Key result
Deep learning classifier predicts antihypertensive drug effectiveness with a 0.97 F1 score.
Why the study?
Predicting optimal antihypertensive therapy in heart failure is hindered by response variability, genetic studies are costly and hard to translate, and electronic health record data remain underused for evaluating drug effectiveness.
Can deep learning classifiers using EHR data predict antihypertensive drug effectiveness in heart failure patients?
Observational (n=9,500)
Can deep learning classifiers using EHR data predict antihypertensive drug effectiveness in heart failure patients?
Deep learning classifiers applied to EHR data can accurately predict the effectiveness of different antihypertensive classes in patients with heart failure.
May inform personalized antihypertensive selection in HF; leaves open external validation and outcome trials.
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.
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Chowdhury et al. (2023) conducted an observational in Heart failure and arterial hypertension (n=9,500). Supervised deep learning classifiers was evaluated on F1 performance of the classifier. A supervised deep learning classifier trained on electronic health records of approximately 9500 patients predicted antihypertensive drug effectiveness with a best F1 performance of 0.97.
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