Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study

BMC Medical Research Methodology

Research

Hieu T. Nguyen¹  Henrique D. Vasconcellos²  Kimberley Keck²  João A.C. Lima³  Eliseo Guallar⁴  Cora E. Lewis⁵  Donald M. Lloyd-Jones⁶  Bharath Ambale-Venkatesh⁷  Steven Sidney⁸  Colin O. Wu⁹  Jared P. Reis⁹  Pamela J. Schreiner^1,10 

[1] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA;Department of Cardiology, Johns Hopkins University, Baltimore, MD, USA;Department of Cardiology, Johns Hopkins University, Baltimore, MD, USA;Department of Radiology, Johns Hopkins University, Baltimore, MD, USA;Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD, USA;Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA;Department of Preventive Medicine, Northwestern University, Chicago, IL, USA;Department of Radiology, Johns Hopkins University, Baltimore, MD, USA;Division of Research, Kaiser Permanente, Oakland, CA, USA;National Heart, Lung, and Blood Institute, Bethesda, MD, USA;School of Public Health, University of Minnesota, Minneapolis, MN, USA;

关键词: Longitudinal data;    Explainable AI;    Survival analysis;    Risk prediction;    Repeated measures;    Personalized medicine;    Time-varying covariates;    SHAP;    TIME;    CARDIA;   

DOI  :  10.1186/s12874-023-01845-4

 received in 2022-09-19, accepted in 2023-01-18,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMultivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability.MethodsWe investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models.ResultsIn a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86–0.87 at 5 years, 0.79–0.81 at 10 years) than using baseline or last observed CS data (0.80–0.86 at 5 years, 0.73–0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering.ConclusionOur analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods.Trial registrationClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.

【 授权许可】

CC BY   
© The Author(s) 2023

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