Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients

Liu, Nan; Ong, Marcus Eng Hock; Ho, Andrew Fu Wah; Pek, Pin Pin; Lu, Tsung-Chien; Khruekarnchana, Pairoj; Song, Kyoung Jun; Tanaka, Shiro; Naroo, Ghulam Yasin; Gan, Han Nee; Koh, Zhi Xiong; Huei‐Ming, Matthew; Hisamuddin, N A R Nik; Karim, Sarah Abdul; Julina, M.N.; Omer, Azal; Yagdir, T.; Khunkhlai, Nalinas; Monsomboon, Apichaya; Piyasuwankul, Thammapad; Wong, Kwanhathai Darin; Mao, Desmond Renhao; Tham, Lai Peng; Kajino, Kentaro; Nishiuchi, Tatsuya; Cheah, Si Oon; Chia, Michael Yih Chong; Tiah, Ling; Shin, Sang Do; Koh, P.C.I.; Ryoo, Hyun Wook

doi:10.1016/j.resuscitation.2020.01.029

Cited by 16 publications

(18 citation statements)

References 19 publications

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“…Differences between expected and observed ROSC rates for non-shockable rhythms were already reported in previous validation studies, 6 , 8 and they were attributed to the different protocols in regards to the cessation of resuscitation attempts.…”

Section: Discussionsupporting

confidence: 60%

“…The external validations of the RACA score performed in recent years in Europe 6 , 7 and Asia, 8 gave similar results concerning the discrimination capacity of the model. On the other hand, the Finnish external validation study reported a better calibration of the model for patients with shockable versus non-shockable rhythms, 6 and it also resulted poorly calibrated when applied to the Pan-Asian Resuscitation Outcomes Study (PAROS) registry, 8 which required an adjustment of the original constant coefficient. Additional validation studies, especially focusing on the calibration of the model in different contexts, could help to understand these differences.…”

Section: Introductionmentioning

confidence: 52%

“…Discrimination was examined by calculation of the area under the receiver operating characteristic curve (AUROC). The observed AUROC for the study population, together with those reported in the other external validation studies available from the literature, 6 , 7 , 8 and the internal validation cohort of the original study 5 were compared using a studentized range test. 14…”

Section: Methodsmentioning

confidence: 99%

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External validation and insights about the calibration of the return of spontaneous circulation after cardiac arrest (RACA) score

et al. 2022

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Section: Discussionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 52%

Section: Methodsmentioning

confidence: 99%

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External validation and insights about the calibration of the return of spontaneous circulation after cardiac arrest (RACA) score

et al. 2022

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“…[20][21][22] An Asian study has found that the original RACA score has a good discrimination performance in Asian patients, but a poor calibration with the constant regression coefficient given ( Table 2). 23 CaRdiac Arrest Survival Score. CaRdiac Arrest Survival Score (CRASS) 8 was developed to predict survival with good neurological outcome after OHCA who had ROSC or ongoing CPR at hospital admission.…”

Section: For All Resuscitated Ohca With or Without Roscmentioning

confidence: 99%

“…20–22 An Asian study has found that the original RACA score has a good discrimination performance in Asian patients, but a poor calibration with the constant regression coefficient given (Table 2). 23…”

Section: Examples Of Validated Ohca Prognostic Prediction Models Withmentioning

confidence: 99%

Evaluation of prognostic prediction models for out-of-hospital cardiac arrest

Siu

2020

Hong Kong Journal of Emergency Medicine

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Introduction: Accurate prognostic prediction of out-of-hospital cardiac arrest is challenging but important for the emergency team and patient’s family members. A number of prognostic prediction models specifically designed for out-of-hospital cardiac arrest are developed and validated worldwide. Objective: This narrative review provides an overview of the prognostic prediction models out-of-hospital cardiac arrest patients for use in the emergency department. Discussion: Out-of-hospital cardiac arrest prognostic prediction models are potentially useful in clinical, administrative and research settings. Development and validation of such models require prehospital and hospital predictor and outcome variables which are best in the standardised Utstein Style. Logistic regression analysis is traditionally employed for model development but machine learning is emerging as the new tool. Examples of such models available for use in the emergency department include ROSC After Cardiac Arrest, CaRdiac Arrest Survival Score, Utstein-Based Return of Spontaneous Circulation, Out-of-Hospital Cardiac Arrest, Cardiac Arrest Hospital Prognosis and Cardiac Arrest Survival Score. The usefulness of these models awaits future studies.

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Effects of physician‐present prehospital care in patients with out‐of‐hospital cardiac arrest on return of spontaneous circulation: A retrospective, observational study in Saga, Japan

Shinada,

Matsuoka,

Miike

et al. 2024

Health Science Reports

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Background and AimsEmergency medical services for out‐of‐hospital cardiac arrest (OHCA) vary according to region and country, and patient prognosis differs accordingly. In Japan, physicians may provide prehospital care. However, the effect of physician‐present prehospital care on achieving return of spontaneous circulation (ROSC) in patients with cardiac arrest is not clear. Here, we aimed to examine the effect of physician‐present prehospital care on the prognosis of patients with OHCA at our hospital compared with physician‐absent care.MethodsIn this retrospective, observational study, patients aged ≥18 years with non‐traumatic OHCA from a single center in Saga City, Japan, between April 2011 and December 2019, were included. Patients were divided into two groups, based on prehospital physician presence or absence. Logistic regression analysis was used to determine the association between physician‐present prehospital care and ROSC.ResultsOf 820 patients with OHCA, 151 had a physician present and 669 did not. Logistic regression analysis with no adjustment showed that the odds ratio (OR) of physician‐present prehospital care for an increased ROSC rate was 1.74 (95% confidence interval [CI]: 1.22−2.48, p = 0.002). Logistic‐regression analysis adjusted for ROSC‐related factors indicated an OR of 1.05 (95% CI: 0.47−2.34, p = 0.914) for physician‐present prehospital care to ROSC.ConclusionPhysician‐present prehospital care may not necessarily lead to increased ROSC rates. However, insufficient data limited our study findings. Further studies involving larger sample sizes are warranted.

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Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients

Cited by 16 publications

References 19 publications

External validation and insights about the calibration of the return of spontaneous circulation after cardiac arrest (RACA) score

External validation and insights about the calibration of the return of spontaneous circulation after cardiac arrest (RACA) score

Evaluation of prognostic prediction models for out-of-hospital cardiac arrest

Effects of physician‐present prehospital care in patients with out‐of‐hospital cardiac arrest on return of spontaneous circulation: A retrospective, observational study in Saga, Japan

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