Dynamic prediction of survival status in patients undergoing cardiac catheterization using a joint modeling approach Open Access
Xia, Derun (Spring 2023)
Published
Abstract
Background: Traditional cardiovascular disease risk factors have a limited ability to precisely predict patient survival outcomes. To better stratify the risk of patients with established coronary artery disease (CAD), it is useful to develop dynamic prediction tools that can update the prediction by incorporating time-varying data to enhance disease management.
Objective: To dynamically predict myocardial infarction (MI) or cardiovascular death (CV-death) and all-cause death among patients undergoing cardiac catheterization using their electronic health records (EHR) data over time and evaluate the prediction accuracy of the model.
Methods: Data from 6119 participants were obtained from Emory Cardiovascular Biobank (EmCAB). We constructed the joint model with multiple longitudinal variables to dynamically predict MI/CV-death and all-cause death. The cumulative effect and slope of longitudinally measured variables were considered in the model. The time-dependent area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the discriminating capability, and the time-dependent Brier score was used to assess prediction error.
Results: In addition to existing risk factors including disease history, changes in several clinical variables that are routinely collected in the EHR showed significant contributions to adverse events. For example, the decrease in glomerular filtration rate (GFR), body mass index (BMI), high-density lipoprotein (HDL), systolic blood pressure (SBP) and increase in troponin-I increased the hazard of MI/CV-death and all-cause death. More rapid decrease in GFR and BMI (corresponding to decrease in slope) increased the hazard of MI/CV-death and all-cause death. More rapid increase in diastolic blood pressure (DBP) and more rapid decrease in SBP increased the hazard of all-cause death. The time-dependent AUCs of the traditional Cox proportional model were higher than those of the joint model for MI/CV-death and all-cause death. The Brier scores of the joint model were also higher than those of the Cox proportional model.
Conclusion: Joint modeling that incorporates longitudinally measured variables to achieve dynamic risk prediction is better than conventional risk assessment models and can be clinically useful. The joint model did not appear to perform better than a Cox regression model in our study. Possible reasons include data availability, selection bias, and quality uncertainty in the EHR. Future studies should address these issues when developing dynamic prediction models.
Table of Contents
Table of contents
1 Introduction. 5
2 Methods. 6
2.1 Study design and participants. 6
2.2 Statistical Analysis. 6
3 Results. 9
4 Discussion. 10
References. 11
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