The self-regulating nature of occupancy in ICUs: stochastic homoeostasis

Varney, Josephine; Bean, Nigel; Mackay, M. F.

doi:10.1007/s10729-018-9448-4

Cited by 8 publications

(6 citation statements)

References 50 publications

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“…At the time of analysis, ward bed occupancy in New South Wales for the mixed-Delta-Omicron epidemic had reached more than double the peak occupancy reported during the Delta-only epidemic (Figure 1). While we do not have any data on the availability of beds at specific hospitals, prior research indicates that increased pressure on health systems leads to self-regulation where earlier discharges will be more likely [24], which would be reflected in our results as a shorter length of stay.…”

Section: Discussionmentioning

confidence: 96%

Hospital length of stay in a mixed Omicron and Delta epidemic in New South Wales, Australia

Tobin

Wood

Jayasundara

et al. 2022

Preprint

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Aim: To estimate the length of stay distributions of hospitalised COVID-19 cases during a mixed Omicron-Delta epidemic in New South Wales, Australia (16 Dec 2021 -- 7 Feb 2022), and compare these to estimates produced over a Delta-only epidemic in the same population (1 Jul 2021 -- 15 Dec 2022). Background: The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the `length of stay') is a key factor in determining how incident caseloads translate into health system burden as measured through ward and ICU occupancy. Results: Using data on the hospital stays of 19,574 individuals, we performed a competing-risk survival analysis of COVID-19 clinical progression. During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0-39, 40-69 and 70+ respectively, 2.16 (95\% CI: 2.12--2.21), 3.93 (95\% CI: 3.78--4.07) and 7.61 days (95\% CI: 7.31--8.01), compared to 3.60 (95\% CI: 3.48--3.81), 5.78 (95\% CI: 5.59--5.99) and 12.31 days (95\% CI: 11.75--12.95) across the preceding Delta epidemic (15 Jul 2021 -- 15 Dec 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95\% CI: 1.80--2.30), 2.92 (95\% CI: 2.50--3.67) and 6.02 days (95\% CI: 4.91--7.01) for the same age groups.

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Section: Discussionmentioning

confidence: 96%

Hospital length of stay in a mixed Omicron and Delta epidemic in New South Wales, Australia

Tobin

Wood

Jayasundara

et al. 2022

Preprint

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“…1 ). While data was not available on the number of unoccupied beds at specific hospitals in New South Wales, prior research indicates that increased pressure on health systems can lead to self-regulation where earlier discharges will be more likely [ 26 ], which would be reflected in our results as a shorter length of stay.…”

Section: Discussionmentioning

confidence: 98%

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia

et al. 2023

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Background The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the ‘length of stay’) is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of hospitalised COVID-19 cases in New South Wales, Australia, comparing estimates between a period where Delta was the dominant variant and a subsequent period where Omicron was dominant. Methods Using data on the hospital stays of 19,574 individuals who tested positive to COVID-19 prior to admission, we performed a competing-risk survival analysis of COVID-19 clinical progression. Results During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0–39, 40–69 and 70 +, respectively, 2.16 (95% CI: 2.12–2.21), 3.93 (95% CI: 3.78–4.07) and 7.61 days (95% CI: 7.31–8.01), compared to 3.60 (95% CI: 3.48–3.81), 5.78 (95% CI: 5.59–5.99) and 12.31 days (95% CI: 11.75–12.95) across the preceding Delta epidemic (1 July 2021–15 December 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80–2.30), 2.92 (95% CI: 2.50–3.67) and 6.02 days (95% CI: 4.91–7.01) for the same age groups. Conclusions Hospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic, contributing to a lessened health system burden despite a greatly increased infection burden. Our results demonstrate the utility of survival analysis in producing real-time estimates of hospital length of stay for assisting in situational assessment and planning of the COVID-19 response.

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“…Simulation models have been widely used in research articles focusing on hospital bed management (Ahmad et al, 2014;Clissold et al, 2015;Hajlasz & Mielczarek, 2020;He et al, 2019;Helbig et al, 2015;Holm et al, 2013;Khanna et al, 2016;Landa et al, 2017;Mallor & Azcarate, 2014;Monks et al, 2016;Oliveira et al, 2020;Qin et al, 2017;Seymour et al, 2015;Varney et al, 2019). These computer-based tools mimic real-world systems and processes and are particularly useful for predicting and analyzing patient flow and resource utilization in hospital settings.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

Wikman-Jorgensen,

Ruiz,

Giner-Galvañ

et al. 2024

JIEM

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Purpose: This study aims to address the pressing need for accurate forecasting of healthcare resource demands during the COVID-19 pandemic. It presents a novel approach that combines a stochastic Markov model and a discrete event simulation model to dynamically predict hospital admissions and daily occupancy of hospital and ICU beds.Design/methodology/approach: The research builds upon existing work related to predicting COVID-19 spread and patient influx to hospital emergency departments. The proposed model was developed and validated at San Juan de Alicante University Hospital from July 10, 2020, to January 10, 2022, and externally validated at Hospital Vega Baja. The model involves an admissions generator based on a stochastic Markov model, feeding data into a discrete event simulation model in the R programming language. The probabilities of hospital admission were calculated based on age-stratified positive SARS-COV-2 results from the health department's catchment population. The discrete event simulation model simulates distinct patient pathways within the hospital to estimate bed occupancy for the upcoming week. The performance of the model was measured using the median absolute difference (MAD) between predicted and actual demand.Findings: When applied to data from San Juan hospital, the admissions generator demonstrated a MAD of 6 admissions/week (interquartile range [IQR] 2-11). The MAD between the model's predictions and actual bed occupancy was 20 beds/day (IQR 5-43), equivalent to 5% of total hospital beds. For ICU occupancy, the MAD was 4 beds/day (IQR 2-7), constituting 25% of ICU beds. Evaluation with data from Hospital Vega Baja showcased an admissions generator MAD of 2.42 admissions/week (IQR 1.02-7.41). The MAD between the model's predictions and actual bed occupancy was 18 beds/day (IQR 19.57-38.89), approximately 5.1% of hospital beds. The ICU occupancy MAD was 3 beds/day (IQR 1-5), making up 21.4% of ICU beds.Practical implications: The dynamic predictions of hospital admissions, ward beds, and ICU occupancy for COVID-19 patients proved highly valuable to hospital managers, facilitating early and informed planning of resource allocation. Additionally, this study underscores the importance of utilizing simulation techniques to predict and manage hospital occupancy levels, thereby enhancing decision-making in hospital bed management, not only during pandemics but also during regular periods.Originality/value: This study introduces a novel hybrid approach that combines stochastic modeling and discrete event simulation to forecast healthcare resource demands during the COVID-19 pandemic. The methodology's effectiveness in predicting admissions and bed occupancy contributes to improved resource planning and situational awareness.

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The self-regulating nature of occupancy in ICUs: stochastic homoeostasis

Cited by 8 publications

References 50 publications

Hospital length of stay in a mixed Omicron and Delta epidemic in New South Wales, Australia

Hospital length of stay in a mixed Omicron and Delta epidemic in New South Wales, Australia

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

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