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Journal articles on the topic 'Disease progression modeling'

Author: Grafiati

Published: 25 May 2024

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1

Reeve, Russell, Lei Pang, Bradley Ferguson, Michael O’Kelly, Seth Berry, and Wei Xiao. "Rheumatoid Arthritis Disease Progression Modeling." Therapeutic Innovation & Regulatory Science 47, no. 6 (2013): 641–50. http://dx.doi.org/10.1177/2168479013499571.

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Inoue, Lurdes Y. T., Ruth Etzioni, Christopher Morrell, and Peter Müller. "Modeling Disease Progression With Longitudinal Markers." Journal of the American Statistical Association 103, no. 481 (2008): 259–70. http://dx.doi.org/10.1198/016214507000000356.

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Plevritis, Sylvia K. "Modeling disease progression in outcomes research." Academic Radiology 6 (January 1999): S132—S133. http://dx.doi.org/10.1016/s1076-6332(99)80108-1.

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Young, Alexandra L., Felix J. S. Bragman, Bojidar Rangelov, et al. "Disease Progression Modeling in Chronic Obstructive Pulmonary Disease." American Journal of Respiratory and Critical Care Medicine 201, no. 3 (2020): 294–302. http://dx.doi.org/10.1164/rccm.201908-1600oc.

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Rooney, William D., Yosef A. Berlow, William T. Triplett, et al. "Modeling disease trajectory in Duchenne muscular dystrophy." Neurology 94, no. 15 (2020): e1622-e1633. http://dx.doi.org/10.1212/wnl.0000000000009244.

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ObjectiveTo quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles.MethodsMRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal changes in biomarkers were modeled with a cumulative distribution function using a nonlinear mixed-effects approach.ResultsMRS FF and MRI qT2 increased with DMD disease duration, with the progression time constants differing markedly between individuals and across muscles. The average age at half-maximal muscle involvement (μ) occurred 4.8 years earlier in vastus lateralis than soleus, and these measures were strongly associated with loss-of-ambulation age. Corticosteroid treatment was found to delay μ by 2.5 years on average across muscles, although there were marked differences between muscles with more slowly progressing muscles showing larger delay.ConclusionsMRS FF and MRI qT2 provide sensitive noninvasive measures of DMD progression. Modeling changes in these biomarkers across multiple muscles can be used to detect and monitor the therapeutic effects of corticosteroids on disease progression and to provide prognostic information on functional outcomes. This modeling approach provides a method to transform these MRI biomarkers into well-understood metrics, allowing concise summaries of DMD disease progression at individual and population levels.ClinicalTrials.gov identifier:NCT01484678.

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Zhou, Jiayu, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. "Modeling disease progression via multi-task learning." NeuroImage 78 (September 2013): 233–48. http://dx.doi.org/10.1016/j.neuroimage.2013.03.073.

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Mehdipour Ghazi, Mostafa, Mads Nielsen, Akshay Pai, et al. "Robust parametric modeling of Alzheimer’s disease progression." NeuroImage 225 (January 2021): 117460. http://dx.doi.org/10.1016/j.neuroimage.2020.117460.

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Sun, Zhaonan, Soumya Ghosh, Ying Li, et al. "A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data." JAMIA Open 2, no. 1 (2019): 123–30. http://dx.doi.org/10.1093/jamiaopen/ooy060.

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Abstract Objective Chronic diseases often have long durations with slow, nonlinear progression and complex, and multifaceted manifestation. Modeling the progression of chronic diseases based on observational studies is challenging. We developed a framework to address these challenges by building probabilistic disease progression models to enable better understanding of chronic diseases and provide insights that could lead to better disease management. Materials and Methods We developed a framework to build probabilistic disease progression models using observational medical data. The framework consists of two steps. The first step determines the number of disease states. The second step builds a probabilistic disease progression model with the determined number of states. The model discovers typical states along the trajectory of the target disease, learns the characteristics of these states, and transition probabilities between the states. We applied the framework to an integrated observational HD dataset curated from four recent observational HD studies. Results The resulting HD progression model identified nine disease states. Compared to state-of-art HD staging system, the model 1) covers wider range of HD progression; 2) is able to quantitatively describe complex changes around the time of clinical diagnosis; 3) discovers multiple potential HD progression pathways; and 4) reveals expected time durations of the identified states. Discussion and Conclusion The proposed framework addresses practical challenges in observational data and can help enhance the understanding of progression of chronic diseases. The framework could be applied to other chronic diseases with the help of clinical knowledge.

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Gomeni, Roberto, Monica Simeoni, Marina Zvartau-Hind, Michael C. Irizarry, Daren Austin, and Michael Gold. "Modeling Alzheimer's disease progression using the disease system analysis approach." Alzheimer's & Dementia 8, no. 1 (2011): 39–50. http://dx.doi.org/10.1016/j.jalz.2010.12.012.

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Cook, Sarah F., and Robert R. Bies. "Disease Progression Modeling: Key Concepts and Recent Developments." Current Pharmacology Reports 2, no. 5 (2016): 221–30. http://dx.doi.org/10.1007/s40495-016-0066-x.

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Ma, Xiaoke, Long Gao, and Kai Tan. "Modeling disease progression using dynamics of pathway connectivity." Bioinformatics 30, no. 16 (2014): 2343–50. http://dx.doi.org/10.1093/bioinformatics/btu298.

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Soper, Braden C., Jose Cadena, Sam Nguyen, et al. "Dynamic modeling of hospitalized COVID-19 patients reveals disease state–dependent risk factors." Journal of the American Medical Informatics Association 29, no. 5 (2022): 864–72. http://dx.doi.org/10.1093/jamia/ocac012.

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Abstract Objective The study sought to investigate the disease state–dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Materials and Methods A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient’s clinical progression. Results The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. Discussion Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. Conclusion Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

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Yang, Liuqing, Xifeng Wang, Qi Guo, et al. "Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease." Statistics in Biopharmaceutical Research 13, no. 3 (2021): 337–43. http://dx.doi.org/10.1080/19466315.2021.1884129.

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Pičulin, Matej, Tim Smole, Bojan Žunkovič, et al. "Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning." JMIR Medical Informatics 10, no. 2 (2022): e30483. http://dx.doi.org/10.2196/30483.

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Background Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). Objective Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. Methods The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. Results The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R2 from 0.3 to 0.6. Conclusions By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.

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Noyes, K., A. Bajorska, AR Chappel, et al. "PMC48 “UNNATURAL” HISTORY: MODELING DISEASE PROGRESSION USING OBSERVATIONAL DATA." Value in Health 12, no. 3 (2009): A28. http://dx.doi.org/10.1016/s1098-3015(10)73199-5.

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Venkatraghavan, Vikram, Esther E. Bron, Wiro J. Niessen, and Stefan Klein. "Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling." NeuroImage 186 (February 2019): 518–32. http://dx.doi.org/10.1016/j.neuroimage.2018.11.024.

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Ojha, Vaghawan Prasad, Shantia Yarahmadian, and Madhav Om. "Stochastic Modeling and Simulation of Filament Aggregation in Alzheimer’s Disease." Processes 12, no. 1 (2024): 157. http://dx.doi.org/10.3390/pr12010157.

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Alzheimer’s disease has been a serious problem for humankind, one without a promising cure for a long time now, and researchers around the world have been working to better understand this disease mathematically, biologically and computationally so that a better cure can be developed and finally humanity can get some relief from this disease. In this study, we try to understand the progression of Alzheimer’s disease by modeling the progression of amyloid-beta aggregation, leading to the formation of filaments using the stochastic method. In a noble approach, we treat the progression of filaments as a random chemical reaction process and apply the Monte Carlo simulation of the kinetics to simulate the progression of filaments of lengths up to 8. By modeling the progression of disease as a progression of filaments and treating this process as a stochastic process, we aim to understand the inherent randomness and complex spatial–temporal features and the convergence of filament propagation process. We also analyze different reaction events and observe the events such as primary as well as secondary elongation, aggregations and fragmentation using different propensities for different possible reactions. We also introduce the random switching of the propensity at random time, which further changes the convergence of the overall dynamics. Our findings show that the stochastic modeling can be utilized to understand the progression of amyloid-beta aggregation, which eventually leads to larger plaques and the development of Alzheimer disease in the patients. This method can be generalized for protein aggregation in any disease, which includes both the primary and secondary aggregation and fragmentation of proteins.

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Kühnel, Line, Anna‐Karin Berger, Bo Markussen, and Lars L. Raket. "Simultaneous modeling of Alzheimer's disease progression via multiple cognitive scales." Statistics in Medicine 40, no. 14 (2021): 3251–66. http://dx.doi.org/10.1002/sim.8932.

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Goodison, Steve, Mark E. Sherman, and Yijun Sun. "Computational disease progression modeling can provide insights into cancer evolution." Oncoscience 7, no. 3-4 (2020): 21–22. http://dx.doi.org/10.18632/oncoscience.501.

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Kotze, L. "PNS222 IMPUTATION TECHNIQUES FOR MISSING COVARIATES WHEN MODELING DISEASE PROGRESSION." Value in Health 22 (May 2019): S323. http://dx.doi.org/10.1016/j.jval.2019.04.1578.

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Karlsson, Kristin E., Justin J. Wilkins, Fredrik Jonsson, Per-Henrik Zingmark, Mats O. Karlsson, and E. Niclas Jonsson. "Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales." AAPS Journal 12, no. 4 (2010): 683–91. http://dx.doi.org/10.1208/s12248-010-9230-0.

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Green, C., and S. Zhang. "Modeling Disease Progression In Alzheimer's Dementia To Inform HTA (CEA)." Value in Health 17, no. 7 (2014): A563. http://dx.doi.org/10.1016/j.jval.2014.08.1866.

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Liu, Xiaoli, Peng Cao, André R. Gonçalves, Dazhe Zhao, and Arindam Banerjee. "Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso." ACM Transactions on Knowledge Discovery from Data 12, no. 6 (2018): 1–35. http://dx.doi.org/10.1145/3230668.

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Donohue, Michael C., Anthony Gamst, Clifford Jack, et al. "F3-02-02: MODELING LONG-TERM DISEASE PROGRESSION WITH COVARIATES." Alzheimer's & Dementia 10 (July 2014): P203—P204. http://dx.doi.org/10.1016/j.jalz.2014.04.253.

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Janke, Andrew L., Greig de Zubicaray, Stephen E. Rose, Mark Griffin, Jonathan B. Chalk, and Graham J. Galloway. "4D deformation modeling of cortical disease progression in Alzheimer's dementia." Magnetic Resonance in Medicine 46, no. 4 (2001): 661–66. http://dx.doi.org/10.1002/mrm.1243.

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Ozkan, Alican, Gwenn Merry, David B. Chou, et al. "878 MODELING INFLAMMATORY BOWEL DISEASE PROGRESSION IN HUMAN ORGAN-CHIPS." Gastroenterology 164, no. 6 (2023): S—195. http://dx.doi.org/10.1016/s0016-5085(23)01430-0.

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Sukkar, Rafid, Bradley Wyman, Elyse Katz, Yanwei Zhang, and David Raunig. "P1-118: Modeling Alzheimer's disease progression using hidden markov models." Alzheimer's & Dementia 7 (July 2011): S147. http://dx.doi.org/10.1016/j.jalz.2011.05.397.

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Platero, Carlos. "Categorical predictive and disease progression modeling in the early stage of Alzheimer’s disease." Journal of Neuroscience Methods 374 (May 2022): 109581. http://dx.doi.org/10.1016/j.jneumeth.2022.109581.

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Nie, Liqiang, Luming Zhang, Lei Meng, Xuemeng Song, Xiaojun Chang, and Xuelong Li. "Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease." IEEE Transactions on Neural Networks and Learning Systems 28, no. 7 (2017): 1508–19. http://dx.doi.org/10.1109/tnnls.2016.2520964.

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Baum, Larry, and Eric Baum. "Progressive Diseases: Interpretation of Genetic Data." Journal of Theoretical Medicine 2, no. 1 (1999): 1–7. http://dx.doi.org/10.1080/17486709909490784.

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Simple modeling is proposed to represent the screening of gene polymorphisms for association with a progressive disease of insidious onset such as Alzheimer's disease. The modeling demonstrates that when a polymorphism affects the rate of progression as well as the risk of disease, the correct interpretation of DNA data requires an accurate sampling of the living, diseased population. Furthermore, in this population, the effect of the polymorphism on disease risk cannot be distinguished from a corresponding effect on the rate of progression of the disease, and a null result does not preclude a significant effect of the gene on the disease. By contrast, when the population is sampled either at time of diagnosis or at autopsy, the effect of the polymorphism on disease frequency can be directly related to the frequency of the polymorphism in the sample, but evaluating the rate of disease progression requires additional data. When the only available data are obtained from a live patient population, substantial differences in interpretation can result from subtle differences in the patient selection protocol. When existing DNA databases are used in which this protocol is not well characterized, there is a corresponding uncertainty introduced into the deduced effect of the polymorphism on disease risk and rate of progression.

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Pfeiffer, John, Tim Foley, Eduardo Braun, et al. "Abstract 1917: Accurate modeling of HER2 positive breast cancer disease progression with a biophysical modeling software." Cancer Research 82, no. 12_Supplement (2022): 1917. http://dx.doi.org/10.1158/1538-7445.am2022-1917.

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Abstract Breast cancer (BC) progression during NAT is associated with development of distant metastases, positive LN status, and decreased OS/RFS. These can occur in the context of clinical trials and therapy de-escalation, where the focus is on delivering effective NAT to patients while reducing drug toxicity. The risks added by disease progression underscore the need for early identification of NAT progressors. To this end, we replicated the NeoSphere study in silico using TumorScope (TS), a biophysical modeling software, focusing on predicting disease progression during NAT. The NeoSphere trial studied the efficacy of docetaxel (T), pertuzumab (P), and trastuzumab (H) in combination with one another over 254 operable BC patients distributed across four study arms. We selected past BC patients with accompanying standard of care clinical data that matched NeoSphere sample composition based on patient and tumor characteristics. A total, 144 patients were included across four study arms (TH, THP, HP, and TP). Parameters from the NeoSphere study were mirrored where possible. Simulation generated volume trajectories of individual tumor’s response to therapy. Disease progressors were identified based on tumor volume at the final simulation timepoint compared to the first simulation timepoint. We then compared group means and proportions between progressors and responders using Welch’s two-sample t-test, and Fisher’s exact test, respectively. We replicated the NeoSphere trial using TS. pCR rates across study arms closely mirrored those of the actual trial. In the HP arm of our trial, we identified 12 (12/144) progressors. No difference was found when comparing it to that observed in the NeoSphere trial (p=1.00, OR=1.12). As expected, percent change in tumor volume from initial to final timepoints for the progressor group was significantly higher than the responder group (n=121, t=19.2, p=1.5x10-10, mean progressor=38.7, mean responder=-75.9). The progressor group was enriched with higher grade tumors (t=2.85, p=0.01), as well as HR-negative tumors (p=0.002, OR=7.54) compared to the responder group, and had lower HER2 receptor FISH ratios (t=-3.4, p=0.002). There were no differences observed between groups age, cancer subtype, or AJCC tumor stage (p&gt;0.05). After trial replication, we identified clinical features that separated progressors from responders, which are being assessed for development of individualized predictive biomarkers of disease progression. While work is ongoing in the field to identify biomarkers of BC progression, it is evident that single markers are not sufficient. Comprehensive, multi-modal biomarkers of disease progression must be developed and applied to patient sub-populations to garner effective predictions. Using biophysical simulations, we are able to investigate the impact of drug delivery/sensitivity, metabolism, and spatial heterogeneity on BC progression. Citation Format: John Pfeiffer, Tim Foley, Eduardo Braun, Anu Antony, Lance Munn, Joseph R. Peterson, John A. Cole, The SimBioSys Team. Accurate modeling of HER2 positive breast cancer disease progression with a biophysical modeling software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1917.

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Sidhu, Ishnoor, Sonali P. Barwe, Raju K. Pillai, and Anilkumar Gopalakrishnapillai. "Harnessing the Power of Induced Pluripotent Stem Cells and Gene Editing Technology: Therapeutic Implications in Hematological Malignancies." Cells 10, no. 10 (2021): 2698. http://dx.doi.org/10.3390/cells10102698.

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In vitro modeling of hematological malignancies not only provides insights into the influence of genetic aberrations on cellular and molecular mechanisms involved in disease progression but also aids development and evaluation of therapeutic agents. Owing to their self-renewal and differentiation capacity, induced pluripotent stem cells (iPSCs) have emerged as a potential source of short in supply disease-specific human cells of the hematopoietic lineage. Patient-derived iPSCs can recapitulate the disease severity and spectrum of prognosis dictated by the genetic variation among patients and can be used for drug screening and studying clonal evolution. However, this approach lacks the ability to model the early phases of the disease leading to cancer. The advent of genetic editing technology has promoted the generation of precise isogenic iPSC disease models to address questions regarding the underlying genetic mechanism of disease initiation and progression. In this review, we discuss the use of iPSC disease modeling in hematological diseases, where there is lack of patient sample availability and/or difficulty of engraftment to generate animal models. Furthermore, we describe the power of combining iPSC and precise gene editing to elucidate the underlying mechanism of initiation and progression of various hematological malignancies. Finally, we discuss the power of iPSC disease modeling in developing and testing novel therapies in a high throughput setting.

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Kim, Darae, Dongwoo Chae, Chi Young Shim, et al. "Predicting Disease Progression in Patients with Bicuspid Aortic Stenosis Using Mathematical Modeling." Journal of Clinical Medicine 8, no. 9 (2019): 1302. http://dx.doi.org/10.3390/jcm8091302.

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We aimed to develop a mathematical model to predict the progression of aortic stenosis (AS) and aortic dilatation (AD) in bicuspid aortic valve patients. Bicuspid AS patients who underwent at least two serial echocardiograms from 2005 to 2017 were enrolled. Mathematical modeling was undertaken to assess (1) the non-linearity associated with the disease progression and (2) the importance of first visit echocardiogram in predicting the overall prognosis. Models were trained in 126 patients and validated in an additional cohort of 43 patients. AS was best described by a logistic function of time. Patients who showed an increase in mean pressure gradient (MPG) at their first visit relative to baseline (denoted as rapid progressors) showed a significantly faster disease progression overall. The core model parameter reflecting the rate of disease progression, α, was 0.012/month in the rapid progressors and 0.0032/month in the slow progressors (p < 0.0001). AD progression was best described by a simple linear function, with an increment rate of 0.019 mm/month. Validation of models in a separate prospective cohort yielded comparable R squared statistics for predicted outcomes. Our novel disease progression model for bicuspid AS significantly increased prediction power by including subsequent follow-up visit information rather than baseline information alone.

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Cao, Yanguang, Debra C. DuBois, Hao Sun, Richard R. Almon, and William J. Jusko. "Modeling Diabetes Disease Progression and Salsalate Intervention in Goto-Kakizaki Rats." Journal of Pharmacology and Experimental Therapeutics 339, no. 3 (2011): 896–904. http://dx.doi.org/10.1124/jpet.111.185686.

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Maitland, M. L., K. Wu, M. R. Sharma, et al. "Estimation of Renal Cell Carcinoma Treatment Effects From Disease Progression Modeling." Clinical Pharmacology & Therapeutics 93, no. 4 (2012): 345–51. http://dx.doi.org/10.1038/clpt.2012.263.

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Asena, Tilahun Ferede, and Ayele Taye Goshu. "Comparison of Sojourn Time Distributions in Modeling HIV/AIDS Disease Progression." Biometrical Letters 54, no. 2 (2017): 155–74. http://dx.doi.org/10.1515/bile-2017-0009.

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Summary An application of semi-Markov models to AIDS disease progression was utilized to find best sojourn time distributions. We obtained data on 370 HIV/AIDS patients who were under follow-up from September 2008 to August 2015, from Yirgalim General Hospital, Ethiopia. The study reveals that within the “good” states, the transition probability of moving from a given state to the next worst state has a parabolic pattern that increases with time until it reaches a maximum and then declines over time. Compared with the case of exponential distribution, the conditional probability of remaining in a good state before moving to the next good state grows faster at the beginning, peaks, and then declines faster for a long period. The probability of remaining in the same good disease state declines over time, though maintaining higher values for healthier states. Moreover, the Weibull distribution under the semi-Markov model leads to dynamic probabilities with a higher rate of decline and smaller deviations. In this study, we found that the Weibull distribution is flexible in modeling and preferable for use as a waiting time distribution for monitoring HIV/AIDS disease progression.

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Walker, Rachel, Jaime Mejia, Jae K. Lee, et al. "Personalizing Gastric Cancer Screening With Predictive Modeling of Disease Progression Biomarkers." Applied Immunohistochemistry & Molecular Morphology 27, no. 4 (2019): 270–77. http://dx.doi.org/10.1097/pai.0000000000000598.

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Jacqmin, Philippe, Ronald Gieschke, Isabelle Delor, et al. "Mathematical Disease Progression Modeling in Type 2/3 Spinal Muscular Atrophy." Muscle & Nerve 58, no. 4 (2018): 528–35. http://dx.doi.org/10.1002/mus.26178.

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Sun, Ming, and Yuanjia Wang. "Nonlinear model with random inflection points for modeling neurodegenerative disease progression." Statistics in Medicine 37, no. 30 (2018): 4721–42. http://dx.doi.org/10.1002/sim.7951.

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Hong, Yun Jeong, Bora Yoon, Yong S. Shim, et al. "Predictors of Clinical Progression of Subjective Memory Impairment in Elderly Subjects: Data from the Clinical Research Centers for Dementia of South Korea (CREDOS)." Dementia and Geriatric Cognitive Disorders 40, no. 3-4 (2015): 158–65. http://dx.doi.org/10.1159/000430807.

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Background/Aims: The aims of this study were to determine baseline factors related to the progression of subjective memory impairment (SMI) in elderly subjects and to develop a new modeling scale to predict progression. Methods: Elderly subjects with SMI were recruited from the nationwide Clinical Research Centers for Dementia of South Korea (CREDOS) multicenter cohort and divided into two groups: (1) progressed to mild cognitive impairment or Alzheimer's disease or (2) stable without progression. Baseline clinical characteristics were compared between the groups, and the most relevant predictors of progression were assessed. A new modeling scale combining the predictors was developed. Results: In total, 129 subjects with SMI were analyzed. The follow-up duration was 0.5-4.7 years, and the median time to event was 3.64 years. The progressing group (n = 29) differed from the stable group (n = 100) in terms of baseline age, apolipoprotein E4 (APOE4) status, and some cognitive domains. Older age, a lower Mini-Mental State Examination recall score, APOE4 carrier, and a lower verbal delayed recall score were the most relevant predictors of progression, and a new modeling scale with these 4 predictors provided a better explanation of progression. Conclusion: SMI subjects with a higher risk of progression can be identified using a new modeling scale and might need further evaluations and more frequent follow-up.

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Ross, Jennifer M., Roger Ying, Connie L. Celum, et al. "Modeling HIV disease progression and transmission at population-level: The potential impact of modifying disease progression in HIV treatment programs." Epidemics 23 (June 2018): 34–41. http://dx.doi.org/10.1016/j.epidem.2017.12.001.

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Thomson, J. L., and W. E. Copes. "Modeling Disease Progression of Camellia Twig Blight Using a Recurrent Event Model." Phytopathology® 99, no. 4 (2009): 378–84. http://dx.doi.org/10.1094/phyto-99-4-0378.

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To improve control of camellia twig blight (CTB) using sanitation methods, a more complete epidemiologic understanding of this disease is necessary. Three CTB disease stages were modeled using recurrent event analysis. Wound inoculated stems were observed at regular intervals for appearance of disease symptoms. Survival times (time from inoculation until symptom appearance) for the three disease stages (mild, moderate, and severe) were regressed against stem diameter, monthly mean hours/day within a specified temperature range (15 to 30°C), and season (spring, summer, fall, and winter). For all three CTB disease stages, stem diameter had a protective effect on survival times, while monthly mean hours/day in the specified temperature range and warmer seasons were risk factors. Based upon median ratios, the mild disease stage developed 2 to 3 times faster in spring, summer, and fall than in winter. Similarly, moderate and severe disease stages developed 2 to 2.5 times faster. For all three disease stages, seasonal differences in stage development were smaller among fall, spring, and summer, varying from 1 to 1.6 times faster. Recurrent event modeling of CTB progression provides knowledge concerning developmental expression of this disease, information necessary for creating a comprehensive, integrated disease management program.

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Caldwell, Kim A., Corey W. Willicott, and Guy A. Caldwell. "Modeling neurodegeneration in Caenorhabditiselegans." Disease Models & Mechanisms 13, no. 10 (2020): dmm046110. http://dx.doi.org/10.1242/dmm.046110.

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ABSTRACTThe global burden of neurodegenerative diseases underscores the urgent need for innovative strategies to define new drug targets and disease-modifying factors. The nematode Caenorhabditis elegans has served as the experimental subject for multiple transformative discoveries that have redefined our understanding of biology for ∼60 years. More recently, the considerable attributes of C. elegans have been applied to neurodegenerative diseases, including amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease and Huntington's disease. Transgenic nematodes with genes encoding normal and disease variants of proteins at the single- or multi-copy level under neuronal-specific promoters limits expression to select neuronal subtypes. The anatomical transparency of C. elegans affords the use of co-expressed fluorescent proteins to follow the progression of neurodegeneration as the animals age. Significantly, a completely defined connectome facilitates detailed understanding of the impact of neurodegeneration on organismal health and offers a unique capacity to accurately link cell death with behavioral dysfunction or phenotypic variation in vivo. Moreover, chemical treatments, as well as forward and reverse genetic screening, hasten the identification of modifiers that alter neurodegeneration. When combined, these chemical-genetic analyses establish critical threshold states to enhance or reduce cellular stress for dissecting associated pathways. Furthermore, C. elegans can rapidly reveal whether lifespan or healthspan factor into neurodegenerative processes. Here, we outline the methodologies employed to investigate neurodegeneration in C. elegans and highlight numerous studies that exemplify its utility as a pre-clinical intermediary to expedite and inform mammalian translational research.

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Andrade-Restrepo, Martin, Paul Lemarre, Laurent Pujo-Menjouet, Leon Matar Tine та Sorin Ionel Ciuperca. "Modeling the spatial propagation of Aβ oligomers in Alzheimer’s Disease". ESAIM: Proceedings and Surveys 67 (2020): 30–45. http://dx.doi.org/10.1051/proc/202067003.

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Recent advances in the study of Alzheimer’s Disease and the role of Aβ amyloid formation have caused the focus of biologists to progressively shift towards the smaller protein assemblies, the oligomers. These appear very early on in the disease progression and they seem to be the most infectious species for the neurons. We suggest a model of spatial propagation of Aβ oligomers in the vicinity of a few neurons, without considering the formation of large ﬁbrils or plaques. We also include a simple representation of the oligomers neurotoxic effect. A numerical study reveals that the oligomers spatial dynamics are very sensitive to the balance between their diffusion and their replication, and that the outcome in terms of the progression of AD strongly depends on it.

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REYES-SILVEYRA, JORGE, ARMIN R. MIKLER, JUSTIN ZHAO, and ANGEL BRAVO-SALGADO. "MODELING INFECTIOUS OUTBREAKS IN NON-HOMOGENEOUS POPULATIONS." Journal of Biological Systems 19, no. 04 (2011): 591–606. http://dx.doi.org/10.1142/s0218339011004007.

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Emerging diseases, novel strains of reemerging diseases, and bioterrorism threats necessitate the development of computational models that can supply health care providers with tools to facilitate analysis and simulation of the progression of infectious diseases in a population. Most computational models assume homogeneous mixing within populations. However, a more realistic approach to the simulation of infectious disease outbreaks includes the stratification of populations in which the interactions between individuals are affinity-based. To examine the effects of heterogeneous populations on the outbreak dynamics, we developed a hybrid model that includes clustered individuals which represent differentiated populations. This facilitates the study of the effects of distinct behavioral properties on the dynamics of an infectious disease epidemic. Our results indicate that non-uniform interactions and affinity-driven behavior can drastically change the outbreak dynamics in the population.

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Li, Lu, Jiho Sohn, Robert J. Genco, et al. "Computational approach to modeling microbiome landscapes associated with chronic human disease progression." PLOS Computational Biology 18, no. 8 (2022): e1010373. http://dx.doi.org/10.1371/journal.pcbi.1010373.

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A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn’s disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn’s disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn’s disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

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Tabberer, Maggie, Sebastian Gonzalez-McQuire, Hana Muellerova, et al. "Development of a Conceptual Model of Disease Progression for Use in Economic Modeling of Chronic Obstructive Pulmonary Disease." Medical Decision Making 37, no. 4 (2016): 440–52. http://dx.doi.org/10.1177/0272989x16662009.

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Background. To develop and validate a new conceptual model (CM) of chronic obstructive pulmonary disease (COPD) for use in disease progression and economic modeling. The CM identifies and describes qualitative associations between disease attributes, progression and outcomes. Methods. A literature review was performed to identify any published CMs or literature reporting the impact and association of COPD disease attributes with outcomes. After critical analysis of the literature, a Steering Group of experts from the disciplines of health economics, epidemiology and clinical medicine was convened to develop a draft CM, which was refined using a Delphi process. The refined CM was validated by testing for associations between attributes using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). Results. Disease progression attributes included in the final CM were history and occurrence of exacerbations, lung function, exercise capacity, signs and symptoms (cough, sputum, dyspnea), cardiovascular disease comorbidities, ‘other’ comorbidities (including depression), body composition (body mass index), fibrinogen as a biomarker, smoking and demographic characteristics (age, gender). Mortality and health-related quality of life were determined to be the most relevant final outcome measures for this model, intended to be the foundation of an economic model of COPD. Conclusion. The CM is being used as the foundation for developing a new COPD model of disease progression and to provide a framework for the analysis of patient-level data. The CM is available as a reference for the implementation of further disease progression and economic models.

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Exuzides, Alex, Chris Colby, Andrew H. Briggs, et al. "Statistical Modeling of Disease Progression for Chronic Obstructive Pulmonary Disease Using Data from the ECLIPSE Study." Medical Decision Making 37, no. 4 (2015): 453–68. http://dx.doi.org/10.1177/0272989x15610781.

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Background. To develop statistical models predicting disease progression and outcomes in chronic obstructive pulmonary disease (COPD), using data from ECLIPSE, a large, observational study of current and former smokers with COPD. Methods. Based on a conceptual model of COPD disease progression and data from 2164 patients, associations were made between baseline characteristics, COPD disease progression attributes (exacerbations, lung function, exercise capacity, and symptoms), health-related quality of life (HRQoL), and survival. Linear and nonlinear functional forms of random intercept models were used to characterize these relationships. Endogeneity was addressed by time-lagging variables in the regression models. Results. At the 5% significance level, an exacerbation history in the year before baseline was associated with increased risk of future exacerbations (moderate: +125.8%; severe: +89.2%) and decline in lung function (forced expiratory volume in 1 second [FEV1]) (–94.20 mL per year). Each 1% increase in FEV1 % predicted was associated with decreased risk of exacerbations (moderate: –1.1%; severe: –3.0%) and increased 6-minute walk test distance (6MWD) (+1.5 m). Increases in baseline exercise capacity (6MWD, per meter) were associated with slightly increased risk of moderate exacerbations (+0.04%) and increased FEV1 (+0.62 mL). Symptoms (dyspnea, cough, and/or sputum) were associated with an increased risk of moderate exacerbations (+13.4% to +31.1%), and baseline dyspnea (modified Medical Research Council score ≥2 v. <2) was associated with lower FEV1 (–112.3 mL). Conclusions. A series of linked statistical regression equations have been developed to express associations between indicators of COPD disease severity and HRQoL and survival. These can be used to represent disease progression, for example, in new economic models of COPD.

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Costa, Bárbara, and Nuno Vale. "Exploring HERV-K (HML-2) Influence in Cancer and Prospects for Therapeutic Interventions." International Journal of Molecular Sciences 24, no. 19 (2023): 14631. http://dx.doi.org/10.3390/ijms241914631.

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This review investigates the intricate role of human endogenous retroviruses (HERVs) in cancer development and progression, explicitly focusing on HERV-K (HML-2). This paper sheds light on the latest research advancements and potential treatment strategies by examining the historical context of HERVs and their involvement in critical biological processes such as embryonic development, immune response, and disease progression. This review covers computational modeling for drug–target binding assessment, systems biology modeling for simulating HERV-K viral cargo dynamics, and using antiviral drugs to combat HERV-induced diseases. The findings presented in this review contribute to our understanding of HERV-mediated disease mechanisms and provide insights into future therapeutic approaches. They emphasize why HERV-K holds significant promise as a biomarker and a target.

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Dunson, David B., and Donna D. Baird. "Bayesian Modeling of Incidence and Progression of Disease from Cross-Sectional Data." Biometrics 58, no. 4 (2002): 813–22. http://dx.doi.org/10.1111/j.0006-341x.2002.00813.x.

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