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TRV-2026-1083Certified recordPeer-reviewed

Interpretable machine learning-enabled risk stratification for pulmonary arterial hypertension associated with unrepaired congenital heart disease: results from a national prospective registry

Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated with unrepaired congenital heart disease (PAH-CHD). We aimed to develop and internally validate an interpretable machine learning (ML)-based risk model for adults with unrepaired PAH-CHD, comparing it against the recent European Society of Cardiology (ESC) model. Methods Utilizing a nationwide prospective registry in China, we included adults with unrepaired PAH-CHD. Five survival models were trained and i…

Health · G Space — documented gain · certified 2026-09-14 · v1 · article view · machine-readable

Current reading — gain

Interpretable random survival forest model predicted all-cause mortality in adults with unrepaired PAH-CHD and stratified survival in both Eisenmenger and non-Eisenmenger subgroups where the ESC model did not.

What this doesn’t fix

Model was developed and internally validated only within a Chinese nationwide registry of adults with unrepaired PAH-CHD, without external validation reported.

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Truvace Impact Record TRV-2026-1083, v1: “Interpretable machine learning-enabled risk stratification for pulmonary arterial hypertension associated with unrepaired congenital heart disease: results from a national prospective registry.” Truvace, 2026-09-14. /record/TRV-2026-1083 (accessed at citation time). sha256 e6d469a8d7b9ba58…

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