Mortality among patients admitted to strained intensive care units

Inadequate intensive care physician supply in France: a point-prevalence prospective study

BACKGROUND

The COVID-19 pandemic has highlighted the importance of intensive care units (ICUs) and their organization in healthcare systems. However, ICU capacity and availability are ongoing concerns beyond the pandemic, particularly due to an aging population and increasing complexity of care. This study aimed to assess the current and future shortage of ICU physicians in France, ten years after a previous evaluation. A national e-survey was conducted among French ICUs in January 2022 to collect data on ICU characteristics, medical staffing, individual physician characteristics, and education and training capacities.

RESULTS

Among 290 ICUs contacted, 242 responded (response rate: 83%), representing 4943 ICU beds. The survey revealed an overall of 300 full time equivalent (FTE) ICU physician vacancies in the country. Nearly two-thirds of the participating ICUs reported at least one physician vacancy and 35% relied on traveling physicians to cover shifts. The ICUs most affected by physician vacancies were the ICUs of non-university affiliated public hospitals. The retirements expected in the next five years represented around 10% of the workforce. The median number of physicians per ICU was 7.0, corresponding to a ratio of 0.36 physician (FTE) per ICU bed. In addition, 27% of ICUs were at risk of critical dysfunction or closure due to vacancies and impending retirements.

CONCLUSION

The findings highlight the urgent need to address the shortage of ICU physicians in France. Compared to a similar study conducted in 2012, the inadequacy between ICU physician supply and demand has increased, resulting in a higher number of vacancies. Our study suggests that, among others, increasing the number of ICM residents trained each year could be a crucial step in addressing this issue. Failure to take appropriate measures may lead to further closures of ICUs and increased risks to patients in this healthcare system.

Patient Factors Associated With Prolonged Length of Stay After Traumatic Brain Injury

Background For traumatic brain injury (TBI) survivors, recovery can lead to significant time spent in the inpatient/rehabilitation settings. Hospital length of stay (LOS) after TBI is a crucial metric of resource utilization and treatment costs. Risk factors for prolonged LOS (PLOS) after TBI require further characterization. Methodology We conducted a retrospective analysis of patients with diagnosed TBI at an urban trauma center. PLOS was defined as the 95th percentile of the LOS of the cohort. Patients with and without PLOS were compared using clinical/injury factors. Analyses included descriptive statistics, non-parametric analyses, and multivariable logistic regression for PLOS status. Results The threshold for PLOS was >24 days. In the cohort of 1,343 patients, 77 had PLOS. PLOS was significantly associated with longer mean intensive care unit (ICU) stays (16.4 vs. 1.5 days), higher mean injury severity scores (18.6 vs. 13.8), lower mean Glasgow coma scale scores (11.3 vs. 13.7) and greater mean complication burden (0.7 vs. 0.1). PLOS patients were more likely to have moderate/severe TBI, Medicaid insurance, and were less likely to be discharged home. In the regression model, PLOS was associated with ICU stay, inpatient disposition, ventilator use, unplanned intubation, and inpatient alcohol withdrawal. Conclusions TBI patients with PLOS were more likely to have severe injuries, in-hospital complications, and Medicaid insurance. PLOS was predicted by ICU stay, intubation, alcohol withdrawal, and disposition to inpatient/post-acute care facilities. Efforts to reduce in-hospital complications and expedite discharge may reduce LOS and accompanying costs. Further validation of these results is needed from larger multicenter studies.

Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning

Background

Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP.

Methods

The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation.

Results

A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0-63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824-0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%.

Conclusions

This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.

Association Between Nurse Copatient Illness Severity and Mortality in the ICU

OBJECTIVES

In the context of traditional nurse-to-patient ratios, ICU patients are typically paired with one or more copatients, creating interdependencies that may affect clinical outcomes. We aimed to examine the effect of copatient illness severity on ICU mortality.

DESIGN

We conducted a retrospective cohort study using electronic health records from a multihospital health system from 2018 to 2020. We identified nurse-to-patient assignments for each 12-hour shift using a validated algorithm. We defined copatient illness severity as whether the index patient's copatient received mechanical ventilation or vasoactive support during the shift. We used proportional hazards regression with time-varying covariates to assess the relationship between copatient illness severity and 28-day ICU mortality.

SETTING

Twenty-four ICUs in eight hospitals.

PATIENTS

Patients hospitalized in the ICU between January 1, 2018, and August 31, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The main analysis included 20,650 patients and 84,544 patient-shifts. Regression analyses showed a patient's risk of death increased when their copatient received both mechanical ventilation and vasoactive support (hazard ratio [HR]: 1.30; 95% CI, 1.05-1.61; p = 0.02) or vasoactive support alone (HR: 1.82; 95% CI, 1.39-2.38; p < 0.001), compared with situations in which the copatient received neither treatment. However, if the copatient was solely on mechanical ventilation, there was no significant increase in the risk of death (HR: 1.03; 95% CI, 0.86-1.23; p = 0.78). Sensitivity analyses conducted on cohorts with varying numbers of copatients consistently showed an increased risk of death when a copatient received vasoactive support.

CONCLUSIONS

Our findings suggest that considering copatient illness severity, alongside the existing practice of considering individual patient conditions, during the nurse-to-patient assignment process may be an opportunity to improve ICU outcomes.

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

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.

Creation and Expansion of a Mixed Patient Intermediate Care Unit to Improve ICU Capacity

OBJECTIVES

ICU capacity strain is associated with worsened outcomes. Intermediate care units (IMCs) comprise one potential option to offload ICUs while providing appropriate care for intermediate acuity patients, but their impact on ICU capacity has not been thoroughly characterized. The aims of this study are to describe the creation of a medical-surgical IMC and assess how the IMC affected ICU capacity.

DESIGN

Descriptive report with retrospective cohort review.

SETTING

Six hundred seventy-three-bed tertiary care academic medical center with 77 ICU beds.

PATIENTS

Adult inpatients who were admitted to the IMC.

INTERVENTIONS

An interdisciplinary working group created an IMC which was located on a general ward. The IMC was staffed by hospitalists and surgeons and supported by critical care consultants. The initial maximum census was three, but this number increased to six in response to heightened critical care demand. IMC admission criteria also expanded to include advanced noninvasive respiratory support defined as patients requiring high-flow nasal cannula, noninvasive positive pressure ventilation, or mechanical ventilation in patients with tracheostomies.

MEASUREMENTS AND MAIN RESULTS

The primary outcome entailed the number of ICU bed-days saved. Adverse outcomes, including ICU transfer, intubation, and death, were also recorded. From August 2021 to July 2022, 230 patients were admitted to the IMC. The most frequent IMC indications were respiratory support for medical patients and post-operative care for surgical patients. A total of 1023 ICU bed-days were made available. Most patients were discharged from the IMC to a general ward, while 8% of all patients required transfer to an ICU within 48 hours of admission. Intubation (2%) and death (1%) occurred infrequently within 48 hours of admission. Respiratory support was the indication associated with the most ICU transfers.

CONCLUSIONS

Despite a modest daily census, an IMC generated substantial ICU bed capacity during a time of peak critical care demand.

Patient Characteristics and Clinical and Economic Outcomes Associated with Unplanned Medical and Surgical Intensive Care Unit Admissions: A Retrospective Analysis

Purpose

To characterize medical and surgical patient characteristics, as well as clinical and economic outcomes, associated with unplanned intensive care unit (ICU) admissions.

Patients and Methods

This was a retrospective matched cohort analysis that utilized the PINC AITM Healthcare Database, which collects deidentified data from 25% of United States (US) hospital admissions. Discharge records were assessed for medical and surgical admissions in 2021. An unplanned ICU admission was defined as direct transfer from a medical, surgical, or telemetry unit to the ICU. Patients with and without an unplanned ICU admission were 1:1 propensity score matched. Differences between patients with and without unplanned ICU admissions were assessed using two-sample t-tests for continuous measures and Chi-square tests for categorical measures.

Results

A total of 3,807,124 qualifying admissions were identified. Medical admissions with unplanned ICU transfers were more likely to be urgent/emergent (odds ratio [OR] 2.9, 95% confidence interval [CI 2.7-3.0], p<0.0001), with patient characteristics including male sex (1.4, [1.4-1.4], p<0.0001), obesity (1.7, [1.6-1.7], p<0.0001), and increased Charlson Comorbidity Index (CCI=1: 1.8, [1.8-1.9], p<0.0001; CCI≥5: 3.2, [3.1-3.3], p<0.0001). Surgical admissions with unplanned ICU transfers were more likely to be urgent/emergent (3.1, [2.9-3.2], p<0.0001) and with patients of higher CCI (2.5, [2.3-2.6], p<0.0001 to a CCI of≥5 (7.9, [7.4-8.4], p<0.0001). Between matched medical patients, mean differences in length of stay, cost, and mortality were 4.1 days (p<0.0001), $13,424 (p<0.0001), and 21% (p<0.0001), respectively. Between matched surgical patients, mean differences in these outcomes were 6.4 days (p<0.0001), $21,448 (p<0.0001), and 14% (p<0.0001), respectively.

Conclusion

Emergency care in patients with a higher co-morbid burden is more likely to lead to unplanned ICU admission, putting patients at a significantly increased chance of mortality, longer length of stay, and increased costs. Improving care and monitoring of patients outside the ICU may help detect early changes in pathophysiology and enable early intervention.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Inter-hospital Transfer Decision-making During the COVID-19 Pandemic: a Qualitative Study

BACKGROUND

Inter-hospital patient transfers to hospitals with greater resource availability and expertise may improve clinical outcomes. However, there is little guidance regarding how patient transfer requests should be prioritized when hospital resources become scarce.

OBJECTIVE

To understand the experiences of healthcare workers involved in the process of accepting inter-hospital patient transfers during a pandemic surge and determine factors impacting inter-hospital patient transfer decision-making.

DESIGN

We conducted a qualitative study consisting of semi-structured interviews between October 2021 and February 2022.

PARTICIPANTS

Eligible participants were physicians, nurses, and non-clinician administrators involved in the process of accepting inter-hospital patient transfers. Participants were recruited using maximum variation sampling.

APPROACH

Semi-structured interviews were conducted with healthcare workers across Michigan.

KEY RESULTS

Twenty-one participants from 15 hospitals were interviewed (45.5% of eligible hospitals). About half (52.4%) of participants were physicians, 38.1% were nurses, and 9.5% were non-clinician administrators. Three domains of themes impacting patient transfer decision-making emerged: decision-maker, patient, and environmental factors. Decision-makers described a lack of guidance for transfer decision-making. Patient factors included severity of illness, predicted chance of survival, need for specialized care, and patient preferences for medical care. Decision-making occurred within the context of environmental factors including scarce resources at accepting and requesting hospitals, organizational changes to transfer processes, and alternatives to patient transfer including use of virtual care. Participants described substantial moral distress related to transfer triaging.

CONCLUSIONS

A lack of guidance in transfer processes may result in considerable variation in the patients who are accepted for inter-hospital transfer and in substantial moral distress among decision-makers involved in the transfer process. Our findings identify potential organizational changes to improve the inter-hospital transfer process and alleviate the moral distress experienced by decision-makers.

Intensivist physician-to-patient ratios and mortality in the intensive care unit

PURPOSE

A high daily census may hinder the ability of physicians to deliver quality care in the intensive care unit (ICU). We sought to determine the relationship between intensivist-to-patient ratios and mortality among ICU patients.

METHODS

We performed a retrospective cohort study of intensivist-to-patient ratios in 29 ICUs in 10 hospitals in the United States from 2018 to 2020. We used meta-data from progress notes in the electronic health record to determine an intensivist-specific caseload for each ICU day. We then fit a multivariable proportional hazards model with time-varying covariates to estimate the relationship between the daily intensivist-to-patient ratio and ICU mortality at 28 days.

RESULTS

The final analysis included 51,656 patients, 210,698 patient days, and 248 intensivist physicians. The average caseload per day was 11.8 (standard deviation: 5.7). There was no association between the intensivist-to-patient ratio and mortality (hazard ratio for each additional patient: 0.987, 95% confidence interval: 0.968-1.007, p = 0.2). This relationship persisted when we defined the ratio as caseload over the sample-wide average (hazard ratio: 0.907, 95% confidence interval: 0.763-1.077, p = 0.26) and cumulative days with a caseload over the sample-wide average (hazard ratio: 0.991, 95% confidence interval: 0.966-1.018, p = 0.52). The relationship was not modified by the presence of physicians-in-training, nurse practitioners, and physician assistants (p value for interaction term: 0.14).

CONCLUSIONS

Mortality for ICU patients appears resistant to high intensivist caseloads. These results may not generalize to ICUs organized differently than those in this sample, such as ICUs outside the United States.

Limitation of life-sustaining therapies in critically ill patients with COVID-19: a descriptive epidemiological investigation from the COVID-ICU study

BACKGROUND

Limitations of life-sustaining therapies (LST) practices are frequent and vary among intensive care units (ICUs). However, scarce data were available during the COVID-19 pandemic when ICUs were under intense pressure. We aimed to investigate the prevalence, cumulative incidence, timing, modalities, and factors associated with LST decisions in critically ill COVID-19 patients.

METHODS

We did an ancillary analysis of the European multicentre COVID-ICU study, which collected data from 163 ICUs in France, Belgium and Switzerland. ICU load, a parameter reflecting stress on ICU capacities, was calculated at the patient level using daily ICU bed occupancy data from official country epidemiological reports. Mixed effects logistic regression was used to assess the association of variables with LST limitation decisions.

RESULTS

Among 4671 severe COVID-19 patients admitted from February 25 to May 4, 2020, the prevalence of in-ICU LST limitations was 14.5%, with a nearly six-fold variability between centres. Overall 28-day cumulative incidence of LST limitations was 12.4%, which occurred at a median of 8 days (3-21). Median ICU load at the patient level was 126%. Age, clinical frailty scale score, and respiratory severity were associated with LST limitations, while ICU load was not. In-ICU death occurred in 74% and 95% of patients, respectively, after LST withholding and withdrawal, while median survival time was 3 days (1-11) after LST limitations.

CONCLUSIONS

In this study, LST limitations frequently preceded death, with a major impact on time of death. In contrast to ICU load, older age, frailty, and the severity of respiratory failure during the first 24 h were the main factors associated with decisions of LST limitations.

Necessity of Mandatory Postoperative Intensive Care Unit Management after Clipping Surgery for Unruptured Intracranial Aneurysms

OBJECTIVE

Many neurosurgeons routinely perform postoperative intensive care unit (ICU) management after clipping of unruptured intracranial aneurysms (UIAs). However, whether routine postoperative ICU care is necessary remains a clinical question. Therefore, we investigated which factors acted as risk factors that actually required ICU care after microsurgical clipping of unruptured aneurysms.

METHODS

We included a total of 532 patients who underwent clipping surgery for UIA between January 2020 and December 2020. The patients were divided into two groups: those who really required ICU care (41 patients, 7.7%) and those who did not (491 patients, 92.3%). A backward stepwise logistic regression model was used to identify factors that were independently associated with ICU care requirement.

RESULTS

The mean hospital stay duration and the operation time were significantly longer in the ICU requirement group than in the no ICU requirement group (9.9 ± 10.7 vs. 6.3 ± 3.7 days, p = 0.041), (259.9 ± 128.4 vs. 210.5 ± 46.1 min, p = 0.019). The transfusion rate was significantly higher (p = 0.024) in the ICU requirement group. Multivariable logistic regression analysis identified male sex (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.15-4.76; p = 0.0195), operation time (OR, 1.01; 95% CI, 1.00-1.01; p = 0.0022), and transfusion (OR, 2.35; 95% CI, 1.00-5.51; p = 0.0500) as independent risk factors for requiring ICU care after clipping.

CONCLUSIONS

Postoperative ICU management may not be mandatory after clipping surgery for UIAs. Our results suggest that postoperative ICU management may be more required in the male sex, patients with longer operation times, and those who received a transfusion.

Economic evaluation of a medical ambulatory care service using a single group interrupted time-series design

RATIONALE

Increasing demand for hospital services can lead to overcrowding and delays in treatment, poorer outcomes and a high cost-burden. The medical ambulatory care service (MACS) provides out of hospital patient care, including diagnostic and therapeutic interventions for patients that require urgent attention, but which can be safely administered in the ambulatory environment. The programme is yet to be rigorously evaluated.

AIMS/OBJECTIVES

The aim of this study is to evaluate the impact of the MACS programme on emergency department (ED) presentations, hospital admissions, length-of-stay and health service costs from a health system perspective.

METHOD

We used a single group interrupted time series methodology with a multiple baseline approach to analyse the impact of the MACS clinic on ED presentations, hospital admissions, length-of-stay and cost outcomes for general practitioners (GP)-referred, ED-referred and ward-referred patients under two counterfactual scenarios: an increasing trend in health utilization based on preperiod predictions or a stabilization of utilization rates.

RESULTS

The time trend of hospital utilization differed after attending MACS for all three referral groups. The time trend for the GP-referred group declined significantly by 0.36 ED presentations per 100 patients per 30 days [95% confidence interval (CI): -0.52 to -0.2], while inpatient length of stay declined significantly by 1.56 and 3.70 days, respectively, per 100 ED-referred and ward-referred patients per 30 days (95% CI: -2.51 to -0.57 and -5.71 to -1.69, respectively). Under two different counterfactual scenarios, the predicted net savings for MACS across three patient groups were $78,685 (95% CI: $54,807-$102,563) and $547,639 (95% CI: $503,990-$591,287) per 100 patients over 18 months.

CONCLUSION

MACS was found to be cost-effective for GP and ward-referred groups, but the expected impact for ED-referred patients is sensitive to assumptions. Expansion of the service for GP-referred patients is expected to reduce hospitalizations the most and generate the largest net cost savings.

Effect of tracheostomy timing in pediatric patients with traumatic brain injury

BACKGROUND

Traumatic brain injury (TBI) is a prevalent cause of disability and death in the pediatric population, often requiring prolonged mechanical ventilation. Patients with significant TBI or intracranial hemorrhage require advanced airway management to protect against aspiration, hypoxia, and hypercarbia, eventually necessitating tracheostomy. While tracheostomy is much less common in children compared to adults, its prevalence among pediatric populations has been steadily increasing. Although early tracheostomy has demonstrated improved outcomes in adult patients, optimal tracheostomy timing in the pediatric population with TBI remains to be definitively established.

OBJECTIVE

This retrospective cohort analysis aims to evaluate pediatric TBI patients who undergo tracheostomy and to investigate the impact of tracheostomy timing on outcomes.

DESIGN/METHODS

The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID), collected between in 2016 and 2019, was queried using International Classification of Disease 10th edition (ICD10) codes for patients with traumatic brain injury who had received a tracheostomy. Baseline demographics, insurance status, and procedural day data were analyzed with univariate and multivariate regression analyses. Propensity score matching was performed to estimate the incidence of medical complications and mortality related to early versus late tracheostomy timing (as defined by median = 9 days).

RESULTS

Of the 68,793 patients (mean age = 14, IQR 4-18) who suffered a TBI, 1,956 (2.8%) received a tracheostomy during their hospital stay. TBI patients who were tracheostomized were older (mean age = 16.5 vs 11.4 years), more likely to have injuries classified as severe TBIs and more likely to have accumulated more than one indicator of parenchymal injury as measured by the Composite Stroke Severity Scale (CSSS >1) than non-tracheostomized TBI patients. TBI patients with a tracheostomy were more likely to encounter serious complications such as sepsis, acute kidney injury (AKI), meningitis, or acute respiratory distress syndrome (ARDS). They were also more likely to necessitate an external ventricular drain (EVD) or decompressive hemicraniectomy (DHC) than TBI patients without a tracheostomy. Tracheostomy was also negatively associated with routine discharge. Procedural timing was assessed in 1,867 patients; older children (age >15 years) were more likely to undergo earlier placements (p < 0.001). Propensity score matching (PSM) comparing early versus late placement was completed by controlling for age, gender, and TBI severity. Those who were subjected to late tracheostomy (>9 days) were more likely to face complications such as AKI or deep vein thrombosis (DVT) as well as a host of respiratory conditions such as pulmonary embolism, aspiration pneumonitis, pneumonia, or ARDS. While the timing did not significantly impact mortality across the PSM cohorts, late tracheostomy was associated with increased length of stay (LOS) and ventilator dependence.

CONCLUSIONS

Tracheostomy, while necessary for some patients who have sustained a TBI, is itself associated with several risks that should be assessed in context of each individual patient's overall condition. Additionally, the timing of the intervention may significantly impact the trajectory of the patient's recovery. Early intervention may reduce the incidence of serious complications as well as length of stay and dependence on a ventilator and facilitate a timelier recovery.

Strain on the ICU resources and patient outcomes in the COVID-19 pandemic: A Swedish national registry cohort study

BACKGROUND

The Coronavirus 2019 (COVID-19) pandemic has led to an unprecedented strain on the ICU resources. It is not known how the ICU resources employed in treating COVID-19 patients are related to inpatient characteristics, use of organ support or mortality.

OBJECTIVES

To investigate how the use of ICU resources relate to use of organ support and mortality in COVID-19 patients.

DESIGN

A national register-based cohort study.

SETTING

All Swedish ICUs from March 2020 to November 2021.

PATIENTS

All patients admitted to Swedish ICUs with a primary diagnosis of COVID-19 reported to the national Swedish Intensive Care Register (SIR).

MAIN OUTCOME MEASURES

Organ support (mechanical ventilation, noninvasive ventilation, high-flow oxygen therapy, prone positioning, surgical and percutaneous tracheostomy, central venous catheterisation, continuous renal replacement therapy and intermittent haemodialysis), discharge at night, re-admission, transfer and ICU and 30-day mortality.

RESULTS

Seven thousand nine hundred and sixty-nine patients had a median age of 63 years, and 70% were men. Median daily census was 167% of habitual census, daily new admissions were 20% of habitual census and the median occupancy was 82%. Census and new admissions were associated with mechanical ventilation, OR 1.37 (95% CI 1.28 to 1.48) and OR 1.44 (95% CI 1.13 to 1.84), respectively, but negatively associated with noninvasive ventilation, OR 0.83 (95% CI 0.77 to 0.89) and OR 0.40 (95% CI 0.30 to 52) and high-flow oxygen therapy, OR 0.72 (95% CI 0.67 to 0.77) and OR 0.77 (95% CI 0.61 to 0.97). Occupancy above 90% of available beds was not associated with mechanical ventilation or noninvasive ventilation, but with high-flow oxygen therapy, OR 1.36 (95% CI 1.21 to 1.53). All measures of pressure on resources were associated with transfer to other hospitals, but none were associated with discharge at night, ICU mortality or 30-day mortality.

CONCLUSIONS

Pressure on ICU resources was associated with more invasive respiratory support, indicating that during these times, ICU resources were reserved for sicker patients.

COVID-19: Lessons Learned, Lessons Unlearned, Lessons for the Future

The COVID-19 pandemic has affected clinicians in many different ways. Clinicians have their own experiences and lessons that they have learned from their work in the pandemic. This article outlines a few lessons learned from the eyes of CHEST Critical Care Editorial Board members, namely practices which will be abandoned, novel practices to be adopted moving forward, and proposed changes to the health care system in general. In an attempt to start the discussion of how health care can grow from the pandemic, the editorial board members outline their thoughts on these lessons learned.

Characterizing Equity of Intensive Care Unit Admissions for Sepsis and Acute Respiratory Failure

Rationale: Patients who identify as from racial or ethnic minority groups who have sepsis or acute respiratory failure (ARF) experience worse outcomes relative to nonminority patients, but processes of care accounting for disparities are not well-characterized. Objectives: Determine whether reductions in intensive care unit (ICU) admission during hospital-wide capacity strain occur preferentially among patients who identify with racial or ethnic minority groups. Methods: This retrospective cohort among 27 hospitals across the Philadelphia metropolitan area and Northern California between 2013 and 2018 included adult patients with sepsis and/or ARF who did not require life support at the time of hospital admission. An updated model of hospital-wide capacity strain was developed that permitted determination of relationships between patient race, ethnicity, ICU admission, and strain. Results: After adjustment for demographics, disease severity, and study hospital, patients who identified as Asian or Pacific Islander had the highest adjusted ICU admission odds relative to patients who identified as White in both the sepsis and ARF populations (odds ratio, 1.09; P = 0.006 and 1.26; P < 0.001). ICU admission was also elevated for patients with ARF who identified as Hispanic (odds ratio, 1.11; P = 0.020). Capacity strain did not modify differences in ICU admission for patients who identified with a minority group in either disease population (all interactions, P > 0.05). Conclusions: Systematic differences in ICU admission patterns were observed for patients that identified as Asian, Pacific Islander, and Hispanic. However, ICU admission was not restricted from these groups, and capacity strain did not preferentially reduce ICU admission from patients identifying with minority groups. Further characterization of provider decision-making can help contextualize these findings as the result of disparate decision-making or a mechanism of equitable care.

Critical Care Clinicians' Experiences of Patient Safety During the COVID-19 Pandemic

OBJECTIVE

It is unknown how hospital- and systems-level factors have impacted patient safety in the intensive care unit (ICU) during the COVID-19 pandemic. We sought to understand how the pandemic has exacerbated preexisting patient safety issues and created novel patient safety challenges in ICUs in the United States.

METHODS

We performed a national, multi-institutional, mixed-methods survey of critical care clinicians to elicit experiences related to patient safety during the pandemic. The survey was disseminated via email through the Society of Critical Care Medicine listserv. Data were reported as valid percentages, compared by COVID caseload and peak of the pandemic; free-text responses were analyzed and coded for themes.

RESULTS

We received 335 survey responses. On general patient safety, 61% felt that conditions were more hazardous when compared with the prepandemic period. Those who took care of mostly COVID-19 patients were more likely to perceive that care was more hazardous (odds ratio, 4.89; 95% CI, 2.49-9.59) compared with those who took care of mostly non-COVID-19 or no COVID-19 patients. In free-text responses, providers identified patient safety risks related to pandemic adaptations, such as ventilator-related lung injury, medication and diagnostic errors, oversedation, oxygen device removal, and falls.

CONCLUSIONS

Increased COVID-19 case burden was significantly associated with perceptions of a less safe patient care environment by frontline ICU clinicians. Results of the qualitative analysis identified specific patient safety hazards in ICUs across the United States as downstream consequences of hospital and provider strain during periods of the COVID-19 pandemic.

Care of the Seriously Ill Patient with SARS-CoV-2

In late 2019, SARS-CoV-2 caused the greatest global health crisis in a century, impacting all aspects of society. As the COVID-19 pandemic evolved throughout 2020 and 2021, multiple variants emerged, contributing to multiple surges in cases of COVID-19 worldwide. In 2021, highly effective vaccines became available, although the pandemic continues into 2022. There has been tremendous expansion of basic, translational, and clinical knowledge about SARS-CoV-2 and COVID-19 since the pandemic's onset. Treatment options have been rapidly explored, attempting to repurpose preexisting medications in tandem with development and evaluation of novel agents. Care of the seriously ill patient is examined.

ICU Admission Tool for Congenital Heart Catheterization (iCATCH): A Predictive Model for High Level Post-Catheterization Care and Patient Management

OBJECTIVES

Currently, there are no prediction tools available to identify patients at risk of needing high-complexity care following cardiac catheterization for congenital heart disease. We sought to develop a method to predict the likelihood a patient will require intensive care level resources following elective cardiac catheterization.

DESIGN

Prospective single-center study capturing important patient and procedural characteristics for predicting discharge to the ICU. Characteristics significant at the 0.10 level in the derivation dataset (July 1, 2017 to December 31, 2019) were considered for inclusion in the final multivariable logistic regression model. The model was validated in the testing dataset (January 1, 2020 to December 31, 2020). The novel pre-procedure cardiac status (PCS) feature, collection started in January 2019, was assessed separately in the final model using the 2019 through 2020 dataset.

SETTING

Tertiary pediatric heart center.

PATIENTS

All elective cases coming from home or non-ICU who underwent a cardiac catheterization from July 2017 to December 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 2,192 cases were recorded in the derivation dataset, of which 11% of patients ( n = 245) were admitted to the ICU, while 64% ( n = 1,413) were admitted to a medical unit and 24% ( n = 534) were discharged home. In multivariable analysis, the following predictors were identified: 1) weight less than 5 kg and 5-9.9 kg, 2) presence of systemic illness, 3) recent cardiac intervention less than 90 days, and 4) ICU Admission Tool for Congenital Heart Catheterization case type risk categories (1-5), with C -statistics of 0.79 and 0.76 in the derivation and testing cohorts, respectively. The addition of the PCS feature fit into the final model resulted in a C -statistic of 0.79.

CONCLUSIONS

The creation of a validated pre-procedural risk prediction model for ICU admission following congenital cardiac catheterization using a large volume, single-center, academic institution will improve resource allocation and prediction of capacity needs for this complex patient population.

Comprehensive Improvement of Cardiology Inpatient Transfers: A Bed-availability Triggered Approach

Introduction

Patient transfers pose a potential risk during hospitalizations. Structured communication practices are necessary to ensure effective handoffs, but occur amidst competing priorities and constraints. We sought to design and implement a multidisciplinary process to enhance communication between pediatric cardiovascular intensive care unit and cardiology floor teams with a comprehensive approach evaluating efficiency, safety, and culture.

Methods

We conducted a prospective quality improvement study to enact a bed-availability triggered bedside handoff process. The primary aim was to reduce the time between handoff and unit transfer. Secondary metrics captured the impact on safety (reported safety events, overnight transfers, bounce backs, and I-PASS utilization), efficiency (transfer latency, unnecessary patient handoffs, and cumulative time providers were engaged in handoffs), and culture (team members perceptions of satisfaction, collaboration, and handoff efficiency via survey data).

Results

Eighty-two preimplementation surveys, 26 stakeholder interviews, and 95 transfers were completed during the preintervention period. During the postintervention period, 145 handoffs were audited. We observed significant reductions in transfer latency, unnecessary handoffs, and cumulative provider handoff time. Overnight transfers decreased, and no negative impact was observed in reported safety events or bouncebacks. Survey results showed a positive impact on collaboration, efficiency, and satisfaction among team members.

Conclusions

Developing safer handoff practices require a collaborative, structured, and stepwise approach. Advances are attainable in high-volume centers, and comprehensive measurement of change is necessary to ensure a positive impact on the overall patient and provider environment.

A critical care outreach team under strain - Evaluation of the service provided to patients with haematological malignancy during the Covid-19 pandemic

Purpose

Critical Care Outreach Teams (CCOTs) have been associated with improved outcomes in patients with haematological malignancy (HM). This study aims to describe CCOT activation by patients with HM before and during the Covid-19 pandemic, assess amny association with worse outcomes, and examine the psychological impact on the CCOT.

Materials and methods

A retrospective, mixed-methods analysis was performed in HM patients reviewed by the CCOT over a two-year period, 01 July 2019 to 31 May 2021.

Results

The CCOT increased in size during the surge period and reviewed 238 HM patients, less than in the pre- and post-surge periods. ICU admission in the baseline, surge and the non-surge periods were 41.7%, 10.4% and 47.9% respectively. ICU mortality was 22.5%, 0% and 21.7% for the same times. Time to review was significantly decreased (p = 0.012). Semi-structured interviews revealed four themes of psychological distress: 1) time-critical work; 2) non-evidence based therapies; 3) feelings of guilt; 4) increased decision-making responsibility.

Conclusions

Despite the increase in total hospital referrals, the number of patients with HM that were reviewed during the surge periods decreased, as did their ICU admission rate and mortality. The quality of care provided was not impaired, as reflected by the number of patients receiving bedside reviews and the shorter-than-pre-pandemic response time.

Comparison of clinical characteristics and hospital mortality in critically ill patients without COVID-19 before and during the COVID-19 pandemic: a multicenter, retrospective, propensity score-matched study

Background

The high transmission and fatality rates of coronavirus disease 2019 (COVID-19) strain intensive care resources and affect the treatment and prognosis of critically ill patients without COVID-19. Therefore, this study evaluated the differences in characteristics, clinical course, and prognosis of critically ill medical patients without COVID-19 before and during the COVID-19 pandemic.

Methods

This retrospective cohort study included patients from three university-affiliated tertiary hospitals. Demographic data and data on the severity, clinical course, and prognosis of medical patients without COVID-19 admitted to the intensive care unit (ICU) via the emergency room (ER) before (from January 1 to May 31, 2019) and during (from January 1 to May 31, 2021) the COVID-19 pandemic were obtained from electronic medical records. Propensity score matching was performed to compare hospital mortality between patients before and during the pandemic.

Results

This study enrolled 1161 patients (619 before and 542 during the pandemic). During the COVID-19 pandemic, the Simplified Acute Physiology Score (SAPS) 3 and Sequential Organ Failure Assessment (SOFA) scores, assessed upon ER and ICU admission, were significantly higher than those before the pandemic (p < 0.05). The lengths of stay in the ER, ICU, and hospital were also longer (p < 0.05). Finally, the hospital mortality rates were higher during the pandemic than before (215 [39.7%] vs. 176 [28.4%], p < 0.001). However, in the propensity score-matched patients, hospital mortality did not differ between the groups (p = 0.138). The COVID-19 pandemic did not increase the risk of hospital mortality (odds ratio [OR] 1.405, 95% confidence interval [CI], 0.937–2.107, p = 0.100). SAPS 3, SOFA score, and do-not-resuscitate orders increased the risk of in-hospital mortality in the multivariate logistic regression model.

Conclusions

In propensity score-matched patients with similarly severe conditions, hospital mortality before and during the COVID-19 pandemic did not differ significantly. However, hospital mortality was higher during the COVID-19 pandemic in unmatched patients in more severe conditions. These findings imply collateral damage to non-COVID-19 patients due to shortages in medical resources during the COVID-19 pandemic. Thus, strategic management of medical resources is required to avoid these consequences.

Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge

OBJECTIVE

To explore the relationship between novel, time-varying predictors for healthcare delivery strain (eg, counts of patient orders per hour) and imminent discharge and in-hospital mortality.

MATERIALS AND METHODS

We conducted a retrospective cohort study using data from adults hospitalized at 21 Kaiser Permanente Northern California hospitals between November 1, 2015 and October 31, 2020 and the nurses caring for them. Patient data extracted included demographics, diagnoses, severity measures, occupancy metrics, and process of care metrics (eg, counts of intravenous drip orders per hour). We linked these data to individual registered nurse records and created multiple dynamic, time-varying predictors (eg, mean acute severity of illness for all patients cared for by a nurse during a given hour). All analyses were stratified by patients' initial hospital unit (ward, stepdown unit, or intensive care unit). We used discrete-time hazard regression to assess the association between each novel time-varying predictor and the outcomes of discharge and mortality, separately.

RESULTS

Our dataset consisted of 84 162 161 hourly records from 954 477 hospitalizations. Many novel time-varying predictors had strong associations with the 2 study outcomes. However, most of the predictors did not merely track patients' severity of illness; instead, many of them only had weak correlations with severity, often with complex relationships over time.

DISCUSSION

Increasing availability of process of care data from automated electronic health records will permit better quantification of healthcare delivery strain. This could result in enhanced prediction of adverse outcomes and service delays.

CONCLUSION

New conceptual models will be needed to use these new data elements.

Mortality of Mechanically Ventilated COVID-19 Patients in Traditional versus Expanded ICUs in NY

RATIONALE

During the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City, the number of mechanically ventilated COVID-19 patients rapidly surpassed the capacity of traditional Intensive Care Units (ICUs), resulting in health systems utilizing other areas as expanded ICUs to provide critical care.

OBJECTIVES

To evaluate the mortality of patients admitted to expanded ICUs compared with those admitted to traditional ICUs.

METHODS

Multicenter, retrospective, cohort study of mechanically ventilated patients with COVID-19 admitted to the ICUs at 11 Northwell Health hospitals in the greater New York City area between March 1, 2020 and April 30, 2020.

MEASUREMENTS

In-hospital mortality up to 28 days after intubation of COVID-19 patients.

RESULTS

Among 1,966 mechanically ventilated patients with COVID-19, 1,198 (61%) died within 28 days after intubation, 46 (2%) were transferred to other hospitals outside of the Northwell Health system, 722 (37%) survived in the hospital until 28 days or were discharged after recovery. The risk of mortality of mechanically ventilated patients admitted to expanded ICUs was not different from those admitted to traditional ICUs (HR, 1.07; 95% CI, 0.95-1.20; p = 0.28), while hospital occupancy for critically ill patients itself was associated with increased risk of mortality (HR, 1.28; 95% CI, 1.12-1.45; p < 0.001).

CONCLUSIONS

Although increased hospital occupancy for critically ill patients itself was associated with increased mortality, the risk of 28-day in-hospital mortality of mechanically ventilated patients with COVID-19 who were admitted to expanded ICUs was not different from those admitted to traditional ICUs.

Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach

Background

ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team’s cognitive capacity.

Methods

The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team’s decision making.

Results

Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19.

Conclusions

Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team’s cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01548-7.

Association of Surge Conditions with Mortality Among Critically Ill Patients with COVID-19

Objective

To determine whether surge conditions were associated with increased mortality.

Design

Multicenter cohort study.

Setting

U.S. ICUs participating in STOP-COVID.

Patients

Consecutive adults with COVID-19 admitted to participating ICUs between March 4 and July 1, 2020.

Interventions

None

Measurements and Main Results

The main outcome was 28-day in-hospital mortality. To assess the association between admission to an ICU during a surge period and mortality, we used two different strategies: (1) an inverse probability weighted difference-in-differences model limited to appropriately matched surge and non-surge patients and (2) a meta-regression of 50 multivariable difference-in-differences models (each based on sets of randomly matched surge- and non-surge hospitals). In the first analysis, we considered a single surge period for the cohort (March 23 – May 6). In the second, each surge hospital had its own surge period (which was compared to the same time periods in matched non-surge hospitals).

Our cohort consisted of 4342 ICU patients (average age 60.8 [sd 14.8], 63.5% men) in 53 U.S. hospitals. Of these, 13 hospitals encountered surge conditions. In analysis 1, the increase in mortality seen during surge was not statistically significant (odds ratio [95% CI]: 1.30 [0.47-3.58], p = .6). In analysis 2, surge was associated with an increased odds of death (odds ratio 1.39 [95% CI, 1.34-1.43], p < .001).

Conclusions

Admission to an ICU with COVID-19 in a hospital that is experiencing surge conditions may be associated with an increased odds of death. Given the high incidence of COVID-19, such increases would translate into substantial excess mortality.

Total patient load, regional disparities and in-hospital mortality of intubated COVID-19 patients in Greece, from September 2020 to May 2021

AIMS

While healthcare services have been expanding capacity during the COVID-19 pandemic, quality of care under increasing patient loads has received less attention. We examined in-hospital mortality of intubated COVID-19 patients in Greece, in relation to total intubated patient load, intensive care unit (ICU) availability and hospital region.

METHODS

Anonymized surveillance data were analyzed from all intubated COVID-19 patients in Greece between 1 September 2020 and 6 May 2021. Poisson regression was used to estimate the hazard of dying as a function of fixed and time-varying covariates.

RESULTS

Mortality was significantly increased above 400 patients, with an adjusted hazard ratio of 1.25 (95% confidence interval (CI): 1.03-1.51), rising progressively up to 1.57 (95% CI: 1.22-2.02) for 800+ patients. Hospitalization outside an ICU or away from the capital region of Attica were also independently associated with significantly increased mortality.

CONCLUSIONS

Our results indicate that in-hospital mortality of severely ill COVID-19 patients is adversely affected by high patient load even without exceeding capacity, as well as by regional disparities. This highlights the need for more substantial strengthening of healthcare services, focusing on equity and quality of care besides just expanding capacity.

Association of patient-to-intensivist ratio with hospital mortality in Australia and New Zealand

Purpose

The impact of intensivist workload on intensive care unit (ICU) outcomes is incompletely described and assessed across healthcare systems and countries. We sought to examine the association of patient-to-intensivist ratio (PIR) with hospital mortality in Australia/New Zealand (ANZ) ICUs.

Methods

We conducted a retrospective study of adult admissions to ANZ ICUs (August 2016–June 2018) using two cohorts: “narrow”, based on previously used criteria including restriction to ICUs with a single daytime intensivist; and “broad”, refined by individual ICU daytime staffing information. The exposure was average daily PIR and the outcome was hospital mortality. We used summary statistics to describe both cohorts and multilevel multivariable logistic regression models to assess the association of PIR with mortality. In each, PIR was modeled using restricted cubic splines to allow for non-linear associations. The broad cohort model included non-PIR physician and non-physician staffing covariables.

Results

The narrow cohort of 27,380 patients across 67 ICUs (predicted mortality: median 1.2% [IQR 0.4–1.4%]; mean 5.9% [sd 13.2%]) had a median PIR of 10.1 (IQR 7–14). The broad cohort of 91,206 patients across 73 ICUs (predicted mortality: 1.9% [0.6–6.5%]; 7.6% [14.9%]) had a median PIR of 7.8 (IQR 5.8–10.2). We found no association of PIR with mortality in either the narrow (PIR 1st spline term odds ratio [95% CI]: 1 [0.94, 1.06], Wald testing of spline terms p = 0.61) or the broad (1.02 [0.97, 1.07], p = 0.4) cohort.

Conclusion

We found no association of PIR with hospital mortality across ANZ ICUs. The low cohort predicted mortality may limit external validity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-021-06575-z.

COVID-19 hospital prevalence as a risk factor for mortality: an observational study of a multistate cohort of 62 hospitals

Background

The associated mortality with COVID-19 has improved compared with the early pandemic period. The effect of hospital COVID-19 patient prevalence on COVID-19 mortality has not been well studied.

Methods

We analysed data for adults with confirmed SARS-CoV-2 infection admitted to 62 hospitals within a multistate health system over 12 months. Mortality was evaluated based on patient demographic and clinical risk factors, COVID-19 hospital prevalence and calendar time period of the admission, using a generalised linear mixed model with site of care as the random effect.

Results

38 104 patients with COVID-19 were hospitalised, and during their encounters, the prevalence of COVID-19 averaged 16% of the total hospitalised population. Between March–April 2020 and January–February 2021, COVID-19 mortality declined from 19% to 12% (p<0.001). In the adjusted multivariable analysis, mid and high COVID-19 inpatient prevalence were associated with a 25% and 41% increase in the odds (absolute contribution to probability of death of 2%–3%) of COVID-19 mortality compared with patients with COVID-19 in facilities with low prevalence (<10%), respectively (high prevalence >25%: adjusted OR (AOR) 1.41, 95% CI 1.23 to 1.61; mid-prevalence (10%–25%): AOR 1.25, 95% CI 1.13 to 1.38). Mid and high COVID-19 prevalence accounted for 76% of patient encounters.

Conclusions

Although inpatient mortality for patients with COVID-19 has sharply declined compared with earlier in the pandemic, higher COVID-19 hospital prevalence remained a common risk factor for COVID-19 mortality. Hospital leaders need to reconsider how we provide support to care for patients in times of increased volume and complexity, such as those experienced during COVID-19 surges.

The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage

PURPOSE OF REVIEW

Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic.

RECENT FINDINGS

Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic.

SUMMARY

The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.

Comparison of the clinical features in open and closed format intensive care units: A systematic review and meta-analysis

IMPORTANCE

The difference in clinical outcomes between closed and open designs of intensive care units (ICUs) is still an open question.

OBJECTIVE

We conducted a systematic review and meta-analysis to compare total mortality, hospital and ICU length of stay (LOS) and mortality as primary outcomes, and severity of illness based on physiological variables, organ failure assessment, age, duration of mechanical ventilation and ventilator-associated pneumonia frequency as secondary outcomes in closed and open ICUs.

EVIDENCE REVIEW

Medline, PubMed, Scopus, Web of Science, Cochrane database, Iran-doc and Elm-net according to the MeSH terms were searched from 1988 to October 2019. The standardised mean difference (SMD), relative risk (RR) with 95% confidence interval (CI) were applied to display summary statistics of primary and secondary outcomes.

FINDINGS

A total of 90 studies with 444,042 participants were analysed. ICU mortality (RR: 1.16, CI: 1.07-1.27, p < 0.001), hospital mortality (RR: 1.12, CI: 1.03-1.22, p = 0.010) and ICU LOS (SMD: 0.43, CI: 0.01-0.85, p = 0.040) were significantly higher in open ICUs. Total mortality (RR: 0.91, CI: 0.77-1.08, p = 0.28) and hospital LOS (SMD: 1.14, CI: 1.31-3.59, p = 0.36) showed no significant difference between the two types of ICU. The secondary outcome measures were also comparable between the two ICU formats (p > 0.05).

CONCLUSIONS AND RELEVANCE

The results demonstrated superiority of closed versus open ICUs in hospital and ICU mortality rates and ICU LOS, with no difference in total mortality, hospital LOS or severity of illness parameters. The superiority of the closed ICU format may be a result of the intensivist-led patient care and should therefore be implemented by clinicians to decrease ICU mortality rates and LOS for critically ill patients.

Assessment of a novel marker of ICU strain, the ICU Activity Index, during the COVID-19 pandemic in Victoria, Australia

Objectives: To validate a real-time Intensive Care Unit (ICU) Activity Index as a marker of ICU strain from daily data available from the Critical Health Resource Information System (CHRIS), and to investigate the association between this Index and the need to transfer critically ill patients during the coronavirus disease 2019 (COVID-19) pandemic in Victoria, Australia. Design: Retrospective observational cohort study. Setting: All 45 hospitals with an ICU in Victoria, Australia. Participants: Patients in all Victorian ICUs and all critically ill patients transferred between Victorian hospitals from 27 June to 6 September 2020. Main outcome measure: Acute interhospital transfer of one or more critically ill patients per day from one site to an ICU in another hospital. Results: 150 patients were transported over 61 days from 29 hospitals (64%). ICU Activity Index scores were higher on days when critical care transfers occurred (median, 1.0 [IQR, 0.4-1.7] v 0.6 [IQR, 0.3-1.2]; P < 0.001). Transfers were more common on days of higher ICU occupancy, higher numbers of ventilated or COVID-19 patients, and when more critical care staff were unavailable. The highest ICU Activity Index scores were observed at hospitals in north-western Melbourne, where the COVID-19 disease burden was greatest. After adjusting for confounding factors, including occupancy and lack of available ICU staff, a rising ICU Activity Index score was associated with an increased risk of a critical care transfer (odds ratio, 4.10; 95% CI, 2.34-7.18; P < 0.001). Conclusions: The ICU Activity Index appeared to be a valid marker of ICU strain during the COVID-19 pandemic. It may be useful as a real-time clinical indicator of ICU activity and predict the need for redistribution of critical ill patients.

Interventions to Reduce Hospital Length of Stay in High-risk Populations: A Systematic Review

IMPORTANCE

Many strategies to reduce hospital length of stay (LOS) have been implemented, but few studies have evaluated hospital-led interventions focused on high-risk populations. The Agency for Healthcare Research and Quality (AHRQ) Learning Health System panel commissioned this study to further evaluate system-level interventions for LOS reduction.

OBJECTIVE

To identify and synthesize evidence regarding potential systems-level strategies to reduce LOS for patients at high risk for prolonged LOS.

EVIDENCE REVIEW

Multiple databases, including MEDLINE and Embase, were searched for English-language systematic reviews from January 1, 2010, through September 30, 2020, with updated searches through January 19, 2021. The scope of the protocol was determined with input from AHRQ Key Informants. Systematic reviews were included if they reported on hospital-led interventions intended to decrease LOS for high-risk populations, defined as those with high-risk medical conditions or socioeconomically vulnerable populations (eg, patients with high levels of socioeconomic risk, who are medically uninsured or underinsured, with limited English proficiency, or who are hospitalized at a safety-net, tertiary, or quaternary care institution). Exclusion criteria included interventions that were conducted outside of the hospital setting, including community health programs. Data extraction was conducted independently, with extraction of strength of evidence (SOE) ratings provided by systematic reviews; if unavailable, SOE was assessed using the AHRQ Evidence-Based Practice Center methods guide.

FINDINGS

Our searches yielded 4432 potential studies. We included 19 systematic reviews reported in 20 articles. The reviews described 8 strategies for reducing LOS in high-risk populations: discharge planning, geriatric assessment, medication management, clinical pathways, interdisciplinary or multidisciplinary care, case management, hospitalist services, and telehealth. Interventions were most frequently designed for older patients, often those who were frail (9 studies), or patients with heart failure. There were notable evidence gaps, as there were no systematic reviews studying interventions for patients with socioeconomic risk. For patients with medically complex conditions, discharge planning, medication management, and interdisciplinary care teams were associated with inconsistent outcomes (LOS, readmissions, mortality) across populations. For patients with heart failure, clinical pathways and case management were associated with reduced length of stay (clinical pathways: mean difference reduction, 1.89 [95% CI, 1.33 to 2.44] days; case management: mean difference reduction, 1.28 [95% CI, 0.52 to 2.04] days).

CONCLUSIONS AND RELEVANCE

This systematic review found inconsistent results across all high-risk populations on the effectiveness associated with interventions, such as discharge planning, that are often widely used by health systems. This systematic review highlights important evidence gaps, such as the lack of existing systematic reviews focused on patients with socioeconomic risk factors, and the need for further research.

Psychological Safety in Intensive Care Unit Rounding Teams

Rationale: Psychological safety is the condition by which members of an organization feel safe to voice concerns and take risks. Although psychological safety is an important determinant of team performance, little is known about its role in the intensive care unit (ICU). Objectives: To identify the factors associated with psychological safety and the potential influence of psychological safety on team performance in critical care. Methods: We performed daily surveys of healthcare providers in 12 ICUs within an integrated health system over a 2-week period. Survey domains included psychological safety, leader familiarity, leader inclusiveness, role clarity, job strain, and teamwork. These data were linked to daily performance on lung-protective ventilation and spontaneous breathing trials. We used regression models to examine the antecedents of psychological safety as well as the influence of psychological safety on both perceived teamwork and actual performance. Results: We received 553 responses from 270 unique providers. At the individual provider level, higher leader inclusiveness (adjusted β = 0.32; 95% confidence interval [CI], 0.24 to 0.41) and lower job strain (adjusted β = -0.07, 95% CI, -0.13 to -0.02) were independently associated with greater psychological safety. Higher psychological safety was independently associated with greater perception of teamwork (adjusted β = 0.30; 95% CI, 0.25 to 0.36). There was no association between team psychological safety and performance on either spontaneous breathing trials (incident rate ratio for each 1-unit change in team psychological safety, 0.85; 95% CI, 0.81 to 1.10) or lung-protective ventilation (incident rate ratio, 0.77; 95% CI, 0.57 to 1.04). Conclusions: Psychological safety is associated with several modifiable factors in the ICU but is not associated with actual use of evidence-based practices.

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

Association between acute psychiatric bed availability in the Veterans Health Administration and veteran suicide risk: a retrospective cohort study

BACKGROUND

Veteran suicides have increased despite mental health investments by the Veterans Health Administration (VHA).

OBJECTIVE

To examine relationships between suicide and acute inpatient psychiatric bed occupancy and other community, hospital and patient factors.

METHODS

Retrospective cohort study using administrative and publicly available data for contextual community factors. The study sample included all veterans enrolled in VHA primary care in 2011-2016 associated with 111 VHA hospitals with acute inpatient psychiatric units. Acute psychiatric bed occupancy, as a measure of access to care, was the main exposure of interest and was categorised by quarter as per cent occupied using thresholds of ≤85%, 85.1%-90%, 90.1%-95% and >95%. Hospital-level analyses were conducted using generalised linear mixed models with random intercepts for hospital, modelling number of suicides by quarter with a negative binomial distribution.

RESULTS

From 2011 to 2016, the national incidence of suicide among enrolled veterans increased from 39.7 to 41.6 per 100 000 person-years. VHA psychiatric bed occupancy decreased from a mean of 68.2% (IQR 56.5%-82.2%) to 65.4% (IQR 53.9%-79.9%). VHA hospitals with the highest occupancy (>95%) in a quarter compared with ≤85% had an adjusted incident rate ratio (IRR) for suicide of 1.10 (95% CI 1.01 to 1.19); no increased risk was observed for 85.1%-90% (IRR 0.96; 95% CI 0.89 to 1.03) or 90.1%-95% (IRR 0.96; 95% CI 0.89 to 1.04) compared with ≤85% occupancy. Of hospital and community variables, suicide risk was not associated with number of VHA or non-VHA psychiatric beds or amount spent on community mental health. Suicide risk increased by age categories, seasons, geographic regions and over time.

CONCLUSIONS

High VHA hospital occupancy (>95%) was associated with a 10% increased suicide risk for veterans whereas absolute number of beds was not, suggesting occupancy is an important access measure. Future work should clarify optimal bed occupancy to meet acute psychiatric needs and ensure adequate bed distribution.

Intensive care unit strain and mortality risk in patients admitted from the ward in Australia and New Zealand

PURPOSE

ICU strain (low number of available beds) may be associated with a delay and altered threshold for ICU admission and adverse patient outcomes. We aimed to investigate the impact of ICU strain on hospital mortality in critically ill patients admitted from wards across Australia and New Zealand.

MATERIALS AND METHODS

Ward patient admitted to ICU and ICU bed data at 137 hospitals were accessed between January 2013 and December 2016. ICU strain was classified as low (≤0.5 patients admitted per available ICU bed in a 6-h block), medium (0.5 to ≤1) or high (>1). Logistic regression models were used to examine the relationship between ICU strain and hospital mortality.

RESULTS

57,844 ICU admissions were analysed, with the majority (64.4%) admitted to medium-strain ICUs. Those admitted to high-strain ICUs spent longer in hospital prior to ICU than medium-strain or low-strain ICUs. After adjusting for confounders those admitted to high-strain ICUs [OR 1.24 (95%CI 1.14-1.35)] or medium-strain ICUs [OR 1.18 (95%CI 1.09-1.27)], (p < 0.001) had a higher risk of death compared low-strain ICUs.

CONCLUSION

ICU strain is associated with longer times in hospital prior to ICU admission and was associated with increased risk of death in patients admitted from ward.

Increased time from physiological derangement to critical care admission associates with mortality

Background

Rapid response systems aim to achieve a timely response to the deteriorating patient; however, the existing literature varies on whether timing of escalation directly affects patient outcomes. Prior studies have been limited to using ‘decision to admit’ to critical care, or arrival in the emergency department as ‘time zero’, rather than the onset of physiological deterioration. The aim of this study is to establish if duration of abnormal physiology prior to critical care admission [‘Score to Door’ (STD) time] impacts on patient outcomes.

Methods

A retrospective cross-sectional analysis of data from pooled electronic medical records from a multi-site academic hospital was performed. All unplanned adult admissions to critical care from the ward with persistent physiological derangement [defined as sustained high National Early Warning Score (NEWS) > / = 7 that did not decrease below 5] were eligible for inclusion. The primary outcome was critical care mortality. Secondary outcomes were length of critical care admission and hospital mortality. The impact of STD time was adjusted for patient factors (demographics, sickness severity, frailty, and co-morbidity) and logistic factors (timing of high NEWS, and out of hours status) utilising logistic and linear regression models.

Results

Six hundred and thirty-two patients were included over the 4-year study period, 16.3% died in critical care. STD time demonstrated a small but significant association with critical care mortality [adjusted odds ratio of 1.02 (95% CI 1.0–1.04, p = 0.01)]. It was also associated with hospital mortality (adjusted OR 1.02, 95% CI 1.0–1.04, p = 0.026), and critical care length of stay. Each hour from onset of physiological derangement increased critical care length of stay by 1.2%. STD time was influenced by the initial NEWS, but not by logistic factors such as out-of-hours status, or pre-existing patient factors such as co-morbidity or frailty.

Conclusion

In a strictly defined population of high NEWS patients, the time from onset of sustained physiological derangement to critical care admission was associated with increased critical care and hospital mortality. If corroborated in further studies, this cohort definition could be utilised alongside the ‘Score to Door’ concept as a clinical indicator within rapid response systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03650-1.

Early warning scores and critical care transfer - patient heterogeneity, low sensitivity, high mortality

Background

Emergency warning systems (EWS) are becoming a standard of care, but have unproven screening value in early critical illness. Similarly, emergency response team (ERT) care is of uncertain value. These questions are most controversial in mixed patient populations, where screening performance might vary, and intensivist-led ERT care might divert resources from existing patients.

Aims

To examine triggering events, disposition and outcome data for an intensivist-staffed EWS-ERT system.

Methods

We analysed process and outcome data over three years, classing EWS-triggered patients into three categories (non-escalated, escalated ward care and critical care transfer). The relationships between EWS data, pre-triggering clinical data, and patient disposition and outcome were examined.

Results

There were 1675 calls in 1190 patients. Most occurred later during admission, with critical care transfer in a minority; the rest were followed by escalated or non-escalated ward care. Patients transferred to critical care had high mortality (40.3%); less than half of patient transfers occurred following triggering EWS score predicted overall hospital mortality, but not mortality after critical care.

Conclusions

In a diverse hospital population, most triggering patients did not receive critical care and most critical care transfers occurred without triggering. Triggering was an insensitive screening measure for critical illness, followed by poor outcome. Higher scores predicted higher probability of transfer, but not later mortality, suggesting that EWS is being used as a decision aid but is not a true severity of illness score. Other, non-EWS data are needed for earlier detection and for prioritizing access to critical care.

Outcomes of Patients With Coronavirus Disease 2019 Receiving Organ Support Therapies: The International Viral Infection and Respiratory Illness Universal Study Registry

OBJECTIVES

To describe the outcomes of hospitalized patients in a multicenter, international coronavirus disease 2019 registry.

DESIGN

Cross-sectional observational study including coronavirus disease 2019 patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus-2 infection between February 15, 2020, and November 30, 2020, according to age and type of organ support therapies.

SETTING

About 168 hospitals in 16 countries within the Society of Critical Care Medicine's Discovery Viral Infection and Respiratory Illness University Study coronavirus disease 2019 registry.

PATIENTS

Adult hospitalized coronavirus disease 2019 patients who did and did not require various types and combinations of organ support (mechanical ventilation, renal replacement therapy, vasopressors, and extracorporeal membrane oxygenation).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Primary outcome was hospital mortality. Secondary outcomes were discharge home with or without assistance and hospital length of stay. Risk-adjusted variation in hospital mortality for patients receiving invasive mechanical ventilation was assessed by using multilevel models with hospitals as a random effect, adjusted for age, race/ethnicity, sex, and comorbidities. Among 20,608 patients with coronavirus disease 2019, the mean (± sd) age was 60.5 (±17), 11,1887 (54.3%) were men, 8,745 (42.4%) were admitted to the ICU, and 3,906 (19%) died in the hospital. Hospital mortality was 8.2% for patients receiving no organ support (n = 15,001). The most common organ support therapy was invasive mechanical ventilation (n = 5,005; 24.3%), with a hospital mortality of 49.8%. Mortality ranged from 40.8% among patients receiving only invasive mechanical ventilation (n =1,749) to 71.6% for patients receiving invasive mechanical ventilation, vasoactive drugs, and new renal replacement therapy (n = 655). Mortality was 39% for patients receiving extracorporeal membrane oxygenation (n = 389). Rates of discharge home ranged from 73.5% for patients who did not require organ support therapies to 29.8% for patients who only received invasive mechanical ventilation, and 8.8% for invasive mechanical ventilation, vasoactive drugs, and renal replacement; 10.8% of patients older than 74 years who received invasive mechanical ventilation were discharged home. Median hospital length of stay for patients on mechanical ventilation was 17.1 days (9.7-28 d). Adjusted interhospital variation in mortality among patients receiving invasive mechanical ventilation was large (median odds ratio 1.69).

CONCLUSIONS

Coronavirus disease 2019 prognosis varies by age and level of organ support. Interhospital variation in mortality of mechanically ventilated patients was not explained by patient characteristics and requires further evaluation.

Clinical characteristics and outcomes of critically ill patients with COVID-19 in Kobe, Japan: a single-center, retrospective, observational study

PURPOSE

Coronavirus disease 2019 (COVID-19) has placed a great burden on critical care services worldwide. Data regarding critically ill COVID-19 patients and their demand of critical care services outside of initial COVID-19 epicenters are lacking. This study described clinical characteristics and outcomes of critically ill COVID-19 patients and the capacity of a COVID-19-dedicated intensive care unit (ICU) in Kobe, Japan.

METHODS

This retrospective observational study included critically ill COVID-19 patients admitted to a 14-bed COVID-19-dedicated ICU in Kobe between March 3, 2020 and June 21, 2020. Clinical and daily ICU occupancy data were obtained from electrical medical records. The last follow-up day was June 28, 2020.

RESULTS

Of 32 patients included, the median hospital follow-up period was 27 (interquartile range 19-50) days. The median age was 68 (57-76) years; 23 (72%) were men and 25 (78%) had at least one comorbidity. Nineteen (59%) patients received invasive mechanical ventilation for a median duration of 14 (8-27) days. Until all patients were discharged from the ICU on June 5, 2020, the median daily ICU occupancy was 50% (36-71%). As of June 28, 2020, six (19%) died during hospitalization. Of 26 (81%) survivors, 23 (72%) were discharged from the hospital and three (9%) remained in the hospital.

CONCLUSION

During the first months of the outbreak in Kobe, most critically ill patients were men aged ≥ 60 years with at least one comorbidity and on mechanical ventilation; the ICU capacity was not strained, and the case-fatality rate was 19%.

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

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.

Value of Advance Care Directives for Patients With Serious Illness in the Era of COVID Pandemic: A Review of Challenges and Solutions

Advance care directives (ACDs) are instructions regarding what types of medical treatments a patient desires and/or who they would like to designate as a healthcare surrogate to make important healthcare decisions when the patient is mentally incapacitated. At end-of-life, when faced with poor prognosis for a meaningful health-related quality of life, most patients indicate their preference to abstain from aggressive, life-sustaining treatments. Patients whose wishes are left unsaid often receive burdensome life sustain therapy by default, prolonging patient suffering. The CoVID pandemic has strained our healthcare resources and raised the need for prioritization of life-sustaining therapy. This highlights the urgency of ACDs more than ever. Despite ACDs' potential to provide patients with care that aligns with their values and preferences and reduce resource competition, there has been relatively little conversation regarding the overlap of ACDs and CoVID-19. There is low uptake among patients, lack of training for healthcare professionals, and inequitable adoption in vulnerable populations. However, solutions are forthcoming and may include electronic medical record completion, patient outreach efforts, healthcare worker programs to increase awareness of at-risk minority patients, and restructuring of incentives and reimbursement policies. This review carefully describes the above challenges and unique opportunities to address them in the CoVID-19 era. If solutions are leveraged appropriately, ACDs have the potential to address the described challenges and ethically resolve resource conflicts during the current crisis and beyond.

Association between intensive care unit occupancy at discharge, afterhours discharges, and clinical outcomes: a historical cohort study

PURPOSE

There is a paucity of evidence evaluating whether intensive care unit (ICU) discharge occupancy is associated with clinical outcomes. It is unknown whether increased discharge occupancy leads to greater afterhours discharges and downstream consequences. We explore the association between ICU discharge occupancy and afterhours discharges, 72-hr readmission, and 30-day mortality.

METHODS

This single-centre, historical cohort study included all patients discharged from the Vancouver General Hospital ICU between 5 April 2010 and 13 September 2017. Data were obtained from the British Columbia Critical Care Database. Occupancy was defined as the number of ICU bed hours utilized divided by the available bed hours for that day. Any discharge between 22:00 and 6:59 was considered afterhours. Logistic regression models adjusting for important covariates were constructed.

RESULTS

We included 8,862 ICU discharges representing 7,288 individual patients. There were 1,180 (13.3%) afterhours discharges, 408 (4.6%) 72-hr readmissions, and 574 (6.5%) 30-day post-discharge deaths. Greater discharge occupancy was associated with afterhours discharges (per 10% increase: adjusted odds ratio [aOR], 1.12; 95% confidence interval [CI], 1.03 to 1.20; P = 0.005). Discharge occupancy was not associated with 72-hr readmission (per 10% increase: aOR, 0.97; 95% CI, 0.87 to 1.09; P = 0.62) or 30-day mortality (per 10% increase: aOR, 1.05; 95% CI, 0.95 to 1.16; P = 0.32). Afterhours discharge was not associated with 72-hr readmission (aOR, 1.15; 95% CI, 0.86 to 1.54; P = 0.34) or 30-day mortality (aOR, 1.05; 95% CI, 0.82 to 1.36; P = 0.69).

CONCLUSIONS

Greater ICU discharge occupancy was associated with a significant increase in afterhours discharges. Nevertheless, neither discharge occupancy nor afterhours discharge were associated with 72-hr readmission or 30-day mortality.

Just-in-Time Simulation to Guide Workflow Design for Coronavirus Disease 2019 Difficult Airway Management

OBJECTIVES

The coronavirus disease 2019 pandemic has required that hospitals rapidly adapt workflows and processes to limit disease spread and optimize the care of critically ill children.

DESIGN AND SETTING

As part of our institution's coronavirus disease 2019 critical care workflow design process, we developed and conducted a number of simulation exercises, increasing in complexity, progressing to intubation wearing personal protective equipment, and culminating in activation of our difficult airway team for an airway emergency.

PATIENTS AND INTERVENTIONS

In situ simulations were used to identify and rework potential failure points to generate guidance for optimal airway management in coronavirus disease 2019 suspected or positive children. Subsequent to this high-realism difficult airway simulation was a real-life difficult airway event in a patient suspected of coronavirus disease 2019 less than 12 hours later, validating potential failure points and effectiveness of rapidly generated guidance.

MEASUREMENTS AND MAIN RESULTS

A number of potential workflow challenges were identified during tabletop and physical in situ manikin-based simulations. Experienced clinicians served as participants, debriefed, and provided feedback that was incorporated into local site clinical pathways, job aids, and suggested practices. Clinical management of an actual suspected coronavirus disease 2019 patient with difficult airway demonstrated very similar success and anticipated failure points. Following debriefing and assembly of a success/failure grid, a coronavirus disease 2019 airway bundle template was created using these simulations and clinical experiences for others to adapt to their sites.

CONCLUSIONS

Integration of tabletop planning, in situ simulations, and debriefing of real coronavirus disease 2019 cases can enhance planning, training, job aids, and feasible policies/procedures that address human factors, team communication, equipment choice, and patient/provider safety in the coronavirus disease 2019 pandemic era.

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

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.