Associations of Intensive Care Unit Capacity Strain with Disposition and Outcomes of Patients with Sepsis Presenting to the Emergency Department

Anesi, George L; Liu, Vincent X; Gabler, Nicole B.; Delgado, M. Kit; Kohn, Rachel; Weissman, Gary E.; Bayes, Brian; Escobar, Gabriel J.; Halpern, Scott D.

doi:10.1513/annalsats.201804-241oc

Cited by 55 publications

(44 citation statements)

References 20 publications

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“…Routine variations in capacity strain alter care processes and some clinical outcomes in the emergency department, hospital wards, and intensive care unit (ICU) (2)(3)(4)(5)(6)(7)(8)(9). However, pandemic-induced strains on capacity far exceed levels observed during peaks in routine demand, requiring new frameworks to guide hospital investments in and allocations of resources to prevent grave consequences of strain.…”

mentioning

confidence: 99%

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Weissman

Crane-Droesch

Chivers

et al. 2020

Annals of Internal Medicine

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267

195

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Background:The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.Objective: To estimate the timing of surges in clinical demand and the best-and worst-case scenarios of local COVID-19induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.Setting: 3 hospitals in an academic health system. Patients:All people living in the greater Philadelphia region. Measurements:The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best-and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. Conclusion:Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

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mentioning

confidence: 99%

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Weissman

Crane-Droesch

Chivers

et al. 2020

Annals of Internal Medicine

Self Cite

267

195

View full text Add to dashboard Cite

show abstract

“…This finding was robust in multiple sensitivity analyses and after adjustment for patient characteristics as well as other ''strain'' variables, such as intensive care unit turnover, intensive care unit daily census and ward occupancy [29]. The authors interpreted this to mean that a 10% increase in intensive care unit occupancy was associated with a 13% decrease in the odds of admission to the intensive care unit [29].…”

Section: Factors Influencing Patient Triage Decisionsmentioning

confidence: 82%

“…However, in a prospective observational study in France by Robert et al [32], the authors found that patients admitted to the intensive care unit after being initially refused admission due to capacity constraints had higher 28-day and 60-day mortality [32]. Similarly, Anesi et al found that patients with sepsis admitted to the general ward from the emergency department during times of higher intensive care unit occupancy had higher odds of in-hospital mortality (odds ratio 1.61, 95% confidence interval 1.21-2.14) [29]. The results from both of these studies strongly suggest that not only are triage decisions influenced by capacity, but patient outcomes may suffer as a downstream consequence of denying access to the intensive care unit.…”

Section: Association Between Intensive Care Unit Bed Supply and Patiementioning

confidence: 98%

“…Lastly, dynamic variables such as real-time intensive care unit capacity also influence the tendency for patients to be admitted to the intensive care unit [28][29][30][31][32]. Anesi et al examined retrospective hospital-level data from three hospitals in the University of Pennsylvania hospital system and reported a negative association between higher intensive care unit capacity and lower likelihood of patients with sepsis being admitted to the intensive care unit [29]. This finding was robust in multiple sensitivity analyses and after adjustment for patient characteristics as well as other ''strain'' variables, such as intensive care unit turnover, intensive care unit daily census and ward occupancy [29].…”

Section: Factors Influencing Patient Triage Decisionsmentioning

confidence: 99%

“…Anesi et al examined retrospective hospital-level data from three hospitals in the University of Pennsylvania hospital system and reported a negative association between higher intensive care unit capacity and lower likelihood of patients with sepsis being admitted to the intensive care unit [29]. This finding was robust in multiple sensitivity analyses and after adjustment for patient characteristics as well as other ''strain'' variables, such as intensive care unit turnover, intensive care unit daily census and ward occupancy [29]. The authors interpreted this to mean that a 10% increase in intensive care unit occupancy was associated with a 13% decrease in the odds of admission to the intensive care unit [29].…”

Section: Factors Influencing Patient Triage Decisionsmentioning

confidence: 99%

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Goldilocks, the Three Bears and Intensive Care Unit Utilization: Delivering Enough Intensive Care But Not Too Much. A Narrative Review

2020

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Professional societies have developed recommendations for patient triage protocols, but wide variations in triage patterns for many acute conditions exist among hospitals in the United States. Differences in hospitals' triage patterns can be attributed to factors such as physician behavior, hospital policy and realtime conditions such as intensive care unit capacity. The patient safety concern is that patients evaluated for admission to the intensive care unit during times of high intensive care unit capacity may have adverse outcomes related to delays in care. Because standardization of a national triage policy is not feasible due to differing resources available at each hospital, local guidelines should prevail that take into account hospitals' local resources. The goal would be to better match intensive care unit bed supply with demand.

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Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic

et al. 2021

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IMPORTANCE Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 critical care capacity and mortality. OBJECTIVE To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain. DESIGN, SETTING, AND PARTICIPANTS This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020. EXPOSURES Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient's hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient's stay divided by the maximum number of patients with COVID-19 in the ICU. MAIN OUTCOMES AND MEASURES All-cause mortality was recorded through 30 days after discharge from the hospital. RESULTS Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361(12.8%) patients dying in August (P < .001). Patients with COVID-19 who were treated in the ICU during periods of increased COVID-19 ICU demand had increased risk of mortality compared with patients treated during periods of low COVID-19 ICU demand (ie, demand of Յ25%); the adjusted hazard ratio for all-cause mortality was 0.99 (95% CI, 0.81-1.22; P = .93) for patients treated when COVID-19 ICU demand was more than 25% to 50%, 1.19 (95% CI, 0.95-1.48; P = .13) when COVID-19 ICU demand was more than 50% to 75%, and 1.94 (95% CI, 1.46-2.59; P < .001) when COVID-19 ICU demand was more than 75% to 100%. No association between COVID-19 ICU demand and mortality was observed for patients with COVID-19 not in the ICU. The association between COVID-19 ICU load and mortality was not consistent over time (ie, early vs late in the pandemic). CONCLUSIONS AND RELEVANCEThis cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased (continued) Key Points Question Is greater coronavirus disease 2019 (COVID-19) intensive care unit (ICU) strain associated with increased COVID-19 mortality? Findings In this cohort study of 8516 patients with COVID-19 admitted to 88 US Veterans Affairs hospitals, strains on critical care capacity were assoc...

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Associations of Intensive Care Unit Capacity Strain with Disposition and Outcomes of Patients with Sepsis Presenting to the Emergency Department

Cited by 55 publications

References 20 publications

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Goldilocks, the Three Bears and Intensive Care Unit Utilization: Delivering Enough Intensive Care But Not Too Much. A Narrative Review

Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic

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