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Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study
DC Field | Value | Language |
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dc.contributor.author | Liu, Nan | - |
dc.contributor.author | Liu, Mingxuan | - |
dc.contributor.author | Chen, Xinru | - |
dc.contributor.author | Ning, Yilin | - |
dc.contributor.author | Lee, Jin Wee | - |
dc.contributor.author | Siddiqui, Fahad Javaid | - |
dc.contributor.author | Saffari, Seyed Ehsan | - |
dc.contributor.author | Ho, Andrew Fu Wah | - |
dc.contributor.author | Shin, Sang Do | - |
dc.contributor.author | Ma, Matthew Huei-Ming | - |
dc.contributor.author | Tanaka, Hideharu | - |
dc.contributor.author | Ong, Marcus Eng Hock | - |
dc.date.accessioned | 2022-09-29T03:18:31Z | - |
dc.date.available | 2022-09-29T03:18:31Z | - |
dc.date.created | 2022-06-15 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | EClinicalMedicine, Vol.48, p. 101422 | - |
dc.identifier.issn | 2589-5370 | - |
dc.identifier.uri | https://hdl.handle.net/10371/184651 | - |
dc.description.abstract | © 2022 The AuthorsBackground: 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 patients with OHCA in Asia. Methods: This retrospective study examined patients who suffered from OHCA between Jan 1, 2009 and Jun 17, 2018 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: 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, which 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] 0.799–0.814), 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: This research received funding from SingHealth Duke-NUS ACP Programme Funding (15/FY2020/P2/06-A79). | - |
dc.language | 영어 | - |
dc.publisher | Elsevier | - |
dc.title | Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.eclinm.2022.101422 | - |
dc.citation.journaltitle | EClinicalMedicine | - |
dc.identifier.wosid | 000808129700019 | - |
dc.identifier.scopusid | 2-s2.0-85129980398 | - |
dc.citation.startpage | 101422 | - |
dc.citation.volume | 48 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Shin, Sang Do | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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