Outcome assessment for out-of-hospital cardiac arrest patients in Singapore and Japan with initial shockable rhythm.

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.

Adding heart rate n-variability (HRnV) to clinical assessment potentially improves prediction of serious bacterial infections in young febrile infants at the emergency department: a prospective observational study.

Background: We aim to investigate the utility of heart rate variability (HRV) and heart rate n-variability (HRnV) in addition to vital signs and blood biomarkers, among febrile young infants at risk of serious bacterial infections (SBIs). Methods: We performed a prospective observational study between December 2017 and November 2021 in a tertiary paediatric emergency department (ED). We included febrile infants <90 days old with a temperature ≥38 ℃. We obtained HRV and HRnV parameters via a single lead electrocardiogram. HRV measures beat-to-beat (R-R) oscillation and reflects autonomic nervous system (ANS) regulation. HRnV includes overlapping and non-overlapping R-R intervals and provides additional physiological information. We defined SBIs as meningitis, bacteraemia and urinary tract infections (UTIs). We performed area under curve (AUC) analysis to assess predictive performance. Results: We recruited 330 and analysed 312 infants. The median age was 35.5 days (interquartile range 13.0-61.0); 74/312 infants (23.7%) had SBIs with the most common being UTIs (66/72, 91.7%); 2 infants had co-infections. No patients died and 32/312 (10.3%) received fluid resuscitation. Adding HRV and HRnV to demographics and vital signs at ED triage successively improved the AUC from 0.765 [95% confidence interval (CI): 0.705-0.825] to 0.776 (95% CI: 0.718-0.835) and 0.807 (95% CI: 0.752-0.861) respectively. The final model including demographics, vital signs, HRV, HRnV and blood biomarkers had an AUC of 0.874 (95% CI: 0.828-0.921). Conclusions: Addition of HRV and HRnV to current assessment tools improved the prediction of SBIs among febrile infants at ED triage. We intend to validate our findings and translate them into tools for clinical care in the ED.

Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration.

PURPOSE: The SingHealth-Duke-GlaxoSmithKline COPD and Asthma Real-world Evidence (SDG-CARE) collaboration was formed to accelerate the use of Singaporean real-world evidence in research and clinical care. A centerpiece of the collaboration was to develop a near real-time database from clinical and operational data sources to inform healthcare decision making and research studies on asthma and chronic obstructive pulmonary disease (COPD). METHODS: Our multidisciplinary team, including clinicians, epidemiologists, data scientists, medical informaticians and IT engineers, adopted the hybrid waterfall-agile project management methodology to develop the SingHealth COPD and Asthma Data Mart (SCDM). The SCDM was developed within the organizational data warehouse. It pulls and maps data from various information systems using extract, transform and load (ETL) pipelines. Robust user testing and data verification was also performed to ensure that the business requirements were met and that the ETL pipelines were valid. RESULTS: The SCDM includes 199 data elements relevant to asthma and COPD. Data verification was performed and found the SCDM to be reliable. As of December 31, 2019, the SCDM contained 36,407 unique patients with asthma and COPD across the spectrum from primary to tertiary care in our healthcare system. The database updates weekly to add new data of existing patients and to include new patients who fulfil the inclusion criteria. CONCLUSIONS: The SCDM was systematically developed and tested to support the use RWD for clinical and health services research in asthma and COPD. This can serve as a platform to provide research and operational insights to improve the care delivered to our patients.

Protocol for a multi-site randomized controlled trial of a stepped-care intervention for emergency department patients with panic-related anxiety.

BACKGROUND: Approximately 40% of Emergency Department (ED) patients with chest pain meet diagnostic criteria for panic-related anxiety, but only 1-2% are correctly diagnosed and appropriately managed in the ED. A stepped-care model, which focuses on providing evidence-based interventions in a resource-efficient manner, is the state-of-the art for treating panic disorder patients in medical settings such as primary care. Stepped-care has yet to be tested in the ED setting, which is the first point of contact with the healthcare system for most patients with panic symptoms. METHODS: This multi-site randomized controlled trial (RCT) aims to evaluate the clinical, patient-centred, and economic effectiveness of a stepped-care intervention in a sample of 212 patients with panic-related anxiety presenting to the ED of Singapore's largest public healthcare group. Participants will be randomly assigned to either: 1) an enhanced care arm consisting of a stepped-care intervention for panic-related anxiety; or 2) a control arm consisting of screening for panic attacks and panic disorder. Screening will be followed by baseline assessments and blocked randomization in a 1:1 ratio. Masked follow-up assessments will be conducted at 1, 3, 6, and 12 months. Clinical outcomes will be panic symptom severity and rates of panic disorder. Patient-centred outcomes will be health-related quality of life, daily functioning, psychiatric comorbidity, and health services utilization. Economic effectiveness outcomes will be the incremental cost-effectiveness ratio of the stepped-care intervention relative to screening alone. DISCUSSION: This trial will examine the impact of early intervention for patients with panic-related anxiety in the ED setting. The results will be used to propose a clinically-meaningful and cost-effective model of care for ED patients with panic-related anxiety. TRIAL REGISTRATION: ClinicalTrials.gov NCT03632356. Retrospectively registered 15 August 2018.

Emergency department utilisation among older adults-Protocol for a systematic review of determinants and conceptual frameworks.

BACKGROUND: Older adults aged 65 years and above have a disproportionately higher utilization of emergency healthcare, of which Emergency Department (ED) visits are a key component. They experience higher degree of multimorbidity and mobility issues compared to younger patients, and are consequently more likely to experience a health event which requires an ED visit. During their visit, older adults tend to require more extensive workup, therefore spending a greater amount of time in the ED. Compared to the younger population, older adults are more susceptible to adverse events following discharge. Considering these factors, investigating the determinants of ED utilisation would be valuable. In this paper, we present a protocol for a systematic review of the determinants of ED utilisation among communitydwelling older adults aged 65 years and above, applying Andersen and Newman's model of healthcare utilisation. Furthermore, we aim to present other conceptual frameworks for healthcare utilisation and propose a holistic approach for understanding the determinants of ED utilisation by older persons. METHODS: The protocol is developed in accordance with the standards of Campbell Collaboration guidelines for systematic reviews, with reference to the Cochrane Handbook for Systematic Review of Interventions. Medline, Embase and Scopus will be searched for studies published from 2000 to 2020. Studies evaluating more than one determinant for ED utilisation among older adults aged 65 years and above will be included. Search process and selection of studies will be presented in a PRISMA flow chart. Statistically significant (p < 0.05) determinants of ED utilisation will be grouped according to individual and societal determinants. Quality of the studies will be assessed using Newcastle Ottawa Scale (NOS). DISCUSSION: In Andersen and Newman's model, individual determinants include predisposing factors, enabling and illness factors, and societal determinants include technology and social norms. Additional conceptual frameworks for healthcare utilisation include Health Belief Model, Social Determinants of Health and Big Five personality traits. By incorporating the concepts of these models, we hope to develop a holistic approach of conceptualizing the factors that influence ED utilisation among older people. SYSTEMATIC REVIEW REGISTRATION: This protocol is registered on 8 May 2021 with PROSPERO's International Prospective Register of Systematic Reviews (CRD42021253770).

Leveraging open data to reconstruct the Singapore Housing Index and other building-level markers of socioeconomic status for health services research.

BACKGROUND: Socioeconomic status (SES) is an important determinant of health, and SES data is an important confounder to control for in epidemiology and health services research. Individual level SES measures are cumbersome to collect and susceptible to biases, while area level SES measures may have insufficient granularity. The 'Singapore Housing Index' (SHI) is a validated, building level SES measure that bridges individual and area level measures. However, determination of the SHI has previously required periodic data purchase and manual parsing. In this study, we describe a means of SHI determination for public housing buildings with open government data, and validate this against the previous SHI determination method. METHODS: Government open data sources (e.g. DATA: gov.sg, Singapore Land Authority OneMAP API, Urban Redevelopment Authority API) were queried using custom Python scripts. Data on residential public housing block address and composition from the HDB Property Information dataset (data.gov.sg) was matched to postal code and geographical coordinates via OneMAP API calls. The SHI was calculated from open data, and compared to the original SHI dataset that was curated from non-open data sources in 2018. RESULTS: Ten thousand seventy-seven unique residential buildings were identified from open data. OneMAP API calls generated valid geographical coordinates for all (100%) buildings, and valid postal code for 10,012 (99.36%) buildings. There was an overlap of 10,011 buildings between the open dataset and the original SHI dataset. Intraclass correlation coefficient was 0.999 for the two sources of SHI, indicating almost perfect agreement. A Bland-Altman plot analysis identified a small number of outliers, and this revealed 5 properties that had an incorrect SHI assigned by the original dataset. Information on recently transacted property prices was also obtained for 8599 (85.3%) of buildings. CONCLUSION: SHI, a useful tool for health services research, can be accurately reconstructed using open datasets at no cost. This method is a convenient means for future researchers to obtain updated building-level markers of socioeconomic status for policy and research.

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.

Modeling Emergency Department crowding: Restoring the balance between demand for and supply of emergency medicine.

Emergency Departments (EDs) worldwide are confronted with rising patient volumes causing significant strains on both Emergency Medicine and entire healthcare systems. Consequently, many EDs are in a situation where the number of patients in the ED is temporarily beyond the capacity for which the ED is designed and resourced to manage-a phenomenon called Emergency Department (ED) crowding. ED crowding can impair the quality of care delivered to patients and lead to longer patient waiting times for ED doctor's consult (time to provider) and admission to the hospital ward. In Singapore, total ED attendance at public hospitals has grown significantly, that is, roughly 5.57% per year between 2005 and 2016 and, therefore, emergency physicians have to cope with patient volumes above the safe workload. The purpose of this study is to create a virtual ED that closely maps the processes of a hospital-based ED in Singapore using system dynamics, that is, a computer simulation method, in order to visualize, simulate, and improve patient flows within the ED. Based on the simulation model (virtual ED), we analyze four policies: (i) co-location of primary care services within the ED, (ii) increase in the capacity of doctors, (iii) a more efficient patient transfer to inpatient hospital wards, and (iv) a combination of policies (i) to (iii). Among the tested policies, the co-location of primary care services has the largest impact on patients' average length of stay (ALOS) in the ED. This implies that decanting non-emergency lower acuity patients from the ED to an adjacent primary care clinic significantly relieves the burden on ED operations. Generally, in Singapore, there is a tendency to strengthen primary care and to educate patients to see their general practitioners first in case of non-life threatening, acute illness.

Comparative prospective study of the performance of chest pain scores and clinical assessment in an emergency department cohort in Singapore.

OBJECTIVE: Chest pain scores allow emergency department (ED) physicians to identify low-risk patients for whom discharge can be safely expedited. Although these have been extensively validated in Western cohorts, data in patients of Asian heritage are lacking. This study aimed to determine the accuracy of HEART, ED Assessment of Chest Pain Score (EDACS), and Global Registry of Acute Coronary Events (GRACE) in risk-stratifying which chest pain patients are at risk of major adverse cardiovascular events within 30 days (composite of all-cause mortality, acute myocardial infarction and coronary revascularization). METHODS: This single-center prospective cohort-study that enrolled 1200 patients was conducted by a large urban tertiary center in Singapore. Chest pain scores were reported before disposition by research assistants blinded to the physician's clinical assessment. Outcomes were assessed independently by a blinded cardiologist and emergency physician, while another cardiologist adjudicated in the case of discrepancies. RESULTS: Of the 1195 patients analyzed, 135 (11.3%) suffered major adverse cardiovascular events within 30 days. HEART, which ruled out major adverse cardiovascular events in 52.8% of patients with 88.1% sensitivity, and EDACS, which ruled out major adverse cardiovascular events in 57.5% of patients with 83.7% sensitivity, proved comparable to clinical judgment that ruled out major adverse cardiovascular events in 73.0% of patients with 85.5% sensitivity. GRACE was weaker-ruling out major adverse cardiovascular events in 79.2% of patients with a dismal sensitivity of 45.0%. The correlation-statistic for HEART (79.4%) was superior to EDACS (69.9%) and GRACE (69.2%). CONCLUSIONS: HEART more accurately identified low-risk chest pain patients in an Asian ED, demonstrating comparable performance characteristics to clinical judgment. This has major implications on the use of chest pain scores to safely expedite disposition decisions for low-risk chest pain patients.

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction.

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department.

BACKGROUND: Although the quick Sequential Organ Failure Assessment (qSOFA) score was recently introduced to identify patients with suspected infection/sepsis, it has limitations as a predictive tool for adverse outcomes. We hypothesized that combining qSOFA score with heart rate variability (HRV) variables improves predictive ability for mortality in septic patients at the emergency department (ED). METHODS: This was a retrospective study using the electronic medical record of a tertiary care hospital in Singapore between September 2014 and February 2017. All patients aged 21 years or older who were suspected with infection/sepsis in the ED and received electrocardiography monitoring with ZOLL X Series Monitor (ZOLL Medical Corporation, Chelmsford, MA) were included. We fitted a logistic regression model to predict the 30-day mortality using one of the HRV variables selected from one of each three domains those previously reported as strong association with mortality (i.e. standard deviation of NN [SDNN], ratio of low frequency to high frequency power [LF/HF], detrended fluctuation analysis α-2 [DFA α-2]) in addition to the qSOFA score. The predictive accuracy was assessed with other scoring systems (i.e. qSOFA alone, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve. RESULTS: A total of 343 septic patients were included. Non-survivors were significantly older (survivors vs. non-survivors, 65.7 vs. 72.9, p <0.01) and had higher qSOFA (0.8 vs. 1.4, p <0.01) as compared to survivors. There were significant differences in HRV variables between survivors and non-survivors including SDNN (23.7s vs. 31.8s, p = 0.02), LF/HF (2.8 vs. 1.5, p = 0.02), DFA α-2 (1.0 vs. 0.7, P < 0.01). Our prediction model using DFA-α-2 had the highest c-statistic of 0.76 (95% CI, 0.70 to 0.82), followed by qSOFA of 0.68 (95% CI, 0.62 to 0.75), National Early Warning Score at 0.67 (95% CI, 0.61 to 0.74), and Modified Early Warning Score at 0.59 (95% CI, 0.53 to 0.67). CONCLUSIONS: Adding DFA-α-2 to the qSOFA score may improve the accuracy of predicting in-hospital mortality in septic patients who present to the ED. Further multicenter prospective studies are required to confirm our results.

Can we understand population healthcare needs using electronic medical records?

INTRODUCTION: The identification of population-level healthcare needs using hospital electronic medical records (EMRs) is a promising approach for the evaluation and development of tailored healthcare services. Population segmentation based on healthcare needs may be possible using information on health and social service needs from EMRs. However, it is currently unknown if EMRs from restructured hospitals in Singapore provide information of sufficient quality for this purpose. We compared the inter-rater reliability between a population segment that was assigned prospectively and one that was assigned retrospectively based on EMR review. METHODS: 200 non-critical patients aged ≥ 55 years were prospectively evaluated by clinicians for their healthcare needs in the emergency department at Singapore General Hospital, Singapore. Trained clinician raters with no prior knowledge of these patients subsequently accessed the EMR up to the prospective rating date. A similar healthcare needs evaluation was conducted using the EMR. The inter-rater reliability between the two rating sets was evaluated using Cohen's Kappa and the incidence of missing information was tabulated. RESULTS: The inter-rater reliability for the medical 'global impression' rating was 0.37 for doctors and 0.35 for nurses. The inter-rater reliability for the same variable, retrospectively rated by two doctors, was 0.75. Variables with a higher incidence of missing EMR information such as 'social support in case of need' and 'patient activation' had poorer inter-rater reliability. CONCLUSION: Pre-existing EMR systems may not capture sufficient information for reliable determination of healthcare needs. Thus, we should consider integrating policy-relevant healthcare need variables into EMRs.

A Cost-Effectiveness Analysis of a Randomized Control Trial of a Tailored, Multifactorial Program to Prevent Falls Among the Community-Dwelling Elderly.

OBJECTIVE: To perform a cost-effectiveness analysis of a multifactorial, tailored intervention to reduce falls among a heterogeneous group of high-risk elderly people. DESIGN: Randomized control trial. SETTINGS: Communities. PARTICIPANTS: Adults aged at least 65 years (N=354) seen at the emergency department (ED) for a fall or fall-related injury and discharged home. INTERVENTIONS: The intervention group received a tailored program of physical therapy focused on progressive training in strength, balance, and gait for a period of 3 months. They also received screening and referrals for low vision, polypharmacy, and environmental hazards. The Short Physical Performance Battery (SPPB) test was assessed at regular intervals to allocate participants into either a home-based or group center-based program. The control group received usual care prescribed by a physician and educational materials on falls prevention. MAIN OUTCOME MEASURES: The incremental cost-effectiveness ratio (ICER) over the 9-month study period based on intervention costs and utility in terms of quality-adjusted life years (QALYs) calculated from EuroQol-5D scores. RESULTS: The ICER was 120,667 Singapore dollars (S$) per QALY gained (S$362/0.003 QALYs), above benchmark values (S$70,000). However, the intervention was more effective and cost-saving among those with SPPB scores of greater than 6 at baseline, higher cognitive function, better vision and no more than 1 fall in the preceding 6 months. The intervention was also cost-effective among those with 0-1 critical comorbidities (S$22,646/QALY). CONCLUSION: The intervention was, overall, not cost-effective, compared to usual care. However, the program was cost-effective among healthier subgroups, and even potentially cost-saving among individuals with sufficient reserve to benefit.

Simulation-based decision support framework for dynamic ambulance redeployment in Singapore.

OBJECTIVE: Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper. METHODS: The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage. RESULTS: Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases. CONCLUSION: This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated.

Electric bicycle-related injuries presenting to a provincial hospital in China: A retrospective study.

The use of electric bicycles (EBs) in China is growing. In the present study, we aimed to characterize the pattern and outcomes of EB-related injuries presenting to a major general hospital in China.This was a retrospective review of EB-related injuries presenting to Zhejiang Provincial People's Hospital from 2008 to 2011. Cases were identified from medical records according to diagnosis codes. Data captured included demographics, injury characteristics, and outcomes.A total of 3156 cases were reviewed in the present study. There were 1460 cases of traffic accidents, of which 482 cases were EB-related (32.7%). In addition, most of EB-related cases (44.6%) belonged to the 41- to 60-year-old age group. Median injury severity score was 10. Moreover, 34.9% underwent surgery and 24.7% were admitted to intensive care unit. The median hospitalization cost was 14,269 USD. Fracture (56.5%) was the most frequently diagnosed injury type, and head was the most commonly injured body region (31.1%).EB-related injuries have become a major health concern, making up a sizeable proportion of injuries presenting to the emergency department. Therefore, it is necessary to establish injury prevention and strategies for EB road safety. Implementation of policy such as compulsory helmet use, as well as popularization of EB road safety education should be considered to improve the current situation of EB-related injuries in China.

Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity.

BACKGROUND: To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool. METHOD: This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital's electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index. RESULTS: 74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as 'requiring inpatient dialysis during index admission, and 'treatment with intravenous furosemide 40 milligrams or more' during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77-0.79) versus 0.70 (95% CI: 0.69-0.71). CONCLUSION: Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity.

Predicting frequent hospital admission risk in Singapore: a retrospective cohort study to investigate the impact of comorbidities, acute illness burden and social determinants of health.

OBJECTIVES: To evaluate the impact of comorbidities, acute illness burden and social determinants of health on predicting the risk of frequent hospital admissions. DESIGN: Multivariable logistic regression was used to associate the predictive variables extracted from electronic health records and frequent hospital admission risk. The model's performance of our predictive model was evaluated using a 10-fold cross-validation. SETTING: A single tertiary hospital in Singapore. PARTICIPANTS: All adult patients admitted to the hospital between 1 January 2013 and 31 May 2014 (n=25 244). MAIN OUTCOME MEASURE: Frequent hospital admissions, defined as 3 or more inpatient admissions within 12 months of discharge. Area under the receiver operating characteristic curve (AUC) of the predictive model, and the sensitivity, specificity and positive predictive values for various cut-offs. RESULTS: 4322 patients (17.1%) met the primary outcome. 11 variables were observed as significant predictors and included in the final regression model. The strongest independent predictor was treatment with antidepressants in the past 1 year (adjusted OR 2.51, 95% CI 2.26 to 2.78). Other notable predictors include requiring dialysis and treatment with intravenous furosemide during the index admission. The predictive model achieved an AUC of 0.84 (95% CI 0.83 to 0.85) for predicting frequent hospital admission risk, with a sensitivity of 73.9% (95% CI 72.6% to 75.2%), specificity of 79.1% (78.5% to 79.6%) and positive predictive value of 42.2% (95% CI 41.1% to 43.3%) at the cut-off of 0.235. CONCLUSIONS: We have identified several predictors for assessing the risk of frequent hospital admissions that achieved high discriminative model performance. Further research is necessary using an external validation cohort.

Public access defibrillation: improving accessibility and outcomes.

BACKGROUND: Worldwide, out-of-hospital cardiac arrest (OHCA) remains a serious problem. Public access defibrillation (PAD) has been shown to be effective in improving survival in OHCA with good neurological outcome. SOURCES OF DATA: Original articles, reviews and national/international guidelines. AREAS OF AGREEMENT: Limitations to how much we can improve ambulance response times mean that the public have an essential role to play in OHCA survival. Training of laypersons in the use of automated external defibrillators (AEDs) has been shown to improve outcomes. Placement of AEDs should be related to underlying population demographics. AREAS OF CONTROVERSY: Placements of AEDs face cost constraints. PAD programs also face challenges in the upkeep of AEDs. Concerns about legal liability for lay rescuers to act remain. GROWING POINTS: Systematic programs should be in place to train the public in PAD. All AEDs should be listed in national registries and available for usage in an emergency. AREAS TIMELY FOR DEVELOPING RESEARCH: 'Smart' technology is being developed to improve accessibility of AEDs.

Variation of current protocols for managing out-of-hospital cardiac arrest in prehospital settings among Asian countries.

BACKGROUND/PURPOSE: Protocols for managing patients with out-of-hospital cardiac arrest (OHCA) may vary due to legal, cultural, or socioeconomic concerns. We sought to assess international variation in policies and protocols related to OHCA. METHODS: A brief survey was developed by consensus. Elicited information included protocols for managing patients with nontraumatic OHCA or traumatic OHCA, policies for using automated external defibrillators (AEDs) during transportation of patients with ongoing resuscitation, and application of terminations of resuscitation (TOR) rules in prehospital settings in the respondent's city or country. The populations of interest were emergency physicians, medical directors of emergency medical services (EMS), and policy makers. RESULTS: Responses were obtained from eight cities in six Asian countries. Only one (12.5%) city applied TOR rules for OHCAs. Do-not-resuscitate (DNR) orders were valid in prehospital settings in five (62.5%) cities. All cities used AEDs for nontraumatic OHCAs; seven (87.5%) cities did not routinely use AEDs for traumatic OHCAs. For nontraumatic OHCAs, four (50%) cities performed 2 minutes of on-scene cardiopulmonary resuscitation (CPR) and then transported the patients with ongoing resuscitation to hospitals; three (37.5%) cities performed 4 minutes of on-scene CPR; one (12.5%) city allowed variation in the duration of on-scene CPR. CONCLUSION: International variation in practices and polices related to OHCAs do exist. Concerns regarding prehospital TOR rules include medical evidence, legal considerations, EMS manpower, public perception, medical oversight, education, EMS characteristics, and cost-effectiveness analysis. Further research is needed to achieve consensus regarding management protocols, especially for EMS that perform resuscitation during transportation of OHCA patients.