Open this publication in new window or tab >>Department of Clinical Medicine, Medicine Solna, Karolinska Institutet, Framstegsgatan, 171 64 Solna, Sweden.
Department of Anesthesiology and Intensive Care, Sahlgrenska University Hospital , Blå stråket 5, 413 45 Gothenburg, Sweden;Department of Anaesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Blå stråket 5, 413 45 Gothenburg , Sweden.
Department of Anesthesiology and Intensive Care, Sahlgrenska University Hospital , Blå stråket 5, 413 45 Gothenburg, Sweden;Department of Anaesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Blå stråket 5, 413 45 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden;Department of Cardiology, Sahlgrenska University Hospital , Blå stråket 5, Västra Götalands län, 413 45 Gothenburg , Sweden.
Center for Resuscitation Science, Department of Clinical Science and Education, Karolinska Institutets, Södersjukhuset , Jägargatan 20, staircase 1, 171 77 Stockholm, Sweden;Function Perioperative Medicine and Intensive Care, Karolinska University Hospital , Tomtebodavägen 18, 171 76 Stockholm , Sweden.
Department of Anesthesiology and Intensive Care, Sahlgrenska University Hospital , Blå stråket 5, 413 45 Gothenburg, Sweden;Department of Anaesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Blå stråket 5, 413 45 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden;Department of Cardiology, Sahlgrenska University Hospital , Blå stråket 5, Västra Götalands län, 413 45 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden;Department of Cardiology, Sahlgrenska University Hospital , Blå stråket 5, Västra Götalands län, 413 45 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden;The Swedish Registry for Cardiopulmonary Resuscitation , Medicinaregatan 18G, 413 90 Gothenburg , Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, staircase H, Sahlgrenska University Hospital, 413 45 Gothenburg , Sweden;Department of Cardiology, Sahlgrenska University Hospital , Blå stråket 5, Västra Götalands län, 413 45 Gothenburg , Sweden;The Swedish Registry for Cardiopulmonary Resuscitation , Medicinaregatan 18G, 413 90 Gothenburg , Sweden.
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2024 (English)In: The European Heart Journal - Digital Health, E-ISSN 2634-3916, Vol. 5, no 3, p. 270-277Article in journal (Refereed) Published
Abstract [en]
Aims
Out-of-hospital cardiac arrest (OHCA) is a major health concern worldwide. Although one-third of all patients achieve a return of spontaneous circulation and may undergo a difficult period in the intensive care unit, only 1 in 10 survive. This study aims to improve our previously developed machine learning model for early prognostication of survival in OHCA.
Methods and results
We studied all cases registered in the Swedish Cardiopulmonary Resuscitation Registry during 2010 and 2020 (n = 55 615). We compared the predictive performance of extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), logistic regression, CatBoost, random forest, and TabNet. For each framework, we developed models that optimized (i) a weighted F1 score to penalize models that yielded more false negatives and (ii) a precision–recall area under the curve (PR AUC). LightGBM assigned higher importance values to a larger set of variables, while XGB made predictions using fewer predictors. The area under the curve receiver operating characteristic (AUC ROC) scores for LightGBM were 0.958 (optimized for weighted F1) and 0.961 (optimized for a PR AUC), while for XGB, the scores were 0.958 and 0.960, respectively. The calibration plots showed a subtle underestimation of survival for LightGBM, contrasting with a mild overestimation for XGB models. In the crucial range of 0–10% likelihood of survival, the XGB model, optimized with the PR AUC, emerged as a clinically safe model.
Conclusion
We improved our previous prediction model by creating a parsimonious model with an AUC ROC at 0.96, with excellent calibration and no apparent risk of underestimating survival in the critical probability range (0–10%). The model is available at www.gocares.se.
Keywords
Out-of-hospital cardiac arrest, Machine learning, Extreme gradient boosting, LightGBM
National Category
Cardiology and Cardiovascular Disease
Identifiers
urn:nbn:se:hb:diva-33055 (URN)10.1093/ehjdh/ztae016 (DOI)
Funder
Knut and Alice Wallenberg FoundationUniversity of GothenburgRegion Västra Götaland
2025-01-092025-01-092025-02-10Bibliographically approved