Machine-learning based scoring system to predict cardiogenic shock in acute coronary syndrome

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00603919" target="_blank" >RIV/67985807:_____/25:00603919 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1093/ehjdh/ztaf002" target="_blank" >https://doi.org/10.1093/ehjdh/ztaf002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/ehjdh/ztaf002" target="_blank" >10.1093/ehjdh/ztaf002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine-learning based scoring system to predict cardiogenic shock in acute coronary syndrome

Popis výsledku v původním jazyce

Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality rates approaching 50%. The ability to identify high-risk patients prior to the development of CS may allow for pre-emptive measures to prevent the development of CS. The objective was to derive and externally validate a simple, machine learning (ML) based scoring system using variables readily available at first medical contact to predict the risk of developing CS during hospitalisation in patients with ACS. Observational multicentre study on ACS patients hospitalized at intensive care units. Derivation cohort included over 40,000 patients from Beth Israel Deaconess Medical Center, Boston, USA. Validation cohort included 5,123 patients from the Sheba Medical Center, Ramat Gan, Israel. The final derivation cohort consisted of 3,228 and the final validation cohort of 4,904 ACS patients without CS at hospital admission. Development of CS was adjudicated manually based on the patients’ reports. From 9 ML models based on 13 variables (heart rate, respiratory rate, oxygen saturation, blood glucose level, systolic blood pressure, age, sex, shock index, heart rhythm, type of acute coronary syndrome, history of hypertension, congestive heart failure and hypercholesterolemia), logistic regression with elastic net regularization had the highest externally validated predictive performance (c-statistics: 0.844, 95% CI, 0.841–0.847). STOP SHOCK score is a simple ML based tool available at first medical contact showing high performance for prediction of developing CS during hospitalization in ACS patients. The web application is available at https://stopshock.org/#calculator.

Název v anglickém jazyce

Machine-learning based scoring system to predict cardiogenic shock in acute coronary syndrome

Popis výsledku anglicky

Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality rates approaching 50%. The ability to identify high-risk patients prior to the development of CS may allow for pre-emptive measures to prevent the development of CS. The objective was to derive and externally validate a simple, machine learning (ML) based scoring system using variables readily available at first medical contact to predict the risk of developing CS during hospitalisation in patients with ACS. Observational multicentre study on ACS patients hospitalized at intensive care units. Derivation cohort included over 40,000 patients from Beth Israel Deaconess Medical Center, Boston, USA. Validation cohort included 5,123 patients from the Sheba Medical Center, Ramat Gan, Israel. The final derivation cohort consisted of 3,228 and the final validation cohort of 4,904 ACS patients without CS at hospital admission. Development of CS was adjudicated manually based on the patients’ reports. From 9 ML models based on 13 variables (heart rate, respiratory rate, oxygen saturation, blood glucose level, systolic blood pressure, age, sex, shock index, heart rhythm, type of acute coronary syndrome, history of hypertension, congestive heart failure and hypercholesterolemia), logistic regression with elastic net regularization had the highest externally validated predictive performance (c-statistics: 0.844, 95% CI, 0.841–0.847). STOP SHOCK score is a simple ML based tool available at first medical contact showing high performance for prediction of developing CS during hospitalization in ACS patients. The web application is available at https://stopshock.org/#calculator.

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

European Heart Journal - Digital Health

ISSN

2634-3916

e-ISSN

2634-3916

Svazek periodika

6

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

240-251

Kód UT WoS článku

001406740900001

EID výsledku v databázi Scopus

2-s2.0-105000377072