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Background One of Singapore’s national strategies for the COVID-19 pandemic was containment. Efforts included a fourteen-day quarantine of close contacts, were subjected to an entry and exit SARS-CoV-2 PCR test, the latter being done between 11-14 days post exposure. Additionally, symptomatic contacts were tested for SARS-CoV-2. We aim to determine the trend in COVID-19 incubation periods during three distinct pandemic waves corresponding to different SARS-CoV-2 variants. Incubation Period Incubation period of the prevalent SARS-CoV-2 variant in circulation Methods This is an ecological study and information collected from the SingHealth COVID-19 Registry, a database of all inpatients admitted to any of the SingHealth hospitals. For patients under quarantine, the start date of the quarantine period was assumed to be the last date of exposure to the index case. Incubation period was determined by the duration between date of exposure and date of the first positive SARS-CoV-2 PCR test. The prevalent strain in circulation was identified from the Singapore database in the GISAID collection. Only variants of concern, as categorized by WHO, Alpha (23rd Jan 2020 – 1st Mar 2021), Delta (5th May 2021 – 31st Oct 2021) and Omicron (1st Jan 2022 – Present) were considered. For the Omicron variant, quarantine was discontinued, hence the last date of arrival from international travel was assumed to be the date of exposure. Results From January 2020 to March 2022, there were 19,905 patients in the COVID-19 registry, of whom 11,235 were under quarantine and 8,612 had preceding international travel. Of the 11,235 patients under quarantine, 8,189 patients were infected when SARS-CoV-2 Alpha variant and 3,046 patients were infected when SARS-CoV-2 Delta variant were in circulation. Of the 8,612 patients with preceding travel, 6,503 patients were infected when SARS-CoV-2 Omicron variant was in circulation. The median incubation period for the Alpha variant was 11 days (IQR: 7-14 days) versus 3 days (IQR: 2-4 days) for the Delta variant versus 3 days (IQR: 0-5 days) for the Omicron variant. Pairwise comparisons between the variants were (p-value = < .001) Conclusion The significant differences between incubation periods of the SARS-CoV-2 variants in circulation poses a challenge to containment efforts and has emphasize the importance of dynamic national strategies. Disclosures All Authors: No reported disclosures.

Stretchers are commonly used for transporting cardiac arrest patients, but their use may be limited in confined spaces, like elevators. Use of transfer sheet as an alternative has not been explored. We aimed to compare manual chest compression quality between these two methods.

Aim To understand the impact of COVID‐19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios. Background COVID‐19 created an increased demand for isolation beds and nursing workforce globally. Methods This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization. Results In January to March 2020, isolation bed capacity was low (=

Background: We describe the development of a dynamic simulation modelling framework to support agile resource planning during the COVID-19 pandemic. The framework takes into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes to deal with the ever-changing outbreak scenarios.Methods: A specific use case based on short-term bed resource planning is described within the proposed framework. The simulation model was calibrated against historical data for the Singapore COVID-19 situation. The time period for model calibration was from 1st April till 30th April 2020. The model was used to project for bed resource needs over the period from 1st May 2020 till 31st May 2020. Multivariate sensitivity analysis was also conducted for ICU and general isolation bed demand, length-of-stay (LOS), and age-adjusted conversion rates across different care needs. The unmet needs under various scenarios were also evaluated for planning purposes.Results: Several variants of the agile resource planning model were developed to adapt to the fast-changing COVID-19 situation in Singapore. The use case demonstrated an agile adaptation of the model to account for previously unexpected scenarios. The rapid evolution of the pandemic locally revealed streams of new infections that arose from two distinct sources. The model projections were calibrated with the latest data for short-term projections. The agility in flexing plans and collaborative management structures to rapidly deploy human and capital resources to surge the level of care during the COVID-19 pandemic have proven utility in guiding the allocation of scarce healthcare resources and helped system resiliency.Conclusions: The rapidly evolving COVID-19 pandemic in Singapore has necessitated the development of an agile and adaptable modelling framework that can be quickly calibrated to changes both from demand and supply. The modelling framework is able to deploy systems modelling concepts in a holistic manner. This facilitates the evaluation of complex cause-and-effect relationships. A robust collaborative framework, coupled with the availability of in-depth domain knowledge and accurate and updated data availability ensures a model is realistic, timely and useful.

Context Hospice care can be delivered in different settings, but many patients choose to receive it at home because of familiar surroundings. Despite their preferences, not every home hospice patient manages to die at home. Objective To examine the independent factors associated with home hospice patient dying at home. Methods Retrospective analysis of Hospice Care Association's database. Hospice Care Association is the largest home hospice provider in Singapore. The study included all patients who were admitted into home hospice service from January 1, 2004 to December 31, 2013. Cox proportional hazards modeling with time as constant was used to study the relationship between independent variables and home death. Results A total of 19,721 patients were included in the study. Females (adjusted risk ratio [ARR] 1.09, 95% CI 1.04–1.15), older patients (ARR 1.01, 95% CI 1.00–1.01), shorter duration of home hospice stay (ARR 0.88, 95% CI 0.82–0.94), fewer episodes of hospitalization (ARR 0.81, 95% CI 0.75–0.86), living with caregivers (ARR 1.54, 95% CI 1.05–2.26), doctor (ARR 1.05, 95% CI 1.01–1.08) and nurse (ARR 1.06, 95% CI 1.04–1.08) visits were positive predictors of dying-at-home. Diagnosis of cancer (ARR 0.93, 95% CI 0.86–1.00) was a negative predictor of dying-at-home. Conclusion Female, older age, living with a caregiver, non-cancer diagnosis, more doctor and nurse visits, shorter duration of home hospice stays, and fewer episodes of acute hospitalizations are predictive of dying-at-home for home hospice patients.

Although chest pain is one of the leading presenting complaints in the Emergency Department (ED), only 12-15% account for Acute Coronary Syndrome (ACS). Hence early triage and exclusion of ACS is crucial to improve ED throughput and workflow. We aim to determine and compare the accuracy of the