{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T12:55:23Z","timestamp":1781355323898,"version":"3.54.1"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:00:00Z","timestamp":1781308800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To develop and evaluate machine learning\u2013based models for predicting in-hospital mortality in post-cardiotomy cardiogenic shock patients supported with extracorporeal life support, aiming to improve prognostication and optimize clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were obtained from the Extracorporeal Life Support Organization (ELSO) registry across 111 centres, including 5,982 adult patients who received extracorporeal life support for post-cardiotomy cardiogenic shock between January 2010 and December 2020. Data preprocessing comprised dataset integration, complete case analysis, and variable selection. Six machine learning algorithms, boosting, decision tree, k-nearest neighbours, random forest, na\u00efve Bayes, and neural networks, were trained to predict in-hospital mortality and secondary clinical outcomes. The dataset was randomly divided into training (60%), validation (20%), and test (20%) cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The boosting algorithm achieved the highest area under the curve (AUC = 0.759), followed by random forest (AUC = 0.688). Key predictors of in-hospital mortality included: age (survivors vs. non-survivors: 57.76 \u00b1 14.63 vs. 61.25 \u00b1 13.41 years, p &amp;lt; 0.001), lactate during support (3.07 \u00b1 2.88 vs. 5.79 \u00b1 5.30 mmol\/L, p &amp;lt; 0.001), arterial pH (7.302 \u00b1 0.122 vs. 7.278 \u00b1 0.139, p &amp;lt; 0.001), and BMI (29.12 \u00b1 6.57 vs. 30.00 \u00b1 7.12 kg\/m\u00b2, p &amp;lt; 0.001). ECMO duration differed between groups (142.14 \u00b1 150.91 vs. 150.50 \u00b1 162.09 hours, p = 0.023); however, this on-support variable reflects clinical trajectory rather than a pre-initiation predictor and was excluded in a sensitivity analysis (random forest AUC = 0.697). Prediction of transplant-related outcomes was limited by class imbalance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning models demonstrated moderate predictive performance for in-hospital mortality in post-cardiotomy cardiogenic shock patients on extracorporeal life support. The random forest model demonstrated moderate discriminative performance, highlighting the relevance of readily available clinical variables. External validation and calibration analysis are required before clinical implementation. The model is best interpreted as a tool for dynamic risk assessment during ECMO support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eschf\/xvag145","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T11:44:10Z","timestamp":1779536650000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Based Prediction of Hospital Mortality in Post-Cardiotomy Cardiogenic Shock with Mechanical Support"],"prefix":"10.1093","author":[{"given":"Ahmad","family":"Mahajna","sequence":"first","affiliation":[{"name":"Cardio-Thoracic Surgery Department, Maastricht University Medical Centre , Maastricht ,","place":["The Netherlands"]},{"name":"Cardiovascular Research Institute Maastricht (CARIM), Maastricht University , Maastricht ,","place":["Netherlands"]},{"name":"Cardiac Surgery Department, Rambam Medical Center , Haifa ,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michal","family":"Kawczynski","sequence":"additional","affiliation":[{"name":"Cardio-Thoracic Surgery Department, Maastricht University Medical Centre , Maastricht ,","place":["The Netherlands"]},{"name":"Cardiovascular Research Institute Maastricht (CARIM), Maastricht University , Maastricht ,","place":["Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2238-1399","authenticated-orcid":false,"given":"Silvia","family":"Mariani","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Institute Maastricht (CARIM), Maastricht University , Maastricht ,","place":["Netherlands"]},{"name":"Cardiac Surgery Unit, Cardio-Thoracic and Vascular Department, Fondazione IRCCS San Gerardo dei Tintori , Monza ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1621-7848","authenticated-orcid":false,"given":"Bas C T","family":"van Bussel","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Institute Maastricht (CARIM), Maastricht University , Maastricht ,","place":["Netherlands"]},{"name":"Department of Intensive Care Medicine, Maastricht University Medical Centre + , Maastricht ,","place":["the Netherlands"]},{"name":"Care and Public Health Research Institute (Caphri), Maastricht University , Maastricht ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Elena","family":"De Piero","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Institute Maastricht (CARIM), Maastricht University , Maastricht ,","place":["Netherlands"]},{"name":"Critical Care Department, King Faisal Specialist Hospital and Research Center , Riyadh ,","place":["Saudi 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,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Soltesz","sequence":"additional","affiliation":[{"name":"Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic , Ohio ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giles","family":"Peek","sequence":"additional","affiliation":[{"name":"UF Health Congenital Heart Center, University of Florida, Gainesville , Florida ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Brodie","sequence":"additional","affiliation":[{"name":"Johns Hopkins School of Medicine , Baltimore, Maryland ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Bermudez","sequence":"additional","affiliation":[{"name":"Hospital of the University of Pennsylvania , Philadelphia, Pennsylvania ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Haft","sequence":"additional","affiliation":[{"name":"Cardiac Surgery, University of Michigan Health , Ann Arbor, Michigan ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7840-801X","authenticated-orcid":false,"given":"Hiroo","family":"Takayama","sequence":"additional","affiliation":[{"name":"Adult Cardiac Surgery, New York-Presbyterian\/Columbia University Irving Medical Center , New York ,","place":["US"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0984-9011","authenticated-orcid":false,"given":"Milan","family":"Milojevic","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute , Belgrade 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Surgery, Medical University of Vienna , Vienna ,","place":["Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2055-8293","authenticated-orcid":false,"given":"Dominik","family":"Wiedemann","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Medical University of Vienna , Vienna ,","place":["Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8848-4112","authenticated-orcid":false,"given":"Jae-Seung","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Cardiothoracic Surgery, Asan Medical Center , Seoul ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoan","family":"Lamarche","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Montreal Heart Institute, Montreal , Quebec 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Surgery, University Hospital D\u00fcsseldorf , D\u00fcsseldorf ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evgenij","family":"Potapov","sequence":"additional","affiliation":[{"name":"Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charit\u00e9 (DHZC) , Berlin ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Silvestry","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Arizona College of Medicine \u2013 Tucson , Arizona ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louis","family":"Stein","sequence":"additional","affiliation":[{"name":"RWJBarnabas Health Medical Group, affiliated with Newark Beth Israel Medical Center , Newark, New Jersey 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