Clinical Characteristics Associated with Acute Heart Failure Dispositions and Outcomes from the Emergency Department

Clinical Characteristics Associated with Acute Heart Failure Dispositions and Outcomes from the Emergency Department | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical Characteristics Associated with Acute Heart Failure Dispositions and Outcomes from the Emergency Department Veronica Singh, Sameera Senanayake, Nicholas Graves, Audry Shan Yin Lee, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8664554/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Acute heart failure (AHF) is a leading cause of Emergency Department (ED) attendance among older adults and contributes substantially to hospitalisations and resource strain. Globally, more than 80% of acute heart failure presentations to EDs result in inpatient admissions, despite wide variability in severity and risk of short-term adverse outcomes. Understanding AHF disposition patterns may be helpful for identifying opportunities to optimise care pathways. This study describes the clinical characteristics associated with ED disposition decisions for AHF and compares short- and long-term outcomes across discharge, ward admissions, and Higher Care Unit (HCU) admissions. Methods We conducted a retrospective cohort study using ED presentation data from a major Singapore public hospital between June 2016 and December 2021. Patients with SNOMED-coded heart failure diagnoses were included. Demographics, clinical presentation, comorbidities, and administrative factors were analysed. Primary outcomes were mortality, ED revisits, and readmissions at 30 days and one year. Multinomial logistic regression was used to examine factors associated with discharge versus ward or HCU admission. Results Among 3,723 AHF presentations, 4% were discharged, 87% admitted to the ward, and 9% to an HCU. The median age was 72 (62–81) years and 40.7% were women. Higher acuity, abnormal vital signs, multimorbidity, and myocardial infarction history were associated with HCU admission. Directly discharged patients had much higher 30-day ED revisit rates (31% vs 22% ward vs 19% HCU) but similar one-year revisit rates. One-year mortality was lower for discharged patients (13.5%) than for those admitted. Conclusion AHF patients present with complex clinical needs and are predominantly admitted in Singapore. However, high admission rates in the context of hospital overcrowding and ED bed block highlight an opportunity to develop structured alternative care pathways, such as observation units, transitional care, and home-based hospitalisation, to reduce unnecessary inpatient utilisation and improve continuity of care safely. Acute heart failure emergency department disposition revisits hospitalisation Figures Figure 1 Figure 2 Figure 3 Introduction Globally, Acute heart failure (AHF) is the most common cause of hospitalisation among the elderly ( 1 ). Heart failure (HF) is a chronic condition associated with substantial mortality and morbidity. It affects approximately 63.4 million people globally ( 2 ). It has a prevalence, of 10%, among those above 70 years of age with more severe adverse events. HF is characterised by frequent exacerbations, which lead most patients to seek care in Emergency Departments (ED). The ED plays a crucial role in the initial patient evaluation, stabilisation and clinical management. Emergency physicians then decide on the subsequent placement of patients from the ED. Patients can be hospitalised to general wards, intensive care units, intermediate care, admitted to observation wards within the ED, or discharged home. These disposition decisions impact patient care pathways, mortality and health systems utilisation. However, the decision to admit or discharge patients can be complex for AHF due to the wide range of clinical presentations and the presence of multiple comorbidities. The primary challenge in making these disposition decisions is identifying patients at low risk of adverse events who are safe to be discharged home. Unnecessary hospitalisations increase costs and resource burden, whereas inappropriate discharges put patients at a higher risk of adverse events. More than 80% of AHF patients presenting to the United States EDs are hospitalised ( 3 ). Notably, up to 20% of hospitalised AHF patients are hospitalised within 30 days ( 4 , 5 ). Although revisits to the ED are reported to be higher for patients discharged home, ranging from 21 to 44% within 30 days ( 6 – 9 ). Observation units have been progressively utilised in some hospitals over the past decade to safely reduce unnecessary hospitalisations. They allow for a short period of treatment and evaluation within the ED before being discharged home or admitted inpatient. While they are reported to have lower inpatient lengths of stay, there is limited data on their mortality and readmission outcomes ( 10 , 11 ). Singapore has a rapidly ageing, multi-ethnic population with chronic multimorbidity patterns. HF is estimated to affect 4.5% of the population in Singapore, compared to 1–2% in other developed countries ( 12 ). Additionally, heart failure patients in Singapore were found to have high one-year mortality and increased acute care utilisation ( 13 ). The total ED attendance at public hospitals has grown at an annual rate of approximately 5.6% from 2005 to 2016. This increase in emergency care utilisation is occurring at a disproportionately higher rate than the overall population growth rate, posing a considerable challenge for managing the ED. There is limited reliable data on the prevalence, demographics, outcomes, and healthcare utilisation patterns of HF patients in Asian populations, as most heart failure registries and risk scores arise from Western populations ( 12 , 14 , 15 ). This lack of evidence, with the inherent heterogeneity of the AHF population, leads to variation in clinical decision-making. Given the significant healthcare burden of HF in Singapore and beyond, it is imperative to gain a deeper understanding of its ED presentations. Determining the patient characteristics and outcomes, such as mortality and recidivism rates, associated with dispositions can enhance the delivery of acute medical care. The aims of this study were to describe the characteristics of acute heart failure patients presenting to the ED in Singapore and how these related to dispositions. We also sought to describe the short and long-term outcomes for each disposition. Primary outcomes of interest were patient mortality, revisits to the ED, readmissions and combined adverse events. Secondary outcomes were in-hospital mortality and LOS of admitted patients. Methods Study design and population We conducted a single-centre retrospective cohort study using electronic health record data from Singapore General Hospital (SGH) of all patients who visited the ED from 1 June 2016 to 31 December 2021. SGH is Singapore’s largest general hospital with approximately 120,000 cases seen in the ED every year. It is part of the SingHealth group, a public healthcare network of general and speciality hospitals, community hospitals and polyclinics. This study was approved by the National University of Singapore Institutional Review Board (NUS-IRB-2024-643), and patient consent was not required. Patients with an ED primary or secondary diagnosis of heart failure were identified using SNOMED CT diagnosis codes (Supplementary Material). SNOMED CT is a comprehensive and precise clinical terminology designed to record clinical information in electronic health records ( 16 ). Dead on arrival patients, patients aged below 18 years, and transfers to other hospitals were excluded. We included the index heart failure-related ED presentation and all subsequent visits of each patient for analysis. Data was analysed on a per-episode basis, with multiple heart failure-related ED visits by a patient considered separate events. Data Sources Data was sourced from the Emergency Medicine Real-world Anonymised Data Repository (EM-RADAR). The registry was initiated in January 2021 to collect comprehensive information on patients from emergency departments at SingHealth institutions. The datasets are generated based on unique patient codes and registration dates from the SingHealth data warehouse, eHintS ( 17 ). The data is de-identified by the SingHealth Trusted Third Party team as per the existing data governance policies ( 18 , 19 ). Data extracted included patient demographics such as age, gender and race, vital measurements, comorbidities and administrative factors. The first vital measurements for heart rate, respiratory rate, temperature, systolic blood pressure, diastolic blood pressure and SpO2 of each ED episode were analysed. ED dispositions were categorised into three levels based on the level of care intensity: ( 1 ) discharge, ( 2 ) ward, and ( 3 ) higher levels of care. Patients who were not admitted to the hospital or the emergency observation unit were considered discharged home. Discharge can occur with a referral to specialised outpatient clinics, referral to other hospital, without a referral, against the doctor’s advice, or otherwise. Transfers to the medical intensive care, surgical intensive care, cardiothoracic intensive care, high dependency unit, intermediate care area, or coronary care unit within six hours of admission from the ED were considered disposition to higher levels of care, i.e. higher care units (HCUs). Admission to all other hospital wards was defined as ward admission. Patients who absconded were not considered as a disposition and excluded from the analysis. Given the small sample of patients transferred to the ED observation unit (n = 5), they were not included in the analysis. Comorbidities were obtained from hospital diagnosis records of all ED, inpatient and outpatient visits of the patient prior to the current episode. The Charlson Comorbidity Index (CCI) is an index score that assigns a weighted score to 17 comorbidities, based on the relative risk of 1-year mortality. It is hence an indicator of multimorbidity and a predictor of mortality ( 18 , 19 ). BMI was calculated from the height and weight measured during triage, and the comorbidity of obesity was coded for patients with a BMI of 30 and above. Administrative factors include mode of arrival to the ED, registration and discharge timestamps, disposition descriptions and triage priority level codes. Triage levels are P1 (resuscitation and critically ill), P2 (major emergency, non-ambulant), P3 (minor emergency, ambulant) and P4 (non-emergency). The primary outcomes were post-discharge all-cause mortality, revisit to the ED and readmissions at 30-day and one-year follow-ups. The follow-up duration was determined from the date of discharge home from either the ED (discharged patients) or the hospital (admitted patients). Hence, patients who suffered in-hospital death were excluded from post-discharge events. Readmissions are defined as hospital admissions during a repeat emergency visit. Secondary outcomes included the rate of in-hospital mortality, heart failure-related revisits and lengths of stay. Statistical analysis Baseline characteristics of the study population were analysed from the index visit, for discharged, ward and HCU patients. Descriptive summaries of categorical variables were reported as frequencies and percentages. Means and standard deviations were reported for continuous variables. Mortality, revisit, and readmission outcomes were compared by reporting rates and confidence intervals for each disposition. Median and inter-quartile range (IQR) were reported for ED and inpatient lengths of stay. Kaplan-Meier survival curves were performed for mortality and ED revisits within 30 days and one year for the three disposition groups. Comparisons between the discharged, ward and HCU curves were performed using the log-rank test. To investigate the factors associated with disposition decisions, we constructed a multinomial logistic regression comparing discharge, HCU admission and ward admission, with ward as the reference category. Observations with missing data (n = 20) were excluded from the regression analysis. Age, heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, and SpO2 variables were rescaled by 10 units. The models were adjusted by year of admission. We employed a four-stage hierarchical framework considering the hierarchical relationships between explanatory variables. Each step of the model extends to include an additional set of variables as follows: Model 1: Age + gender + race Model 2: Model 1 + vitals Model 3: Model 2 + comorbidities Model 4: Model 2 + CCI Model 5: Model 2 + administrative factors All analyses were conducted using Python version 3.12. Results There were 3,728 episodes of patients with heart failure diagnoses in the ED, of which we excluded four dead-on-arrival patients, one patient below 18 years of age, and one transfer to another hospital. The final study cohort was 3,723 cases, comprising 3,007 unique patients. Most cases were admitted to the ward (86.9%), whereas approximately 8.8% were transferred to the HCU and 4% were discharged from the ED. Five patients were admitted to the observation unit before being discharged or admitted, and four patients absconded before a disposition decision (Fig. 1 ). Overall, the median age was 72 years (IQR 62–81), and 40.7% were women. The five most common comorbidities were hypertension (54.2%), diabetes (38.5%), hyperlipidaemia (38.5%), atrial fibrillation (34.6%) and myocardial infarction (27.2%). These comorbidities were highly prevalent in the HCU cohort, followed by the ward and then the discharged cohort. The mean systolic blood pressure was above the normal range across all cohorts (136.6 ± 26.7). Most patients had a high triage acuity level of P1 or P2, with 90% HCU patients having a P1 triage code (Table 1 ). Table 1 Baseline demographic and clinical characteristics of the study population Characteristic All (3000)^ Discharge (128) Ward (2589) HCU (284) Age (yrs), median (IQR) 72 (62–81) 67 (58-77.5) 73 (63–81) 69 (58–78) Female, n (%) 1220 (40.7) 48 (37.8) 1070 (41.3) 102 (35.9) Race, n (%) Chinese 2132 (71.1) 81 (63.8) 1845 (71.3) 206 (72.5) Indian 373 (12.4) 17 (13.4) 323 (12.5) 33 (11.6) Malay 357 (11.9) 20 (15.7) 301 (11.6) 36 (12.7) Other Races 138 (4.6) 9 (7.1) 120 (4.6) 9 (3.2) Triage Class Code, n (%) Priority 1 1805 (60.2) 57 (44.9) 1490 (57.6) 258 (90.8) Priority 2 1115 (37.2) 60 (47.2) 1030 (39.8) 25 (8.8) Priority 3 (medium) and P4 (low) 79 (2.6) 10 (7.9) 68 (2.6) 1 (0.4) Arrival by EMS, n (%) 499 (16.6) 11 (8.7) 414 ( 16 ) 74 (26.1) Vitals, mean (sd) BP, Diastolic (mmHg) 75.1 (15.8) 73.6 (14.1) 75.1 (15.4) 76.1 (19.5) BP, Systolic (mmHg) 136.6 (26.7) 135.1 (27.4) 137 (25.9) 133.5 (32.2) Heart Rate (beats/min) 84.8 (20.5) 81.8 (18.1) 83.6 (19.4) 97.1 (26.2) Respiration Rate 19.4 (3.8) 18.7 (2.2) 19.2 (3.2) 21.7 (6.9) SpO2% 97.4 (2.4) 97.6 (2.2) 97.4 (2.1) 97.4 (4.1) Temperature (°C) 36.5 (0.7) 36.5 (0.6) 36.5 (0.6) 36.5 ( 1 ) Comorbidities, n (%) MI 815 (27.2) 25 (19.7) 648 ( 25 ) 142 (50) Atrial fibrillation 1042 (34.7) 30 (23.6) 910 (35.1) 102 (35.9) Diabetes 1156 (38.5) 30 (23.6) 1007 (38.9) 119 (41.9) Hypertension 1624 (54.1) 44 (34.6) 1435 (55.4) 145 (51.1) Hyperlipidaemia 1154 (38.5) 33 ( 26 ) 1004 (38.8) 117 (41.2) Stroke 232 (7.7) 7 (5.5) 204 (7.9) 21 (7.4) Cancer 272 (9.1) 5 (3.9) 252 (9.7) 15 (5.3) PVD 141 (4.7) 3 (2.4) 125 (4.8) 13 (4.6) COPD 611 (20.4) 31 (24.4) 525 (20.3) 55 (19.4) Chronic Kidney Disease 661 ( 22 ) 14 ( 11 ) 585 (22.6) 62 (21.8) Liver Disease 168 (5.6) 7 (5.5) 148 (5.7) 13 (4.6) Obesity 332 (11.1) 8 (6.3) 303 (11.7) 21 (7.4) CCI, mean (sd) 5.1 (2.6) 4 (2.2) 5.2 (2.6) 4.8 (2.7) ^Data are presented for the population at the index episode. The All column excludes absconded and observation unit cases. BP: blood pressure, SpO2: Oxygen saturation, MI: Myocardial Infarction, PVD: Peripheral Vascular Disease, COPD: Chronic Obstructive Pulmonary Disease, CCI: Charlson Comorbidity Index In the multinomial logistic regression analyses with ward admission as the reference category, increasing age was associated with lower odds of discharge (OR 0.84; 95% CI 0.74–0.96) and HCU admission (OR 0.83; 95% CI 0.76–0.91) [Table 2 ; model 1]. Controlling demographic factors, vital signs at presentation were also significantly associated with the outcomes [Table 2 ; model 2]. Lower diastolic blood pressure was associated with a greater likelihood of HCU admission compared with ward admission (OR 0.91; 95% CI 0.83–0.99). An increase in heart rate and respiratory rates by 10 units increased the odds of HCU admission versus ward admission by 31% and 163%, respectively. The respiration rate was also significantly associated with lower odds of discharge from the ED. Similarly, a history of myocardial infarction was associated with higher odds of HCU admission (OR 3.1; 95% CI 2.38–4), and chronic kidney disease was associated with reduced odds of discharge (OR 0.55; 95% CI 0.33–0.95), relative to ward admission [Table 2 ; model 3]. A higher Charlson Comorbidity Index was associated with lower odds of discharge (OR 0.82, 95% CI 0.75–0.9) but was not significantly associated with HCU admission [Table 2 ; model 4]. Disposition decisions closely followed triage acuity levels [Table 2 ; model 5]. Patients with less severe triage codes (P2, P3, and P4) had significantly lower odds of admission to the ICU compared to those with P1 codes. Whereas, P3 and P4 codes were associated with a 352% increase in the odds of discharge home. Table 2 Factors associated with discharge or Higher Care Unit admission compared to ward admission Discharge HCU Odds Ratio (95% CI) a Odds Ratio (95% CI) a Model 1: Age + Gender +Race Age (per 10 years) 0.84 (0.74 to 0.96)** 0.83 (0.76 to 0.91)*** Female 0.94 (0.66 to 1.33) 0.88 (0.69 to 1.13) Chinese Reference Reference Indian 0.97 (0.59 to 1.6) 0.72 (0.5 to 1.04) Malay 1.14 (0.7 to 1.88) 0.84 (0.58 to 1.22) Other Races 1.62 (0.84 to 3.12) 0.61 (0.32 to 1.18) Model 2: Model 1 + Vitals BP, Diastolic (mmHg) 0.94 (0.83 to 1.07) 0.91 (0.83 to 0.99)* BP, Systolic (mmHg) 1.03 (0.96 to 1.11) 1.01 (0.96 to 1.07) Heart Rate (beats/min) 0.95 (0.86 to 1.05) 1.31 (1.23 to 1.38)*** Respiration Rate 0.44 (0.19 to 0.98)* 2.63 (1.94 to 3.55)*** SpO2 (%) 1.47 (0.64 to 3.39) 1.28 (0.79 to 2.08) Temperature (°C) 1.02 (0.76 to 1.37) 1.05 (0.89 to 1.25) Model 3: Model 2 + Comorbidities MI 0.9 (0.59 to 1.37) 3.1 (2.38 to 4.03)*** Atrial fibrillation 0.68 (0.46 to 1.02) 1.06 (0.8 to 1.39) Diabetes 0.66 (0.43 to 1.03) 1.17 (0.87 to 1.56) Hypertension 0.72 (0.47 to 1.1) 0.87 (0.63 to 1.19) Hyperlipidaemia 0.95 (0.6 to 1.5) 1.08 (0.8 to 1.47) Stroke 0.87 (0.41 to 1.81) 0.87 (0.55 to 1.37) Cancer 0.47 (0.2 to 1.09) 0.71 (0.43 to 1.19) PVD 0.78 (0.3 to 2.01) 0.87 (0.51 to 1.51) COPD 1.36 (0.92 to 2.03) 0.84 (0.62 to 1.14) Chronic Kidney Disease 0.55 (0.33 to 0.95)* 0.95 (0.7 to 1.3) Liver Disease 0.98 (0.44 to 2.15) 0.77 (0.45 to 1.32) Obesity 0.49 (0.24 to 0.99)* 0.56 (0.35 to 0.89)* Model 4: Model 2 + CCI Charlson Comorbidity Index 0.82 (0.75 to 0.9)*** 1.02 (0.97 to 1.08) Model 5: Model 2+ Triage Class +Arrival mode +Shift time Priority 1 Reference Reference Priority 2 1.5 (1.04 to 2.14)* 0.15 (0.1 to 0.23)*** Priority 3 and 4 3.52 (1.78 to 6.96)*** 0.09 (0.01 to 0.65)* Arrival by ambulance 0.67 (0.38 to 1.18) 1.34 (1.0 to 1.8) Referred (yes) 1.45 (0.93 to 2.27) 0.71 (0.44 to 1.15) Shift 8 am to 4 pm Reference Reference Shift 4 pm to 12 am 0.83 (0.56 to 1.23) 1.12 (0.85 to 1.49) Shift 12 am to 8 am 0.8 (0.44 to 1.43) 1.5 (1.07 to 2.09)* CI - confidence interval; BP - blood pressure; SpO2: Oxygen saturation. a Ward admissions as reference *p < .05, ** p<.01, *** p < .001 The 30-day mortality rate was 4.7% for discharged patients and 2.9% and 3.7% for ward and HCU admissions, respectively (Table 3 ). The overall one-year mortality was 20.6%. Directly discharged patients had lower one-year mortality (13.5%) compared to ward (21.1%) and HCU patients (19%) (Fig. 2 ). HCU patients had higher rates of in-hospital mortality (11% vs 3.3%) and higher median length of stay (5.7 vs 3.6) compared to ward patients. Conversely, discharged patients had considerably higher all-cause revisits within 30 days than patients admitted to ward and HCU. Comparative survival analysis shows that the cumulative probability of discharge patients revisiting the ED is significantly greater than that of admitted patients (p = 0.014) (Fig. 3 ). Similarly, 30-day heart failure-related revisits were highest for discharge patients, followed by ward and HCU. Approximately 40% of 30-day revisits among discharged patients were heart-failure related. More than half of the patients revisited the ED at 1-year follow-up, with similar rates for discharge and ward admissions. Table 3 Clinical outcomes of heart failure patients by ED disposition All (3713)^ Discharge (149) Ward (3239) HCU (326) % (95% CI) % (95% CI) % (95% CI) % (95% CI) 30-day mortality 3 (2.5–3.6) 4.7 (1.3–8.1) 2.9 (2.3–3.4) 3.7 (1.6–5.7) 1-year mortality 20.6 (19.3–21.9) 13.5 ( 8 – 19 ) 21.1 (19.7–22.5) 19 (14.8–23.3) In-hospital mortality 3.8 (3.2–4.5) - 3.3 (2.7–3.9) 11 (7.6–14.4) 30-day all-cause ED revisit 21.9 (20.6–23.2) 31.1 (23.6–38.5) 21.8 (20.3–23.2) 19 (14.8–23.3) 1-year all-cause ED revisit 56.7 (55.1–58.3) 57.4 (49.5–65.4) 57.7 (56- 59.4) 46 (40.6–51.4) 30-day HF-related ED revisit 4.4 (3.8–5.1) 12.8 (7.4–18.2) 4.3 (3.6- 5) 2.1 (0.6–3.7) 1-year HF-related ED revisit 14.7 (13.5–15.8) 21.6 (15- 28.3) 14.9 (13.6–16.1) 9.8 (6.6–13) 30-day readmission 20.1 (18.7–21.4) 26.4 (19.3–33.4) 19.9 (18.5–21.3) 18.6 (14.1–23.1) 1-year readmission 54.6 (52.8–56.1) 49.3 (41.3–57.4) 55.3 (53.5–57) 48.6 (42.9–54.4) ED LOS (hours), median (IQR) 4.8 (3.5–6.5) 3.4 (2.5–4.3) 4.9 (3.6–6.7) 4.5 (3.5–6) IP LOS (days), median (IQR) 3.7 (2.2–6.4) 3.6 (2.1–5.9) 5.7 (3.7–9.9) ^ Data are presented for all ED heart failure episodes. The All column excludes absconded and observation unit cases. CI - confidence interval; ED - emergency department; HF - heart failure; LOS – length of stay; IQR – interquartile range. Discussion This study provides an overview of how AHF are managed in the ED, highlighting the clinical and operational factors that shape disposition decisions. The cohort reflects an older population with substantial multimorbidity and clinically significant presentations, consistent with international evidence that AHF disproportionately affects older adults with complex chronic disease profiles. In our analysis, markers of greater clinical severity, such as unstable vital signs, higher Charlson Comorbidity Index scores, and high-acuity triage classifications, were associated with inpatient admission, particularly to the HCU. Conversely, discharged patients tended to have lower comorbidity burdens and more moderate presentations. These findings reinforce that disposition decisions in the ED are primarily shaped by acute physiological status and underlying disease complexity. However, the notable variation in revisit rates between disposition groups also indicates that current clinical decision-making may not fully capture the short-term risk of decompensation, suggesting gaps in existing risk stratification and post-discharge support pathways. While the short-term mortality of discharged patients was variable, they were at a significantly increased risk of returning to the ED within 30 days compared to the hospitalised cohort. Not only did discharged patients come back to the ED more within 30 days, but a higher proportion of them were also revisiting for heart failure. These differences in revisit rates disappeared over the one-year follow-up period. This may indicate that discharged AHF patients, even after management in acute care, were more vulnerable to exacerbations in the following weeks. It could also be related to variations between inpatient and acute-care discharge protocols, which provide opportunities to facilitate a smoother transition to primary care and offer better patient education. There is often fragmentation of care in health systems, and the transition from acute to primary care requires well-coordinated efforts. Community-based transitional care programs are promising models to address post-discharge care planning needs, patient education, and provide continued clinical monitoring and management. They facilitate the coordinated continuation of care from specialists, nurses, and allied health professionals, and are shown to lower acute hospital utilisation ( 20 – 22 ). There are ongoing implementations of such interventions in some Singapore hospitals; however, more synthesised regional evidence is needed on their clinical and cost-effectiveness for heart failure. Investigating implementation challenges related to logistics and financing and conducting economic evaluations are essential future steps. Admission rates from the ED for AHF patients are substantial worldwide, ranging from approximately 70% to 90%. In our Singapore setting, the majority of AHF patients were admitted, with only 4% discharged home. The decision to discharge patients requires careful consideration of several factors. Most heart failure patients present with high acuity and it may be challenging to identify those who are clinically safe for discharge. High hospitalisation rates may also be influenced by the fact that directly discharged patients have an increased likelihood of short-term revisits. Few risk stratification tools have been developed for HF patients in Western ED settings ( 15 , 23 , 24 ). However, there remains a lack of tools and uniform guidelines focused on identifying HF patients at low risk of short-term adverse events. Singapore’s hospital system is overburdened due to rapid population ageing and a rise in chronic diseases such as heart failure, and both factors are expected to increase further by 2050 ( 25 ). Therefore, alternative care pathways that prevent unnecessary hospitalisations and alleviate ED crowding from repeat presentations will be beneficial. Observation units can potentially help achieve this goal for more stable patients. They facilitate extended assessment, access to diagnostic testing and therapies, thus preventing inpatient ward utilisation. They are shown to be associated with reduced ED revisits and shorter hospital stays ( 10 , 11 ). The Hospital-at-home model can also be a viable strategy for hospitalisation avoidance by providing coordinated medical care at the patient’s home setting. It is reported to effectively improve outcomes in various cohorts ( 25 – 27 ). Further evaluation is required to explore their large-scale implementation and integration with existing care pathways in Singapore. Compared with similar studies in other countries, both short- and long-term mortality rates were lower for Singaporean AHF patients ( 7 , 8 , 23 ). High revisit rates after direct discharge have also been reported in other countries ( 6 , 7 , 9 ). Few studies examine long-term revisit or readmission patterns in the ED. We noted that more than half the patients revisited within one year, with similar rates across dispositions. This difference in short- and long-term revisit patterns further underscores the importance of ongoing care interventions currently being implemented ( 28 ). Our study results may help clinicians better understand the ED presentations of HF patients and their outcomes after disposition. The study setting in a Singapore tertiary hospital makes the findings representative of the real-world presentation of AHF in the ED. It contributes to the literature with evidence on Asian populations. These results can assist the development of standardised risk stratification tools and disposition guidelines in Singapore. This study has several limitations that should be considered when interpreting the findings. First, case identification relied on SNOMED diagnostic coding in the electronic health record, which may not capture all presentations of acute heart failure and may misclassify patients with overlapping cardiorespiratory conditions. Second, although the analysis adjusted for demographic and clinical factors, unmeasured confounders such as socioeconomic status, medication adherence, frailty, and social support were not available and may influence both disposition decisions and outcomes. Third, readmission outcomes were defined based on ED-linked hospital admissions and therefore do not include patients admitted directly through outpatient clinics or private sector hospitals, potentially underestimating true rehospitalisation rates. Finally, the single-centre design may limit the generalisability of the findings to other hospital settings with different patient populations, resource constraints, and care pathways. Conclusion This study provides evidence on the presentation patterns, disposition decisions, and outcomes of acute heart failure patients in a Singapore tertiary emergency department. Disposition was principally influenced by clinical severity at presentation and the burden of multimorbidity, with the majority of patients admitted to inpatient wards. Patients discharged directly from the ED experienced substantially higher short-term revisit rates, signalling that current discharge selection processes may not fully account for early decompensation risk. At the same time, high overall hospitalisation rates occur in a health system already facing ED bed pressures and capacity constraints. Collectively, these findings suggest scope to refine risk stratification and expand structured alternatives to inpatient admission, such as observation units, transitional care programmes, and home-based hospitalisation models to ensure safe discharge, improve continuity of care, and reduce avoidable acute care utilisation. Efforts to develop and evaluate such pathways will be important for sustaining high-quality, patient-centred heart failure care in Singapore. Abbreviations AHF: acute heart failure, BP: blood pressure, CCI: Charlson comorbidity index, COPD: chronic obstructive pulmonary disease, ED: emergency department, HCU: higher care unit, HF: heart failure, IQR: inter-quartile range, MI: myocardial infarction, PVD: peripheral vascular disease, SGH: Singapore General Hospital, SpO2: oxygen saturation, OR: odds ratio Declarations Ethics approval and consent to participate Ethics approval for this study was granted by the National University of Singapore Institutional Review Board (NUS-IRB-2024-643), and patient consent was not required. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The EMRADAR database is not publicly available. The data and code used in this study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This review is funded through SK’s start-up grant from the National University of Singapore. Authors' contributions SK conceptualised the study, provided overall supervision, and contributed to manuscript drafting. VS undertook data collection, conducted the analysis, and led the manuscript write-up. SS contributed to study conceptualisation, data analysis, and manuscript drafting. NG, AL and MO contributed to the study conceptualisation and critically reviewed and edited the manuscript. LF contributed to data analysis and manuscript drafting. SL and NR were responsible for data collection, facilitated access to the data, and contributed to manuscript drafting. References McMurray JJV, Adamopoulos S, Anker SD, Auricchio A, Böhm M, Dickstein K, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2012;14(8):803–69. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858. Storrow AB, Jenkins CA, Self WH, Alexander PT, Barrett TW, Han JH, et al. The Burden of Acute Heart Failure on U.S. Emergency Departments. JACC Heart Fail. 2014 June;2(3):269–77. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among Patients in the Medicare Fee-for-Service Program. N Engl J Med. 2009;360(14):1418–28. Ross JS, Chen J, Lin Z, Bueno H, Curtis JP, Keenan PS, et al. Recent National Trends in Readmission Rates After Heart Failure Hospitalization. Circ Heart Fail. 2010;3(1):97–103. Miró Ò, Gil V, Martín-Sánchez FJ, Jacob J, Herrero P, Alquézar A, et al. Short-term outcomes of heart failure patients with reduced and preserved ejection fraction after acute decompensation according to the final destination after emergency department care. Clin Res Cardiol. 2018;107(8):698–710. Ezekowitz JA, Bakal JA, Kaul P, Westerhout CM, Armstrong PW. Acute heart failure in the emergency department: Short and long-term outcomes of elderly patients with heart failure. Eur J Heart Fail. 2008;10(3):308–14. Miró Ò, Rossello X, Gil V, Martín-Sánchez FJ, Llorens P, Herrero-Puente P, et al. Analysis of How Emergency Physicians’ Decisions to Hospitalize or Discharge Patients With Acute Heart Failure Match the Clinical Risk Categories of the MEESSI-AHF Scale. Ann Emerg Med. 2019;74(2):204–15. Poliwoda J, Eagles D, Yadav K, Nemnom MJ, Walmsley CG, Mielniczuk L, et al. Outcomes of acute heart failure patients managed in the emergency department. Can J Emerg Med. 2023;25(9):752–60. Pang PS, Berger DA, Mahler SA, Li X, Pressler SJ, Lane KA, et al. Short-Stay Units vs Routine Admission From the Emergency Department in Patients With Acute Heart Failure: The SSU-AHF Randomized Clinical Trial. JAMA Netw Open. 2024;7(1):e2350511–2350511. Sánchez-Marcos C, Jacob J, Llorens P, Rodríguez B, Martín-Sánchez FJ, Herrera S et al. Analysis of the effectiveness and safety of short-stay units in the hospitalization of patients with acute heart failure. Propensity Score SSU-EAHFE. Análisis Ef Segur Las Unidades Estancia Corta En Hosp Pacientes Con Insufic Cardíaca Aguda Propensity Score UCE-EAHFE. 2022;222(8):443–57. Lam CSP. Heart failure in Southeast Asia: facts and numbers. ESC Heart Fail. 2015;2(2):46–9. Yan S, Kwan YH, Thumboo J, Low LL. Characteristics and Health Care Utilization of Different Segments of a Multiethnic Asian Population in Singapore. JAMA Netw Open 2019 Sept 6;2(9):e1910878. Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2022;118(17):3272–87. Fountoulaki K, Ventoulis I, Drokou A, Georgarakou K, Parissis J, Polyzogopoulou E. Emergency department risk assessment and disposition of acute heart failure patients: existing evidence and ongoing challenges. Heart Fail Rev. 2023 July 1;28(4):781–93. SNOMED International [Internet]. [cited 2025 Sept 4]. What is SNOMED CT. Available from: https://www.snomed.org/what-is-snomed-ct Electronic Health Intelligence. System (eHints) [Internet]. [cited 2025 Sept 30]. Available from: https://www.synapxe.sg/healthtech/data-and-analytics/electronic-health-intelligence-system 202507-SingHealth-Group. -Data-Protection-Policy.pdf [Internet]. [cited 2025 Aug 6]. Available from: https://www.singhealth.com.sg/content/dam/singhealth-web/singhealth/about-singhealth/corporate-governance/documents/202507-SingHealth-Group-Data-Protection-Policy.pdf PDPC | PDPA Overview [Internet]. [cited 2025 Aug 6]. Available from: https://www.pdpc.gov.sg/overview-of-pdpa/the-legislation/personal-data-protection-act Sattouf AA, Farahat R, Khatri AA, Sattouf AA, Farahat RM, Khatri A. Effectiveness of Transitional Care Interventions for Heart Failure Patients: A Systematic Review With Meta-Analysis. Cureus [Internet]. 2022 Sept 29 [cited 2024 June 19];14. Available from: https://www.cureus.com/articles/114868-effectiveness-of-transitional-care-interventions-for-heart-failure-patients-a-systematic-review-with-meta-analysis#!/ Feltner C, Jones CD, Cené CW, Zheng ZJ, Sueta CA, Coker-Schwimmer EJL, et al. Transitional Care Interventions to Prevent Readmissions for Persons With Heart Failure. Ann Intern Med. 2014 June;3(11):774–84. Ong CY, Ng JJA, Ng KKSJ, Tay PY, Lee MHJ. Transitional Care Program in Reducing Acute Hospital Utilization in Singapore. Healthcare. 2024;12(21):2144. Garg N, Pekmezaris R, Stevens G, Becerra AZ, Kozikowski A, Patel V, et al. Performance of Emergency Heart Failure Mortality Risk Grade in the Emergency Department. West J Emerg Med Integrating Emerg Care Popul Health. 2021;22(3):672–7. Stiell IG, Clement CM, Brison RJ, Rowe BH, Borgundvaag B, Aaron SD, et al. A risk scoring system to identify emergency department patients with heart failure at high risk for serious adverse events. Acad Emerg Med. 2013;20(1):17–26. Low LL, Vasanwala FF, Ng LB, Chen C, Lee KH, Tan SY. Effectiveness of a transitional home care program in reducing acute hospital utilization: a quasi-experimental study. BMC Health Serv Res. 2015;15(1):100. Qaddoura A, Yazdan-Ashoori P, Kabali C, Thabane L, Haynes RB, Connolly SJ et al. Efficacy of Hospital at Home in Patients with Heart Failure: A Systematic Review and Meta-Analysis. PLoS ONE. 2015 June 8;10(6):e0129282. Shepperd S, Doll H, Angus RM, Clarke MJ, Iliffe S, Kalra L et al. Hospital at home admission avoidance - Shepperd, S – 2008 | Cochrane Library. [cited 2024 Nov 5]; Available from: https://www.cochranelibrary.com/cdsr/doi/ 10.1002/14651858.CD007491/full Tan CC, Lam CSP, Matchar DB, Zee YK, Wong JEL. Singapore’s health-care system: key features, challenges, and shifts. Lancet 2021 Sept 18;398(10305):1091–104. Additional Declarations No competing interests reported. Supplementary Files HFdispositionssupplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Feb, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Editor invited by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 02 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8664554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596876446,"identity":"9649a102-e190-40b0-aee4-b70b6e6f4c62","order_by":0,"name":"Veronica Singh","email":"","orcid":"","institution":"Duke-NUS Medical School","correspondingAuthor":false,"prefix":"","firstName":"Veronica","middleName":"","lastName":"Singh","suffix":""},{"id":596876447,"identity":"05c44b67-b740-41c0-be60-7b8e02c2301c","order_by":1,"name":"Sameera 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School","correspondingAuthor":false,"prefix":"","firstName":"Sanjeewa","middleName":"","lastName":"Kularatna","suffix":""}],"badges":[],"createdAt":"2026-01-22 02:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8664554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8664554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103533376,"identity":"f4bb4622-5574-4774-9cb1-0f08672405d4","added_by":"auto","created_at":"2026-02-26 17:40:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260254,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study cohort\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8664554/v1/6643b3ca61bb49607d547f9e.png"},{"id":103533341,"identity":"1c19d4f9-6ccb-4ab6-98f9-8dd88872c3ae","added_by":"auto","created_at":"2026-02-26 17:40:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3389988,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier survival estimates of mortality at 30-day and 1-year follow-up.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8664554/v1/afab09cf65e45bbde0ca1dd2.png"},{"id":103533378,"identity":"c9352e42-ba09-4c46-810b-1df2105ca0b3","added_by":"auto","created_at":"2026-02-26 17:40:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3403594,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier survival estimates of revisits at 30-day and 1-year follow-up.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8664554/v1/ae49bd47c7fb945723b1444c.png"},{"id":103533428,"identity":"8496ff0d-9d2e-4f8c-b495-5baaecb2682d","added_by":"auto","created_at":"2026-02-26 17:40:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7881134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8664554/v1/42a7c374-085f-491b-af66-20046d24efe7.pdf"},{"id":103533383,"identity":"c94852bf-7d89-4f48-9b9d-e9dec49cfd1a","added_by":"auto","created_at":"2026-02-26 17:40:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13862,"visible":true,"origin":"","legend":"","description":"","filename":"HFdispositionssupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8664554/v1/d2eb1389d29303f6a4ef3a5d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Characteristics Associated with Acute Heart Failure Dispositions and Outcomes from the Emergency Department","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, Acute heart failure (AHF) is the most common cause of hospitalisation among the elderly (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Heart failure (HF) is a chronic condition associated with substantial mortality and morbidity. It affects approximately 63.4\u0026nbsp;million people globally (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It has a prevalence, of 10%, among those above 70 years of age with more severe adverse events. HF is characterised by frequent exacerbations, which lead most patients to seek care in Emergency Departments (ED). The ED plays a crucial role in the initial patient evaluation, stabilisation and clinical management. Emergency physicians then decide on the subsequent placement of patients from the ED. Patients can be hospitalised to general wards, intensive care units, intermediate care, admitted to observation wards within the ED, or discharged home. These disposition decisions impact patient care pathways, mortality and health systems utilisation.\u003c/p\u003e \u003cp\u003eHowever, the decision to admit or discharge patients can be complex for AHF due to the wide range of clinical presentations and the presence of multiple comorbidities. The primary challenge in making these disposition decisions is identifying patients at low risk of adverse events who are safe to be discharged home. Unnecessary hospitalisations increase costs and resource burden, whereas inappropriate discharges put patients at a higher risk of adverse events. More than 80% of AHF patients presenting to the United States EDs are hospitalised (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Notably, up to 20% of hospitalised AHF patients are hospitalised within 30 days (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Although revisits to the ED are reported to be higher for patients discharged home, ranging from 21 to 44% within 30 days (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObservation units have been progressively utilised in some hospitals over the past decade to safely reduce unnecessary hospitalisations. They allow for a short period of treatment and evaluation within the ED before being discharged home or admitted inpatient. While they are reported to have lower inpatient lengths of stay, there is limited data on their mortality and readmission outcomes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSingapore has a rapidly ageing, multi-ethnic population with chronic multimorbidity patterns. HF is estimated to affect 4.5% of the population in Singapore, compared to 1\u0026ndash;2% in other developed countries (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, heart failure patients in Singapore were found to have high one-year mortality and increased acute care utilisation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The total ED attendance at public hospitals has grown at an annual rate of approximately 5.6% from 2005 to 2016. This increase in emergency care utilisation is occurring at a disproportionately higher rate than the overall population growth rate, posing a considerable challenge for managing the ED.\u003c/p\u003e \u003cp\u003eThere is limited reliable data on the prevalence, demographics, outcomes, and healthcare utilisation patterns of HF patients in Asian populations, as most heart failure registries and risk scores arise from Western populations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This lack of evidence, with the inherent heterogeneity of the AHF population, leads to variation in clinical decision-making. Given the significant healthcare burden of HF in Singapore and beyond, it is imperative to gain a deeper understanding of its ED presentations. Determining the patient characteristics and outcomes, such as mortality and recidivism rates, associated with dispositions can enhance the delivery of acute medical care.\u003c/p\u003e \u003cp\u003eThe aims of this study were to describe the characteristics of acute heart failure patients presenting to the ED in Singapore and how these related to dispositions. We also sought to describe the short and long-term outcomes for each disposition. Primary outcomes of interest were patient mortality, revisits to the ED, readmissions and combined adverse events. Secondary outcomes were in-hospital mortality and LOS of admitted patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eWe conducted a single-centre retrospective cohort study using electronic health record data from Singapore General Hospital (SGH) of all patients who visited the ED from 1 June 2016 to 31 December 2021. SGH is Singapore\u0026rsquo;s largest general hospital with approximately 120,000 cases seen in the ED every year. It is part of the SingHealth group, a public healthcare network of general and speciality hospitals, community hospitals and polyclinics. This study was approved by the National University of Singapore Institutional Review Board (NUS-IRB-2024-643), and patient consent was not required.\u003c/p\u003e \u003cp\u003ePatients with an ED primary or secondary diagnosis of heart failure were identified using SNOMED CT diagnosis codes (Supplementary Material). SNOMED CT is a comprehensive and precise clinical terminology designed to record clinical information in electronic health records (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Dead on arrival patients, patients aged below 18 years, and transfers to other hospitals were excluded. We included the index heart failure-related ED presentation and all subsequent visits of each patient for analysis. Data was analysed on a per-episode basis, with multiple heart failure-related ED visits by a patient considered separate events.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eData was sourced from the Emergency Medicine Real-world Anonymised Data Repository (EM-RADAR). The registry was initiated in January 2021 to collect comprehensive information on patients from emergency departments at SingHealth institutions. The datasets are generated based on unique patient codes and registration dates from the SingHealth data warehouse, eHintS (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The data is de-identified by the SingHealth Trusted Third Party team as per the existing data governance policies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData extracted included patient demographics such as age, gender and race, vital measurements, comorbidities and administrative factors. The first vital measurements for heart rate, respiratory rate, temperature, systolic blood pressure, diastolic blood pressure and SpO2 of each ED episode were analysed.\u003c/p\u003e \u003cp\u003eED dispositions were categorised into three levels based on the level of care intensity: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) discharge, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ward, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) higher levels of care. Patients who were not admitted to the hospital or the emergency observation unit were considered discharged home. Discharge can occur with a referral to specialised outpatient clinics, referral to other hospital, without a referral, against the doctor\u0026rsquo;s advice, or otherwise. Transfers to the medical intensive care, surgical intensive care, cardiothoracic intensive care, high dependency unit, intermediate care area, or coronary care unit within six hours of admission from the ED were considered disposition to higher levels of care, i.e. higher care units (HCUs). Admission to all other hospital wards was defined as ward admission. Patients who absconded were not considered as a disposition and excluded from the analysis. Given the small sample of patients transferred to the ED observation unit (n\u0026thinsp;=\u0026thinsp;5), they were not included in the analysis.\u003c/p\u003e \u003cp\u003eComorbidities were obtained from hospital diagnosis records of all ED, inpatient and outpatient visits of the patient prior to the current episode. The Charlson Comorbidity Index (CCI) is an index score that assigns a weighted score to 17 comorbidities, based on the relative risk of 1-year mortality. It is hence an indicator of multimorbidity and a predictor of mortality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). BMI was calculated from the height and weight measured during triage, and the comorbidity of obesity was coded for patients with a BMI of 30 and above. Administrative factors include mode of arrival to the ED, registration and discharge timestamps, disposition descriptions and triage priority level codes. Triage levels are P1 (resuscitation and critically ill), P2 (major emergency, non-ambulant), P3 (minor emergency, ambulant) and P4 (non-emergency).\u003c/p\u003e \u003cp\u003eThe primary outcomes were post-discharge all-cause mortality, revisit to the ED and readmissions at 30-day and one-year follow-ups. The follow-up duration was determined from the date of discharge home from either the ED (discharged patients) or the hospital (admitted patients). Hence, patients who suffered in-hospital death were excluded from post-discharge events. Readmissions are defined as hospital admissions during a repeat emergency visit. Secondary outcomes included the rate of in-hospital mortality, heart failure-related revisits and lengths of stay.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the study population were analysed from the index visit, for discharged, ward and HCU patients. Descriptive summaries of categorical variables were reported as frequencies and percentages. Means and standard deviations were reported for continuous variables. Mortality, revisit, and readmission outcomes were compared by reporting rates and confidence intervals for each disposition. Median and inter-quartile range (IQR) were reported for ED and inpatient lengths of stay. Kaplan-Meier survival curves were performed for mortality and ED revisits within 30 days and one year for the three disposition groups. Comparisons between the discharged, ward and HCU curves were performed using the log-rank test.\u003c/p\u003e \u003cp\u003eTo investigate the factors associated with disposition decisions, we constructed a multinomial logistic regression comparing discharge, HCU admission and ward admission, with ward as the reference category. Observations with missing data (n\u0026thinsp;=\u0026thinsp;20) were excluded from the regression analysis. Age, heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, and SpO2 variables were rescaled by 10 units. The models were adjusted by year of admission. We employed a four-stage hierarchical framework considering the hierarchical relationships between explanatory variables. Each step of the model extends to include an additional set of variables as follows:\u003c/p\u003e \u003cp\u003eModel 1: Age\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;race\u003c/p\u003e \u003cp\u003eModel 2: Model 1\u0026thinsp;+\u0026thinsp;vitals\u003c/p\u003e \u003cp\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;comorbidities\u003c/p\u003e \u003cp\u003eModel 4: Model 2\u0026thinsp;+\u0026thinsp;CCI\u003c/p\u003e \u003cp\u003eModel 5: Model 2\u0026thinsp;+\u0026thinsp;administrative factors\u003c/p\u003e \u003cp\u003eAll analyses were conducted using Python version 3.12.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 3,728 episodes of patients with heart failure diagnoses in the ED, of which we excluded four dead-on-arrival patients, one patient below 18 years of age, and one transfer to another hospital. The final study cohort was 3,723 cases, comprising 3,007 unique patients. Most cases were admitted to the ward (86.9%), whereas approximately 8.8% were transferred to the HCU and 4% were discharged from the ED. Five patients were admitted to the observation unit before being discharged or admitted, and four patients absconded before a disposition decision (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, the median age was 72 years (IQR 62\u0026ndash;81), and 40.7% were women. The five most common comorbidities were hypertension (54.2%), diabetes (38.5%), hyperlipidaemia (38.5%), atrial fibrillation (34.6%) and myocardial infarction (27.2%). These comorbidities were highly prevalent in the HCU cohort, followed by the ward and then the discharged cohort. The mean systolic blood pressure was above the normal range across all cohorts (136.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.7). Most patients had a high triage acuity level of P1 or P2, with 90% HCU patients having a P1 triage code (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic and clinical characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(3000)^\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003cp\u003e(128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWard\u003c/p\u003e \u003cp\u003e(2589)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCU\u003c/p\u003e \u003cp\u003e(284)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yrs), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (62\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (58-77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (63\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (58\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1220 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1070 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2132 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1845 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206 (72.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriage Class Code, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePriority 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1805 (60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1490 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258 (90.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePriority 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1115 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1030 (39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePriority 3 (medium) and P4 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArrival by EMS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e499 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e414 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (26.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitals, mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP, Diastolic (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.1 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.6 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.1 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.1 (19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP, Systolic (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.6 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.1 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.5 (32.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.8 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.8 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.6 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.1 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.4 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.7 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.4 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.6 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.4 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.4 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.5 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.5 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e815 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e648 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1042 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e910 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1156 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1007 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (41.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1624 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1435 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145 (51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1154 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1004 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117 (41.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e611 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e525 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (19.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e661 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e585 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI, mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e^Data are presented for the population at the index episode. The All column excludes absconded and observation unit cases.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eBP: blood pressure, SpO2: Oxygen saturation, MI: Myocardial Infarction, PVD: Peripheral Vascular Disease, COPD: Chronic Obstructive Pulmonary Disease, CCI: Charlson Comorbidity Index\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn the multinomial logistic regression analyses with ward admission as the reference category, increasing age was associated with lower odds of discharge (OR 0.84; 95% CI 0.74\u0026ndash;0.96) and HCU admission (OR 0.83; 95% CI 0.76\u0026ndash;0.91) [Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; model 1]. Controlling demographic factors, vital signs at presentation were also significantly associated with the outcomes [Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; model 2]. Lower diastolic blood pressure was associated with a greater likelihood of HCU admission compared with ward admission (OR 0.91; 95% CI 0.83\u0026ndash;0.99). An increase in heart rate and respiratory rates by 10 units increased the odds of HCU admission versus ward admission by 31% and 163%, respectively. The respiration rate was also significantly associated with lower odds of discharge from the ED.\u003c/p\u003e \u003cp\u003eSimilarly, a history of myocardial infarction was associated with higher odds of HCU admission (OR 3.1; 95% CI 2.38\u0026ndash;4), and chronic kidney disease was associated with reduced odds of discharge (OR 0.55; 95% CI 0.33\u0026ndash;0.95), relative to ward admission [Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; model 3]. A higher Charlson Comorbidity Index was associated with lower odds of discharge (OR 0.82, 95% CI 0.75\u0026ndash;0.9) but was not significantly associated with HCU admission [Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; model 4].\u003c/p\u003e \u003cp\u003eDisposition decisions closely followed triage acuity levels [Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; model 5]. Patients with less severe triage codes (P2, P3, and P4) had significantly lower odds of admission to the ICU compared to those with P1 codes. Whereas, P3 and P4 codes were associated with a 352% increase in the odds of discharge home.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with discharge or Higher Care Unit admission compared to ward admission\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1: Age\u0026thinsp;+\u0026thinsp;Gender +Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per 10 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.74 to 0.96)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.76 to 0.91)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.66 to 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.69 to 1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.59 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.5 to 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (0.7 to 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.58 to 1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62 (0.84 to 3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.32 to 1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2: Model 1\u0026thinsp;+\u0026thinsp;Vitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP, Diastolic (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.83 to 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.83 to 0.99)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP, Systolic (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.96 to 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.96 to 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.86 to 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31 (1.23 to 1.38)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.19 to 0.98)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.63 (1.94 to 3.55)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47 (0.64 to 3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.79 to 2.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.76 to 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.89 to 1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;Comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.59 to 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1 (2.38 to 4.03)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.46 to 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.8 to 1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.43 to 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.87 to 1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.47 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.63 to 1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.6 to 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.8 to 1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.41 to 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.55 to 1.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.2 to 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.43 to 1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.3 to 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.51 to 1.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36 (0.92 to 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.62 to 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.33 to 0.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.7 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.44 to 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77 (0.45 to 1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.24 to 0.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.35 to 0.89)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4: Model 2\u0026thinsp;+\u0026thinsp;CCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.75 to 0.9)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.97 to 1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5: Model 2+ Triage Class +Arrival mode +Shift time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePriority 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePriority 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (1.04 to 2.14)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 (0.1 to 0.23)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePriority 3 and 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52 (1.78 to 6.96)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.01 to 0.65)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArrival by ambulance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.38 to 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.0 to 1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferred (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (0.93 to 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.44 to 1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShift 8 am to 4 pm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShift 4 pm to 12 am\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.56 to 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.85 to 1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShift 12 am to 8 am\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.44 to 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.07 to 2.09)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCI - confidence interval; BP - blood pressure; SpO2: Oxygen saturation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003ea\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eWard admissions as reference\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e*p \u003c .05, ** p\u003c.01, *** p \u003c .001\u003c/h3\u003e\n\u003cp\u003eThe 30-day mortality rate was 4.7% for discharged patients and 2.9% and 3.7% for ward and HCU admissions, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overall one-year mortality was 20.6%. Directly discharged patients had lower one-year mortality (13.5%) compared to ward (21.1%) and HCU patients (19%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HCU patients had higher rates of in-hospital mortality (11% vs 3.3%) and higher median length of stay (5.7 vs 3.6) compared to ward patients.\u003c/p\u003e \u003cp\u003eConversely, discharged patients had considerably higher all-cause revisits within 30 days than patients admitted to ward and HCU. Comparative survival analysis shows that the cumulative probability of discharge patients revisiting the ED is significantly greater than that of admitted patients (p\u0026thinsp;=\u0026thinsp;0.014) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, 30-day heart failure-related revisits were highest for discharge patients, followed by ward and HCU. Approximately 40% of 30-day revisits among discharged patients were heart-failure related. More than half of the patients revisited the ED at 1-year follow-up, with similar rates for discharge and ward admissions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical outcomes of heart failure patients by ED disposition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(3713)^\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003cp\u003e(149)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWard\u003c/p\u003e \u003cp\u003e(3239)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCU\u003c/p\u003e \u003cp\u003e(326)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (2.5\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7 (1.3\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9 (2.3\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.7 (1.6\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.6 (19.3\u0026ndash;21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5 (\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1 (19.7\u0026ndash;22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (14.8\u0026ndash;23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.8 (3.2\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3 (2.7\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (7.6\u0026ndash;14.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day all-cause ED revisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.9 (20.6\u0026ndash;23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1 (23.6\u0026ndash;38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8 (20.3\u0026ndash;23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (14.8\u0026ndash;23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year all-cause ED revisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.7 (55.1\u0026ndash;58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.4 (49.5\u0026ndash;65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.7 (56- 59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46 (40.6\u0026ndash;51.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day HF-related ED revisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4 (3.8\u0026ndash;5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8 (7.4\u0026ndash;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3 (3.6- 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1 (0.6\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year HF-related ED revisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.7 (13.5\u0026ndash;15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6 (15- 28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.9 (13.6\u0026ndash;16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8 (6.6\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.1 (18.7\u0026ndash;21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4 (19.3\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.9 (18.5\u0026ndash;21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.6 (14.1\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.6 (52.8\u0026ndash;56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3 (41.3\u0026ndash;57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.3 (53.5\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.6 (42.9\u0026ndash;54.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED LOS (hours), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.8 (3.5\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (2.5\u0026ndash;4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9 (3.6\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5 (3.5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP LOS (days), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7 (2.2\u0026ndash;6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.6 (2.1\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.7 (3.7\u0026ndash;9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e^\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eData are presented for all ED heart failure episodes. The All column excludes absconded and observation unit cases.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCI - confidence interval; ED - emergency department; HF - heart failure; LOS \u0026ndash; length of stay; IQR \u0026ndash; interquartile range.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides an overview of how AHF are managed in the ED, highlighting the clinical and operational factors that shape disposition decisions. The cohort reflects an older population with substantial multimorbidity and clinically significant presentations, consistent with international evidence that AHF disproportionately affects older adults with complex chronic disease profiles. In our analysis, markers of greater clinical severity, such as unstable vital signs, higher Charlson Comorbidity Index scores, and high-acuity triage classifications, were associated with inpatient admission, particularly to the HCU. Conversely, discharged patients tended to have lower comorbidity burdens and more moderate presentations. These findings reinforce that disposition decisions in the ED are primarily shaped by acute physiological status and underlying disease complexity. However, the notable variation in revisit rates between disposition groups also indicates that current clinical decision-making may not fully capture the short-term risk of decompensation, suggesting gaps in existing risk stratification and post-discharge support pathways.\u003c/p\u003e \u003cp\u003eWhile the short-term mortality of discharged patients was variable, they were at a significantly increased risk of returning to the ED within 30 days compared to the hospitalised cohort. Not only did discharged patients come back to the ED more within 30 days, but a higher proportion of them were also revisiting for heart failure. These differences in revisit rates disappeared over the one-year follow-up period. This may indicate that discharged AHF patients, even after management in acute care, were more vulnerable to exacerbations in the following weeks. It could also be related to variations between inpatient and acute-care discharge protocols, which provide opportunities to facilitate a smoother transition to primary care and offer better patient education. There is often fragmentation of care in health systems, and the transition from acute to primary care requires well-coordinated efforts. Community-based transitional care programs are promising models to address post-discharge care planning needs, patient education, and provide continued clinical monitoring and management. They facilitate the coordinated continuation of care from specialists, nurses, and allied health professionals, and are shown to lower acute hospital utilisation (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). There are ongoing implementations of such interventions in some Singapore hospitals; however, more synthesised regional evidence is needed on their clinical and cost-effectiveness for heart failure. Investigating implementation challenges related to logistics and financing and conducting economic evaluations are essential future steps.\u003c/p\u003e \u003cp\u003eAdmission rates from the ED for AHF patients are substantial worldwide, ranging from approximately 70% to 90%. In our Singapore setting, the majority of AHF patients were admitted, with only 4% discharged home. The decision to discharge patients requires careful consideration of several factors. Most heart failure patients present with high acuity and it may be challenging to identify those who are clinically safe for discharge. High hospitalisation rates may also be influenced by the fact that directly discharged patients have an increased likelihood of short-term revisits. Few risk stratification tools have been developed for HF patients in Western ED settings (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, there remains a lack of tools and uniform guidelines focused on identifying HF patients at low risk of short-term adverse events.\u003c/p\u003e \u003cp\u003eSingapore\u0026rsquo;s hospital system is overburdened due to rapid population ageing and a rise in chronic diseases such as heart failure, and both factors are expected to increase further by 2050 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, alternative care pathways that prevent unnecessary hospitalisations and alleviate ED crowding from repeat presentations will be beneficial. Observation units can potentially help achieve this goal for more stable patients. They facilitate extended assessment, access to diagnostic testing and therapies, thus preventing inpatient ward utilisation. They are shown to be associated with reduced ED revisits and shorter hospital stays (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The Hospital-at-home model can also be a viable strategy for hospitalisation avoidance by providing coordinated medical care at the patient\u0026rsquo;s home setting. It is reported to effectively improve outcomes in various cohorts (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Further evaluation is required to explore their large-scale implementation and integration with existing care pathways in Singapore.\u003c/p\u003e \u003cp\u003eCompared with similar studies in other countries, both short- and long-term mortality rates were lower for Singaporean AHF patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). High revisit rates after direct discharge have also been reported in other countries (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Few studies examine long-term revisit or readmission patterns in the ED. We noted that more than half the patients revisited within one year, with similar rates across dispositions. This difference in short- and long-term revisit patterns further underscores the importance of ongoing care interventions currently being implemented (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study results may help clinicians better understand the ED presentations of HF patients and their outcomes after disposition. The study setting in a Singapore tertiary hospital makes the findings representative of the real-world presentation of AHF in the ED. It contributes to the literature with evidence on Asian populations. These results can assist the development of standardised risk stratification tools and disposition guidelines in Singapore. This study has several limitations that should be considered when interpreting the findings. First, case identification relied on SNOMED diagnostic coding in the electronic health record, which may not capture all presentations of acute heart failure and may misclassify patients with overlapping cardiorespiratory conditions. Second, although the analysis adjusted for demographic and clinical factors, unmeasured confounders such as socioeconomic status, medication adherence, frailty, and social support were not available and may influence both disposition decisions and outcomes. Third, readmission outcomes were defined based on ED-linked hospital admissions and therefore do not include patients admitted directly through outpatient clinics or private sector hospitals, potentially underestimating true rehospitalisation rates. Finally, the single-centre design may limit the generalisability of the findings to other hospital settings with different patient populations, resource constraints, and care pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides evidence on the presentation patterns, disposition decisions, and outcomes of acute heart failure patients in a Singapore tertiary emergency department. Disposition was principally influenced by clinical severity at presentation and the burden of multimorbidity, with the majority of patients admitted to inpatient wards. Patients discharged directly from the ED experienced substantially higher short-term revisit rates, signalling that current discharge selection processes may not fully account for early decompensation risk. At the same time, high overall hospitalisation rates occur in a health system already facing ED bed pressures and capacity constraints. Collectively, these findings suggest scope to refine risk stratification and expand structured alternatives to inpatient admission, such as observation units, transitional care programmes, and home-based hospitalisation models to ensure safe discharge, improve continuity of care, and reduce avoidable acute care utilisation. Efforts to develop and evaluate such pathways will be important for sustaining high-quality, patient-centred heart failure care in Singapore.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAHF: acute heart failure, BP: blood pressure, CCI: Charlson comorbidity index, COPD: chronic obstructive pulmonary disease, ED: emergency department, HCU: higher care unit, HF: heart failure, IQR: inter-quartile range, MI: myocardial infarction, PVD: peripheral vascular disease, SGH: Singapore General Hospital, SpO2: oxygen saturation, OR: odds ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for this study was granted by the National University of Singapore Institutional Review Board (NUS-IRB-2024-643), and patient consent was not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eClinical trial number\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe EMRADAR database is not publicly available. The data and code used in this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis review is funded through SK\u0026rsquo;s start-up grant from the National University of Singapore.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eSK conceptualised the study, provided overall supervision, and contributed to manuscript drafting. VS undertook data collection, conducted the analysis, and led the manuscript write-up. SS contributed to study conceptualisation, data analysis, and manuscript drafting. NG, AL and MO contributed to the study conceptualisation and critically reviewed and edited the manuscript. LF contributed to data analysis and manuscript drafting. SL and NR were responsible for data collection, facilitated access to the data, and contributed to manuscript drafting.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcMurray JJV, Adamopoulos S, Anker SD, Auricchio A, B\u0026ouml;hm M, Dickstein K, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2012;14(8):803\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789\u0026ndash;858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStorrow AB, Jenkins CA, Self WH, Alexander PT, Barrett TW, Han JH, et al. The Burden of Acute Heart Failure on U.S. Emergency Departments. JACC Heart Fail. 2014 June;2(3):269\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJencks SF, Williams MV, Coleman EA. Rehospitalizations among Patients in the Medicare Fee-for-Service Program. N Engl J Med. 2009;360(14):1418\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss JS, Chen J, Lin Z, Bueno H, Curtis JP, Keenan PS, et al. Recent National Trends in Readmission Rates After Heart Failure Hospitalization. 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Analysis of How Emergency Physicians\u0026rsquo; Decisions to Hospitalize or Discharge Patients With Acute Heart Failure Match the Clinical Risk Categories of the MEESSI-AHF Scale. Ann Emerg Med. 2019;74(2):204\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoliwoda J, Eagles D, Yadav K, Nemnom MJ, Walmsley CG, Mielniczuk L, et al. Outcomes of acute heart failure patients managed in the emergency department. Can J Emerg Med. 2023;25(9):752\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePang PS, Berger DA, Mahler SA, Li X, Pressler SJ, Lane KA, et al. Short-Stay Units vs Routine Admission From the Emergency Department in Patients With Acute Heart Failure: The SSU-AHF Randomized Clinical Trial. 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JAMA Netw Open 2019 Sept 6;2(9):e1910878.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2022;118(17):3272\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFountoulaki K, Ventoulis I, Drokou A, Georgarakou K, Parissis J, Polyzogopoulou E. Emergency department risk assessment and disposition of acute heart failure patients: existing evidence and ongoing challenges. Heart Fail Rev. 2023 July 1;28(4):781\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSNOMED International [Internet]. [cited 2025 Sept 4]. What is SNOMED CT. 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Lancet 2021 Sept 18;398(10305):1091\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute heart failure, emergency department, disposition, revisits, hospitalisation","lastPublishedDoi":"10.21203/rs.3.rs-8664554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8664554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAcute heart failure (AHF) is a leading cause of Emergency Department (ED) attendance among older adults and contributes substantially to hospitalisations and resource strain. Globally, more than 80% of acute heart failure presentations to EDs result in inpatient admissions, despite wide variability in severity and risk of short-term adverse outcomes. Understanding AHF disposition patterns may be helpful for identifying opportunities to optimise care pathways. This study describes the clinical characteristics associated with ED disposition decisions for AHF and compares short- and long-term outcomes across discharge, ward admissions, and Higher Care Unit (HCU) admissions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a retrospective cohort study using ED presentation data from a major Singapore public hospital between June 2016 and December 2021. Patients with SNOMED-coded heart failure diagnoses were included. Demographics, clinical presentation, comorbidities, and administrative factors were analysed. Primary outcomes were mortality, ED revisits, and readmissions at 30 days and one year. Multinomial logistic regression was used to examine factors associated with discharge versus ward or HCU admission.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong 3,723 AHF presentations, 4% were discharged, 87% admitted to the ward, and 9% to an HCU. The median age was 72 (62\u0026ndash;81) years and 40.7% were women. Higher acuity, abnormal vital signs, multimorbidity, and myocardial infarction history were associated with HCU admission. Directly discharged patients had much higher 30-day ED revisit rates (31% vs 22% ward vs 19% HCU) but similar one-year revisit rates. One-year mortality was lower for discharged patients (13.5%) than for those admitted.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAHF patients present with complex clinical needs and are predominantly admitted in Singapore. However, high admission rates in the context of hospital overcrowding and ED bed block highlight an opportunity to develop structured alternative care pathways, such as observation units, transitional care, and home-based hospitalisation, to reduce unnecessary inpatient utilisation and improve continuity of care safely.\u003c/p\u003e","manuscriptTitle":"Clinical Characteristics Associated with Acute Heart Failure Dispositions and Outcomes from the Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 17:39:14","doi":"10.21203/rs.3.rs-8664554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"333889523575894990240915386796940257858","date":"2026-02-24T23:01:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T14:03:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12959911669439564545811964411267937155","date":"2026-02-24T13:57:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T13:24:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T10:00:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T12:35:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T06:23:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-02-02T06:15:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9929c88-09bd-4120-a3af-14e74206e5d1","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T17:39:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 17:39:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8664554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8664554","identity":"rs-8664554","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}