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Artículos de revistas sobre el tema "Disease progression modeling"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 19 de julio de 2025

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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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

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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13

Azizi, Tahmineh. "Mathematical Modeling of Cancer Progression." AppliedMath 4, no. 3 (2024): 1065–79. http://dx.doi.org/10.3390/appliedmath4030057.

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Cancer, a complex disease characterized by uncontrolled cell growth and metastasis, remains a formidable challenge to global health. Mathematical modeling has emerged as a critical tool to elucidate the underlying biological mechanisms driving tumor initiation, progression, and treatment responses. By integrating principles from biology, physics, and mathematics, mathematical oncology provides a quantitative framework for understanding tumor growth dynamics, microenvironmental interactions, and the evolution of cancer cells. This study explores the key applications of mathematical modeling in oncology, encompassing tumor growth kinetics, intra-tumor heterogeneity, personalized medicine, clinical trial optimization, and cancer immunology. Through the development and application of computational models, researchers aim to gain deeper insights into cancer biology, identify novel therapeutic targets, and optimize treatment strategies. Ultimately, mathematical oncology holds the promise of transforming cancer care by enabling more precise, personalized, and effective therapies.

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14

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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15

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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16

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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17

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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18

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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19

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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20

Das, Satrajit. "Modelling the Progression of Chronic Diseases Using Hidden Markov Models with Electronic Health Records." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48436.

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Progressive diseases like diabetes, chronic kidney disease (CKD), and cardiovascular diseases (CVD) have progressively worsening over time. It is vital to capture and forecast the hidden transitions between disease stages for early intervention. This paper reports a model of disease progression based on Hidden Markov Models (HMMs) learned from longitudinal Electronic Health Records (EHRs). By modeling patient data routinely collected in clinical practice, we predict the probability of unobserved states of disease and future progression trajectories. Our findings show that HMMs can usefully detect latent disease states and transitions, yielding important information on individual disease trajectories and contributing to personalized treatment planning.

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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

Takahashi, *Koki, Yoshihiro Noda, Daichi Sone, et al. "DISEASE PROGRESSION MODELING OF BRAIN MACROSTRUCTURE IN TREATMENT-RESISTANT DEPRESSION." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i63. https://doi.org/10.1093/ijnp/pyae059.108.

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Abstract Background Progressive abnormalities in brain structures have consistently been reported in depression. However, the distinct trajectories of progressive structural abnormalities in TRD remain unknown. Moreover, treatment-resistant depression (TRD) is suggested to have a heterogeneous pathophysiology. The Subtype and Stage Inference (SuStaIn) algorithm, an unsupervised machine learning technique, has shown premise in distinguishing disease biotypes with different progression trajectories. Aims & Objectives This study aimed to identify patterns of structural abnormalities in TRD with SuStaIn and associate each of them with clinical characteristics. Method The study received approval from the ethical committee at Keio University School of Medicine. All participants gave written informed consent. We included 129 adult patients with TRD (45.0±12.5 years old, 59 females [46%]) and 93 healthy controls (HC) (46.2±17.7 years old, 37 females [40%]). Participants underwent magnetic resonance imaging (MRI) scans with a Siemens Prisma 3T MRI scanner. Cortical thickness and gray matter volume from 13 cortical regions and bilateral hippocampus reported by mega-analysis studies (Schmaal et al., 2017; Schmaal et al., 2016) were calculated using FreeSurfer 6.0 and then z-scored using HC data.We applied the SuStaIn algorithm with 10-fold cross-validation using pySuStaIn. The optimal number of subtypes was determined through cross validation information criterion and test set log likelihood across folds. Furthermore, we explored the relationship between SuStaIn output (subtype and stage) and clinico-demographic variables. Results The SuStaIn algorithm identified two subtypes with distinct progression patterns. Subtype 1 (medial frontal cortex (mFC) type) (n=65) exhibited mild cortical thinning in the bilateral medial orbitofrontal cortex (mOFC) followed by the rostral anterior cingulate cortex (rACC). Subtype 2 (hippocampus and posterior cingulate cortex (Hip &PCC) type) (n=21) showed a marked reduction in the bilateral hippocampal volume followed by mild cortical thinning in the left PCC. Hip &PCC type had an earlier stage than mFC type (p&lt;0.001). No significant group difference in scores of the Montgomery-Å sberg Depression Rating Scale (p=0.73), onset age (p=0.33), or duration of untreated depression (p=0.79) was observed. There was no correlation between stage and these clinical variables in the whole group. In Hip &PCC type, stage correlated with visuospatial constructional ability assessed by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (r=-0.57, p=0.007). Discussion & Conclusion Previous studies suggest that abnormalities in the mOFC and Hip are the starting points for progressive structural changes in depression. Our results indicate that they may separately emerge in the two distinct subtypes. Additionally, both subtypes revealed progressive structural abnormalities in the regions in the hippocampal-medial prefrontal cortex network, a brain network crucial for depression. Hip &PCC type had an earlier stage than mFC type, suggesting less severe and more localized neurodegeneration in Hip &PCC type than mFC type. Only in Hip &PCC type, stage progression was linked to visuospatial dysfunction. Given the association between reduced Hip volume and cognitive impairment in MDD, the trajectories of structural changes might provide insights into the heterogeneity of cognitive impairment in TRD.

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32

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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33

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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34

Singh, Anant Manish. "A Multivariate Joint Modeling Framework for Disease Progression in Chronic Illnesses Using Longitudinal Data." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48268.

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Abstract: Disease progression modeling in chronic illnesses presents significant challenges due to the inherent complexity, heterogeneity and multivariate nature of longitudinal medical data. Traditional approaches often focus on single disease outcomes or fail to capture complex dependencies between multiple biomarkers measured over time. This research introduces a novel multivariate joint modeling framework that integrates advanced Bayesian methods with deep learning techniques to model disease progression trajectories across multiple correlated outcomes. Our framework extends existing methodologies by incorporating three key innovations: (1) a flexible multivariate longitudinal component using latent variables to capture dependencies between biomarkers, (2) a non-parametric disease trajectory module based on Gaussian processes with deep kernels to model non-linear progression patterns and (3) an interpretable patient-specific risk stratification component. We validate our approach using real-world longitudinal data from multiple chronic disease cohorts including Parkinson's disease, diabetes and chronic kidney disease. Results demonstrate that our framework outperforms existing methods in prediction accuracy (improving RMSE by 18.7% and MAE by 15.3%), provides more robust handling of irregular sampling and missing data and reveals clinically meaningful disease subtypes through trajectory clustering. Furthermore, our model demonstrates superior calibration of uncertainty estimates and maintains interpretability through feature importance metrics. This work addresses significant gaps in disease progression modeling by providing a unified framework that balances predictive power, clinical interpretability and computational efficiency thereby supporting more personalized clinical decision-making for chronic disease management. Keywords: disease progression modeling, multivariate longitudinal data, Bayesian joint models, Gaussian processes, deep learning, chronic illness, trajectory clustering, personalized medicine

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35

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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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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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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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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Xiaoxia, Li, and Heidari Farzin. "Understanding the Progression of Lewy Body Dementia with Longitudinal Data Modeling." Journal of Management Science and Business Intelligence 5, no. 2 (2020): 1–6. https://doi.org/10.5281/zenodo.3996530.

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Lewy Body Dementia is the second most common dementia type after Alzheimer's disease. The cognitive decline caused by Lewy Body Dementia affects the patients' performance in daily living and the ability to make decisions. It is critical to understand the course of the disease progression and estimate the cognitive decline trajectory for disease management and care planning. This study analyzed the factors that can be used for the Clinical Dementia Rating score estimation and developed a highly customized model using the data obtained from an existing Lewy Body Dementia database. The model will help the understanding of the course of the disease development and is proved to be robust and reliable in making estimation in cognitive decline for patients with Lewy Body Dementia.

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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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41

Bhatti, Priha. "Predictive Modeling of Chronic Kidney Disease Progression with Ensemble Learning Techniques." Sukkur IBA Journal of Emerging Technologies 7, no. 2 (2025): 71–82. https://doi.org/10.30537/sjet.v7i2.1449.

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The present study aims to tackle the significant issue of prompt identification of chronic kidney disease (CKD), a highly prevalent and potentially fatal medical illness. Given the crucial function of the kidneys in maintaining homeostasis, we put forth a novel ensemble learning model to forecast the onset of chronic kidney disease (CKD). Utilizing an extensive dataset, the study employs ten carefully designed stages, covering data analysis, missing data management, normalization, and training of machine learning models. The model that we have proposed exhibits superior performance compared to the existing approaches, attaining a noteworthy accuracy rate of 98.74%. Additionally, it demonstrates a sensitivity rate of 100%, a specificity rate of 96.54%, and an F1 score of 99.02%. The visual representation of the confusion matrix effectively showcases the strong performance of the model. The results of this study indicate that our ensemble technique holds promise as a valuable tool for the prompt detection of chronic kidney disease (CKD). It has the potential to improve diagnostic accuracy in clinical settings and alleviate the financial burden associated with advanced CKD treatments.

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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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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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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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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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