A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy

We present a scalable forecasting framework with a Monte Carlo simulation to forecast the short-term bed occupancy of patients with confirmed and suspected COVID-19 in intensive care units and regular wards. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care from May 2020 to March 2021.

Remdesivir and dexamethasone as tools to relieve hospital care systems stressed by COVID-19: A modelling study on bed resources and budget impact

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.

The cross-scale correlations between individuals and nations in COVID-19 mortality

It is challenging to quantitatively clarify the determining medical and social factors of COVID-19 mortality, which varied by 2-3 orders of magnitude across countries. Here, we present evidence that the whole-cycle patterns of mortality follow a logistic law for 52 countries. A universal linear law is found between the ICU time in the early stage and the most important quantity regarding the epidemic: its duration. Saturation mortality is found to have a power law relationship with median age and bed occupancy, which quantitatively explains the great variation in mortality based on the two key thresholds of median age (=38) and bed occupancy (=15%). We predict that deaths will be reduced by 36% when the number of beds is doubled for countries with older populations. Facing the next wave of the epidemic, this model can make early predictions on the epidemic duration and medical supply reservation.

The cross-scale correlations between individuals and nations in COVID-19 mortality

It is challenging to quantitatively clarify the determining medical and social factors of COVID-19 mortality, which varied by 2-3 orders of magnitude across countries. Here, we present evidence that the whole-cycle patterns of mortality follow a logistic law for 52 countries. A universal linear law is found between the ICU time in the early stage and the most important quantity regarding the epidemic: its duration. Saturation mortality is found to have a power law relationship with median age and bed occupancy, which quantitatively explains the great variation in mortality based on the two key thresholds of median age (=38) and bed occupancy (=15%). We predict that deaths will be reduced by 36% when the number of beds is doubled for countries with older populations. Facing the next wave of the epidemic, this model can make early predictions on the epidemic duration and medical supply reservation.

P-O11 Surgical Ambulatory Care: Reducing surgical admissions whilst maintaining patient satisfaction. Experiences over a four year period

Background The tri-association document; The future of Emergency General Surgery (2015) has a number of key recommendations for the provision of emergency general surgical care. Key recommendations include for senior surgeons to triage referrals and to utilise a “hot clinic” model. Prior to 2016 in the authors’ hospital, all General Practitioner/community referrals were formally admitted to General Surgery. A consultant led ambulatory clinic with dedicated Advanced Nurse Practitioner support was instituted in October 2016. It offers preliminary assessment, phlebotomy and priority access to routine imaging modalities. The clinic is located in a tertiary hospital serving a population of 500,000. Methods A retrospective audit of prospectively collected referral and outcome lists for the Surgical Ambulatory clinic was conducted for the time periods of October 2016 to June 2021. The two primary outcomes were defined as admission to the General Surgical ward and discharge to the community/non-general surgical specialty. Secondary outcomes for patient satisfaction were measured by randomly distributing over a six week period a patient satisfaction survey. The survey was designed in accordance with trust guidance, was anonymous and would cover multiple lead Consultant encounters as a cohort. Results In total, 9069 patients presented to the surgical ambulatory clinic over a period of 44 months. 2347 (26%) were admitted to the General Surgical ward whilst 6717 (74%) were discharged directly from the clinic. 71% of survey responders rated their experiences of the ambulatory clinic as “Excellent”, 19% “Very Good”, 0.5% “Good” and 0.5% “Poor.” Conclusions The introduction of an ambulatory care model has demonstrated a marked reduction in surgical admissions whilst remaining favourable to the patient populace. This has a direct impact on overall bed occupancy rates. In the age of COVID-19, efforts must me made to reduce the the number of potential inpatient interactions to protect those most at risk. A reduced admission and bed occupancy rate will contribute to the reduction of this risk.

Early Information on Active Cases in Zero Rejection Efforts for COVID-19 Patients in West Java Province 2021 Using the Feedforwards Multilayer Perceptron Neural Network

West Java noted, as of August 14, 2021, 653,741 people were confirmed positive for COVID-19. On the same date, the number of active COVID-19 cases in West Java was 65,000. There is a significant increase in active cases of COVID-19 in 2021 in West Java. In the period 5 June – 17 July 2021, there was an increase in the number of active cases by 95,532. In that period, active cases increased by 484%, and the Bed Occupancy Ratio (BOR) in West Java had jumped in June 2021 with the highest number of 91.6%, this figure far exceeded the WHO recommendation of 60% before finally continuing to decline and finally in August was at 30.69%. This has an impact on the incidence of patient rejection at the COVID-19 referral hospital. Active cases talk about COVID-19 patients who need medical treatment and new cases talk about the rate of spread of COVID-19 in West Java, so these two things are very strategic to study. In this study, active cases and new case were predicted using Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive cases, recovered and died of COVID-19 sufferers in 34 provinces in Indonesia in the period 2 March 2020 - 14 August 2021. The results of the study found, from the results of the evaluation using data testing the number of active cases in the last 19 weeks, namely April 10 – August 14, 2021, MLP is accurate in predicting the number of active cases for the first coming week 17 times, and the next two weeks for the second week 12 times with an absolute percentage error (APE) < 20%. As for weekly new cases, MLP has been accurate 10 times for the next one week and 9 times for the next two weeks. It is hoped that the results of this study can be useful for the government as a reference in conditioning the hospital bed capacity to deal with active cases of COVID-19 in West Java in the next two weeks so that no COVID-19 patients are rejected by the hospital because the hospital is full.

Variation in Laboratory Utilization and Correlation with Hospital Bed Utilization: Experience of a Trauma-Care Hospital during the COVID-19 Pandemic

Objectives The present study was planned with the following objectives: (i) to calculate the difference in frequency of laboratory test ordered and use of consumables between the prepandemic and pandemic phases, (ii) to determine and compare the monthly average number of tests ordered per patient between the prepandemic and pandemic phases, and (iii) to correlate the monthly test ordering frequency with the monthly bed occupancy rate in both phases. Materials and Methods Records of laboratory tests ordered and use of consumables were collected for the prepandemic phase (1.8.2019 to 31.3.2020) and the pandemic phase (1.4.2020 to 31.10.2020). The absolute and relative differences were calculated. Monthly average number of tests ordered per patient and bed occupancy rate between prepandemic and pandemic phases was determined, compared, and correlated. Statistical Analysis The absolute and the relative differences between the two periods were calculated. The continuous variables were analyzed between groups using Mann–Whitney U test. Spearman correlation was used to correlate the monthly test ordering frequency with the monthly bed occupancy rate in both phases. Results A total of 946,421 tests were ordered, of which 370,270 (39%) tests were ordered during the pandemic period. There was a decrease in the number of the overall laboratory tests ordered (12%), and in the use of blood collection tubes (34%), and an increase in the consumption of sanitizers (18%), disinfectants (3%), masks (1633%), and gloves (7011%) during the pandemic period. Also, the monthly average number of tests ordered per patients significantly reduced (p-value < 0.001). Test ordering frequency had strong positive correlation with bed occupancy rate during pandemic (Spearman co-efficient = 0.73, p-value = 0.03). Conclusions An overall decline in laboratory utilization during pandemic period was observed. Understanding and correlating the trends with hospital bed utilization can maximize the productivity of the laboratory and help in better preparedness for the challenges imposed during similar exigencies.

Impact of the COVID-19 pandemic on emergency department attendances and acute medical admissions

Background To better understand the impact of the COVID-19 pandemic on hospital healthcare, we studied activity in the emergency department (ED) and acute medicine department of a major UK hospital. Methods Electronic patient records for all adult patients attending ED (n = 243,667) or acute medicine (n = 82,899) during the pandemic (2020–2021) and prior year (2019) were analysed and compared. We studied parameters including severity, primary diagnoses, co-morbidity, admission rate, length of stay, bed occupancy, and mortality, with a focus on non-COVID-19 diseases. Results During the first wave of the pandemic, daily ED attendance fell by 37%, medical admissions by 30% and medical bed occupancy by 27%, but all returned to normal within a year. ED attendances and medical admissions fell across all age ranges; the greatest reductions were seen for younger adults in ED attendances, but in older adults for medical admissions. Compared to non-COVID-19 pandemic admissions, COVID-19 admissions were enriched for minority ethnic groups, for dementia, obesity and diabetes, but had lower rates of malignancy. Compared to the pre-pandemic period, non-COVID-19 pandemic admissions had more hypertension, cerebrovascular disease, liver disease, and obesity. There were fewer low severity ED attendances during the pandemic and fewer medical admissions across all severity categories. There were fewer ED attendances with common non-respiratory illnesses including cardiac diagnoses, but no change in cardiac arrests. COVID-19 was the commonest diagnosis amongst medical admissions during the first wave and there were fewer diagnoses of pneumonia, myocardial infarction, heart failure, cellulitis, chronic obstructive pulmonary disease, urinary tract infection and other sepsis, but not stroke. Levels had rebounded by a year later with a trend to higher levels of stroke than before the pandemic. During the pandemic first wave, 7-day mortality was increased for ED attendances, but not for non-COVID-19 medical admissions. Conclusions Reduced ED attendances in the first wave of the pandemic suggest opportunities for reducing low severity presentations to ED in the future, but also raise the possibility of harm from delayed or missed care. Reassuringly, recent rises in attendance and admissions indicate that any deterrent effect of the pandemic on attendance is diminishing.