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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

See all articles by Nan Liu

Nan Liu

Duke-National University of Singapore Medical School - Centre for Quantitative Medicine

Mingxuan Liu

National University of Singapore (NUS) - Duke-NUS Medical School

Xinru Chen

National University of Singapore (NUS) - Duke-NUS Medical School

Yilin Ning

National University of Singapore (NUS) - Duke-NUS Medical School

Jin Wee Lee

National University of Singapore (NUS) - Duke-NUS Medical School

Fahad Javaid Siddiqui

National University of Singapore (NUS) - Duke-NUS Medical School

Seyed Ehsan Saffari

National University of Singapore (NUS) - Duke-NUS Medical School

Andrew Fu Wah Ho

National University of Singapore (NUS) - Duke-NUS Medical School

Sang Do Shin

Seoul National University - Department of Emergency Medicine

Matthew Huei-Ming Ma

National Taiwan University - Department of Emergency Medicine

Hideharu Tanaka

Kokushikan University - Graduate School of Emergency Medical System

Marcus Eng Hock Ong

National University of Singapore (NUS) - Health Services and Systems Research; Singapore General Hospital - Department of Emergency Medicine

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

Suggested Citation

Liu, Nan and Liu, Mingxuan and Chen, Xinru and Ning, Yilin and Lee, Jin Wee and Siddiqui, Fahad Javaid and Saffari, Seyed Ehsan and Ho, Andrew Fu Wah and Do Shin, Sang and Ma, Matthew Huei-Ming and Tanaka, Hideharu and Ong, Marcus Eng Hock, Development and Validation of an Interpretable Prehospital Return of Spontaneous Circulation (P-ROSC) Score for Out-of-Hospital Cardiac Arrest Patients Using Machine Learning. Available at SSRN: https://ssrn.com/abstract=4046679 or http://dx.doi.org/10.2139/ssrn.4046679

Nan Liu (Contact Author)

Duke-National University of Singapore Medical School - Centre for Quantitative Medicine ( email )

8 College Rd.
Singapore, 169857
Singapore
+65 6601 6503 (Phone)

Mingxuan Liu

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Xinru Chen

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Yilin Ning

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Jin Wee Lee

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Fahad Javaid Siddiqui

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Seyed Ehsan Saffari

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Andrew Fu Wah Ho

National University of Singapore (NUS) - Duke-NUS Medical School ( email )

Singapore
Singapore

Sang Do Shin

Seoul National University - Department of Emergency Medicine ( email )

Seoul
Korea, Republic of (South Korea)

Matthew Huei-Ming Ma

National Taiwan University - Department of Emergency Medicine ( email )

Taipei
Taiwan

Hideharu Tanaka

Kokushikan University - Graduate School of Emergency Medical System ( email )

Tokyo
Japan

Marcus Eng Hock Ong

National University of Singapore (NUS) - Health Services and Systems Research ( email )

Singapore General Hospital - Department of Emergency Medicine ( email )

Singapore