factors for Cerebrovascular Disease and Stroke describes environmental and genetic determinants for cerebrovascular disease and stroke from the perspective of an international group of neurologists, epidemiologists, and geneticists who are at the forefront of research and education on these issues. Unlike other books in the field, which solely deal with physiology, diagnosis, and management of stroke, this essential book discusses prevention factors as well as the causes. This unique book takes a comprehensive approach to risk prediction while integrating epidemiological, genetic, and statistical principles explained in a way that is easy for the clinical trainee to understand. The section on genetic risk factors for various types of stroke is unique in its depth and up-to-date information. Clinicians, residents, fellows and academics in neurology, geriatrics, internal medicine, epidemiology, genetics, public health professionals, and preventative cardiologists, as well as nurses, practitioners and physician assistants will find this a handy source for years to come.
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Eder, Lihi. The clinical course and outcome of psoriatic arthritis. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0021.
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In contrast to early reports, it is now appreciated that psoriatic arthritis (PsA) can present as a destructive, progressive, and disabling arthritis with consequences as severe as those of rheumatoid arthritis. Longitudinal cohort studies of PsA patients contributed important knowledge about long-term outcomes, such as development of structural joint damage, remission achievement, and physical function. These studies identified predictors for improved outcomes including male gender and lower burden of inflammation at presentation while delayed diagnosis, disability, and joint damage are associated with worse long-term outcomes. These findings suggest early diagnosis and aggressive control of inflammation are important as they may prevent the occurrence of subsequent joint damage. The latter is strongly correlated with long-term outcomes, such as reduced physical function and increased mortality. Development of prediction models using clinical measures, laboratory biomarkers, and imaging is warranted to stratify patients with early disease into risk groups for long-term outcomes.
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Inherited Susceptibility To Cancer Clinical Predictive And Ethical Perspectives. Cambridge University Press, 2009.
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1960-, Foulkes William D., and Hodgson S. V, eds. Inherited susceptibility to cancer: Clinical, predictive, and ethical perspectives. Cambridge: Cambridge University Press, 1998.
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Wunsch, Hannah, and Andrew A. Kramer. The role and limitations of scoring systems. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0028.
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Scoring systems for critically-ill patients provide a measure of the severity of illness of patients admitted to intensive care units (ICUs). They are primarily based on patient characteristics, physiological derangement, and/or clinical assessments. Severity scores themselves allow for risk-adjusting outcomes, but they can also be used to provide a prediction of the overall risk of death, length of stay, or other outcome for critically ill patients. This allows for comparison of outcomes between different cohorts of patients or between observed and predicted ICU performance. There are a number of general ICU scoring systems that are in use. All scoring systems have limitations. Future scoring systems may include prediction of longer-term outcomes, and assimilation of granular data temporally and at the molecular level that could result in more personalized severity scores to help guide individual care decisions.
Book chapters on the topic "Clinical risk prediction"
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Subramanian, Vigneshwar, and Michael W. Kattan. "Clinical Risk Assessment and Prediction." In Health Informatics, 17–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18626-5_2.
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de Graaf, Jacqueline, Patrick Couture, and Allan D. Sniderman. "ApoB in Cardiovascular Risk Prediction." In ApoB in Clinical Care, 135–46. Houten: Bohn Stafleu van Loghum, 2015. http://dx.doi.org/10.1007/978-90-368-0980-1_5.
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Presannan, Bhagya, N. Ramasubramanian, and A. Santhana Vijayan. "Disease Risk Prediction from Clinical Texts." In Advances in Intelligent Systems and Computing, 319–25. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_30.
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Cording, Jacinta R., Tony Ward, and Sarah M. Beggs Christofferson. "Risk Prediction and Sex Offending." In Sexually Violent Predators: A Clinical Science Handbook, 225–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04696-5_14.
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Drubay, Damien, Ben Van Calster, and Stefan Michiels. "Development and Validation of Risk Prediction Models." In Principles and Practice of Clinical Trials, 1–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-52677-5_138-1.
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Drubay, Damien, Ben Van Calster, and Stefan Michiels. "Development and Validation of Risk Prediction Models." In Principles and Practice of Clinical Trials, 2003–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-52636-2_138.
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Teo, Kareen, Ching Wai Yong, Joon Huang Chuah, Khairunnisa Hasikin, Maheza Irna Mohd Salim, Yan Chai Hum, and Khin Wee Lai. "Assessing Clinical Usefulness of Readmission Risk Prediction Model." In 6th Kuala Lumpur International Conference on Biomedical Engineering 2021, 389–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90724-2_42.
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Farrell, Rachel, and Peter J. Kelly. "Serum Biomarkers in Prediction of Stroke Risk and Outcome." In Handbook of Stroke Prevention in Clinical Practice, 257–78. Totowa, NJ: Humana Press, 2004. http://dx.doi.org/10.1007/978-1-59259-769-7_16.
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Apfel, C. C., and N. Roewer. "Prediction of Postoperative Nausea and Vomiting Using Clinical Risk Factors." In Problems of the Gastrointestinal Tract in Anesthesia, the Perioperative Period, and Intensive Care, 289–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60200-9_32.
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Hengeveld, M. W., J. van der Wal, and A. J. F. M. Kerkhof. "Clinical Prediction of Suicidal Behavior Among High-Risk Suicide Attempters." In Current Issues of Suicidology, 189–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73358-1_28.
Full textConference papers on the topic "Clinical risk prediction"
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Wang, Fei, Ping Zhang, Buyue Qian, Xiang Wang, and Ian Davidson. "Clinical risk prediction with multilinear sparse logistic regression." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623755.
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Demarco, Maria, Noorie Hyun, Hormuzd Katki, Brian Befano, Li Cheung, Tina R. Raine-Bennett, Barbara Fetterman, et al. "Abstract A28: Risk model for clinical management of HPV-infected women." In Abstracts: AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; November 16-19, 2016; Orlando, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.carisk16-a28.
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Garg, Priya, and Deepti Aggarwal. "Application of Swarm-Based Feature Selection and Extreme Learning Machines in Lung Cancer Risk Prediction." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.1.
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Lung cancer risk prediction models help in identifying high-risk individuals for early CT screening tests. These predictive models can play a pivotal role in healthcare by decreasing lung cancer's mortality rate and saving many lives. Although many predictive models have been developed that use various features, no specific guidelines have been provided regarding the crucial features in lung cancer risk prediction. This study proposes novel risk prediction models using bio-inspired swarm-based techniques for feature selection and extreme learning machines for classification. The proposed models are applied on a public dataset consisting of 1000 patient records and 23 variables, including sociodemographic factors, smoking status, and lung cancer clinical symptoms. The models, validated using 10-fold cross-validation, achieve an AUC score in the range of 0.985 to 0.989, accuracy in the range of 0.986 to 0.99 and F-Measure in range of 0.98 to 0.985. The study also identifies smoking habits, exposure to air pollution, occupational hazards and some clinical symptoms as the most commonly selected lung cancer risk prediction features. The study concludes that the developed lung cancer risk prediction models can be successfully applied for early screening, diagnosis and treatment of high-risk individuals.
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Tiwaskar, S. A., Rutuja Gosavi, Riddhima Dubey, Shaila Jadhav, and Komal Iyer. "Comparison of Prediction Models for Heart Failure Risk: A Clinical Perspective." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697509.
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Cheng, Chih-Wen, and May D. Wang. "Improving personalized clinical risk prediction based on causality-based association rules." In BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808719.2808759.
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Singhal, Pankhuri, Yogasudha Veturi, Renae Judy, Yoson Park, Marijana Vujkovic, Olivia Veatch, Rachel Kember, and Shefali Setia Verma. "Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction." In Pacific Symposium on Biocomputing 2023. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811270611_0037.
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Alhassan, Zakhriya, David Budgen, Riyad Alshammari, Tahani Daghstani, A. Stephen McGough, and Noura Al Moubayed. "Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00087.
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Lowe, G. D. O. "EPIDEMIOLOGY AND RISK PREDICTION OF VENOUS THROMBOEMBOLISM." In XIth International Congress on Thrombosis and Haemostasis. Schattauer GmbH, 1987. http://dx.doi.org/10.1055/s-0038-1642965.
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Uses of epidemiology. Venous thromboembolism continues to be an important cause of death and disability in Western Countries. Its epidemiology may provide clues to etiology, e.g. the increased incidence in oral contraceptive users, and the low prevalence at autopsy in Central Africa or Japan compared to the U.S.A. A second use is the monitoring of time-trends: the diagnosis of pulmonary embolism increased during the 1970s, although the case fatality decreased. A third use is the identification and quantification of risk factors: these could be modified in the hope of prevention, or else used to select high risk groups for selective prophylaxis, e.g. during acute illness. Prevention is the only feasible approach to reducing the burden of venous thromboembolism, since most cases are not diagnosed, and since the value of current treatment is debatable.Case definition. Presents problems: clinical diagnosis is unreliable, and should if possible be supported by objective methods. Autopsy studies are performed on selected populations, at a decreasing rate; the frequency of thromboembolism depends on technique; and pathologists cannot be blinded and are open to bias. It can also be difficult to judge whether a patient dying with pulmonary embolism died from pulmonary embolism. 125I-fibrinogen scans indicate minimal disease, and now present ethical problems in screening due to risks of viral transmission. Venography is invasive and is not readily repeatable, which limits its use as a screening method. Plethysmography merits wider evaluation, since it is non-invasive, and sensitive to major thrombosis.Community epidemiology. Data on the community epidemiology are limited. The risk increases with age. When age is taken into account, there is little sex difference. Overweight in women, use of oral contraceptives and blood group A increase the risk: smoking, varicose veins, blood pressure, cholesterol and glucose do not, on current evidence. Long-term follow-up of patients with proven thromboembolism shows an increased risk of malignancy, hence occult cancer may also be a risk factor. Polycythaemia and certain congenital deficiencies (e.g. antithrombin III) are also well-recognised risk factors, although uncommon.Hospital epidemiology. Data on hospital epidemiology are derived largely from autopsy prevalence, and from short-term incidence of minimal thrombosis detected by 125I—fibrinogen scanning. Old, immobile and traumatised patients are most at risk. Previous thromboembolism, polycythaemia, antithrombin III deficiency, hip and leg fractures, elective hip and leg surgery, hemiplegia, paraplegia, and heart failure carry high risks, and merit consideration for routine prophylaxis. The risk in elective surgery precedes the operation, and increases with age, overweight, malignancy, varicose veins, non-smoking, and operative factors (duration, approach, general anaesthesia, intravenous fluids). Diabetics appear to have no extra risk. Combinations of clinical variables can be used to predict high risk groups for selective prophylaxis, but combination indices require further study. Laboratory variables may increase the predictability of deep vein thrombosis, but the results of published studies are conflicting, and the cost-effectiveness of laboratory prediction should be evaluated.
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Ding, Xiyu, Mei-Hua Hall, and Timothy Miller. "Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries." In Proceedings of the 3rd Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.clinicalnlp-1.4.
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Zang, Chengxi, and Fei Wang. "SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records." In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. http://dx.doi.org/10.1109/icdm51629.2021.00097.
Full textReports on the topic "Clinical risk prediction"
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Kent, David M., Jason Nelson, Jenica N. Upshaw, Gaurav Gulati, Riley Brazil, Esmee Venema, Christine M. Lundquist, et al. Using Different Data Sets to Test How Well Clinical Prediction Models Work to Predict Patients' Risk of Heart Disease. Patient-Centered Outcomes Research Institute (PCORI), September 2021. http://dx.doi.org/10.25302/09.2021.me.160635555.
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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317444.
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The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow and delayed system that captures the data much later after the fact and the agency will always be behind the curve in controlling the adverse incidents and events. This means that the incidences might continue to arise or go out of control. This project attempts to develop a system that would be more proactive in the collection of pharmacovigilance data and more predictive of pharmacovigilance incidences. The pharmacovigilance system should have the capacity to detect and analyze subtle changes in reporting frequencies and in patterns of clinical symptoms and signs that are reported as suspected adverse drug reactions. The method involved carrying out a thorough literature review of the latest trends in pharmacovigilance employed by different regulatory agencies across the world, especially the more stringent regulatory authorities. A review of the system employed by the Pharmacy and Poisons Board of Kenya was also done. Pharmacovigilance data, both primary and secondary, were collected and reviewed. Media reports on adverse drug reactions and poor-quality medicines over the period were also collected and reviewed. An appropriate predictive pharmacovigilance tool was also researched and identified. It was found that the Pharmacy and Poisons Board had a robust system of collecting historical pharmacovigilance data both from the healthcare workers and the general public. However, a more responsive data collection and evaluation system is proposed that will help the agency achieve its pharmacovigilance objectives. On analysis of the data it was found that just above half of all the product complaints, about 55%, involved poor quality medicines; 15% poor performance, 13% presentation, 8% adverse drug reactions, 7% market authorization, 2% expired drugs and 1% adulteration complaints. A regulatory pharmacovigilance prioritization tool was identified, employing a risk impact analysis was proposed for regulatory action.
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Dy, Sydney M., Julie M. Waldfogel, Danetta H. Sloan, Valerie Cotter, Susan Hannum, JaAlah-Ai Heughan, Linda Chyr, et al. Integrating Palliative Care in Ambulatory Care of Noncancer Serious Chronic Illness: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), February 2020. http://dx.doi.org/10.23970/ahrqepccer237.
Full textAbstract:
Objectives. To evaluate availability, effectiveness, and implementation of interventions for integrating palliative care into ambulatory care for U.S.-based adults with serious life-threatening chronic illness or conditions other than cancer and their caregivers We evaluated interventions addressing identification of patients, patient and caregiver education, shared decision-making tools, clinician education, and models of care. Data sources. We searched key U.S. national websites (March 2020) and PubMed®, CINAHL®, and the Cochrane Central Register of Controlled Trials (through May 2020). We also engaged Key Informants. Review methods. We completed a mixed-methods review; we sought, synthesized, and integrated Web resources; quantitative, qualitative and mixed-methods studies; and input from patient/caregiver and clinician/stakeholder Key Informants. Two reviewers screened websites and search results, abstracted data, assessed risk of bias or study quality, and graded strength of evidence (SOE) for key outcomes: health-related quality of life, patient overall symptom burden, patient depressive symptom scores, patient and caregiver satisfaction, and advance directive documentation. We performed meta-analyses when appropriate. Results. We included 46 Web resources, 20 quantitative effectiveness studies, and 16 qualitative implementation studies across primary care and specialty populations. Various prediction models, tools, and triggers to identify patients are available, but none were evaluated for effectiveness or implementation. Numerous patient and caregiver education tools are available, but none were evaluated for effectiveness or implementation. All of the shared decision-making tools addressed advance care planning; these tools may increase patient satisfaction and advance directive documentation compared with usual care (SOE: low). Patients and caregivers prefer advance care planning discussions grounded in patient and caregiver experiences with individualized timing. Although numerous education and training resources for nonpalliative care clinicians are available, we were unable to draw conclusions about implementation, and none have been evaluated for effectiveness. The models evaluated for integrating palliative care were not more effective than usual care for improving health-related quality of life or patient depressive symptom scores (SOE: moderate) and may have little to no effect on increasing patient satisfaction or decreasing overall symptom burden (SOE: low), but models for integrating palliative care were effective for increasing advance directive documentation (SOE: moderate). Multimodal interventions may have little to no effect on increasing advance directive documentation (SOE: low) and other graded outcomes were not assessed. For utilization, models for integrating palliative care were not found to be more effective than usual care for decreasing hospitalizations; we were unable to draw conclusions about most other aspects of utilization or cost and resource use. We were unable to draw conclusions about caregiver satisfaction or specific characteristics of models for integrating palliative care. Patient preferences for appropriate timing of palliative care varied; costs, additional visits, and travel were seen as barriers to implementation. Conclusions. For integrating palliative care into ambulatory care for serious illness and conditions other than cancer, advance care planning shared decision-making tools and palliative care models were the most widely evaluated interventions and may be effective for improving only a few outcomes. More research is needed, particularly on identification of patients for these interventions; education for patients, caregivers, and clinicians; shared decision-making tools beyond advance care planning and advance directive completion; and specific components, characteristics, and implementation factors in models for integrating palliative care into ambulatory care.