Ambient Air Quality and Emergency Hospital Admissions in Singapore: A Time-Series Analysis.

Air pollution exposure may increase the demand for emergency healthcare services, particularly in South-East Asia, where the burden of air-pollution-related health impacts is high. This article aims to investigate the association between air quality and emergency hospital admissions in Singapore. Quasi-Poisson regression was applied with a distributed lag non-linear model (DLNM) to assess the short-term associations between air quality variations and all-cause, emergency admissions from a major hospital in Singapore, between 2009 and 2017. Higher concentrations of SO2, PM2.5, PM10, NO2, and CO were positively associated with an increased risk of (i) all-cause, (ii) cardiovascular-related, and (iii) respiratory-related emergency admissions over 7 days. O3 concentration increases were associated with a non-linear decrease in emergency admissions. Females experienced a higher risk of emergency admissions associated with PM2.5, PM10, and CO exposure, and a lower risk of admissions with NO2 exposure, compared to males. The older adults (≥65 years) experienced a higher risk of emergency admissions associated with SO2 and O3 exposure compared to the non-elderly group. We found significant positive associations between respiratory disease- and cardiovascular disease-related emergency hospital admissions and ambient SO2, PM2.5, PM10, NO2, and CO concentrations. Age and gender were identified as effect modifiers of all-cause admissions.

Use of extracorporeal membrane oxygenation in massive amlodipine overdose.

Calcium-channel blocker overdose can result in profound vasoplegia and cardiogenic shock, which can quickly spiral into multi-organ failure and death. In this case report, we discuss two separate cases of massive amlodipine overdose with polydrug intoxication (Patient A: amlodipine and quetiapine; Patient B: amlodipine, fluoxetine and zopiclone), both of which were complicated by life-threatening vasoplegic shock refractory to supportive therapy (endotracheal intubation, fluid resuscitation, activated charcoal, vasopressors and inotropes), multimodal antidotes (calcium and hyper-insulinemic euglycemic therapy) and even second-line treatment (methylene blue and therapeutic plasma exchange). Despite exhausting all therapeutic options, resuscitation remained futile with no clinical response elicited until veno-arterial extracorporeal membrane oxygenation (ECMO) salvage therapy was initiated in both cases as a bridge-to-recovery. Albumin dialysis was also commenced to further enhance elimination of amlodipine given its high plasma protein-binding properties. Both patients improved drastically once perfusion to vital organs was maintained by ECMO and eventually survived with good neurological outcomes and preserved cardiac contractility on discharge. This case report supports the growing evidence that although ECMO support represents a potentially life-saving salvage therapy for refractory poisoning-induced shock, escalation to ECMO must be considered and instituted early before irreversible multi-organ failure sets in to ensure good clinical outcomes.

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation.

BACKGROUND: There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer waiting times. The triage process plays a crucial role in assessing and stratifying patients' risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. OBJECTIVE: In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHRs) and machine learning. METHODS: To achieve this objective, we will conduct a retrospective, single-center study based on a large, longitudinal data set obtained from the EHRs of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit and inpatient death. With preidentified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning-based AutoScore to develop 3 SERT scores. These 3 scores can be used at different times in the ED, that is, on arrival, during ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. Receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. RESULTS: The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. CONCLUSIONS: The SERT scoring system proposed in this study will be unique and innovative because of its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34201.

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.

BACKGROUND: Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC. METHODS: We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses. RESULTS: 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort. CONCLUSION: We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.

Improved door-to-balloon time for primary percutaneous coronary intervention for patients conveyed via emergency ambulance service.

INTRODUCTION: Early reperfusion of ST-segment elevation myocardial infarction (STEMI) results in better outcomes. Interventions that have resulted in shorter door-to-balloon (DTB) time include prehospital cardiovascular laboratory activation and prehospital electrocardiogram (ECG) transmission, which are only available for patients who arrive via emergency ambulances. We assessed the impact of mode of transport on DTB time in a single tertiary institution and evaluated the factors that affected various components of DTB time. METHODS: We conducted a retrospective cohort study using registry data of patients diagnosed with STEMI in the emergency department (ED) who underwent primary percutaneous coronary intervention. We compared patients who arrived by emergency ambulances with those who came via their own transport. The primary study end point was DTB, defined as the earliest time a patient arrived in the ED to balloon inflation. As deidentified data was used, ethics review was waived. RESULTS: A total of 321 patients were included for analysis after excluding 7 with missing data. The mean age was 61.4±11.4 years old with 49 (15.3%) females. Ninety-nine (30.8%) patients arrived by emergency ambulance. The median DTB time was shorter for patients arriving by ambulance versus own transport (52min, interquartile range [IQR] 45-61 vs 67min, IQR 59-74; P<0.001), with shorter door-to-ECG and door-to-activation time. CONCLUSION: Arrival via emergency ambulance was associated with a decreased DTB for STEMI patients compared to arriving via own transport. There is a need for public education to increase the usage of emergency ambulances for suspected heart attacks to improve outcomes.

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.

Importance: Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations. Objectives: To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate the tool's predictive accuracy compared with several established clinical scores. Design, Setting, and Participants: This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021. Main Outcomes and Measures: Three SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively. Secondary outcomes included 3-day mortality and inpatient mortality. The SERP's predictive power was measured using the area under the curve in the receiver operating characteristic analysis. Results: The study included 224 666 ED episodes in the model training cohort (mean [SD] patient age, 63.60 [16.90] years; 113 426 [50.5%] female), 56 167 episodes in the validation cohort (mean [SD] patient age, 63.58 [16.87] years; 28 427 [50.6%] female), and 42 676 episodes in the testing cohort (mean [SD] patient age, 64.85 [16.80] years; 21 556 [50.5%] female). The mortality rates in the training cohort were 0.8% at 2 days, 2.2% at 7 days, and 5.9% at 30 days. In the testing cohort, the areas under the curve of SERP-30d were 0.821 (95% CI, 0.796-0.847) for 2-day mortality, 0.826 (95% CI, 0.811-0.841) for 7-day mortality, and 0.823 (95% CI, 0.814-0.832) for 30-day mortality and outperformed several benchmark scores. Conclusions and Relevance: In this retrospective cohort study, SERP had better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment in the ED. It has the potential to be widely applied and validated in different circumstances and health care settings.

Containment of COVID-19 and reduction in healthcare-associated respiratory viral infections through a multi-tiered infection control strategy.

BACKGROUND: During the ongoing COVID-19 pandemic, healthcare-associated transmission of respiratory viral infections (RVI) is a concern. To reduce the impact of SARS-CoV-2 and other respiratory viruses on patients and healthcare workers (HCWs) we devised and evaluated a multi-tiered infection control strategy with the goal of preventing nosocomial transmission of SARS-CoV2 and other RVIs across a large healthcare campus. METHODS: From January-June 2020, a multi-tiered infection control strategy was implemented across a healthcare campus in Singapore, comprising the largest acute tertiary hospital as well as four other subspecialty centres, with more than 10,000 HCWs. Drawing on our institution's experience with an outbreak of Severe Acute Respiratory Syndrome (SARS) in 2003, this strategy included improved patient segregation and distancing, and heightened infection prevention and control (IPC) measures including universal masking. All symptomatic patients were tested for COVID-19 and common RVIs. RESULTS: A total of 16,162 admissions campus-wide were screened; 7.1% (1155/16,162) tested positive for COVID-19. Less than 5% of COVID-19 cases (39/1155) were initially detected outside of isolation wards in multi-bedded cohorted wards. Improved distancing and enhanced IPC measures successfully mitigated onward spread even amongst COVID-19 cases detected outside of isolation. COVID-19 rates amongst HCWs were kept low (0.13%, 17/13,066) and reflected community acquisition rather than nosocomial spread. Rates of healthcare-associated-RVI amongst inpatients fell to zero and this decrease was sustained even after the lifting of visitor restrictions. CONCLUSION: This multi-tiered infection control strategies can be implemented at-scale to successfully mitigate healthcare-associated transmission of respiratory viral pathogens.

Disease Outbreak Surge Response: How a Singapore Tertiary Hospital Converted a Multi-story Carpark Into a Flu Screening Area to Respond to the COVID-19 Pandemic.

Coronavirus disease 2019 (COVID-19), first documented in December 2019, was declared a public health emergency by the World Health Organization (WHO) on January 30, 2020 (https://www.who.int/westernpacific/emergencies/covid-19). The disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has affected more than 9 million people and contributed to at least 490,000 deaths globally as of June 2020, with numbers on the rise (https://www.worldometers.info/coronavirus/#countries).Increased numbers of patients seeking medical attention during disease outbreaks can overwhelm healthcare facilities, hence requiring an equivalent response from healthcare services. Surge capacity is a concept that has not only been defined as the "ability to respond to a sudden increase in patient care demands" (Hick et al., Disaster Med Public Health Prep. 2008;2:S51-S57) but also to "effectively and rapidly expand capacity" (Watson et al., Milbank Q. 2013;91(1):78-122).This narrative review discusses how Singapore's largest tertiary hospital has encapsulated the elements of surge capability and transformed a peacetime multi-story carpark into a flu screening area in response to the COVID-19 disease outbreak.

Dynamic emergency department response to the evolving COVID-19 pandemic: the experience of a tertiary hospital in Singapore.

The coronavirus disease 2019 (COVID-19) pandemic has placed large stressors on emergency departments (EDs) worldwide. As the pandemic progressed, EDs faced changing patient epidemiology and numbers. Our ED needed to rapidly transform to deal with the risk of COVID-19. Having limited floor space, we opted for a phased, dynamic response that allowed us to adapt the ED multiple times as the epidemiology of the pandemic evolved. The principles behind our response include guiding ED operations with data, enhancing infection control practices, and being prepared to transform areas of the ED to care for different groups of patients. Our experience can serve to guide other EDs in planning their response to surge capacity and ED operations during such pandemics.

Improving asthma care with Asthma-COPD Afterhours Respiratory Nurse at Emergency (A-CARE).

BACKGROUND: Emergency departments (ED) are important providers of asthma care, particularly after-hours. We identified gaps for quality improvement such as suboptimal adherence rates to three key recommendations from the Global Initiative for Asthma (GINA) guidelines for discharge management asthma guidelines. These were: the prescription of oral and inhaled corticosteroids (OCS and ICS) and issuance of outpatient follow-up for patients discharged from the ED. AIM: To achieve an adherence rate of 80% to GINA guidelines for ED discharge management by providing after-hours asthma counselling services. METHODS: We implemented Asthma-COPD Afterhours Respiratory Nurse at Emergency (A-CARE) according to the Plan-Do-Study-Act (PDSA) framework to provide after-hours asthma counselling and clinical decision support to ED physicians three nights a week. Data on adherence rates to the GINA guidelines were collected and analysed on a run chart. RESULTS: After 17 months' follow-up, a sustained improvement was observed in patients reviewed by A-CARE in the median adherence rates to OCS prescription (58% vs 86%), ICS initiation (27% vs 67%) and issuance of follow-up (69% vs 92%), respectively. The overall impact was, however, limited by a suboptimal referral rate to A-CARE (16%) in a clinical audit of all ED patients with asthma. Nonetheless, in this audit, attendance rates for patients referred to our respiratory department for follow-up were higher in those receiving asthma counselling compared with those who did not (41.7% vs 15.9%, p=0.0388). CONCLUSION: Sustained improvements in the adherence rates to guidelines were achieved for patients reviewed by A-CARE but were limited in overall impact due to suboptimal referral rate. We plan to improve the quality of asthma care by implementing further PDSA cycles to increase the referral rates to A-CARE.

Randomized controlled trial of internal and external targeted temperature management methods in post- cardiac arrest patients.

BACKGROUND: Targeted temperature management post-cardiac arrest is currently implemented using various methods, broadly categorized as internal and external. This study aimed to evaluate survival-to-hospital discharge and neurological outcomes (Glasgow-Pittsburgh Score) of post-cardiac arrest patients undergoing internal cooling verses external cooling. METHODOLOGY: A randomized controlled trial of post-resuscitation cardiac arrest patients was conducted from October 2008-September 2014. Patients were randomized to either internal or external cooling methods. Historical controls were selected matched by age and gender. Analysis using SPSS version 21.0 presented descriptive statistics and frequencies while univariate logistic regression was done using R 3.1.3. RESULTS: 23 patients were randomized to internal cooling and 22 patients to external cooling and 42 matched controls were selected. No significant difference was seen between internal and external cooling in terms of survival, neurological outcomes and complications. However in the internal cooling arm, there was lower risk of developing overcooling (p=0.01) and rebound hyperthermia (p=0.02). Compared to normothermia, internal cooling had higher survival (OR=3.36, 95% CI=(1.130, 10.412), and lower risk of developing cardiac arrhythmias (OR=0.18, 95% CI=(0.04, 0.63)). Subgroup analysis showed those with cardiac cause of arrest (OR=4.29, 95% CI=(1.26, 15.80)) and sustained ROSC (OR=5.50, 95% CI=(1.64, 20.39)) had better survival with internal cooling compared to normothermia. Cooling curves showed tighter temperature control for internal compared to external cooling. CONCLUSION: Internal cooling showed tighter temperature control compared to external cooling. Internal cooling can potentially provide better survival-to-hospital discharge outcomes and reduce cardiac arrhythmia complications in carefully selected patients as compared to normothermia.

Atypical presentation of traumatic aortic injury.

Background. Blunt thoracic aorta injury (BAI) is second only to head injury as cause of mortality in blunt trauma. While most patients do not survive till arrival at the hospital, for the remainder, prompt diagnosis and treatment greatly improve outcomes. We report an atypical presentation of BAI, highlighting the diagnostic challenges of this condition in the emergency department. Case Presentation. A previously well 25-year-old male presented 15 hours after injury hemodynamically stable with delirium. There were no signs or symptoms suggestive of BAI. Sonography showed small bilateral pleural effusions. Chest radiograph showed a normal mediastinum. Eventually, CT demonstrated a contained distal aortic arch disruption. The patient underwent percutaneous endovascular thoracic aortic repair and recovered well. Conclusion. This catastrophic lesion may present with few reliable signs and symptoms; hence, a high index of suspicion is crucial for early diagnosis and definitive surgical management. This paper discusses the diagnostic utility of clinical features, injury mechanism, and radiographic modalities. Consideration of mechanism of injury, clinical features, and chest radiograph findings should prompt advanced chest imaging.

Improving the quality of cardiopulmonary resuscitation by training dedicated cardiac arrest teams incorporating a mechanical load-distributing device at the emergency department.

OBJECTIVE: Determine if implementing cardiac arrest teams trained with a 'pit-crew' protocol incorporating a load-distributing band mechanical CPR device (Autopulse™ ZOLL) improves the quality of CPR, as determined by no-flow ratio (NFR) in the first 10min of resuscitation. METHODS: A phased, prospective, non-randomized, before-after cohort evaluation. Data collection was from April 2008 to February 2011. There were 100 before and 148 after cases. Continuous video and chest compression data of all study subjects were analyzed. All non-traumatic, collapsed patients aged 18 years and above presenting to the emergency department were eligible. Primary outcome was NFR. Secondary outcomes were return of spontaneous circulation (ROSC), survival to hospital admission and neurological outcome at discharge. MAIN RESULTS: After implementation, mean total NFR for the first 5min decreased from 0.42 to 0.27 (decrease=0.15, 95% CI 0.10-0.19, p<0.005), and from 0.24 to 0.18 (decrease=0.06, 95% CI 0.01-0.11, p=0.02) for the next 5min. The mean time taken to apply Autopulse™ decreased from 208.8s to 141.6s (decrease=67.2, 95% CI, 22.3-112.1, p<0.005). The mean CPR ratio increased from 46.4% to 88.4% (increase=41.9%, 95% CI 36.9-46.9, p<0.005) and the mean total NFR for the first 10min decreased from 0.33 to 0.23 (decrease=0.10, 95% CI 0.07-0.14, p<0.005). CONCLUSION: Implementation of cardiac arrest teams was associated with a reduction in NFR in the first 10min of resuscitation. Training cardiac arrest teams in a 'pit-crew' protocol may improve the quality of CPR at the ED.