JMIR Publications

ABSTRACT

Background:

Pulmonary arterial hypertension (PAH) is a rare and progressive disease associated with substantial clinical heterogeneity and high healthcare costs. While machine learning (ML) has been increasingly applied to predict clinical outcomes, approaches that integrate clinical prediction with downstream economic modeling remain limited.

Objective:

This study proposes and evaluates a machine learning–based framework that combines prediction of future functional status with estimation of associated healthcare costs in patients with PAH.

Methods:

In this retrospective cohort study, data from 181 patients with PAH were analyzed. Six supervised ML algorithms (lasso, ridge regression, k-nearest neighbors, decision trees, random forest, and gradient boosting) were trained to predict World Health Organization functional class (WHO-FC) at 12-month follow-up using baseline clinical variables. Missing values in predictor variables were imputed using median substitution. To avoid information leakage, all preprocessing steps were performed within the training data and subsequently applied to the test set. Model performance was evaluated using Pearson correlation coefficient (r), coefficient of determination (R²), and standard error metrics, based on cross-validation and independent test set validation. Predicted WHO-FC values were subsequently linked to externally validated, stratified real-world healthcare cost estimates to approximate future economic burden.

Results:

Among the evaluated models, k-nearest neighbors showed the highest predictive performance (r=0.40; R²=0.16), followed by lasso regression (r=0.30; R²=0.09). Prediction errors were comparable across models. Estimated mean healthcare costs ranged from US $12,481 to $12,614 per patient per month. Despite a significant improvement in WHO-FC over 12 months, estimated healthcare costs remained largely unchanged, reflecting persistent economic burden across functional class strata.

Conclusions:

This study demonstrates the feasibility of a machine learning–based framework that integrates prediction of functional status with healthcare cost estimation in PAH. While predictive performance was moderate, the approach illustrates how ML models can be extended beyond clinical endpoints toward system-level applications such as cost forecasting and resource planning. Further validation in larger, multicenter cohorts is required before clinical or health system implementation.