INTRODUCTION: Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS. METHODS: We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction. RESULTS: We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization. CONCLUSION: We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

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

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

Affiliated to the Sackler Faculty of Medicine Tel Aviv University Tel Aviv Israel

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

Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany

Cardiac Intensive Care Unit Institute for Cardiovascular Diseases of Vojvodina Sremska Kamenica Serbia

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

Cardiology Department Emergency County Clinical Hospital of Oradea Oradea Romania

Clinic of Cardiac Surgery National Institute of Cardiovascular Diseases Bratislava Slovakia

Department for Cardiac Surgery Cardiac Regeneration Research Medical University of Innsbruck Innsbruck Austria

Department of Acute Cardiology National Institute of Cardiovascular Diseases Bratislava Slovakia

Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité Campus Benjamin Franklin Charité Universitätsmedizin Berlin Berlin Germany

Department of Cardiology Faculty of Human Medicine Zagazig University Zagazig Egypt

Department of Cardiology Ibrahim Cardiac Hospital and Research Institute Dhaka Bangladesh

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

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

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

Deutsches Zentrum für Herz Kreislauf Forschung e 5 Berlin Germany

Duke Clinical Research Institute Durham NC United States

Faculty of Medicine Comenius University in Bratislava Bratislava Slovakia

Faculty of Medicine University of Novi Sad Novi Sad Serbia

Global Clinical Scholars Research Training Program Harvard Medical School Boston MA United States

Institute of Cardiovascular Sciences University of Birmingham Medical School Birmingham United Kingdom

Institute of Medical Informatics Charité Universitätsmedizin Berlin Berlin Germany

Premedix Academy Bratislava Slovakia

The Leviev Cardiothoracic and Vascular Center Chaim Sheba Medical Center Ramat Gan Israel

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