ArticleLiterature Review

Impact of ICU strain on outcomes

October 2022
Current Opinion in Critical Care Publish Ahead of Print(6)

DOI:10.1097/MCC.0000000000000993

Authors:

Abhijit Duggal

Cleveland Clinic

Kusum Mathews

Icahn School of Medicine at Mount Sinai

Purpose of review: Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes. Recent findings: This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions. Summary: An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.

Measuring the Impact of ICU Strain on Mortality, After-Hours Discharge, Discharge Delay, Interhospital Transfer, and Readmission in Australia With the Activity Index

Article

Full-text available

Jul 2023

Objectives: ICU resource strain leads to adverse patient outcomes. Simple, well-validated measures of ICU strain are lacking. Our objective was to assess whether the "Activity index," an indicator developed during the COVID-19 pandemic, was a valid measure of ICU strain. Design: Retrospective national registry-based cohort study. Setting: One hundred seventy-five public and private hospitals in Australia (June 2020 through March 2022). Subjects: Two hundred seventy-seven thousand seven hundred thirty-seven adult ICU patients. Interventions: None. Measurements and main results: Data from the Australian and New Zealand Intensive Care Society Adult Patient Database were matched to the Critical Health Resources Information System. The mean daily Activity index of each ICU (census total of "patients with 1:1 nursing" + "invasive ventilation" + "renal replacement" + "extracorporeal membrane oxygenation" + "active COVID-19," divided by total staffed ICU beds) during the patient's stay in the ICU was calculated. Patients were categorized as being in the ICU during very quiet (Activity index < 0.1), quiet (0.1 to < 0.6), intermediate (0.6 to < 1.1), busy (1.1 to < 1.6), or very busy time-periods (≥ 1.6). The primary outcome was in-hospital mortality. Secondary outcomes included after-hours discharge from the ICU, readmission to the ICU, interhospital transfer to another ICU, and delay in discharge from the ICU. Median Activity index was 0.87 (interquartile range, 0.40-1.24). Nineteen thousand one hundred seventy-seven patients died (6.9%). In-hospital mortality ranged from 2.4% during very quiet to 10.9% during very busy time-periods. After adjusting for confounders, being in an ICU during time-periods with higher Activity indices, was associated with an increased risk of in-hospital mortality (odds ratio [OR], 1.49; 99% CI, 1.38-1.60), after-hours discharge (OR, 1.27; 99% CI, 1.21-1.34), readmission (OR, 1.18; 99% CI, 1.09-1.28), interhospital transfer (OR, 1.92; 99% CI, 1.72-2.15), and less delay in ICU discharge (OR, 0.58; 99% CI, 0.55-0.62): findings consistent with ICU strain. Conclusions: The Activity index is a simple and valid measure that identifies ICUs in which increasing strain leads to progressively worse patient outcomes.

The Ongoing Pursuit to Reduce the Burden of IV Vasopressor Infusions

Article

Full-text available

Dec 2024

Roughly one in four patients admitted to the ICU require vasopressor infusions (1). Vasopressors are critical for restoring arterial smooth muscle tone and reestablishing tissue perfusion during shock (2). Yet, they often necessitate central venous catheters for administration—which come with their associated complications—invasive arterial catheters for hemodynamic monitoring and titration, and precious critical care resources. As such, there has been a growing interest in the use of oral vasoactive agents to minimize or offset the burden of IV vasopressors in persons with shock (3). Midodrine is one such agent, an oral pro-drug that is rapidly converted to a deglycinated metabolite that serves as a selective agonist of peripheral alpha receptors. In patients with orthostasis, midodrine produces a dose-dependent increase in systolic blood pressure (4). In this issue of Critical Care Medicine, Kilcommons et al (5) report on a systematic review wherein they analyzed seven randomized trials, including 439 patients with vasopressor-dependent shock, assessing the efficacy of oral midodrine. In random-effects meta-analysis, midodrine was associated with reduced IV vasopressor duration and hospital mortality but similar ICU and hospital lengths of stay compared with placebo or standard of care. The optimal information size was not achieved in the trial sequential analysis performed on vasopressor duration and ICU length of stay, concluding that additional trials are necessary to confirm the findings. Indeed, the certainty in evidence was judged to be low or very low for all the outcomes assessed, indicating that additional research could likely alter the findings. Importantly, adverse event reporting in the included trials was scant. The authors should be applauded for a methodologically sound synthesis of the currently available evidence. However, the findings of Kilcommons et al (5) leave clinicians and researchers with several unanswered questions, including: 1) what are the implications of including midodrine in the treatment of shock and 2) is it possible to identify which patients with shock may benefit most from an oral vasoactive agent like midodrine?

Cuidados de alto valor para pacientes onco-hematológicos graves: o que sabemos até agora?

Article

Jan 2023

High-value care for critically ill oncohematological patients: what do we know thus far?

Article

Full-text available

Jun 2023

The number of patients with cancer requiring intensive care unit admission is increasing around the world. The improvement in the pathophysiological understanding of this group of patients, as well as the increasingly better and more targeted treatment options for their underlying disease, has led to a significant increase in their survival over the past three decades. Within the organizational concepts, it is necessary to know what adds value in the care of critical oncohematological patients. Practices in medicine that do not benefit patients and possibly cause harm are called low-value practices, while high-value practices are defined as high-quality care at relatively low cost. In this article, we discuss ten domains with high-value evidence in the care of cancer patients: (1) intensive care unit admission policies; (2) intensive care unit organization; (3) etiological investigation of hypoxemia; (4) management of acute respiratory failure; (5) management of febrile neutropenia; (6) urgent chemotherapy treatment in critically ill patients; (7) patient and family experience; (8) palliative care; (9) care of intensive care unit staff; and (10) long-term impact of critical disease on the cancer population. The disclosure of such policies is expected to have the potential to change health care standards. We understand that it is a lengthy process, and initiatives such as this paper are one of the first steps in raising awareness and beginning a discussion about high-value care in various health scenarios. Keywords: Neoplasms; Low-value care; Cost of illness; Hospital costs; Critical illness; Patient care management; Intensive care units

Intensive Care Strain Indicators: Recommendations for Critical Care Processes and Research Objectives

Article

Full-text available

Mar 2023

Intensive care units (ICU’s) are particularly susceptible to resource and personnel strain given the complexity and unpredictability of care. This featured prominently in the early course of the SARS-CoV-2 (COVID-19) pandemic, where poor patient outcomes were clearly linked to the increasing severity of ICU strain associated with decreased ICU capacity. Despite attempts at measuring ICU strain, there exists no operational model that ICU directors can implement to monitor strain or researchers can use to examine its effects. This article reviews ICU strain indicators including census load (census, acuity, and admissions), ICU flow characteristics (admission/discharge criteria, sufficient staffing levels, and ICU performance), and consequence mediators (ICU queuing time and high-risk discharges) with attention to common themes and measures. Census load data suggests mortality risk is greater when ICU census starts higher, has high overall acuity, and with greater numbers of admissions especially when they arrive close together. Optimal ICU flow depends on maintaining a “strain mindset” when prioritizing patients, optimal ICU professional staffing, and maintaining high level ICU performance processes. Finally, delaying ICU admissions beyond six hours, or “after hours” or rushed ICU discharges result in increased mortality risk. Incorporating these ICU strain factors into an outcomes-focused model is proposed based on a conceptual framework with future research objectives recommended.

Rearranging the furniture: A blueprint for reappropriating fixed resources to create an emergency department resuscitative care unit

Article

Full-text available

Jun 2024

Emergency department (ED) care teams face challenges in providing timely, high‐quality care to critically ill patients because of competing patient care priorities and a multitude of system strains, including patient boarding. Patients who are boarding in the ED experience increased morbidity and mortality, and this is particularly true for those who are critically ill. Geography‐based models for critical care delivery in the ED range from resuscitation bays to full‐fledged ED intensive care units. Studies have shown that such models can improve patient survival without affecting cost. Here, we describe how we reappropriated limited fixed resources to create a critical care resuscitation unit in a busy, urban, academic ED. Our objective is to provide a blueprint for similar models, paying particular attention to operations, clinical care, education, and financial stability.

COVID-19 Admission Rates and Changes in Care Quality in US Hospitals

Article

May 2024

Importance Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19. Objective To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions. Design, Setting, and Participants This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project State Inpatient Databases. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024. Exposure Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater. Main Outcomes and Measures The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds). Results The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%). Conclusions and Relevance In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

In-Hospital Triage

Article

Apr 2024
CRIT CARE CLIN

Resource allocation

Article

Apr 2023
Curr Opin Anaesthesiol

Purpose of review: The coronavirus disease 2019 pandemic and recent global recessions have brought to the forefront of the medical-political discussion the fact that medical resources are finite and have focused a spotlight on fair allocation and prioritization of healthcare resources describe why this review is timely and relevant. Recent findings: This review presents past and present concepts related to the ethics of resource allocation. Included are discussions regarding the topics of who should determine resource allocation, what types of research require allocation, methods currently in use to determine what resources are appropriate and which should be prioritized.describe the main themes in the literature covered by the article. Summary: Models for resource allocation must differentiate between different types of resources, some of which may require early preparation or distribution. Local availability of specific resources, supplies and infrastructure must be taken into consideration during preparation. When planning for long durations of limited resource availability, the limitations of human resilience must also be considered. Preparation also requires information regarding the needs of the specific population at hand (e.g. age distributions, disease prevalence) and societal preferences must be acknowledged within possible limits.

Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK

Article

Full-text available

Jul 2021

Background Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. Methods In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020). We included all patients aged 18 years and older with clinical signs and symptoms of COVID-19 or confirmed COVID-19 (by RT-PCR test) from assumed community-acquired infection. We did a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and in-hospital mortality, adjusting for confounders (demographics, comorbidities, and severity of illness) and quantifying potential mediators (level of respiratory support and steroid treatment). The primary outcome was weekly in-hospital mortality at 28 days, defined as the proportion of patients who had died within 28 days of admission of all patients admitted in the observed week, and it was assessed in all patients with an outcome. This study is registered with the ISRCTN Registry, ISRCTN66726260. Findings Between March 9, and Aug 2, 2020, we recruited 80 713 patients, of whom 63 972 were eligible and included in the study. Unadjusted weekly in-hospital mortality declined from 32·3% (95% CI 31·8–32·7) in March 9 to April 26, 2020, to 16·4% (15·0–17·8) in June 15 to Aug 2, 2020. Reductions in mortality were observed in all age groups, in all ethnic groups, for both sexes, and in patients with and without comorbidities. After adjustment, there was a 32% reduction in the risk of mortality per 7-week period (odds ratio [OR] 0·68 [95% CI 0·65–0·71]). The higher proportions of patients with severe disease and comorbidities earlier in the first wave (March and April) than in June and July accounted for 10·2% of this reduction. The use of respiratory support changed during the first wave, with gradually increased use of non-invasive ventilation over the first wave. Changes in respiratory support and use of steroids accounted for 22·2%, OR 0·95 (0·94–0·95) of the reduction in in-hospital mortality. Interpretation The reduction in in-hospital mortality in patients with COVID-19 during the first wave in the UK was partly accounted for by changes in the case-mix and illness severity. A significant reduction in in-hospital mortality was associated with differences in respiratory support and critical care use, which could partly reflect accrual of clinical knowledge. The remaining improvement in in-hospital mortality is not explained by these factors, and could be associated with changes in community behaviour, inoculum dose, and hospital capacity strain. Funding National Institute for Health Research and the Medical Research Council.

Interhospital Transfer Outcomes for Critically Ill Patients With Coronavirus Disease 2019 Requiring Mechanical Ventilation

Article

Full-text available

Oct 2021

IMPORTANCE:. Studying interhospital transfer of critically ill patients with coronavirus disease 2019 pneumonia in the spring 2020 surge may help inform future pandemic management. OBJECTIVES:. To compare outcomes for mechanically ventilated patients with coronavirus disease 2019 transferred to a tertiary referral center with increased surge capacity with patients admitted from the emergency department. DESIGN, SETTING, PARTICIPANTS:. Observational cohort study of single center urban academic medical center ICUs. All patients admitted and discharged with coronavirus disease 2019 pneumonia who received invasive ventilation between March 17, 2020, and October 14, 2020. MAIN OUTCOME AND MEASURES:. Demographic and clinical variables were obtained from the electronic medical record. Patients were classified as emergency department admits or interhospital transfers. Regression models tested the association between transfer status and survival, adjusting for demographics and presentation severity. RESULTS:. In total, 298 patients with coronavirus disease 2019 pneumonia were admitted to the ICU and received mechanical ventilation. Of these, 117 were transferred from another facility and 181 were admitted through the emergency department. Patients were primarily male (64%) and Black (38%) or Hispanic (45%). Transfer patients differed from emergency department admits in having English as a preferred language (71% vs 56%; p = 0.008) and younger age (median 57 vs 61 yr; p < 0.001). There were no differences in race/ethnicity or primary payor. Transfers were more likely to receive extracorporeal membrane oxygenation (12% vs 3%; p = 0.004). Overall, 50 (43%) transferred patients and 78 (43%) emergency department admits died prior to discharge. There was no significant difference in hospital mortality or days from intubation to discharge between the two groups. CONCLUSIONS AND RELEVANCE:. In a single-center retrospective cohort, no significant differences in hospital mortality or length of stay between interhospital transfers and emergency department admits were found. While more study is needed, this suggests that interhospital transfer of critically ill patients with coronavirus disease 2019 can be done safely and effectively.

Optimal triage for COVID-19 patients under limited healthcare resources: Development of a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation (Preprint)

Article

Full-text available

Aug 2021

Background: The coronavirus disease 2019 (COVID-19) pandemic has placed an unprecedented burden on healthcare systems. Objective: To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining healthcare system capacity. Methods: A nationwide sample of 5,601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate maintenance of the model performance when clinical and laboratory variables were eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find an optimal threshold within limited resource environments that minimizes mortality rates. Results: The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model's performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1%, compared to the conventional Youden Index. Conclusions: Our adaptive triage model and its threshold optimization capability revealed that COVID-19 management can be achieved via the cooperation of both medical and healthcare management sectors for maximum treatment efficacy. The model is available online for clinical implementation. Clinicaltrial:

Hospital preparedness during epidemics using simulation: the case of COVID-19

Article

Full-text available

Mar 2022
CENT EUR J OPER RES

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study

Article

Full-text available

Aug 2021
BMC MED

Abstract Background The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. Methods A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). Results One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (

Trends in 28-Day Mortality of Critical Care Patients With Coronavirus Disease 2019 in United Kingdom: A National Cohort Study, March 2020 to January 2021

Article

Full-text available

Jul 2021
CRIT CARE MED

Objectives: To determine whether the previously described trend of improving mortality in people with coronavirus disease 2019 in critical care during the first wave was maintained, plateaued, or reversed during the second wave in United Kingdom, when B117 became the dominant strain. Design: National retrospective cohort study. Setting: All English hospital trusts (i.e., groups of hospitals functioning as single operational units), reporting critical care admissions (high dependency unit and ICU) to the Coronavirus Disease 2019 Hospitalization in England Surveillance System. Patients: A total of 49,862 (34,336 high dependency unit and 15,526 ICU) patients admitted between March 1, 2020, and January 31, 2021 (inclusive). Interventions: Not applicable. Measurements and main results: The primary outcome was inhospital 28-day mortality by calendar month of admission, from March 2020 to January 2021. Unadjusted mortality was estimated, and Cox proportional hazard models were used to estimate adjusted mortality, controlling for age, sex, ethnicity, major comorbidities, social deprivation, geographic location, and operational strain (using bed occupancy as a proxy). Mortality fell to trough levels in June 2020 (ICU: 22.5% [95% CI, 18.2-27.4], high dependency unit: 8.0% [95% CI, 6.4-9.6]) but then subsequently increased up to January 2021: (ICU: 30.6% [95% CI, 29.0-32.2] and high dependency unit, 16.2% [95% CI, 15.3-17.1]). Comparing patients admitted during June-September 2020 with those admitted during December 2020-January 2021, the adjusted mortality was 59% (CI range, 39-82) higher in high dependency unit and 88% (CI range, 62-118) higher in ICU for the later period. This increased mortality was seen in all subgroups including those under 65. Conclusions: There was a marked deterioration in outcomes for patients admitted to critical care at the peak of the second wave of coronavirus disease 2019 in United Kingdom (December 2020-January 2021), compared with the post-first-wave period (June 2020-September 2020). The deterioration was independent of recorded patient characteristics and occupancy levels. Further research is required to determine to what extent this deterioration reflects the impact of the B117 variant of concern.

Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK

Article

Full-text available

May 2021

Hospital-Level Variation in Death for Critically Ill Patients with COVID-19

Article

Full-text available

Apr 2021
AM J RESP CRIT CARE

Rationale: Variation in hospital mortality has been described for coronavirus disease (COVID-19), but the factors that explain these differences remain unclear. Objective: Our objective was to use a large, nationally representative data set of critically ill adults with COVID-19 to determine which factors explain mortality variability. Methods: In this multicenter cohort study, we examined adults hospitalized in ICUs with COVID-19 at 70 U.S. hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effect logistic regression was used to identify factors associated with interhospital variation. The median odds ratio was calculated to compare outcomes in higher- versus lower-mortality hospitals. A gradient-boosted machine algorithm was developed for individual-level mortality models. Measurements and Main Results: A total of 4,019 patients were included, 1,537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0–82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (odds ratio decline from 2.06 [95% confidence interval (CI), 1.73–2.37] to 1.22 [95% CI, 1.00–1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was as follows: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%). Conclusions: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors.

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

Article

Full-text available

Jan 2021

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 (COVID-19) 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 ≤25%); 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 Relevance This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.

Intensive Care Unit Strain and Mortality Risk Among Critically Ill Patients With COVID-19—There Is No “Me” in COVID

Article

Full-text available

Jan 2021

Lewis Rubinson

The Effects of ICU Crisis Reorganization on Outcomes in Patients Not Infected With Coronavirus Disease 2019 During the Initial Surge of the Coronavirus Disease 2019 Pandemic

Article

Full-text available

Jan 2021

To determine if ICU reorganization due to the coronavirus disease 2019 pandemic affected outcomes in critically ill patients who were not infected with coronavirus disease 2019. Design: This was a Before-After study, with coronavirus disease 2019-induced ICU reorganization as the intervention. A retrospective chart review of adult patients admitted to a reorganized ICU during the coronavirus disease 2019 surge (from March 23, 2020, to May 06, 2020: intervention group) was compared with patients admitted to the ICU prior to coronavirus disease 2019 surge (from January 10, 2020, to February 23, 2020: before group). Setting: High-intensity cardiac, medical, and surgical ICUs of a community hospital in metropolitan Missouri. Patients: All patients admitted to the ICU during the before and intervention period were included. Patients younger than 18 years old and those admitted after an elective procedure or surgery were excluded. Patients with coronavirus disease 2019 were excluded. Interventions: None. Measurements and main results: We identified a total of 524 eligible patients: 342 patients in the before group and 182 in the intervention group. The 28-day mortality was 25.1% (86/342) and 28.6% (52/182), respectively (p = 0.40). The ICU length of stay, ventilator length of stay, and ventilator-free days were similar in both groups. Rates of patient adverse events including falls, inadvertent endotracheal tube removal, reintubation within 48 hours of extubation, and hospital acquired pressure ulcers occurred more frequently in the study group (20 events, 11%) versus control group (12 events, 3.5%) (p = 0.001). Conclusions: Twenty-eight-day mortality, in patients who required ICU care and were not infected with coronavirus disease 2019, was not significantly affected by ICU reorganization during a pandemic.

Rapid deployment of an emergency department-intensive care unit for the COVID-19 pandemic

Article

Full-text available

Dec 2020

The coronavirus disease 2019 (COVID-19) pandemic mandated rapid, flexible solutions to meet the anticipated surge in both patient acuity and volume. This paper describes one institution's emergency department (ED) innovation at the center of the COVID-19 crisis, including the creation of a temporary ED-intensive care unit (ICU) and development of interdisciplinary COVID-19-specific care delivery models to care for critically ill patients. Mount Sinai Hospital, an urban quaternary academic medical center, had an existing five-bed resuscitation area insufficiently rescue due to its size and lack of negative pressure rooms. Within 1 week, the ED-based observation unit, which has four negative pressure rooms, was quickly converted into a COVID-19-specific unit, split between a 14-bed stepdown unit and a 13-bed ED-ICU unit. An increase in staffing for physicians, physician assistants, nurses, respiratory therapists, and medical technicians, as well as training in critical care protocols and procedures, was needed to ensure appropriate patient care. The transition of the ED to a COVID-19-specific unit with the inclusion of a temporary expanded ED-ICU at the beginning of the COVID-19 pandemic was a proactive solution to the growing challenges of surging patients, complexity, and extended boarding of critically ill patients in the ED. This pandemic underscores the importance of ED design innovation with flexible spacing, interdisciplinary collaborations on structure and services, and NP ventilation systems which will remain important moving forward.

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

Article

Full-text available

Dec 2020

In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this study, the role of the health system in the fight against Covid-19 is discussed. In this context, the number of hospital beds, the number of doctors, life expectancy at 60, universal health service and the share of health expenditures in GDP were used as health indicators. In the study, firstly 2020 data was estimated by using the Artificial Neural Networks simulation method and this year was used in the analysis. The model, with the data of 124 countries, was estimated using the cross-sectional OLS regression method. The estimation results show that the number of hospital beds, number of doctors and life expectancy at the age of 60 have statistically significant and positive effects on the ratio of Covid-19 recovered/cases. Universal health service and share of health expenditures in GDP are not significant statistically on the cases and recovered. Hospital bed capacity is the most effective variable on the recovered/case ratio.

From Intensive Care to Step-Down Units: Managing Patients Throughput in Response to COVID-19

Article

Full-text available

Aug 2020
INT J QUAL HEALTH C

Quality problem: The ongoing COVID-19 pandemic may cause the collapse of healthcare systems because of unprecedented hospitalisation rates. Initial assessment: 8.2 individuals per 1,000 inhabitants have been diagnosed with COVID-19 in our Province. The hospital predisposed 110 beds for COVID-19 patients: on the day of the local peak, 90% of them were occupied and intensive care unit (ICU) faced unprecedented admission rates, fearing system collapse. Choice of solution: Instead of increasing the number of ICU beds, the creation of a step-down unit (SDU) close to the ICU was preferred: the aim was to safely improve the transfer of patients and to relieve ICU from the risk of overload. Implementation: A 9-bed SDU was created next to the ICU, led by intensivists and ICU nurses, with adequate personal protective equipment, monitoring systems and ventilators for respiratory support when needed. A second 6-bed SDU was also created. Evaluation: Patients were clinically comparable to those of most reports from Western Countries now available in the literature. ICU never needed supernumerary beds, no patient died in the SDU, there was no waiting time for ICU admission of critical patients. SDU has been affordable from human resources, safety, and economic points of view. Lessons learned: COVID-19 is like an enduring Mass-Casualty Incident. Solutions tailored on local epidemiology and available resources should be implemented to preserve efficiency and adaptability of our institutions and provide adequate sanitary response.

Trends in Emergency Department Visits and Hospital Admissions in Health Care Systems in 5 States in the First Months of the COVID-19 Pandemic in the US

Article

Full-text available

Aug 2020

Importance As coronavirus disease 2019 (COVID-19) spread throughout the US in the early months of 2020, acute care delivery changed to accommodate an influx of patients with a highly contagious infection about which little was known. Objective To examine trends in emergency department (ED) visits and visits that led to hospitalizations covering a 4-month period leading up to and during the COVID-19 outbreak in the US. Design, Setting, and Participants This retrospective, observational, cross-sectional study of 24 EDs in 5 large health care systems in Colorado (n = 4), Connecticut (n = 5), Massachusetts (n = 5), New York (n = 5), and North Carolina (n = 5) examined daily ED visit and hospital admission rates from January 1 to April 30, 2020, in relation to national and the 5 states’ COVID-19 case counts. Exposures Time (day) as a continuous variable. Main Outcomes and Measures Daily counts of ED visits, hospital admissions, and COVID-19 cases. Results A total of 24 EDs were studied. The annual ED volume before the COVID-19 pandemic ranged from 13 000 to 115 000 visits per year; the decrease in ED visits ranged from 41.5% in Colorado to 63.5% in New York. The weeks with the most rapid rates of decrease in visits were in March 2020, which corresponded with national public health messaging about COVID-19. Hospital admission rates from the ED were stable until new COVID-19 case rates began to increase locally; the largest relative increase in admission rates was 149.0% in New York, followed by 51.7% in Massachusetts, 36.2% in Connecticut, 29.4% in Colorado, and 22.0% in North Carolina. Conclusions and Relevance From January through April 2020, as the COVID-19 pandemic intensified in the US, temporal associations were observed with a decrease in ED visits and an increase in hospital admission rates in 5 health care systems in 5 states. These findings suggest that practitioners and public health officials should emphasize the importance of visiting the ED during the COVID-19 pandemic for serious symptoms, illnesses, and injuries that cannot be managed in other settings.

Reorganising the emergency department to manage the COVID-19 outbreak

Article

Full-text available

Jun 2020

Background: The COVID-19 disease outbreak that first surfaced in Wuhan, China, in December 2019, has taken the world by storm and ravaged almost every country in the world. Emergency departments (ED) in hospitals are on the frontlines, serving an essential function in identifying these patients, isolating them early whilst providing urgent medical care. This outbreak has reinforced the role of Emergency Medicine in public health. This paper documents the challenges faced and measures taken by a tertiary hospital's ED in Singapore, in response to the outbreak. Main body: The ED detected the first case of COVID-19 in Singapore on 22 January 2020 in a Chinese tourist and also the first case of locally transmitted COVID-19 on 3 February 2020. The patient journeys through the patient reception area in the ED and undergoes fever screening before being shunted to isolation areas within the ED. Management and disposition of suspect COVID-19 patients are guided by a close-knit collaboration between ED and department of infectious diseases. With increasing number of patients, back-up plans for expansion of space and staff augmentation have been enacted. Staff safety is also of utmost importance, with provision and guidelines for personal protective equipment and team segregation to ensure no cross-contamination across staff. These have been made possible with an early setup of an operational command and control structure within the ED, managing manpower, logistics, operations, communication and information management and liaison with other clinical departments. Conclusion: With the large numbers of undifferentiated patients managed by the ED to date, more than 820 patients with COVID-19 have been identified in the hospital. Not a single member of the staff of the SGH Emergency Department has come down with the illness. The various measures undertaken by the department have helped to ensure good staff morale and strict adherence to safety procedures. We share the lessons learnt so that others who manage EDs around the world can benefit from our experience.

Estimating the Maximum Capacity of COVID-19 Cases Manageable per Day Given a Health Care System's Constrained Resources

Article

Full-text available

Apr 2020

A Conceptual and Adaptable Approach to Hospital Preparedness for Acute Surge Events Due to Emerging Infectious Diseases

Article

Full-text available

Apr 2020

At the time this article was written, the World Health Organization had declared a global pandemic due to the novel coronavirus disease 2019, the first pandemic since 2009 H1N1 influenza A. Emerging respiratory pathogens are a common trigger of acute surge events-the extreme end of the healthcare capacity strain spectrum in which there is a dramatic increase in care demands and/or decreases in care resources that trigger deviations from normal care delivery processes, reliance on contingencies and external resources, and, in the most extreme cases, nonroutine decisions about resource allocation. This article provides as follows: 1) a conceptual introduction and approach to healthcare capacity strain including the etiologies of patient volume, patient acuity, special patient care demands, and resource reduction; 2) a framework for considering key resources during an acute surge event-the "four Ss" of preparedness: space (beds), staff (clinicians and operations), stuff (physical equipment), and system (coordination); and 3) an adaptable approach to and discussion of the most common domains that should be addressed during preparation for and response to acute surge events, with an eye toward combating novel respiratory viral pathogens.

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

Article

Full-text available

Apr 2020

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-19-induced 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. Primary funding source: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

Indicators of intensive care unit capacity strain: A systematic review

Article

Full-text available

Mar 2018

Background: 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. Methods: 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. Results: 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. Conclusions: 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. Systematic review registration: This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).

Global Variability in Reported Mortality for Critical Illness during the 2009-10 Influenza A(H1N1) Pandemic: A Systematic Review and Meta-Regression to Guide Reporting of Outcomes during Disease Outbreaks

Article

Full-text available

May 2016
PLOS ONE

Purpose: To determine how patient, healthcare system and study-specific factors influence reported mortality associated with critical illness during the 2009-2010 Influenza A (H1N1) pandemic. Methods: Systematic review with meta-regression of studies reporting on mortality associated with critical illness during the 2009-2010 Influenza A (H1N1) pandemic. Data sources: Medline, Embase, LiLACs and African Index Medicus to June 2009-March 2016. Results: 226 studies from 50 countries met our inclusion criteria. Mortality associated with H1N1-related critical illness was 31% (95% CI 28-34). Reported mortality was highest in South Asia (61% [95% CI 50-71]) and Sub-Saharan Africa (53% [95% CI 29-75]), in comparison to Western Europe (25% [95% CI 22-30]), North America (25% [95% CI 22-27]) and Australia (15% [95% CI 13-18]) (P<0.0001). High income economies had significantly lower reported mortality compared to upper middle income economies and lower middle income economies respectively (P<0.0001). Mortality for the first wave was non-significantly higher than wave two (P = 0.66). There was substantial variability in reported mortality among the specific subgroups of patients: unselected critically ill adults (27% [95% CI 24-30]), acute respiratory distress syndrome (37% [95% CI 32-44]), acute kidney injury (44% [95% CI 26-64]), and critically ill pregnant patients (10% [95% CI 5-19]). Conclusion: Reported mortality for outbreaks and pandemics may vary substantially depending upon selected patient characteristics, the number of patients described, and the region and economic status of the outbreak location. Outcomes from a relatively small number of patients from specific regions may lead to biased estimates of outcomes on a global scale.

Association Between Availability of ECMO and Mortality in COVID-19 Patients Eligible for ECMO: A Natural Experiment

Conference Paper

May 2022

Association Between Availability of ECMO and Mortality in COVID-19 Patients Eligible for ECMO: A Natural Experiment

Article

Feb 2022

Does Unprecedented ICU Capacity Strain, As Experienced During the COVID-19 Pandemic, Impact Patient Outcome?

Article

Feb 2022
CRIT CARE MED

Objectives: To determine whether patients admitted to an ICU during times of unprecedented ICU capacity strain, during the COVID-19 pandemic in the United Kingdom, experienced a higher risk of death. Design: Multicenter, observational cohort study using routine clinical audit data. Setting: Adult general ICUs participating the Intensive Care National Audit & Research Centre Case Mix Programme in England, Wales, and Northern Ireland. Patients: One-hundred thirty-thousand six-hundred eighty-nine patients admitted to 210 adult general ICUs in 207 hospitals. Interventions: Multilevel, mixed effects, logistic regression models were used to examine the relationship between levels of ICU capacity strain on the day of admission (typical low, typical, typical high, pandemic high, and pandemic extreme) and risk-adjusted hospital mortality. Measurements and main results: In adjusted analyses, compared with patients admitted during periods of typical ICU capacity strain, we found that COVID-19 patients admitted during periods of pandemic high or pandemic extreme ICU capacity strain during the first wave had no difference in hospital mortality, whereas those admitted during the pandemic high or pandemic extreme ICU capacity strain in the second wave had a 17% (odds ratio [OR], 1.17; 95% CI, 1.05-1.30) and 15% (OR, 1.15; 95% CI, 1.00-1.31) higher odds of hospital mortality, respectively. For non-COVID-19 patients, there was little difference in trend between waves, with those admitted during periods of pandemic high and pandemic extreme ICU capacity strain having 16% (OR, 1.16; 95% CI, 1.08-1.25) and 30% (OR, 1.30; 95% CI, 1.14-1.48) higher overall odds of acute hospital mortality, respectively. Conclusions: For patients admitted to ICU during the pandemic, unprecedented levels of ICU capacity strain were significantly associated with higher acute hospital mortality, after accounting for differences in baseline characteristics. Further study into possible differences in the provision of care and outcome for COVID-19 and non-COVID-19 patients is needed.

Association of community-level social vulnerability with US acute care hospital intensive care unit capacity during COVID-19

Article

Dec 2021

The COVID-19 pandemic has placed unprecedented stress on US acute care hospitals, leading to overburdened ICUs. It remains unknown if increased COVID-19 ICU occupancy is crowding out non-COVID-related care and whether hospitals in vulnerable communities may be more susceptible to ICUs reaching capacity. Using facility-level hospitalization data, we conducted a retrospective observational cohort study of 1753 US acute care hospitals reporting to the US Department of Health and Human Services Protect database from September 4, 2020 to February 25, 2021.63% of hospitals reached critical ICU capacity for at least two weeks during the study period, and the surge of COVID-19 cases appeared to be crowding out non-COVID-19-related intensive care needs. Hospitals in the South (OR = 3.31, 95% CI OR 2.31–4.78) and West (OR = 2.28, 95% CI OR 1.51–3.46) were more likely to reach critical capacity than those in the Northeast, and hospitals in areas with the highest social vulnerability were more than twice as likely to reach capacity as those in the least vulnerable areas (OR = 2.15, 95% CI OR 1.41–3.29). The association between social vulnerability and critical ICU capacity highlights underlying structural inequities in health care access and provides an opportunity for policymakers to take action to prevent strained ICU capacity from compounding COVID-19 inequities.

Resuming Elective Surgery After COVID-19: A Simulation Modelling Framework for Guiding the Phased Opening of Operating Rooms

Article

Dec 2021
INT J MED INFORM

Objective To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes. Materials and Methods Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs. Results The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the best phased resumption strategy. Differences in the performance measures were evaluated based on 95% confidence intervals. The results indicate that two of the gradual phased resumption strategies provided lower peak OR and bed utilizations but required a longer time to return to BAU levels. Minimum peak bed demands could also be reduced by approximately 14 beds daily with the gradual resumption strategy, whilst the maximum peak bed demands by approximately 8.2 beds. Peak OR utilization could be reduced to 92% for gradual resumption as compared to a minimum peak of 94.2% with the full resumption strategy. Conclusions The 2-stage modelling framework coupled with a user-friendly visualization interface were key enablers for understanding the elective surgery postponement patterns during a partial lockdown phase. The DES model enabled the identification and evaluation of optimal phased resumption policies across multiple important operational outcome measures. Lay Abstract During the height of the COVID-19 pandemic, most healthcare systems suspended their non-urgent elective surgery services. This strategy was undertaken as a means to expand surge capacity, through the preservation of structural resources (such as operating theaters, ICU beds, and ventilators), consumables (such as personal protective equipment and medications), and critical healthcare manpower. As a result, some patients had less-essential surgeries postponed due to the pandemic. As the first wave of the pandemic waned, there was an urgent need to quickly develop optimal strategies for the resumption of these surgeries. We developed a 2-stage discrete events simulation (DES) framework based on 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.

Surge capacity and capability of intensive care units across a large healthcare system: An operational blueprint for regional integration

Article

Sep 2021

Objective: Many hospitals were unprepared for the surge of patients associated with the spread of coronavirus disease 2019 (COVID-19) pandemic. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. Setting: A large academic medical center in the Cleveland metropolitan area, with a network of 10 regional hospitals throughout Northeastern Ohio with a daily capacity of more than 500 intensive care unit (ICU) beds. Results: At the beginning of the pandemic, an equitable delivery of healthcare services across the healthcare system was developed. This distribution of resources was implemented with the potential needs and resources of the individual ICUs in mind, and epidemiologic predictions of virus transmissibility. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. We also describe an additional level of surge capacity, which is available to well-integrated institutions called "extension of capacity." This refers to the ability to immediately have access to the beds and resources within a hospital system with minimal administrative burden. Conclusions: Large integrated hospital systems may have an advantage over individual hospitals because they can shift supplies among regional partners, which may lead to faster mobilization of resources, rather than depending on local and national governments. The pandemic response of our healthcare system highlights these benefits.

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories

Article

Aug 2021
VALUE HEALTH

Objectives To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. Methods An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. Results To illustrate the use of the model, a case study was developed for Guy’s and St Thomas’ Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. Conclusions The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Association Between Caseload Surge and COVID-19 Survival in 558 U.S. Hospitals, March to August 2020

Article

Jul 2021

Background: Several U.S. hospitals had surges in COVID-19 caseload, but their effect on COVID-19 survival rates remains unclear, especially independent of temporal changes in survival. Objective: To determine the association between hospitals' severity-weighted COVID-19 caseload and COVID-19 mortality risk and identify effect modifiers of this relationship. Design: Retrospective cohort study. (ClinicalTrials.gov: NCT04688372). Setting: 558 U.S. hospitals in the Premier Healthcare Database. Participants: Adult COVID-19-coded inpatients admitted from March to August 2020 with discharge dispositions by October 2020. Measurements: Each hospital-month was stratified by percentile rank on a surge index (a severity-weighted measure of COVID-19 caseload relative to pre-COVID-19 bed capacity). The effect of surge index on risk-adjusted odds ratio (aOR) of in-hospital mortality or discharge to hospice was calculated using hierarchical modeling; interaction by surge attributes was assessed. Results: Of 144 116 inpatients with COVID-19 at 558 U.S. hospitals, 78 144 (54.2%) were admitted to hospitals in the top surge index decile. Overall, 25 344 (17.6%) died; crude COVID-19 mortality decreased over time across all surge index strata. However, compared with nonsurging (<50th surge index percentile) hospital-months, aORs in the 50th to 75th, 75th to 90th, 90th to 95th, 95th to 99th, and greater than 99th percentiles were 1.11 (95% CI, 1.01 to 1.23), 1.24 (CI, 1.12 to 1.38), 1.42 (CI, 1.27 to 1.60), 1.59 (CI, 1.41 to 1.80), and 2.00 (CI, 1.69 to 2.38), respectively. The surge index was associated with mortality across ward, intensive care unit, and intubated patients. The surge-mortality relationship was stronger in June to August than in March to May (slope difference, 0.10 [CI, 0.033 to 0.16]) despite greater corticosteroid use and more judicious intubation during later and higher-surging months. Nearly 1 in 4 COVID-19 deaths (5868 [CI, 3584 to 8171]; 23.2%) was potentially attributable to hospitals strained by surging caseload. Limitation: Residual confounding. Conclusion: Despite improvements in COVID-19 survival between March and August 2020, surges in hospital COVID-19 caseload remained detrimental to survival and potentially eroded benefits gained from emerging treatments. Bolstering preventive measures and supporting surging hospitals will save many lives. Primary funding source: Intramural Research Program of the National Institutes of Health Clinical Center, the National Institute of Allergy and Infectious Diseases, and the National Cancer Institute.

A Nation-wide Review of Elective Surgery and COVID-Surge Capacity

Article

Nov 2021
J SURG RES

Background The COVID-19 pandemic has resulted in over 225,000 excess deaths in the United States. A moratorium on elective surgery was placed early in the pandemic to reduce risk to patients and staff and preserve critical care resources. This report evaluates the impact of the elective surgical moratorium on case volumes and intensive care unit (ICU) bed utilization. Methods This retrospective review used a national convenience sample to correlate trends in the weekly rates of surgical cases at 170 Veterans Affairs Hospitals around the United States from January 1 to September 30, 2020 to national trends in the COVID-19 pandemic. We reviewed data on weekly number of procedures performed and ICU bed usage, stratified by level of urgency (elective, urgent, emergency), and whether an ICU bed was required within 24 hours of surgery. National data on the proportion of COVID-19 positive test results and mortality rates were obtained from the Center for Disease Control website. Results 198,911 unique surgical procedures performed during the study period. The total number of cases performed from January 1 to March 16 was 86,004 compared with 15,699 from March 17 to May 17. The reduction in volume occurred before an increase in the percentage of COVID-19 positive test results and deaths nationally. There was a 91% reduction from baseline in the number of elective surgeries performed allowing 78% of surgical ICU beds to be available for COVID-19 positive patients. Conclusion The moratorium on elective surgical cases was timely and effective in creating bed capacity for critically ill COVID-19 patients. Further analyses will allow targeted resource allocation for future pandemic planning.

Hospital capacity during the COVID-19 pandemic

Article

Apr 2021

The rapid global spread of the COVID-19 pandemic has posed a significant challenge to various countries in terms of the capacity of hospitals to admit and care for patients during the crisis. To estimate hospital capacity during the COVID-19 pandemic, clinicians working in tertiary hospitals around the world were surveyed regarding available COVID-19 hospital statistics. Data were obtained from 8 tertiary centers in 8 countries including the United States, United Kingdom, Switzerland, Turkey, Singapore, India, Pakistan, and Japan. The correlation between the number of patients with COVID-19 per 1 million population vs. the maximum number of inpatients with COVID-19 in a representative tertiary hospital in each country was determined, as was the correlation between COVID-19 deaths per 1 million population vs. the maximum number of patients with COVID-19 in the intensive care unit (ICU). What was noteworthy was that none of the 8 hospitals reduced emergency room (ER) activity even at the peak of the pandemic although treatment of patients without COVID-19 decreased by 0-70% depending on the extent of the epidemic. Although various measures are being actively implemented to slow the spread of the virus and reduce the strain on the health care system, the reality is that there are still a significant number of hospitals at risk of being overloaded in the event of a future surge in cases.

Analysis of Hospital Resource Availability and COVID-19 Mortality Across the United States

Article

Jan 2021
J HOSP MED

While the impact of coronavirus disease 2019 (COVID-19) has varied greatly across the United States, there has been little assessment of hospital resources and mortality. We examine hospital resources and death counts among hospital referral regions (HRRs) from March 1 to July 26, 2020. This was an analysis of American Hospital Association data with COVID-19 data from the New York Times. Hospital-based resource availabilities were characterized per COVID-19 case. Death count was defined by monthly confirmed COVID-19 deaths. Geographic areas with fewer intensive care unit (ICU) beds (incident rate ratio [IRR], 0.194; 95% CI, 0.076-0.491), nurses (IRR, 0.927; 95% CI, 0.888-0.967), and general medicine/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920) per COVID-19 case were statistically significantly associated with greater deaths in April. This underscores the potential impact of innovative hospital capacity protocols and care models to create resource flexibility to limit system overload early in a pandemic.

Blueprint for Restructuring a Department of Surgery in Concert With the Health Care System During a Pandemic: The University of Wisconsin Experience

Article

Apr 2020

The current health care environment is complex. Systems often cross US state boundaries to provide care to patients with a wide variety of medical needs. The coronavirus disease 2019 pandemic is challenging health care systems across the globe. Systems face varying levels of complexity as they adapt to the new reality. This pandemic continues to escalate in hot spots nationally and internationally, and the worst strain on health care systems may be yet to come. The purpose of this article is to provide a road map developed from lessons learned from the experience in the Department of Surgery at the University of Wisconsin School of Medicine and Public Health and University of Wisconsin Health, based on past experience with incident command structures in military combat operations and Federal Emergency Management Agency responses. We will discuss administrative restructuring leveraging a team-of-teams approach, provide a framework for deploying the workforce needed to deliver all necessary urgent health care and critical care to patients in the system, and consider implications for the future.

Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response

Article

Mar 2020

Higher ICU Capacity Strain Is Associated with Increased Acute Mortality in Closed ICUs

Article

Mar 2020
CRIT CARE MED

Objectives: To determine whether patients admitted to an ICU during times of strain, when compared with its own norm (i.e. accommodating a greater number of patients, higher acuity of illness, or frequent turnover), is associated with a higher risk of death in ICUs with closed models of intensivist staffing. Design: We conducted a large, multicenter, observational cohort study. Multilevel mixed effects logistic regression was used to examine relationships for three measures of ICU strain (bed census, severity-weighted bed census, and activity-weighted bed census) on the day of admission with risk-adjusted acute hospital mortality. Setting: Pooled case mix and outcome database of adult general ICUs participating in the Intensive Care National Audit and Research Centre Case Mix Programme. Measurements and main results: The analysis included 149,310 patients admitted to 215 adult general ICUs in 213 hospitals in United Kingdom, Wales, and Northern Ireland. A relative lower strain in ICU capacity as measured by bed census on the calendar day (daytime hours) of admission was associated with decreased risk-adjusted acute hospital mortality (odds ratio, 0.94; 95% CI, 0.90-0.99; p = 0.01), whereas a nonsignificant association was seen between higher strain and increased acute hospital mortality (odds ratio, 1.04; 95% CI, 1.00-1.10; p = 0.07). The relationship between periods of high ICU strain and acute hospital mortality was strongest when bed census was composed of higher acuity patients (odds ratio, 1.05; 95% CI, 1.01-1.10; p = 0.03). No relationship was seen between high strain and ICU mortality. Conclusions: In closed staffing models of care, variations in bed census within individual ICUs was associated with patient's predicted risk of acute hospital mortality, particularly when its standardized bed census consisted of sicker patients.

Intensive Care Unit Capacity Strain and Outcomes of Critical Illness in a Resource-Limited Setting: A 2-Hospital Study in South Africa

Article

Dec 2018

Objective:: To measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting. Methods:: We performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country's first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression. Results:: No measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; P = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality. Conclusions:: In a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.

Too Many Patients…A Framework to Guide Statewide Allocation of Scarce Mechanical Ventilation During Disasters

Article

Oct 2018

The threat of a catastrophic public health emergency causing life-threatening illness or injury on a massive scale has prompted extensive federal, state, and local preparedness efforts. Modeling studies suggest that an influenza pandemic similar to that of 1918 would require ICU and mechanical ventilation capacity that is significantly greater than what is available. Several groups have published recommendations for allocating life-support measures during a public health emergency. Because there are multiple ethically permissible approaches to allocating scarce life-sustaining resources and because the public will bear the consequences of these decisions, knowledge of public perspectives and moral points of reference on these issues is critical. Here we describe a critical care disaster resource allocation framework developed following a statewide community engagement process in Maryland. It is intended to assist hospitals and public health agencies in their independent and coordinated response to an officially declared catastrophic health emergency in which demand for mechanical ventilators exceeds the capabilities of all surge response efforts and in which there has been an executive order to implement scarce resource allocation procedures. The framework, built on a basic scoring system with modifications for specific considerations, also creates an opportunity for the legal community to review existing laws and liability protections in light of a specific disaster response process.

Association between strained capacity and mortality among patients admitted to intensive care: A path-analysis modeling strategy

Article

Aug 2017
J CRIT CARE

Purpose: To evaluate the associations between strained ICU capacity and patient outcomes. Methods: Multi-center population-based cohort study of nine integrated ICUs in Alberta, Canada. Path-analysis modeling was adopted to investigate direct and indirect associations between strain (available beds ≤1; occupancy ≥95%) and outcomes. Mixed-effects multivariate regression was used to measure the association between strain and acuity (APACHE II score), and both acuity and strain measures on ICU mortality and length of stay. Results: 12,265 admissions comprise the study cohort. Available beds ≤1 and occupancy ≥95% occurred for 22.3% and 17.0% of admissions. Lower bed availability was associated with higher APACHE II score (p<0.0001). The direct effect of ≤1 available beds at ICU admission on ICU mortality was 11.6% (OR 1.116; 95% CI, 0.995-1.252). Integrating direct and indirect effects resulted in a 16.5% increased risk of ICU mortality (OR 1.165; 95% CI, 1.036-1.310), which exceeded the direct effect by 4.9%. Findings were similar with strain defined as occupancy ≥95%. Strain was associated with shorter ICU stay, primarily mediated by greater acuity. Conclusions: Strained capacity was associated with increased ICU mortality, partly mediated through greater illness acuity. Future work should consider both the direct and indirect relationships of strain on outcomes.

Association of Intensive Care Unit Patient-to-Intensivist Ratios With Hospital Mortality

Article

Jan 2017

Importance: The patient-to-intensivist ratio (PIR) across intensive care units (ICUs) is not standardized and the association of PIR with patient outcome is not well established. Understanding the impact of PIR on outcomes is necessary to optimize senior medical staffing and deliver high-quality care. Objective: To test the hypotheses that: (1) there is significant variation in the PIR across ICUs and (2) higher PIRs are associated with higher hospital mortality for ICU patients. Design, setting, and participants: Retrospective cohort analysis of patients (≥16 years) admitted to ICUs staffed by a single intensivist during daytime hours in the United Kingdom from 2010 to 2013. Exposures: Patient-to-intensivist ratios, which we defined for each patient as the number of patients cared for by the intensivist each day averaged over the patient's stay. Main outcomes and measures: Using standard summary statistics, we evaluated PIR variation across ICUs. We used multivariable, mixed-effect, logistic regression analysis to evaluate the association between PIR and hospital mortality at ultimate discharge from acute hospital (primary outcome) and at ICU discharge. Finding: Among 49 686 adults in 94 ICUs, median age was 66 (interquartile range [IQR], 52-76) years, and 45.1% were women. The ultimate hospital mortality was 25.7%. The median PIR for patients was 8.5 (IQR, 6.9-10.8; full range, 1.0-23.5), and varied substantially among individual ICUs. The association between PIR and ultimate hospital mortality was U-shaped; there was a reduction in the odds of mortality associated with an increasing PIR up to 7.5 after which the odds of mortality increased again significantly (average patient mortality for lowest PIR, 22%; PIR of 7.5, 15%; highest PIR, 19%; P = .003). A similar U-shaped association was seen for PIR and mortality in the ICU (nadir of mortality at a PIR of 7.8, P < .001). Conclusions and relevance: PIR varied across UK ICUs. The optimal PIR in this cohort of UK ICU patients was 7.5, with significantly increased ICU and hospital mortality above and below this ratio. The number of patients cared for by 1 intensivist may impact patient outcomes.

The Volume-Outcome Relationship in Critical Care

Article

Jul 2015

OBJECTIVE The purpose of this study was to systematically review the research on volume and outcome relationships in critical care.METHODS From January 1, 2001, to April 30, 2014, MEDLINE and EMBASE were searched for studies assessing the relationship between admission volume and clinical outcomes in critical illness. Bibliographies were reviewed to identify other articles of interest, and experts were contacted about missing or unpublished studies. Of 127 studies reviewed, 46 met inclusion criteria, covering seven clinical conditions. Two investigators independently reviewed each article using a standardized form to abstract information on key study characteristics and results.RESULTSOverall, 29 of the studies (63%) reported a statistically significant association between higher admission volume and improved outcomes. The magnitude of the association (mortality OR between the lowest vs highest stratum of volume centers), as well as the thresholds used to characterize high volume, varied across clinical conditions. Critically ill patients with cardiovascular (n = 7, OR = 1.49 [1.11-2.00]), respiratory (n = 12, OR = 1.20 [1.04-1.38]), severe sepsis (n = 4, OR = 1.17 [1.03-1.33]), hepato-GI (n = 3, OR = 1.30 [1.08-1.78]), neurologic (n = 3, OR = 1.38 [1.22-1.57]), and postoperative admission diagnoses (n = 3, OR = 2.95 [1.05-8.30]) were more likely to benefit from admission to higher-volume centers compared with lower-volume centers. Studies that controlled for ICU or hospital organizational factors were less likely to find a significant volume-outcome relationship than studies that did not control for these factors.CONCLUSIONS Critically ill patients generally benefit from care in high-volume centers, with more substantial benefits in selected high-risk conditions. This relationship may in part be mediated by specific ICU and hospital organizational factors.Volume-outcome relationships are well established in many surgical conditions and high-risk procedures in health care.1 Under these relationships, higher numbers of procedures are thought to lead to better patient outcomes through the development of procedural skill.2 Such observations lend conceptual support to the development of regionalized systems of surgical care, in which patients are selectively referred to high-volume providers.3 Selective referral has substantially improved the quality of care for patients in need of these planned high-risk procedures, with improved outcomes over time due in large part to concentration of care.2Given the current shortage of ICU physicians and the overall complexity of critical illness, critical care is also an attractive target for regionalization. However, unlike in many surgical conditions, the volume-outcome relationship in critical illness is still incompletely characterized.4 In the absence of a well-defined volume-outcome relationship, regionalization of critical care may increase costs while delaying definitive therapy for extremely sick patients in need of rapid diagnosis and treatment. Moreover, regionalization is only one potential strategy for region-wide organization of critical care.5 Without a greater understanding of the mechanism of the volume-outcome relationship, which may in part be determined by organizational factors that are correlated with volume, we may miss out on opportunities to improve outcomes for small-volume providers without large-scale reorganization of care.The goal of this study was to perform a systematic review of literature to assess the volume-outcome relationship among critically ill adult patients. In addition to providing summary information, we sought to understand organizational factors that may be potential mechanisms for this effect by analyzing the differences between positive and negative studies.

ICU capacity strain and the quality and allocation of critical care

Article

Dec 2011
CURR OPIN CRIT CARE

Scott Halpern

Increasing demand for critical care, with limited potential for comparable expansion of supply, may strain the abilities of ICUs to provide high-quality care in an equitable fashion. Efforts to counter the untoward consequences for the quality and ethics of critical care delivery are limited by the absence of a specific and validated metric of ICU capacity strain. This manuscript presents a conceptual framework for ICU capacity strain, considers what data elements may contribute to it, and suggests methods for determining the optimal metric. Next, it outlines the range of potential consequences of increased capacity strain, in terms of both the quality and ethics of care delivered. Finally, consideration is given to how untoward consequences of ICU capacity strain might be mitigated through better understanding of what makes some ICUs better able than others to withstand temporal fluctuations in the demand for their services. Development of an appropriately accurate and parsimonious measure of ICU capacity strain may augment the precision of future critical care outcomes research by reducing unexplained variance attributable to temporal fluctuations in ICU-level factors; elucidate organizational characteristics that make some ICUs better able to withstand high-capacity strain without substantive degradations in quality; and enhance the transparency of critical care rationing while helping to improve its equity and efficiency, thereby promoting the ethics of this inevitable practice.

Mechanical Ventilators in US Acute Care Hospitals

Article

Oct 2010

The supply and distribution of mechanical ventilation capacity is of profound importance for planning for severe public health emergencies. However, the capability of US health systems to provide mechanical ventilation for children and adults remains poorly quantified. The objective of this study was to determine the quantity of adult and pediatric mechanical ventilators at US acute care hospitals. A total of 5,752 US acute care hospitals included in the 2007 American Hospital Association database were surveyed. We measured the quantities of mechanical ventilators and their features. Responding to the survey were 4305 (74.8%) hospitals, which accounted for 83.8% of US intensive care unit beds. Of the 52,118 full-feature mechanical ventilators owned by respondent hospitals, 24,204 (46.4%) are pediatric/neonatal capable. Accounting for nonrespondents, we estimate that there are 62,188 full-feature mechanical ventilators owned by US acute care hospitals. The median number of full-feature mechanical ventilators per 100,000 population for individual states is 19.7 (interquartile ratio 17.2-23.1), ranging from 11.9 to 77.6. The median number of pediatric-capable device full-feature mechanical ventilators per 100,000 population younger than 14 years old is 52.3 (interquartile ratio 43.1-63.9) and the range across states is 22.1 to 206.2. In addition, respondent hospitals reported owning 82,755 ventilators other than full-feature mechanical ventilators; we estimate that there are 98,738 devices other than full-feature ventilators at all of the US acute care hospitals. The number of mechanical ventilators per US population exceeds those reported by other developed countries, but there is wide variation across states in the population-adjusted supply. There are considerably more pediatric-capable ventilators than there are for adults only on a population-adjusted basis.

Surge capacity principles

Jan 2014
e1S

Hick

Mass critical care surge response during COVID-19

Jan 2022
429

Dichter

Actions taken by US hospitals to prepare for increased demand for intensive care during the first wave of COVID-19

Jan 2021
519

Kerlin

The coronavirus disease 2019 pandemic's effect on critical care resources and health-care providers

Jan 2021
619

Wahlster

Actions taken by US hospitals to prepare for increased demand for intensive care during the first wave of COVID-19

Jan 2021
519-528

MP Kerlin
DK Costa
BS Davis

Reorganising the emergency department to manage the COVID-19 outbreak

Jan 2020
32

LJJ Quah
BKK Tan
TP Fua

Surge capacity principles

Jan 2014
e1S-e16S

JL Hick
S Einav
D Hanfling

Mass critical care surge response during COVID-19

Jan 2022
429-447

JR Dichter
AV Devereaux
CL Sprung

Impact of ICU strain on outcomes

Abstract

No full-text available

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