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Background Sudden cardiac arrest with or without sudden cardiac death (SCD) represents a heterogeneous spectrum of underlying etiology but is often a catastrophic event. Despite improvements in pre‐hospital response and post‐resuscitation care, outcomes remain grim. Thus, we aim to evaluate the predictors of survival in out‐of‐hospital cardiac arrests (OHCAs) and describe autopsy findings of those with the uncertain cause of death (COD). Methods This is a subgroup analysis of the Singapore cohort from the Pan Asian Resuscitation Outcome Study which studied 933 OHCAs admitted to two Singapore tertiary hospitals from April 2010 to May 2012. Results Of the patients analysed, 30.2% (n = 282) had an initial return of spontaneous circulation (ROSC) at the emergency department, 18.0% (n = 168) had sustained ROSC with subsequent admission and 3.4% (n = 32) had survival to discharge. On multivariate analysis, an initial shockable rhythm, a witnessed event, prehospital defibrillation, and shorter time to hospital predicted ROSC as well as survival to discharge. A total of 163 (17.5%) autopsies were performed of which a cardiac etiology of SCD was noted in 92.1% (n = 151). Ischemic heart disease accounted for 54.3% (n = 89) of the autopsy cohort, with acute myocardial infarction (26.9%, n = 44) and myocarditis (3.7%, n = 6) rounding out the top three causes of demise. Conclusion OHCA remains a clinical presentation that portends a poor prognosis. Of those with uncertain COD, cardiac etiology appears to predominate from autopsy study. Identification of prognostic factors will play an important role in improving individual‐level and systemic‐level variables to further optimize outcomes. Out‐of‐hospital cardiac arrest remains a clinical presentation which portends poor prognosis. Of those with uncertain causes of death, cardiac etiology appears to predominate from autopsy study.

Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients’ ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department.

The use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission. We gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004-2011) and Bangkok (monthly incidence, 2004-2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using r² and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques. The linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an r² = 0.943, and a correlation of 0.869 between fitted and observed. In Singapore, the model has an r² = 0.948, and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok. Internet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.

Introduction: Bystander cardiopulmonary resuscitation (CPR) improves survival in out-of-hospital cardiac arrest. The use of certain songs as mental metronomes for CPR have been validated and recognised by contemporary guidelines. We hypothesise that the National Day song, Count on me Singapore (COMS CPR), is not inferior to standard ‘one-and-two-and-three-and-four’ counting (standard CPR) for timing CPR, in terms of the proportion of participants achieving the guideline compression rate of 100–120/minute. Methods: This was a prospective randomised crossover trial powered to demonstrate non-inferiority in the CPR rate. After a familiarisation session, volunteers were randomly assigned to two groups. Group A performed one cycle of standard CPR while group B performed one cycle of COMS CPR. Participants then crossed over to perform the other method. The Laerdal SkillReporter measured CPR quality. Four weeks later, participants attended a test scenario, using standard CPR or COMS CPR (randomly allocated). Results: Ninety subjects were recruited; 46 were randomly assigned to group A and 44 to group B. Baseline characteristics were similar; 41.1% of COMS CPR achieved 100–120/minute, versus 28.9% of standard CPR (P=0.028). In mixed effects logistical regression, significantly more COMS CPR was performed at 100–120/minute compared to standard CPR (odds ratio 2.44, 95% confidence interval 1.01–5.9, P=0.047). The proportion of insufficient depth was higher in COMS CPR (80.59% vs. 68.01%, P<0.001). There were no differences in other aspects of CPR quality. There were no differences in CPR quality between standard CPR and COMS CPR during the follow-up. Conclusion: COMS CPR was not inferior in terms of the proportion of participants delivering a guideline-compliant rate of chest compression. COMS CPR may have applications to layman CPR education, such as in mass education events.

Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients’ ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital’s score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858–0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy.

Background Singapore and Osaka in Japan have comparable population sizes and prehospital management; however, the frequency of ECPR differs greatly for out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythm. Given this disparity, we hypothesized that the outcomes among the OHCA patients with initial shockable rhythm in Singapore were different from those in Osaka. The aim of this study was to evaluate the outcomes of OHCA patients with initial shockable rhythm in Singapore compared to the expected outcomes derived from Osaka data using machine learning-based prediction models. Methods This was a secondary analysis of two OHCA databases: the Singapore PAROS database (SG-PAROS) and the Osaka-CRITICAL database from Osaka, Japan. This study included adult (18–74 years) OHCA patients with initial shockable rhythm. A machine learning-based prediction model was derived and validated using data from the Osaka-CRITICAL database (derivation data 2012–2017, validation data 2018–2019), and applied to the SG-PAROS database (2010–2016 data), to predict the risk-adjusted probability of favorable neurological outcomes. The observed and expected outcomes were compared using the observed–expected ratio (OE ratio) with 95% confidence intervals (CI). Results From the SG-PAROS database, 1,789 patients were included in the analysis. For OHCA patients who achieved return of spontaneous circulation (ROSC) on hospital arrival, the observed favorable neurological outcome was at the same level as expected (OE ratio: 0.905 [95%CI: 0.784–1.036]). On the other hand, for those who had continued cardiac arrest on hospital arrival, the outcomes were lower than expected (shockable rhythm on hospital arrival, OE ratio: 0.369 [95%CI: 0.258–0.499], and nonshockable rhythm, OE ratio: 0.137 [95%CI: 0.065–0.235]). Conclusion This observational study found that the outcomes for patients with initial shockable rhythm but who did not obtain ROSC on hospital arrival in Singapore were lower than expected from Osaka. We hypothesize this is mainly due to differences in the use of ECPR.

Background Organ donation after brain death is the standard practice in many countries. Rates are low globally. This study explores the potential national number of candidates for uncontrolled donations after cardiac death (uDCD) amongst out-of-hospital cardiac arrest (OHCA) patients and the influence of extracorporeal cardiopulmonary resuscitation (ECPR) on the candidacy of these potential organ donors using Singapore as a case study. Methods Using Singapore data from the Pan-Asian Resuscitation Outcomes Study, we identified all non-traumatic OHCA cases from 2010 to 2016. Four established criteria for identifying uDCD candidates (Madrid, San Carlos Madrid, Maastricht and Paris) were retrospectively applied onto the population. Within these four groups, a condensed ECPR eligibility criteria was employed and thereafter, an estimated ECPR survival rate was applied, extrapolating for possible neurologically intact survivors had ECPR been administered. Results 12,546 OHCA cases (64.8% male, mean age 65.2 years old) qualified for analysis. The estimated number of OHCA patients who were eligible for uDCD ranged from 4.3 to 19.6%. The final projected percentage of potential uDCD donors readjusted for ECPR survivors was 4.2% (Paris criteria worst-case scenario, n  = 532) to 19.4% of all OHCA cases (Maastricht criteria best-case scenario, n  = 2428), for an estimated 14.3 to 65.4 uDCD donors per million population per year (pmp/year). Conclusions In Singapore case study, we demonstrated the potential numbers of candidates for uDCD among resuscitated OHCA cases. This sizeable pool of potential donors demonstrates the potential for an uDCD program to expand the organ donor pool. A small proportion of these patients might however survive had they been administered ECPR. Further research into the factors influencing local organ and patient outcomes following uDCD and ECPR is indicated.

Dynamically reassigning ambulance deployment locations throughout a day to balance ambulance availability and demands can be effective in reducing response times. The objectives of this study were to model dynamic ambulance allocation plans in Singapore based on the system status management (SSM) strategy and to evaluate the dynamic deployment plans using a discrete event simulation (DES) model. The geographical information system–based analysis and mathematical programming were used to develop the dynamic ambulance deployment plans for SSM based on ambulance calls data from January 1, 2011, to June 30, 2011. A DES model that incorporated these plans was used to compare the performance of the dynamic SSM strategy against static reallocation policies under various demands and travel time uncertainties. When the deployment plans based on the SSM strategy were followed strictly, the DES model showed that the geographical information system–based plans resulted in approximately 13-second reduction in the median response times compared to the static reallocation policy, whereas the mathematical programming–based plans resulted in approximately a 44-second reduction. The response times and coverage performances were still better than the static policy when reallocations happened for only 60% of all the recommended moves. Dynamically reassigning ambulance deployment locations based on the SSM strategy can result in superior response times and coverage performance compared to static reallocation policies even when the dynamic plans were not followed strictly.

Globally, Emergency Care Systems (ECS) are a critical resource that needs to be used judiciously as demand can easily exceed supply capacity. Sub-optimal ECS use contributes to Emergency Department (ED) crowding; this adversely affects ECS as well as system-wide service performance. Alternate Care Service Pathways (ACSPs) are innovations intended to mitigate ED crowding by re-routing less-urgent cases to sites of care other than the ED. As in other countries, policymakers in Singapore need to respond to increasing ED utilization and are evaluating the introduction of ACSPs. However, developing ACSPs is costly, entails tinkering with established critical services, and runs the risk of unintended adverse consequences. Through a Causal Loop Diagram (CLD) developed in four stages, we present a view of the current Singapore ECS and the intended role of ACSPs in relieving its stress. This exercise suggests that to be successful ACSPs must change the prevailing mental model of the ED as a “one-stop shop” but should focus on integrating with primary care. The discussions stimulated by the development, critiquing, and revision of the CLD highlighted the importance of accounting for the reservations of stakeholders for changes. The CLD has enhanced shared understanding and will be used to guide quantitative simulation modeling to promote informed policy.

Background Dispatch services (DS’s) form an integral part of emergency medical service (EMS) systems. The role of a dispatcher has also evolved into a crucial link in patient care delivery, particularly in dispatcher assisted cardio-pulmonary resuscitation (DACPR) during out-of-hospital cardiac arrest (OHCA). Yet, there has been a paucity of research into the emerging area of dispatch science in Asia. This paper compares the characteristics of DS’s, and state of implementation of DACPR within the Pan-Asian Resuscitation Outcomes (PAROS) network. Methods A cross-sectional descriptive survey addressing population characteristics, DS structures and levels of service, state of DACPR implementation (including protocols and quality improvement programs) among PAROS DS’s. Results 9 DS’s responded, representing a total of 23 dispatch centres from 9 countries that serve over 80 million people. Most PAROS DS’s operate a tiered dispatch response, have implemented medical oversight, and tend to be staffed by dispatchers with a predominantly medical background. Almost all PAROS DS’s have begun tracking key EMS indicators. 77.8% ( n  = 7) of PAROS DS’s have introduced DACPR. Of the DS’s that have rolled out DACPR, 71.4% ( n  = 5) provided instructions in over one language. All DS’s that implemented DACPR and provided feedback to dispatchers offered feedback on missed OHCA recognition. The majority of DS’s (83.3%; n  = 5) that offered DACPR and provided feedback to dispatchers also implemented corrective feedback, while 66.7% ( n  = 4) offered positive feedback. Compression-only CPR was the standard instruction for PAROS DS’s. OHCA recognition sensitivity varied widely in PAROS DS’s, ranging from 32.6% (95% CI: 29.9–35.5%) to 79.2% (95% CI: 72.9–84.4%). Median time to first compression ranged from 120 s to 220 s. Conclusions We found notable variations in characteristics and state of DACPR implementation between PAROS DS’s. These findings will lay the groundwork for future DS and DACPR studies in the PAROS network.