Machine-learning based scoring system to predict cardiogenic shock in acute coronary syndrome
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Machine-learning based scoring system to predict cardiogenic shock in acute coronary syndrome
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30201 - Cardiac and Cardiovascular systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
European Heart Journal - Digital Health
ISSN
2634-3916
e-ISSN
2634-3916
Volume of the periodical
6
Issue of the periodical within the volume
2
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
240-251
UT code for WoS article
001406740900001
EID of the result in the Scopus database
2-s2.0-105000377072