Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention
Year: 2025
Type: article
Abstract: Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.
Source: Journal of Clinical Medicine
DOI: 10.3390/jcm14238325
Authors: Łukasz Ledziński, Grzegorz Grześk, Michał Ziołkowski, Marcin Waligóra, Marcin Kurzyna, Tatiana Mularek-Kubzdela, Anna Smukowska-Gorynia, Ilona Skoczylas, Łukasz Chrzanowski, Piotr Błaszczak, Miłosz Jaguszewski, Beata Kuśmierczyk-Droszcz, Katarzyna Ptaszyńska, Katarzyna Mizia-Stec, Ewa Malinowska, Małgorzata Peregud-Pogorzelska, Ewa Lewicka, Michał Tomaszewski, Wojciech Jacheć, Michał Florczyk, Ewa Mroczek, Zbigniew Gąsior, Agnieszka Pawlak, Katarzyna Betkier-Lipińska, Piotr Pruszczyk, Olga Dzikowska-Diduch, Katarzyna Widejko, Judyta Winowska-Józwa, Grzegorz Kopeć
Institutions: Nicolaus Copernicus University, Collegium Medicum in Bydgoszcz, Jagiellonian University, John Paul II Hospital, Poznan University of Medical Sciences, Medical University of Silesia, Medical University of Lodz, Gdańsk Medical University, Institute of Cardiology, Medical University of Białystok, University of Warmia and Mazury in Olsztyn, Pomeranian Medical University, Medical University of Lublin, Wroclaw Medical University, Mossakowski Medical Research Institute, Polish Academy of Sciences, National Medical Institute of the Ministry of Interior and Administration, Polish Academy of Sciences, Wojskowy Instytut Medyczny, Medical University of Warsaw
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416665992