PROJECT SUMMARY The application of machine learning (ML) to randomized clinical trials (RCTs) represents a novel avenue for developing tools to enhance precision cardiovascular care. ML-based predictive approaches can learn response profiles based on the clinical characteristics of patients included in RCTs, thereby allowing personalized inference. However, despite the development of promising algorithms from applying these novel methods to high-quality experimental data in RCTs, there is a lack of a clear pathway for their real-world evaluation and implementation. To bridge this gap, we aim to develop an implementation-aligned strategy for care personalization models. We achieve this through our three study aims. In Aim 1, we will empirically evaluate various ML approaches for RCT using adequately powered simulated heterogeneous treatment effects, specifically incorporating covariate distributions observed in real-world patients from two distinct and diverse health systems. Using participant-level data from five diverse NIH-funded RCT datasets, we will evaluate models based on their performance in detecting simulated graded positive control heterogeneous treatments effects in these RCTs as well as in “digital twins” of these RCTs, computationally designed to replicate populations of these conditions in the practice in electronic health records (EHRs). Such an approach is needed to evaluate model generalizability to different populations expected in EHRs. In Aim 2, we enhance the interoperability between RCTs and EHRs, which is required for translating RCT-derived models to EHRs as well as selecting candidate predictors based on their EHR availability. We will accomplish this by mapping covariates from RCTs to a common data model, using a novel sentence transformer to map the descriptions of these covariates to those in the common data model. We will demonstrate real-world RCT covariate distribution at 13 hospitals across two health system EHRs mapped to the same common data model. In Aim 3, we will address the informative missingness of covariates in the real-world data, representing another key challenge limiting the pragmatic evaluation of algorithms developed from RCTs. For this, we will prospectively evaluate novel approaches that adapt models for variable missingness, both random and informative, during the model development process. In this study, we will assess whether “missingness-adapted algorithms” accurately capture the personalized effect estimates for patients, compared with a complete-covariate algorithm whose covariates are captured prospectively through direct patient contact. Collectively, the proposal will develop an end-to-end strategy for evaluating models developed from RCTs to improve their selection for real-world, pragmatic evaluation and implementation in EHRs. The methods will be rigorously tested in multiple RCTs. Moreover, through open-source data sharing, the datasets and the results of our work will be available as benchmarks for the rigorous development of further methodology for detecting personalized effects from RCTs. The proposal will serve as an essential framework for evaluating and translating precision care tools developed from RCTs.

PROJECT NARRATIVE The application of machine learning to randomized clinical trials (RCTs) represents a novel avenue for developing tools to enhance precision cardiovascular care. To realize the potential of care personalization algorithms derived from RCTs to improve outcomes, the proposed investigations will develop an end-to-end strategy for model evaluation and adaption to electronic health records to facilitate real-world, pragmatic validation and implementation. This will be achieved through an empirical approach to evaluating personalized effect estimates derived from RCTs and addressing information streams in RCTs concurrently with real-world data in the electronic health record to enhance translation.