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June 19, 2026European Heart Journal - Digital HealthOpen Access

Machine learning models accurately estimate mPAP and PVR from routine non-invasive data before RHC.

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Why the study?

Can machine learning models accurately predict key haemodynamic parameters in patients with pulmonary arterial hypertension using routinely available non-invasive data?

Population

181 patients with invasively confirmed pulmonary arterial hypertension (PAH)

Design

Cohort

Key result

Machine learning models can estimate mean pulmonary arterial pressure (r=0.80) and pulmonary vascular resistance (r=0.71) from routine clinical data obtained prior to right heart catheterization.

Authors

TKTilmann KramerHWHenning WeisMKMira Krämer

Discussion

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Overview

May support non-invasive mPAP/PVR estimation in PAH; hypothesis-generating and requires prospective validation before clinical use.

Key Points

  • To develop and evaluate machine learning models for predicting key haemodynamic parameters in pulmonary arterial hypertension using non-invasive data.
  • Analyzed data from 181 patients with confirmed pulmonary arterial hypertension using 56 predictive variables.
  • Employed an 80/20 train-test split and fivefold cross-validation across various machine learning models.
  • Models evaluated include lasso regression, ridge regression, k-nearest neighbours, decision trees, random forest, and gradient boosting.
  • Lasso regression achieved the best performance for predicting mean pulmonary arterial pressure (mPAP) with r = 0.80 and R² = 0.64.
  • Ridge regression performed best for predicting pulmonary vascular resistance (PVR) with r = 0.71 and R² = 0.51.
  • Random forest and gradient boosting showed modest performance for cardiac index prediction with r = 0.38 and 0.37 respectively.

Study Design

Type

Observational (n=181)

Structured PICO

Can machine learning models accurately predict key haemodynamic parameters in patients with pulmonary arterial hypertension using routinely available non-invasive data?

P
Population
181 patients with invasively confirmed pulmonary arterial hypertension.
E
Exposure
Machine learning models (lasso regression, ridge regression, k-nearest neighbours, decision trees, random forest, and gradient boosting machine) using 56 non-invasive variables collected within 8 weeks prior to right heart catheterization
O
Outcome
Prediction of mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance (PVR)surrogate

Main Result

Effect estimate: r = 0.80 for mPAP; r = 0.71 for PVR

Machine learning models can estimate mean pulmonary arterial pressure and pulmonary vascular resistance from routine non-invasive clinical data in patients with confirmed PAH.

Limitations

  • External validation is required to confirm generalizability and clinical applicability.
  • External validation is required to confirm generalizability and clinical applicability

Cite This Study

Kramer et al. (2025) conducted an observational in Pulmonary arterial hypertension (n=181). Machine learning models was evaluated on Prediction of mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance (PVR) (r = 0.80 for mPAP; r = 0.71 for PVR). Machine learning models can estimate mean pulmonary arterial pressure (r=0.80) and pulmonary vascular resistance (r=0.71) from routine clinical data obtained prior to right heart catheterization.

synapsesocial.com/papers/6a35982fdd3be7785e70ed1bhttps://doi.org/10.1093/ehjdh/ztaf074
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