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      Strain on Scarce Intensive Care Beds Drives Reduced Patient Volumes, Patient Selection, and Worse Outcome: A National Cohort Study*

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          Abstract

          OBJECTIVES:

          Strain on ICUs during the COVID-19 pandemic required stringent triage at the ICU to distribute resources appropriately. This could have resulted in reduced patient volumes, patient selection, and worse outcome of non-COVID-19 patients, especially during the pandemic peaks when the strain on ICUs was extreme. We analyzed this potential impact on the non-COVID-19 patients.

          DESIGN:

          A national cohort study.

          SETTING:

          Data of 71 Dutch ICUs

          PARTICIPANTS:

          A total of 120,393 patients in the pandemic non-COVID-19 cohort (from March 1, 2020 to February 28, 2022) and 164,737 patients in the prepandemic cohort (from January 1, 2018 to December 31, 2019).

          INTERVENTIONS:

          None.

          MEASUREMENTS AND MAIN RESULTS:

          Volume, patient characteristics, and mortality were compared between the pandemic non-COVID-19 cohort and the prepandemic cohort, focusing on the pandemic period and its peaks, with attention to strata of specific admission types, diagnoses, and severity. The number of admitted non-COVID-19 patients during the pandemic period and its peaks were, respectively, 26.9% and 34.2% lower compared with the prepandemic cohort. The pandemic non-COVID-19 cohort consisted of fewer medical patients (48.1% vs. 50.7%), fewer patients with comorbidities (36.5% vs. 40.6%), and more patients on mechanical ventilation (45.3% vs. 42.4%) and vasoactive medication (44.7% vs. 38.4%) compared with the prepandemic cohort. Case-mix adjusted mortality during the pandemic period and its peaks was higher compared with the prepandemic period, odds ratios were, respectively, 1.08 (95% CI, 1.05–1.11) and 1.10 (95% CI, 1.07–1.13).

          CONCLUSIONS:

          In non-COVID-19 patients the strain on healthcare has driven lower patient volume, selection of fewer comorbid patients who required more intensive support, and a modest increase in the case-mix adjusted mortality.

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          Most cited references35

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          Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review

          Ray Moynihan, Sharon Sanders, Zoe A Michaleff … (2021)
          Objectives To determine the extent and nature of changes in utilisation of healthcare services during COVID-19 pandemic. Design Systematic review. Eligibility Eligible studies compared utilisation of services during COVID-19 pandemic to at least one comparable period in prior years. Services included visits, admissions, diagnostics and therapeutics. Studies were excluded if from single centres or studied only patients with COVID-19. Data sources PubMed, Embase, Cochrane COVID-19 Study Register and preprints were searched, without language restrictions, until 10 August, using detailed searches with key concepts including COVID-19, health services and impact. Data analysis Risk of bias was assessed by adapting the Risk of Bias in Non-randomised Studies of Interventions tool, and a Cochrane Effective Practice and Organization of Care tool. Results were analysed using descriptive statistics, graphical figures and narrative synthesis. Outcome measures Primary outcome was change in service utilisation between prepandemic and pandemic periods. Secondary outcome was the change in proportions of users of healthcare services with milder or more severe illness (eg, triage scores). Results 3097 unique references were identified, and 81 studies across 20 countries included, reporting on >11 million services prepandemic and 6.9 million during pandemic. For the primary outcome, there were 143 estimates of changes, with a median 37% reduction in services overall (IQR −51% to −20%), comprising median reductions for visits of 42% (−53% to −32%), admissions 28% (−40% to −17%), diagnostics 31% (−53% to −24%) and for therapeutics 30% (−57% to −19%). Among 35 studies reporting secondary outcomes, there were 60 estimates, with 27 (45%) reporting larger reductions in utilisation among people with a milder spectrum of illness, and 33 (55%) reporting no difference. Conclusions Healthcare utilisation decreased by about a third during the pandemic, with considerable variation, and with greater reductions among people with less severe illness. While addressing unmet need remains a priority, studies of health impacts of reductions may help health systems reduce unnecessary care in the postpandemic recovery. PROSPERO registration number CRD42020203729.
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            The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.

            W A Knaus, D P Wagner, E A Draper … (1991)
            The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).
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              Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.

              Jack E Zimmerman, Andrew A Kramer, Douglas S. McNair … (2006)
              To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models. : Observational cohort study. A total of 104 intensive care units (ICUs) in 45 U.S. hospitals. A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data. None. We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy. APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Crit Care Med
                Journal ID (iso-abbrev): Crit Care Med
                Journal ID (publisher-id): CCM
                Title: Critical Care Medicine
                Publisher: Lippincott Williams & Wilkins (Hagerstown, MD )
                ISSN (Print): 0090-3493
                ISSN (Electronic): 1530-0293
                Publication date (Electronic): 14 December 2023
                Publication date (Print): April 2024
                Volume: 52
                Issue: 4
                Pages: 574-585
                Affiliations
                [1 ] Department of Medical Informatics, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands.
                [2 ] National Intensive Care Evaluation Foundation, Amsterdam, The Netherlands.
                [3 ] Amsterdam Public Health, Quality of Care, Amsterdam, The Netherlands.
                [4 ] University Medical Center, University of Utrecht, Intensive Care Medicine, Utrecht, The Netherlands.
                [5 ] Amsterdam UMC Location University of Amsterdam, Intensive Care Medicine, Amsterdam, The Netherlands.
                [6 ] Department of Intensive Care Medicine, Maastricht University Medical Centre +, Maastricht, The Netherlands.
                [7 ] Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
                [8 ] Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands.
                Author notes
                For information regarding this article, E-mail: s.brinkman@ 123456amsterdamumc.nl
                Article
                Accession ID: 00006
                DOI: 10.1097/CCM.0000000000006156
                PMC ID: 10930373
                PubMed ID: 38095502
                SO-VID: 3ad82f3b-2c2b-4b5e-bb26-07a11d415233
                Copyright © Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.
                License:

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Categories
                Subject: Clinical Investigations
                Custom metadata
                OPEN-ACCESS TRUE
                SDC T

                Keywords: critical care,intensive care,mortality,non-covid-19,overload,pandemic,strain,surge
                Data availability:
                Keywords: critical care, intensive care, mortality, non-covid-19, overload, pandemic, strain, surge

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