Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical Trials

Medical record review by a physician clinical events committee is the gold standard for identifying cardiovascular outcomes in clinical trials, but is labor-intensive and poorly reproducible. Automated outcome adjudication by artificial intelligence (AI) could enable larger and less expensive clinical trials but has not been validated in global studies.

We developed a novel model for automated AI-based heart failure adjudication (Heart Failure Natural Language Processing) using hospitalizations from 3 international clinical outcomes trials. This model was tested on potential heart failure hospitalizations from the DELIVER trial (Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure), a cardiovascular outcomes trial comparing dapagliflozin with placebo in 6063 patients with heart failure with mildly reduced or preserved ejection fraction. AI-based adjudications were compared with adjudications from a clinical events committee that followed Food and Drug Administration-based criteria.

AI-based adjudication agreed with the clinical events committee in 83% of events. A strategy of human review for events that the AI model deemed uncertain (16%) would have achieved 91% agreement with the clinical events committee while reducing the adjudication workload by 84%. The estimated effect of dapagliflozin on heart failure hospitalization was nearly identical with AI-based adjudication (hazard ratio, 0.76 [95% CI, 0.66-0.88]) compared with clinical events committee adjudication (hazard ratio, 0.77 [95% CI, 0.67-0.89]). The AI model extracted symptoms, signs, and treatments of heart failure from each medical record in tabular format and quoted sentences documenting them.

AI-based adjudication of clinical outcomes has the potential to improve the efficiency of global clinical trials while preserving accuracy and interpretability.