Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework

Wong, Xiang Yi; Ang, Yu Kai; Li, Keqi; Chin, Yip Han; Lam, Sean Shao Wei; Tan, Kenneth; Chua, Matthew Chin Heng; Ong, Marcus Eng Hock; Liu, Nan; Pourghaderi, Ahmad Reza; Ho, Andrew Fu Wah

doi:10.1016/j.resuscitation.2021.11.029

Cited by 21 publications

(21 citation statements)

References 26 publications

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“…They also remain in line with our aim of employing objective variables that are easily obtained in the emergency department, without being subject to recall bias. However, their inclusion did not substantially improve overall model performance (measured by AUC) as shown in the parsimony plot of the original SARICA derivation paper ( 13 ).…”

Section: Discussionmentioning

confidence: 96%

“…Countries with fewer than 500 patients who attained ROSC were excluded due to small effect size. Data from Singapore was excluded as that data had previously formed the derivation and internal validation cohorts ( 13 ). Pediatric arrests (age <18 years) and traumatic arrests were excluded.…”

Section: Methodsmentioning

confidence: 99%

“…The SARICA score was calculated as described in the original SARICA paper ( 13 ). SARICA comprises three variables: age, pre-hospital ROSC and initial shockable rhythm.…”

Section: Methodsmentioning

confidence: 99%

“…However, the clinical relevance of these scores was noted to be limited by difficulty in computation, recall bias and unavailability of data at the time the patient is in the emergency department (ED). In response to this unmet need, we recently developed the Survival After ROSC In Cardiac Arrest (SARICA) score ( 13 ) using real world data from Singapore applied to AutoScore, ( 14 , 15 ) an interpretable machine learning score generator. SARICA consists of three variables: pre-hospital ROSC, age and initial heart rhythm.…”

Section: Introductionmentioning

confidence: 99%

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External validation of the Survival After ROSC in Cardiac Arrest (SARICA) score for predicting survival after return of spontaneous circulation using multinational pan-asian cohorts

et al. 2022

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AimAccurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who attain return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communication with family. A clinical decision tool, Survival After ROSC in Cardiac Arrest (SARICA), was recently developed, showing excellent performance on internal validation. We aimed to externally validate SARICA in multinational cohorts within the Pan-Asian Resuscitation Outcomes Study.Materials and methodsThis was an international, retrospective cohort study of patients who attained ROSC after OHCA in the Asia Pacific between January 2009 and August 2018. Pediatric (age <18 years) and traumatic arrests were excluded. The SARICA score was calculated for each patient. The primary outcome was survival. We used receiver operating characteristics (ROC) analysis to calculate the model performance of the SARICA score in predicting survival. A calibration belt plot was used to assess calibration.ResultsOut of 207,450 cases of OHCA, 24,897 cases from Taiwan, Japan and South Korea were eligible for inclusion. Of this validation cohort, 30.4% survived. The median SARICA score was 4. Area under the ROC curve (AUC) was 0.759 (95% confidence interval, CI 0.753–0.766) for the total population. A higher AUC was observed in subgroups that received bystander CPR (AUC 0.791, 95% CI 0.782–0.801) and of presumed cardiac etiology (AUC 0.790, 95% CI 0.782–0.797). The model was well-calibrated.ConclusionThis external validation study of SARICA demonstrated high model performance in a multinational Pan-Asian cohort. Further modification and validation in other populations can be performed to assess its readiness for clinical translation.

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Section: Discussionmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

“…The SARICA score was calculated as described in the original SARICA paper ( 13 ). SARICA comprises three variables: age, pre-hospital ROSC and initial shockable rhythm.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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External validation of the Survival After ROSC in Cardiac Arrest (SARICA) score for predicting survival after return of spontaneous circulation using multinational pan-asian cohorts

et al. 2022

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“…Another regional project called 'Take Heart America' doubled survival for OHCA patients after encouraging the use of TTM and CAG [34]. A fairly substantial evidence base has accumulated over the past decade supporting key features of CACs related to regionalization, including individual interventions such as TTM [35], 24/7 PCI [36], ECMO [37], and prognostication [38,39], a structured OHCA treatment bundle combining the above [40,41], and highvolume centres [42]. It should be acknowledged, however, that there continues to be active debate in these research areas, such as what specific temperature should be aimed for in TTM [43], and why some high-volume centres have relatively poor outcomes [20].…”

Section: Key Pointsmentioning

confidence: 99%

Cardiac arrest centres: what, who, when, and where?

Yeo

Ong

2022

Current Opinion in Critical Care

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Purpose of reviewCardiac arrest centres (CACs) may play a key role in providing postresuscitation care, thereby improving outcomes in out-of-hospital cardiac arrest (OHCA). There is no consensus on CAC definitions or the optimal CAC transport strategy despite advances in research. This review provides an updated overview of CACs, highlighting evidence gaps and future research directions.Recent findingsCAC definitions vary worldwide but often feature 24/7 percutaneous coronary intervention capability, targeted temperature management, neuroprognostication, intensive care, education, and research within a centralized, high-volume hospital. Significant evidence exists for benefits of CACs related to regionalization. A recent meta-analysis demonstrated clearly improved survival with favourable neurological outcome and survival among patients transported to CACs with conclusions robust to sensitivity analyses. However, scarce data exists regarding ‘who’, ‘when’, and ‘where’ for CAC transport strategies. Evidence for OHCA patients without ST elevation postresuscitation to be transported to CACs remains unclear. Preliminary evidence demonstrated greater benefit from CACs among patients with shockable rhythms. Randomized controlled trials should evaluate specific strategies, such as bypassing nearest hospitals and interhospital transfer.SummaryReal-world study designs evaluating CAC transport strategies are needed. OHCA patients with underlying culprit lesions, such as those with ST-elevation myocardial infarction (STEMI) or initial shockable rhythms, will likely benefit the most from CACs.

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A review of evaluation approaches for explainable AI with applications in cardiology

Salih,

Galazzo,

Gkontra

et al. 2024

Artif Intell Rev

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Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

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Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework

Cited by 21 publications

References 26 publications

External validation of the Survival After ROSC in Cardiac Arrest (SARICA) score for predicting survival after return of spontaneous circulation using multinational pan-asian cohorts

External validation of the Survival After ROSC in Cardiac Arrest (SARICA) score for predicting survival after return of spontaneous circulation using multinational pan-asian cohorts

Cardiac arrest centres: what, who, when, and where?

A review of evaluation approaches for explainable AI with applications in cardiology

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