AIMS: 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 hospitalization in patients with ACS. METHODS AND RESULTS: 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 5123 patients from the Sheba Medical Center, Ramat Gan, Israel. The final derivation cohort consisted of 3228 and the final validation cohort of 4904 ACS patients without CS at hospital admission. Development of CS was adjudicated manually based on the patients' reports. From nine 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 ACS, history of hypertension, congestive heart failure, and hypercholesterolaemia), logistic regression with elastic net regularization had the highest externally validated predictive performance (c-statistics: 0.844, 95% CI, 0.841-0.847). CONCLUSION: 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.

3rd Department of Cardiology National and Kapodistrian University of Athens Athens Greece

3rd Medical Department Cardiology and Intensive Care Medicine Wilhelminen Hospital Vienna Austria

Anesthesia and Intensive Care Fondazione Policlinico San Matteo Hospital IRCCS Pavia Italy

Cardiology and Arrhythmology Clinic Marche Polytechnic University University Hospital 'Umberto 1 Lancisi Salesi' Ancona Italy

Department of Cardiac Surgery National Institute of Cardiovascular Diseases Bratislava Slovakia

Department of Cardiology Charité Universitätsmedizin Berlin Berlin Germany

Department of Clinical Surgical Diagnostic and Paediatric Sciences University of Pavia Pavia Italy

Department of Intensive Care Institute for Cardiovascular Diseases of Vojvodina Sremska Kamenica Serbia

Department of Internal Medicine 2 Division of Cardiology Medical University of Vienna Vienna Austria

Duke Clinical Research Institute Durham NC USA

Faculty of Medicine Comenius University in Bratislava Spitalska 24 813 72 Bratislava Slovakia

Harvard Medical School Boston MA USA

Institute of Computer Science Czech Academy of Sciences Department of Complex Systems Prague Czech Republic

Premedix Academy Medena 18 811 02 Bratislava Slovakia

The Leviev Cardiothoracic and Vascular Center Chaim Sheba Medical Center Tel Aviv Israel

The Sackler Faculty of Medicine Tel Aviv University Tel Aviv Israel

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