A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00179906%3A_____%2F25%3A10503845" target="_blank" >RIV/00179906:_____/25:10503845 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00669806:_____/25:10503845

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=3icGffDils" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=3icGffDils</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-14893-1" target="_blank" >10.1038/s41598-025-14893-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism

Popis výsledku v původním jazyce

Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develop a machine learning model to identify patients at risk for long-term consequences after PE. Using data from the RIETE registry, the largest prospective international PE registry, we developed supervised machine learning models to identify patients at increased risk of CTEPH and post-PE syndrome. Our approach involved data preprocessing, model training via random forest algorithm, and validation through Monte-Carlo cross-validation. The performance of the CTEPH prediction model was benchmarked against an existing score. Of the 57,981 PE patients in the RIETE registry, 5,217 were eligible for inclusion. Median age was 68 years, with 50.6% men. Machine learning was based on 111 predictor variables, with 171 patients (3.3%) developing CTEPH. The CTEPH model demonstrated good performance with an AUC of 0.74 (95%CI: 0.73-0.75), significantly outperforming the existing CTEPH prediction score (0.57; 0.54-0.61). Additionally, 1,310 (25.1%) patients were defined as having post-PE syndrome six months after index PE. The post-PE syndrome model showed poorer performance with an AUC of 0.62 (0.61-0.62). Key predictor variables across both models included chest pain at presentation, PE location, troponin, side of clot, and dyspnea at presentation. Machine learning models show promise in predicting CTEPH but are less effective for post-PE syndrome. Future refinement, including integrating imaging data, is necessary to improve predictive performance and clinical utility.

Název v anglickém jazyce

A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism

Popis výsledku anglicky

Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develop a machine learning model to identify patients at risk for long-term consequences after PE. Using data from the RIETE registry, the largest prospective international PE registry, we developed supervised machine learning models to identify patients at increased risk of CTEPH and post-PE syndrome. Our approach involved data preprocessing, model training via random forest algorithm, and validation through Monte-Carlo cross-validation. The performance of the CTEPH prediction model was benchmarked against an existing score. Of the 57,981 PE patients in the RIETE registry, 5,217 were eligible for inclusion. Median age was 68 years, with 50.6% men. Machine learning was based on 111 predictor variables, with 171 patients (3.3%) developing CTEPH. The CTEPH model demonstrated good performance with an AUC of 0.74 (95%CI: 0.73-0.75), significantly outperforming the existing CTEPH prediction score (0.57; 0.54-0.61). Additionally, 1,310 (25.1%) patients were defined as having post-PE syndrome six months after index PE. The post-PE syndrome model showed poorer performance with an AUC of 0.62 (0.61-0.62). Key predictor variables across both models included chest pain at presentation, PE location, troponin, side of clot, and dyspnea at presentation. Machine learning models show promise in predicting CTEPH but are less effective for post-PE syndrome. Future refinement, including integrating imaging data, is necessary to improve predictive performance and clinical utility.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30201 - Cardiac and Cardiovascular systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

32744

Kód UT WoS článku

001580597500022

EID výsledku v databázi Scopus

2-s2.0-105016908262