# Evaluating Drug Effectiveness for Antihypertensives in Heart Failure Prognosis: Leveraging Composite Clinical Endpoints and Biomarkers from Electronic Health Records

## TL;DR

This study uses electronic health records to evaluate how well antihypertensive drugs work for heart failure patients, using clinical endpoints and biomarkers to train a deep learning model.

## Contribution

The novel approach uses EHR data and deep learning to predict antihypertensive effectiveness without relying on genetic data.

## Key findings

- A supervised deep learning classifier was trained on EHR data from 9500 patients to predict antihypertensive drug effectiveness.
- The classifier achieved an F1 score of 0.97, indicating high accuracy in predicting drug effectiveness.
- Clinical endpoints and biomarkers from EHRs were used to annotate drug effectiveness labels for model training.

## Abstract

Arterial hypertension is a major risk factor for heart failure and antihypertensives such as angiotensin converting enzyme (ACE) inhibitors and β-blockers are considered as its first-line treatment. Drug response prediction models designed to determine the most effective antihypertensive drug for a patient are hindered by the interpatient response variability. Although typically pharmacogenetic data have been used to investigate the association of genetic variants with the antihypertensive response, genomewide association studies are currently expensive and the translation of genotype guided antihypertensive therapy to clinical practice is challenging. With the generation of electronic health records (EHR) data summarized over the patient’s disease prognosis and interventions, it is still an underused resource for antihypertensive effectiveness studies in heart failure management. In this study, we first use the clinical events in the EHR related to the patient’s hard clinical endpoints and biomarkers associated with the heart failure condition to design selection strategies that determine the antihypertensive effectiveness, then develop annotated corpora using the strategies and eventually evaluate supervised deep learning classifiers on the annotated data. We annotated the EHR sequences of approximately 9500 patients with binary labels corresponding to the drug effectiveness across two different antihypertensive classes and our trained classifier was able to obtain the best F1 performance of 0.97.