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Surge capacity: analysis of census fluctuations to estimate the number of intensive care unit beds needed.

ABSTRACT: To compare methods of characterizing intensive care unit (ICU) bed use and estimate the number of beds needed.Three geographic regions in the Canadian province of Manitoba.Retrospective analysis of population-based data from April 1, 2000, to March 31, 2007.We compared three methods to estimate ICU bed requirements. Method 1 analyzed yearly patient-days. Methods 2 and 3 analyzed day-to-day fluctuations in patient census; these differed by whether each hospital needed to independently fulfill its own demand or this resource was shared across hospitals.Three main findings were as follows: (1) estimates based on yearly average usage generally underestimated the number of beds needed compared to analysis of fluctuations in census, especially in the smaller regions where underestimation ranged 25-58 percent; (2) 4-29 percent fewer beds were needed if it was acceptable for demand to exceed supply 18 days/year, versus 4 days/year; and (3) 13-36 percent fewer beds were needed if hospitals within a region could effectively share ICU beds.Compared to using yearly averages, analyzing day-to-day fluctuations in patient census gives a more accurate picture of ICU bed use. Failing to provide adequate "surge capacity" can lead to demand that frequently and severely exceeds supply.

SUBMITTER: Olafson K

PROVIDER: S-EPMC4319880 | biostudies-other | 2015 Feb

REPOSITORIES: biostudies-other

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Surge capacity: analysis of census fluctuations to estimate the number of intensive care unit beds needed.

Olafson Kendiss K Ramsey Clare C Yogendran Marina M Fransoo Randall R Chrusch Carla C Forget Evelyn E Garland Allan A

Health services research 20140715 1

<h4>Objective</h4>To compare methods of characterizing intensive care unit (ICU) bed use and estimate the number of beds needed.<h4>Study setting</h4>Three geographic regions in the Canadian province of Manitoba.<h4>Study design</h4>Retrospective analysis of population-based data from April 1, 2000, to March 31, 2007.<h4>Methods</h4>We compared three methods to estimate ICU bed requirements. Method 1 analyzed yearly patient-days. Methods 2 and 3 analyzed day-to-day fluctuations in patient census ...[more]

PMID: 25040848

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Project description:Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity.We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive.Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies.We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain.This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).

Project description:Mechanical ventilation may be lifesaving, but in certain persons, such as those with advanced dementia, it may prolong patient suffering without a clear survival benefit.To describe the use and outcomes of mechanical ventilation and its association with the increasing numbers of intensive care unit (ICU) beds in the United States for patients with advanced dementia residing in a nursing home 120 days before that hospital admission.This retrospective cohort study evaluated Medicare beneficiaries with advanced dementia hospitalized from January 1, 2000, to December 31, 2013, using the Minimum Data Set assessments linked with Medicare part A claims. A hospital fixed-effect, multivariable logistic regression model examined the effect of changes in ICU beds within individual hospitals and the likelihood of receiving mechanical ventilation, controlling for patients' demographic characteristics, function, and comorbidities.Mechanical ventilation.From 2000 to 2013, a total of 635?008 hospitalizations of 380?060 eligible patients occurred (30.5% male and 69.5% female; mean [SD] age, 84.4 [7.4] years). Use of mechanical ventilation increased from 39 per 1000 hospitalizations in 2000 to 78 per 1000 hospitalizations in 2013 (P?<?.001, test of linear trend). As the number of ICU beds in a hospital increased over time, patients with advanced dementia were more likely to receive mechanical ventilation (ie, adjusted odds ratio per 10 ICU bed increase, 1.06; 95% CI, 1.05-1.07). In 2013, hospitals in the top decile in the number of ICU beds were reimbursed $9611.89 per hospitalization compared with $8050.24 per hospitalization in the lower decile (P?<?.001) without an improvement in 1-year mortality (65.2% vs 64.6%; P?=?54).Among hospitalized nursing home residents with advanced dementia, we found an increase in the use of mechanical ventilation over time without substantial improvement in survival. This increase in the use of mechanical ventilation was associated with an increase in the number of ICU beds within a hospital.

Project description:BackgroundThis paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity.MethodsCompartmental models were used to estimate deaths under different combinations of ICU and ward care required and received in England up to late April 2021. Model parameters were sourced from publicly available government information and organisations collating COVID-19 data. A sub-model was used to estimate the mortality scalars that represent increased mortality due to insufficient ICU or general ward bed capacity. Three illustrative scenarios for admissions numbers, 'Optimistic', 'Middling' and 'Pessimistic', were modelled and compared with the subsequent observations to the 3rd February.ResultsThe key output was the demand and capacity model described. There were no excess deaths from a lack of capacity in the 'Optimistic' scenario. Several of the 'Middling' scenario applications resulted in excess deaths-up to 597 deaths (0.6% increase) with a 20% reduction compared to best estimate ICU capacity. All the 'Pessimistic' scenario applications resulted in excess deaths, ranging from 49,178 (17.0% increase) for a 20% increase in ward bed availability, to 103,735 (35.8% increase) for a 20% shortfall in ward bed availability. These scenarios took no account of the emergence of the new, more transmissible, variant of concern (b.1.1.7).ConclusionsMortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the 'Optimistic' scenario. The 'Middling' scenario could result in some excess deaths-up to a 0.7% increase relative to the total number of deaths. The 'Pessimistic' scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase). Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.

Project description:BackgroundTransfers from one intensive care unit (ICU) to another ICU are associated with increased length of intensive care and hospital stay. Inter-hospital ICU transfers are carried out for three main reasons: clinical transfers, capacity transfers and repatriations. The aim of the study was to show that different ICU transfers differ in risk-adjusted mortality rate with repatriations having the least risk.ResultsObservational cohort study of adult patients transferred between Swedish ICUs during 3 years (2016-2018) with follow-up ending September 2019. Primary and secondary end-points were survival to 30 days and 180 days after discharge from the first ICU. Data from 75 ICUs in the Swedish Intensive Care Register, a nationwide intensive care register, were used for analysis (89% of all Swedish ICUs), covering local community hospitals, district general hospitals and tertiary care hospitals. We included adult patients (16 years or older) admitted to ICU and subsequently discharged by transfer to another ICU. Only the first admission was used. Exposure was discharge to any other ICU (ICU-to-ICU transfer), whether in the same or in another hospital. Transfers were grouped into three predefined categories: clinical transfer, capacity transfer, and repatriation. We identified 15,588 transfers among 112,860 admissions (14.8%) and analysed 11,176 after excluding 4112 repeat transfer of the same individual and 300 with missing risk adjustment. The majority were clinical transfers (62.7%), followed by repatriations (21.5%) and capacity transfers (15.8%). Unadjusted 30-day mortality was 25.0% among capacity transfers compared to 14.5% and 16.2% for clinical transfers and repatriations, respectively. Adjusted odds ratio (OR) for 30-day mortality were 1.25 (95% CI 1.06-1.49 p = 0.01) for capacity transfers and 1.17 (95% CI 1.02-1.36 p = 0.03) for clinical transfers using repatriation as reference. The differences remained 180 days post-discharge.ConclusionsThere was a large proportion of ICU-to-ICU transfers and an increased odds of dying for those transferred due to other reasons than repatriation.

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Surge capacity: analysis of census fluctuations to estimate the number of intensive care unit beds needed.

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Surge capacity: analysis of census fluctuations to estimate the number of intensive care unit beds needed.

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