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Development and Validation of an Interpretable Prehospital Return of Spontaneous Circulation (P-ROSC) Score for Out-of-Hospital Cardiac Arrest Patients Using Machine Learning
23 Pages Posted: 1 Mar 2022
More...Abstract
Background: Return of spontaneous circulation (ROSC) before arrival at the emergency department is an early indicator of successful resuscitation in out-of-hospital cardiac arrest (OHCA). Several ROSC prediction scores have been developed with European cohorts, with unclear applicability in Asian settings. We aimed to develop an interpretable prehospital ROSC (P-ROSC) score for ROSC prediction based on Asian OHCA patients.
Methods: This retrospective study examined patients who suffered OHCA between 2008 and 2019 using data recorded in the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. AutoScore, an interpretable machine learning framework, was used to develop P-ROSC. On the same cohort, the P-ROSC was compared with two clinical scores, the RACA and the UB-ROSC. The predictive power was evaluated using the area under the curve (AUC) in the receiver operating characteristic analysis.
Findings: A total of 170,678 cases were included, of which 14,104 (8.26%) attained prehospital ROSC. The P-ROSC score identified a new variable, prehospital drug administration, that was not included in the RACA score or the UB-ROSC score. Using only five variables, the P-ROSC score achieved an AUC of 0.806 (95% confidence interval [CI], 0.7987 – 0.814), outperforming both RACA and UB-ROSC with AUCs of 0.773 (95% CI, 0.765 – 0.782) and 0.728 (95% CI, 0.718 – 0.738), respectively.
Interpretation: The P-ROSC score is a practical and easily interpreted tool for predicting the probability of prehospital ROSC.
Funding Information: This research received funding from SingHealth Duke-NUS ACP Programme Funding (15/FY2020/P2/06-A79).
Declaration of Interests: MEH Ong reports funding from the ZOLL Medical Corporation for a study involving mechanical cardiopulmonary resuscitation devices; grants from the Laerdal Foundation, Laerdal Medical, and Ramsey Social Justice Foundation for funding of the Pan-Asian Resuscitation Outcomes Study; an advisory relationship with Global Healthcare SG, a commercial entity that manufactures cooling devices; and funding from Laerdal Medical on an observation program to their Community CPR raining Centre Research Program in Norway. MEH Ong has a licensing agreement and a patent filed (Application no: 13/047,348) with ZOLL Medical Corporation for a study titled "Method of predicting acute cardiopulmonary events and survivability of a patient. All other authors have no conflict of interests to declare.
Ethics Approval Statement: The local Institutional Review Boards approved the study. The waiver of informed consent was approved for the collection of data.
Keywords: Out-of-Hospital Cardiac Arrest, Return of Spontaneous Circulation, Interpretable Machine Learning, Score
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