Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention

<b>Background/Objectives</b>: Pulmonary arterial hypertension (PAH) is a complex cardiovascular disease with a high burden of morbidity and mortality. Although several risk prediction models have been proposed, the exact significance of distinct clinical parameters in predicting survival in PAH remains unclear. It is important to emphasize that this study does not aim to validate or contradict existing clinical risk assessment calculators provided by the ESC or other scientific societies. Instead, the goal of this research is to identify and rank clinical parameters according to their importance in predicting mortality in PAH patients using machine learning techniques. <b>Methods</b>: Using the Database of Pulmonary Hypertension in the Polish population (BNP-PL) registry, 1755 adult patients with PAH were selected. Feature engineering was conducted using domain knowledge, guided by European Society of Cardiology (ESC) recommendations. Features were reduced using LASSO regression and sequential feature elimination algorithms. A classification model was built using the XGBoost algorithm, utilizing 17 features. The model was tested on a preselected subset of the BNP-PL data. The Shapley Additive Explanations (SHAP) method was used to explain the model's predictions and to rank feature importance. <b>Results</b>: The model achieved satisfactory results across evaluated metrics, including an area under the curve of 0.767, accuracy of 0.738, specificity of 0.733, and sensitivity of 0.800. SHAP values effectively ranked the features, corroborating the significance of parameters present in the ESC risk stratification tables. Furthermore, local interpretation of results using SHAP enabled individualized assessment of feature importance, enhancing clinical applicability. <b>Conclusions</b>: The proposed artificial intelligence-based model demonstrates satisfactory predictive capability, highlighting the potential of machine learning techniques to support more personalized approaches to the management of PAH patients. This approach offers complementary insights into traditional risk assessment methods, providing clinicians with a novel tool for individualized risk evaluation and decision-making.

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Publication Details

Journal
Journal of Clinical Medicine
Published
2025-11-23
DOI
https://doi.org/10.3390/jcm14238325
Citations
2
Primary Topic
Pulmonary Hypertension Research and Treatments
Type
article
Field-Weighted Citation Impact
3.04
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article

Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention

Beata Kuśmierczyk‐Droszcz, Marcin Waligóra, Piotr Błaszczak, Zbigniew Gąsior et al.
2 citations
Journal of Clinical Medicine
Pulmonary Hypertension Research and Treatments
3.04
article

Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention

Beata Kuśmierczyk‐Droszcz, Marcin Waligóra, Piotr Błaszczak, Zbigniew Gąsior, Ilona Skoczylas, Michał Ziółkowski, Grzegorz Kopeć, Ewa Lewicka, Grzegorz Grześk, Łukasz Chrzanowski, Katarzyna Widejko, Katarzyna Mizia­‐Stec, Małgorzata Peregud‐Pogorzelska, Marcin Kurzyna, Wojciech Jacheć, Katarzyna Ptaszyńska‐Kopczyńska, Ewa Malinowska, Tatiana Mularek‐Kubzdela, Anna Smukowska-Gorynia, Agnieszka Pawlak, Piotr Pruszczyk, Katarzyna Betkier‐Lipińska, Michał Tomaszewski, Łukasz Ledziński, Michał Florczyk, Olga Dzikowska­‐Diduch, Ewa Mroczek, Miłosz Jaguszewski, Judyta Winowska-Józwa
article en
2 citations

Abstract

<b>Background/Objectives</b>: Pulmonary arterial hypertension (PAH) is a complex cardiovascular disease with a high burden of morbidity and mortality. Although several risk prediction models have been proposed, the exact significance of distinct clinical parameters in predicting survival in PAH remains unclear. It is important to emphasize that this study does not aim to validate or contradict existing clinical risk assessment calculators provided by the ESC or other scientific societies. Instead, the goal of this research is to identify and rank clinical parameters according to their importance in predicting mortality in PAH patients using machine learning techniques. <b>Methods</b>: Using the Database of Pulmonary Hypertension in the Polish population (BNP-PL) registry, 1755 adult patients with PAH were selected. Feature engineering was conducted using domain knowledge, guided by European Society of Cardiology (ESC) recommendations. Features were reduced using LASSO regression and sequential feature elimination algorithms. A classification model was built using the XGBoost algorithm, utilizing 17 features. The model was tested on a preselected subset of the BNP-PL data. The Shapley Additive Explanations (SHAP) method was used to explain the model's predictions and to rank feature importance. <b>Results</b>: The model achieved satisfactory results across evaluated metrics, including an area under the curve of 0.767, accuracy of 0.738, specificity of 0.733, and sensitivity of 0.800. SHAP values effectively ranked the features, corroborating the significance of parameters present in the ESC risk stratification tables. Furthermore, local interpretation of results using SHAP enabled individualized assessment of feature importance, enhancing clinical applicability. <b>Conclusions</b>: The proposed artificial intelligence-based model demonstrates satisfactory predictive capability, highlighting the potential of machine learning techniques to support more personalized approaches to the management of PAH patients. This approach offers complementary insights into traditional risk assessment methods, providing clinicians with a novel tool for individualized risk evaluation and decision-making.

Journal of Clinical MedicineVol. 14(23)
University of Szczecin (PL), Poznan University of Medical Sciences (PL), Medical University of Lublin (PL), Jagiellonian University (PL), Medical University of Białystok (PL), Medical University of Silesia (PL), Medical University of Warsaw (PL), Sacred Heart Hospital (IE), Nicolaus Copernicus University (PL), John Paul II Hospital (PL), Wojskowy Instytut Medycyny Lotniczej (PL), Medical University of Lodz (PL), Ministry of Interior and Administration (PL), Institute of Cardiology (PL), ERN GUARD-Heart (NL), Collegium Medicum in Bydgoszcz (PL), Mossakowski Medical Research Institute, Polish Academy of Sciences (PL), University of Warmia and Mazury in Olsztyn (PL), Cardinal Stefan Wyszynski University in Warsaw (PL), Gdańsk Medical University (PL), Pomeranian Medical University (PL)
Openalex Percentile: Top 7%
Pulmonary Hypertension Research and Treatments
3.04
DOIOpen Access PDFOpenAlex
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