Combination AI-Machine Learning to Diagnose Pulmonary Hypertension: A Real-World Evidence Cohort Study

Abstract

BACKGROUND: Pulmonary hypertension (PH) is a highly morbid disease, but underdiagnosis is common outside of expert referral centers. Consequentially, there may be opportunities to automate PH diagnosis using artificial intelligence (AI) clinical decision support tools. Analysis of patient-level right heart catheterization (RHC) data is required to optimize AI-based PH diagnosis but has not been reported previously. METHODS: We performed a retrospective cohort analysis of all RHC studies (January 1, 2016 to December 31, 2024) performed at the University of Maryland Medical System (UMMS), which is a Maryland statewide clinical network of 12 hospitals serving >2 million patients. We developed an automated large language model (LLM)-driven Pattern Repository (LDPR) method, featuring three task-specific LLM agents for extracting unstructured RHC data, which was manually cross-validated independently by two PH experts. To address data missingness, we used machine-learning to develop formulae to calculate mean pulmonary artery pressure (mPAP) from systolic (sPAP) and diastolic (dPAP) PAP, using an 80/20 train-test split. RESULTS: of 0.94 and lowest mean square error of 8.3 mmHg, which outperformed linear equations used currently (all p<0.001). The ML-derived formula was then directed to patients with missing mPAP (N=507) and identified N=382 patients (75.3%) with mPAP >20mmHg, and therefore reclassifying patients from no diagnosis to a diagnosis of PH. CONCLUSION: In this retrospective cohort analysis, combination LLM-ML-based extraction and interpretation of RHC was used to automate PH diagnosis in a large and heterogenous patient population. This approach is an efficient and scalable solution to preventing under-diagnosis of PH and demonstrates the feasibility of generative AI for advancing clinically-actionable tools that can improve cardiovascular disease phenotyping and diagnosis in real-world settings.

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