Effect of Boarding on Mortality in ICUs

Emergency critical care: closing the gap between onset of critical illness and intensive care unit admission

Critical illness is an exquisitely time-sensitive condition and follows a disease continuum, which always starts before admission to the intensive care unit (ICU), in the majority of cases even before hospital admission. Reflecting the common practice in many healthcare systems that critical care is mainly provided in the confined areas of an ICU, any delay in ICU admission of critically ill patients is associated with increased morbidity and mortality. However, if appropriate critical care interventions are provided before ICU admission, this association is not observed. Emergency critical care refers to critical care provided outside of the ICU. It encompasses the delivery of critical care interventions to and monitoring of patients at the place and time closest to the onset of critical illness as well as during transfer to the ICU. Thus, emergency critical care covers the most time-sensitive phase of critical illness and constitutes one missing link in the chain of survival of the critically ill patient. Emergency critical care is delivered whenever and wherever critical illness occurs such as in the pre-hospital setting, before and during inter-hospital transfers of critically ill patients, in the emergency department, in the operating theatres, and on hospital wards. By closing the management gap between onset of critical illness and ICU admission, emergency critical care improves patient safety and can avoid early deaths, reverse mild-to-moderate critical illness, avoid ICU admission, attenuate the severity of organ dysfunction, shorten ICU length of stay, and reduce short- and long-term mortality of critically ill patients. Future research is needed to identify effective models to implement emergency critical care systems in different healthcare systems.

Outcomes of critically ill COVID-19 patients boarding in the emergency department of a tertiary care center in a developing country: a retrospective cohort study

BACKGROUND

Boarding of critically ill patients in the emergency department (ED) has long been known to compromise patient care and affect outcomes. During the COVID-19 pandemic, multiple hospitals worldwide experienced overcrowded emergency rooms. Large influx of patients outnumbered hospital beds and required prolonged length of stay (LOS) in the ED. Our aim was to assess the ED LOS effect on mortality and morbidity, in addition to the predictors of in-hospital mortality, intubation, and complications of critically ill COVID-19 ED boarder patients.

METHODS

This was a retrospective cohort study, investigating 145 COVID-19-positive adult patients who were critically ill, required intensive care unit (ICU), and boarded in the ED of a tertiary care center in Lebanon. Data on patients who boarded in the emergency from January 1, 2020, till January 31, 2021, was gathered and studied.

RESULTS

Overall, 66% of patients died, 60% required intubation, and 88% developed complications. Multiple risk factors were associated with mortality naming age above 65 years, vasopressor use, severe COVID pneumonia findings on CT chest, chemotherapy treatment in the previous year, cardiovascular diseases, chronic kidney diseases, prolonged ED LOS, and low SaO2 < 95% on triage. In addition, our study showed that staying long hours in the ED increased the risk of developing complications.

CONCLUSION

To conclude, all efforts need to be drawn to re-establish mitigation strategies and models of critical care delivery in the ED to alleviate the burden of critical boarders during pandemics, thus decreasing morbidity and mortality rates. Lessons from this pandemic should raise concern for complications seen in ED ICU boarders and allow the promotion of health measures optimizing resource allocation in future pandemic crises.

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.

Association of care by a non-medical intensive care unit provider team with outcomes of medically critically ill patients

OBJECTIVE

To determine the association of boarding of critically ill medical patients on non-medical intensive care unit (ICU) provider teams with outcomes.

DESIGN

A retrospective cohort study.

SETTING

ICUs in a tertiary academic medical center.

PATIENTS

Patients with medical critical illness.

INTERVENTIONS

None.

MEASUREMENT AND MAIN RESULTS

We compared outcomes for critically ill medical patients admitted to a non-medical specialty ICU team (April 1 - August 30, 2020) with those admitted to the medical ICU team (January 1, 2018 - March 31, 2020). The primary outcome was hospital mortality; secondary outcomes were hospital length of stay (LOS) and hospital disposition for survivors. Our cohort consisted of 1241 patients admitted to the medical ICU team and 230 admitted to non-medical ICU teams. Unadjusted hospital mortality (medical ICU, 38.8% vs non-medical ICU, 42.2%, p = 0.33) and hospital LOS (7.4 vs 7.4 days, p = 0.96) were similar between teams. Among survivors, more non-medical ICU team patients were discharged home (72.6% vs 82.0%, p = 0.024). After multivariable adjustment, we found no difference in mortality, LOS, or home discharge between teams. However, among hospital survivors, admission to a non-medical ICU team was associated with a longer LOS (regression coefficient [95% CI] for log-transformed hospital LOS: 0.23 [0.05,0.40], p = 0.022). Certain subgroups-patients aged 50-64 years (odds-ratio [95% CI]: 4.22 [1.84,9.65], p = 0.001), with ≤10 comorbidities (0-5: 2.78 (1.11,6.95], p = 0.029; 6-10: 6.61 [1.38,31.71], p = 0.018), without acute respiratory failure (1.97 [1.20,3.23], p = 0.008)-had higher mortality when admitted to non-medical ICU teams.

CONCLUSIONS

We found no association between admission to non-medical ICU team and mortality for medically critically ill patients. However, survivors experienced longer hospital LOS when admitted to non-medical ICU teams. Middle-aged patients, those with low comorbidity burden, and those without respiratory failure had higher mortality when admitted to non-medical ICU teams.

A prognostic model for 1-month mortality in the postoperative intensive care unit

PURPOSES

Recognizing which patients admitted postsurgically to the intensive care unit (ICU) are at greater risk of mortality assists medical staff to identify who will benefit most from the care. We developed a prediction model for the 1-month mortality of postsurgical ICU patients.

METHODS

From May, 2019 to May, 2020, we conducted a prospective cohort study in the postsurgical ICU of a teaching hospital affiliated with our University of Medical Sciences. The outcome was death within 1 month of admission and the predictors were a variety of anthropometric and clinical features. The subjects of this analysis were 805 consecutive adult postsurgical patients with a mean (SD) age of 54.8 (18.9) years.

RESULTS

Overall, the resulted logistic model was well-fitted [χ² (26) = 772.097, p < 0.001, Nagelkerke R² = 0.814] accurate (88%), and specific (92%). The adjusted odds ratio for body temperature was 0.51, p < 0.001. Patients with comorbidities and those undergoing multiple operations were at a greater risk of mortality, odds = 10.00 and 10.65 (both p < 0.001).

CONCLUSIONS

Higher body temperature at the time of postoperative ICU admission is a protective factor against 1-month mortality. Our study found that patients with several comorbidities and those who have undergone multiple operations are at a greater risk of a poor outcome.

Boarding of Critically Ill Patients in the Emergency Department

Supplemental Digital Content is available in the text.

Objectives:

Emergency department boarding is the practice of caring for admitted patients in the emergency department after hospital admission, and boarding has been a growing problem in the United States. Boarding of the critically ill has achieved specific attention because of its association with poor clinical outcomes. Accordingly, the Society of Critical Care Medicine and the American College of Emergency Physicians convened a Task Force to understand the implications of emergency department boarding of the critically ill. The objective of this article is to review the U.S. literature on (1) the frequency of emergency department boarding among the critically ill, (2) the outcomes associated with critical care patient boarding, and (3) local strategies developed to mitigate the impact of emergency department critical care boarding on patient outcomes.

Data Sources and Study Selection:

Review article.

Data Extraction and Data Synthesis:

Emergency department–based boarding of the critically ill patient is common, but no nationally representative frequency estimates has been reported. Boarding literature is limited by variation in the definitions used for boarding and variation in the facilities studied (boarding ranges from 2% to 88% of ICU admissions). Prolonged boarding in the emergency department has been associated with longer duration of mechanical ventilation, longer ICU and hospital length of stay, and higher mortality. Health systems have developed multiple mitigation strategies to address emergency department boarding of critically ill patients, including emergency department-based interventions, hospital-based interventions, and emergency department–based resuscitation care units.

Conclusions:

Emergency department boarding of critically ill patients was common and was associated with worse clinical outcomes. Health systems have generated a number of strategies to mitigate these effects. A definition for emergency department boarding is proposed. Future work should establish formal criteria for analysis and benchmarking of emergency department–based boarding overall, with subsequent efforts focused on developing and reporting innovative strategies that improve clinical outcomes of critically ill patients boarded in the emergency department.

Use of a Novel Patient-Flow Model to Optimize Hospital Bed Capacity for Medical Patients

BACKGROUND

There is no known method for determining the minimum number of beds in hospital inpatient units (IPs) to achieve patient waiting-time targets. This study aims to determine the relationship between patient waiting time-related performance measures and bed utilization, so as to optimize IP capacity decisions.

METHODS

The researchers simulated a novel queueing model specifically developed for the IPs. The model takes into account salient features of patient-flow dynamics and was validated against hospital census data. The team used the model to evaluate inpatient capacity decisions against multiple waiting time outcomes: (1) daily average, peak-hour average, and daily maximum waiting times; and (2) proportion of patients waiting strictly more than 0, 1, and 2 hours. The results were published in a simple Microsoft Excel toolbox to allow administrators to conduct sensitivity analysis.

RESULTS

To achieve the hospital's goal of rooming patients within 30 to 60 minutes of IP bed requests, the model predicted that the optimal daily average occupancy levels should be 89%-92% (182-188 beds) in the Medicine cohort, 74%-79% (41-43 beds) in the Cardiology cohort, and 72%-78% (23-25 beds) in the Observation cohort. Larger IP cohorts can achieve the same queueing-related performance measure as smaller ones, while tolerating a higher occupancy level. Moreover, patient waiting time increases rapidly as the occupancy level approaches 100%.

CONCLUSION

No universal optimal IP occupancy level exists. Capacity decisions should therefore be made on a cohort-by-cohort basis, incorporating the comprehensive patient-flow characteristics of each cohort. To this end, patient-flow queueing models tailored to the IPs are needed.

The cost of waiting: Association of ED boarding with hospitalization costs

BACKGROUND

Emergency Department (ED) boarding, the practice of holding patients in the ED after they have been admitted to the hospital due to unavailability of inpatient beds, is common and contributes to the public health crisis of ED crowding. Prior work has documented the harms of ED boarding on access and quality of care. Limited studies examine the relationship between ED boarding and an equally important domain of quality-the cost of care. This study evaluates the relationship between ED boarding, ED characteristics and risk-adjusted hospitalization costs utilizing national publicly-reported measures.

METHODS

We conducted a cross-sectional analysis of two 2018 Centers for Medicare and Medicaid Services (CMS) Hospital Compare datasets: 1) Medicare Hospital Spending per Patient and 2) Timely and Effective Care. We constructed a hospital-level multivariate linear regression analysis to examine the association between ED boarding and Medicare spending per beneficiary (MSPB), adjusting for ED length of stay, door to diagnostic evaluation time, and ED patient volume.

RESULTS

A total of 2903 hospitals were included in the analysis. ED boarding was significantly correlated with MSPB (r = 0.1774; p-value: < 0.0001). In multivariate regression, ED boarding was also positively associated with MSPB (Beta: 0.00015; p < 0.0001) after adjustment for other hospital level crowding indicators.

CONCLUSION

We found a strong relationship between measures of ED crowding, including ED boarding, and risk-adjusted hospital spending. Future work should elucidate the mediators of this relationship. Policymakers and administrators should consider the financial harms of ED boarding when devising strategies to improve hospital care access and flow.

Boarding of critically Ill patients in the emergency department

OBJECTIVES

Emergency department boarding is the practice of caring for admitted patients in the emergency department after hospital admission, and boarding has been a growing problem in the United States. Boarding of the critically ill has achieved specific attention because of its association with poor clinical outcomes. Accordingly, the Society of Critical Care Medicine and the American College of Emergency Physicians convened a Task Force to understand the implications of emergency department boarding of the critically ill. The objective of this article is to review the U.S. literature on (1) the frequency of emergency department boarding among the critically ill, (2) the outcomes associated with critical care patient boarding, and (3) local strategies developed to mitigate the impact of emergency department critical care boarding on patient outcomes.

DATA SOURCES AND STUDY SELECTION

Review article.

DATA EXTRACTION AND DATA SYNTHESIS

Emergency department-based boarding of the critically ill patient is common, but no nationally representative frequency estimates has been reported. Boarding literature is limited by variation in the definitions used for boarding and variation in the facilities studied (boarding ranges from 2% to 88% of ICU admissions). Prolonged boarding in the emergency department has been associated with longer duration of mechanical ventilation, longer ICU and hospital length of stay, and higher mortality. Health systems have developed multiple mitigation strategies to address emergency department boarding of critically ill patients, including emergency department-based interventions, hospital-based interventions, and emergency department-based resuscitation care units.

CONCLUSIONS

Emergency department boarding of critically ill patients was common and was associated with worse clinical outcomes. Health systems have generated a number of strategies to mitigate these effects. A definition for emergency department boarding is proposed. Future work should establish formal criteria for analysis and benchmarking of emergency department-based boarding overall, with subsequent efforts focused on developing and reporting innovative strategies that improve clinical outcomes of critically ill patients boarded in the emergency department.

Critical Care Delivery Solutions in the Emergency Department: Evolving Models in Caring for ICU Boarders

The National Academy of Medicine has identified emergency department (ED) crowding as a health care delivery problem. Because the ED is a portal of entry to the hospital, 25% of all ED encounters are related to critical illness. Crowding at both an ED and hospital level can thus lead to boarding of a number of critically ill patients in the ED. EDs are required to not only deliver immediate resuscitative and stabilizing care to critically ill patients on presentation but also provide longitudinal care while boarding for the ICU. Crowding and boarding are multifactorial and complex issues, for which different models for delivery of critical care in the ED have been described. Herein, we provide a narrative review of different models of delivery of critical care reported in the literature and highlight aspects for consideration for successful local implementation.

The Impact of an ICU "Bed Ahead" Policy on ED Length of Stay and Patient Outcomes

OBJECTIVE

To assess the impact of preassigning a single bed in the surgical intensive care unit (SICU) for the next trauma admission.

BACKGROUND

Prolonged emergency department (ED) dwell time before admission to a critical care unit has an adverse effect on patient outcomes and is often due to the lack of an available bed in the intensive care unit (ICU).

METHODS

A "Bed Ahead" policy was instituted at an urban level 1 Trauma Public Safety Net Teaching Hospital to preassign 1 SICU bed for the next trauma patient who warrants a critical care admission. A retrospective review of all trauma patients admitted to the SICU before and after implementation of this policy was performed to assess the impact on ED dwell time, ICU and hospital lengths of stay, complications, and in-hospital mortality.

RESULTS

ED length of stay (ED-LOS); ICU length of stay (ICU-LOS); hospital length of stay (HLOS); complications; and in-hospital mortality were compared before (PRE) and after (POST)implementation of the Bed Ahead policy. Statistically significant improvements were seen in the POST period for ED-LOS, HLOS, complications, and in-hospital mortality.

CONCLUSIONS

Preassigning 1 ICU for the yet to arrive next injured patient decreases ED dwell times, complications, HLOS, and in-hospital mortality.

Associations between hospital occupancy, intensive care unit transfer delay and hospital mortality

PURPOSE

Hospital occupancy (HospOcc) pressures often lead to longer intensive care unit (ICU) stay after physician recognition of discharge readiness. We evaluated the relationships between HospOcc, extended ICU stay, and patient outcomes.

MATERIALS AND METHODS

7-year retrospective cohort study of 8500 alive discharge encounters from 4 adult ICUs of a tertiary hospital. We estimated associations between i) HospOcc and ICU transfer delay; and ii) ICU transfer delay and hospital mortality.

RESULTS

Median (IQR) ICU transfer delay was 4.8 h (1.6-11.7), 1.4% (119) suffered in-hospital death, and 4% (341) were readmitted. HospOcc was non-linearly related with ICU transfer delay, with a spline knot at 80% (mean transfer delay 8.8 h [95% CI: 8.24, 9.38]). Higher HospOcc level above 80% was associated with longer transfer delays, (mean increase 5.4% per % HospOcc increase; 95% CI, 4.7 to 6.1; P < .001). Longer ICU transfer delay was associated with increasing odds of in-hospital death or ICU readmission (odds ratio 1.01 per hour; 95% CI 1.00 to 1.01; P = .04) but not with ICU readmission alone (OR 1.01 per hour; 95% CI 1.00 to 1.01, P = .14).

CONCLUSIONS

ICU transfer delay exponentially increased above a threshold hospital occupancy and may be associated with increased hospital mortality.

Association entre mortalité et attente aux urgences chez les adultes à hospitaliser pour étiologies médicales

Introduction : Notre objectif principal est de comparer, dans un centre hospitalier universitaire (CHU) français et chez les patients hospitalisés pour étiologies médicales à partir de la structure des urgences (SU), le taux de mortalité intrahospitalière entre ceux qui n’attendent pas faute de place en service et ceux en attente (boarding).

Méthode : Il s’agit d’une étude quasi expérimentale, monocentrique, observationnelle, rétrospective, par recueil d’informations à partir des dossiers patients informatisés. Nous avons appliqué un score de propension pour ajuster les critères de jugement aux variables mesurées dans les deux groupes, c’est-à-dire les données : 1) démographiques (âge et sexe) ; 2) médicales (niveau de triage) ; 3) biologiques (numération leucocytaire, hémoglobinémie, natrémie, kaliémie, taux sérique de CRP, créatininémie) ; 4) d’imageries (réalisation ou non de radiographie, d’échographie, d’imagerie par résonance magnétique, de tomodensitométrie).

Résultats : En 2017, la SU du CHU a admis 60 062 patients adultes. Sur les 15 496 patients hospitalisés après admission en SU, 6 997 l’ont été pour une étiologie médicale, dont 2 546 (36 %) sans attente et 4 451 (64 %) après une attente. Après pondération, le taux de mortalité intrahospitalière était plus important dans le groupe en attente : 7,8 vs 6,3 % ; p < 0,05. De même, la durée médiane d’hospitalisation était plus importante dans le groupe en attente : 7,6 [4,7– 12,0] vs 7,1 j [4,3–11,5] ; p < 0,01.

Discussion : Les taux de mortalité et de la durée de séjour intrahospitaliers sont plus importants chez les patients étudiés qui attendent en SU faute de place en service. Nos résultats sont concordants avec la littérature internationale. Il est nécessaire de trouver des solutions pour réduire cette surmortalité.

Increased Healthcare-Associated Infections in a Surgical Intensive Care Unit Related to Boarding Non-Surgical Patients

Background: Hospital over-capacity often forces boarding patients outside of their designated intensive care unit (ICU). Anecdotal evidence suggested medical intensive care unit (MICU) patients boarding in the surgical intensive care unit (SICU) were responsible for increases in healthcare-associated infection (HAI) rates. We studied the effect of ICU boarding on rates of SICU HAIs. Methods: This single-center, retrospective two-year database study compared primary SICU patients (Home) to MICU patients boarding in the SICU (Boarders). Variables studied included age, gender, Acute Physiology and Chronic Health Evaluation III (APACHE III) scores, and comorbidities. Healthcare-associated infections included Clostridium difficile infection, catheter-associated urinary tract infections, central line-associated blood stream infection, and ventilator-associated pneumonia. Student t-test, Fisher exact testing, and a multivariable regression model were used to determine the significance of associations. Results: A total of 2,562 patients were included in the study; 328 (12.8%) were Boarders and 2,234 (87.2%) were Home. Univariable analysis demonstrated that Boarders were older (64.0 ± 16.9 vs. 60.2 ± 17.4), more severely ill (APACHE III score 70.5 ± 31.1 vs. 53.4 ± 21.9), more likely to have cirrhosis, coronary artery disease, and asthma/chronic obstructive pulmonary disease, but less likely to have hypertension. On univariable analysis boarding was associated with an increase HAI rate (19 HAI/1,000 patient days vs. 6.2, p < 0.001). Multivariable regression modeling demonstrated boarding status remained independently associated with HAI (odds ratio [OR] 1.83 95% confidence interval [CI] 1.02-3.27). Cost estimates demonstrated an additional cost of $83,303 per 1,000 patient days. Conclusion: The practice of hospital boarding is associated with development of HAI and increased hospital costs. Efforts at determining the cause of this increase and then reducing HAIs will improve patient care and help hospital budgets.