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

Data-driven coaching to improve statewide outcomes in CABG: before and after interventional study

BACKGROUND

The impact of quality improvement initiatives program (QIP) on coronary artery bypass grafting surgery (CABG) remains scarce, despite improved outcomes in other surgical areas. This study aims to evaluate the impact of a package of QIP on mortality rates among patients undergoing CABG.

MATERIALS AND METHODS

This prospective cohort study utilized data from the multicenter database Registro Paulista de Cirurgia Cardiovascular II (REPLICCAR II), spanning from July 2017 to June 2019. Data from 4018 isolated CABG adult patients were collected and analyzed in three phases: before-implementation, implementation, and after-implementation of the intervention (which comprised QIP training for the hospital team). Propensity Score Matching was used to balance the groups of 2170 patients each for a comparative analysis of the following outcomes: reoperation, deep sternal wound infection/mediastinitis ≤30 days, cerebrovascular accident, acute kidney injury, ventilation time >24 h, length of stay <6 days, length of stay >14 days, morbidity and mortality, and operative mortality. A multiple regression model was constructed to predict mortality outcomes.

RESULTS

Following implementation, there was a significant reduction of operative mortality (61.7%, P =0.046), as well as deep sternal wound infection/mediastinitis ( P <0.001), sepsis ( P =0.002), ventilation time in hours ( P <0.001), prolonged ventilation time ( P =0.009), postoperative peak blood glucose ( P <0.001), total length of hospital stay ( P <0.001). Additionally, there was a greater use of arterial grafts, including internal thoracic ( P <0.001) and radial ( P =0.038), along with a higher rate of skeletonized dissection of the internal thoracic artery.

CONCLUSIONS

QIP was associated with a 61.7% reduction in operative mortality following CABG. Although not all complications exhibited a decline, the reduction in mortality suggests a possible decrease in failure to rescue during the after-implementation period.

Association Between In-hospital Mortality and the Institutional Factors of Intensive Care Units with a Focus on the Intensivist- to-bed Ratio: A Retrospective Cohort Study

Purpose: To elucidate the relationship between in-hospital mortality and the institutional factors of intensive care units (ICUs), with a focus on the intensivist-to-bed ratio. Methods: A retrospective cohort study was conducted using a Japanese ICU database, including adult patients admitted between April 1, 2020 and March 31, 2021. We used a multilevel logistic regression model to investigate the associations between in-hospital mortality and the following institutional factors: the intensivist-to-bed ratios on weekdays or over weekends/holidays, different work shifts, hospital-to-ICU-bed ratio, annual-ICU-admission-to-bed ratio, type of hospital, and the presence of other medical staff. Results: The study population comprised 46 503 patients admitted to 65 ICUs. The in-hospital mortality rate was 8.1%. The median numbers of ICU beds and intensivists were 12 (interquartile range [IQR] 8-14) and 4 (IQR 2-9), respectively. In-hospital mortality decreased significantly as the intensivist-to-bed ratio at 10 am on weekdays increased: the average contrast indicated a 20% (95% confidence interval [CI]: 1%-38%) reduction when the ratio increased from 0 to 0.5, and a 38% (95% CI: 9%-67%) reduction when the ratio increased from 0 to 1. The other institutional factors did not present a significant effect. Conclusions: The intensivist-to-bed ratio at 10 am on weekdays had a significant effect on in-hospital mortality. Further investigation is needed to understand the processes leading to improved outcomes.

Management and outcomes of COVID-19 patients admitted in a newly created ICU and an expert ICU, a retrospective observational study

BACKGROUND

The COVID-19 pandemic abruptly increased the inflow of patients requiring intensive care units (ICU). French health institutions responded by a twofold capacity increase with temporary upgraded beds, supplemental beds in pre-existing ICUs, or newly created units (New-ICU). We aimed to compare outcomes according to admission in expert pre-existing ICUs or in New-ICU.

METHODS

This multicenter retrospective observational study was conducted in two 20-bed expert ICUs of a University Hospital (Expert-ICU) and in one 16-bed New-ICU in a private clinic managed respectively by 3 and 2 physicians during daytime and by one physician during the night shift. All consecutive adult patients with COVID-19-related acute hypoxemic respiratory failure admitted after centralized regional management by a dedicated crisis cell were included. The primary outcome was 180-day mortality. Propensity score matching and restricted cubic spline for predicted mortality over time were performed.

RESULTS

During the study period, 165 and 176 patients were enrolled in Expert-ICU and New-ICU respectively, 162 (98%) and 157 (89%) patients were analyzed. The unadjusted 180-day mortality was 30.8% in Expert-ICU and 28.7% in New-ICU, (log-rank test, p =  0.7). After propensity score matching, 123 pairs (76 and 78%) of patients were matched, with no significant difference in mortality (32% vs. 32%, OR 1.00 [0.89; 1.12], p = 1). Adjusted predicted mortality decreased over time (p < 0.01) in both Expert-ICU and New-ICU.

CONCLUSIONS

In COVID-19 patients with acute hypoxemic respiratory failure, hospitalization in a new ICU was not associated with mortality at day 180.

Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio

BACKGROUND

The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients.

METHODS

Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis.

RESULTS

Our protocol describes the processes and methods for an observational study in the ICU.

CONCLUSIONS

This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.

Perception of the safety culture in a critical area

INTRODUCTION

Critical care Area (CCA) is one of the most complex in the hospital system, requiring a high number of interventions and handling of amounts of information. Therefore, these areas are likely to experience more incidents that compromise patient safety (PS).

AIM

To determine the perception of the healthcare team in a critical care area about the patient safety culture.

METHOD

Cross-sectional descriptive study, September 2021, in a polyvalent CCA with 45 beds, 118 health workers (physicians, nurses, auxiliary nursing care technicians). Sociodemographic variables, knowledge of the person in charge in PS and their general training in PS and incident notification system were collected. The validated Hospital Survey on Patient Safety Culture questionnaire, measuring 12 dimensions was used. Positive responses with an average score ≥75%, were defined as an area of strength while ≥50% negative responses were defined as an area of weakness. Descriptive statistics and bivariate analysis: X2 and t-Student tests, and ANOVA. Significance p ≤ 0.05.

RESULTS

94 questionnaires were collected (79.7% sample). The PS score was 7.1 (1.2) range 1-10. The rotational staff scored the PS with 6.9 (1.2) compared to 7.8 (0.9) for non-rotational staff (p = 0.04). A 54.3% (n = 51) was familiar with the incident reporting procedure, 53% (n = 27) of which had not reported any in the last year. No dimension was defined as strength. There were three dimensions that behaved like a weakness: security perception: 57.7% (95% CI: 52.7-62.6), staffing: 81.7% (95% CI: 77.4-85.2) and management support: 69 .9% (95% CI: 64.3-74.9).

CONCLUSIONS

The assessment of PS in the CCA is moderately high, although the rotational staff has a lower appreciation. Half of the staff do not know the procedure for reporting an incident. The notification rate is low. The weaknesses detected are perception of security, staffing and management support. The analysis of the patient safety culture can be useful to implement improvement measures.

Staffing levels and hospital mortality in England: a national panel study using routinely collected data

OBJECTIVES

Examine the association between multiple clinical staff levels and case-mix adjusted patient mortality in English hospitals. Most studies investigating the association between hospital staffing levels and mortality have focused on single professional groups, in particular nursing. However, single staff group studies might overestimate effects or neglect important contributions to patient safety from other staff groups.

DESIGN

Retrospective observational study of routinely available data.

SETTING AND PARTICIPANTS

138 National Health Service hospital trusts that provided general acute adult services in England between 2015 and 2019.

OUTCOME MEASURE

Standardised mortality rates were derived from the Summary Hospital level Mortality Indicator data set, with observed deaths as outcome in our models and expected deaths as offset. Staffing levels were calculated as the ratio of occupied beds per staff group. We developed negative binomial random-effects models with trust as random effects.

RESULTS

Hospitals with lower levels of medical and allied healthcare professional (AHP) staff (e.g, occupational therapy, physiotherapy, radiography, speech and language therapy) had significantly higher mortality rates (rate ratio: 1.04, 95% CI 1.02 to 1.06, and 1.04, 95% CI 1.02 to 1.06, respectively), while those with lower support staff had lower mortality rates (0.85, 95% CI 0.79 to 0.91 for nurse support, and 1.00, 95% CI 0.99 to 1.00 for AHP support). Estimates of the association between staffing levels and mortality were stronger between-hospitals than within-hospitals, which were not statistically significant in a within-between random effects model.

CONCLUSIONS

In additional to medicine and nursing, AHP staffing levels may influence hospital mortality rates. Considering multiple staff groups simultaneously when examining the association between hospital mortality and clinical staffing levels is crucial.

TRIAL REGISTRATION NUMBER

NCT04374812.

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.

Health and Well-Being of Intensive Care Unit Physicians: How to Ensure the Longevity of a Critical Specialty

A second epidemic of burnout, fatigue, anxiety, and moral distress has emerged concurrently with the coronavirus disease 2019 (COVID-19) pandemic, and critical care physicians are especially affected. This article reviews the history of burnout in health care workers, presents the signs and symptoms, discusses the specific impact of the COVID-19 pandemic on intensive care unit caregivers, and attempts to identify potential strategies to combat the Great Resignation disproportionately affecting health care workers. The article also focuses on how the specialty can amplify the voices and highlight the leadership potential of underrepresented minorities, physicians with disabilities, and the aging physician population.

Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study

Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed.

OBJECTIVES

To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition.

DESIGN SETTING AND PARTICIPANTS

This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016-May 2017) and after (June 2017-November 2017) the transition to a high-intensity ICU staffing model.

MAIN OUTCOMES AND MEASURES

The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission.

RESULTS

For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61-1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57-1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001).

CONCLUSIONS AND RELEVANCE

Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.

The effects of physicians on operational and financial performance in United States hospitals: staffing, human capital and knowledge spillovers

Purpose

The authors examine how physician staffing, human capital and knowledge spillovers are related to multiple dimensions of hospital operational and financial performance at the organizational level.

Design/methodology/approach

The authors use a data set assembled from multiple sources for more than 1,300 US hospitals and employ hierarchical linear regression to test this study’s hypotheses. The authors use multiple quality, efficiency and financial measures of performance for these hospitals.

Findings

The authors find that higher levels of staffing, skills and knowledge spillovers associated with physicians were positively associated with multiple dimensions of hospital performance. The authors find linear and nonlinear relationships between experience and performance, with the relationships primarily negative, and nonlinear relationships between spillovers and quality performance.

Practical implications

Hospital managers should consider increasing physician staffing levels if possible. In addition, the overall Final MIPS Score from the Centers for Medicare and Medicaid Services might be included as a factor in determining which physicians practice in a hospital. Finally, if possible, encouraging physicians to practice at multiple hospitals will likely be beneficial to hospital performance.

Originality/value

This study’s findings are original in that they explore how physician-specific staffing and human capital, which have received comparatively little attention in the literature, are related to several different dimensions of hospital-level operational and financial performance. To the best of the authors’ knowledge, this paper is also the first to examine the relationship between the construct of physician knowledge spillovers and hospital-level operational and financial performance.

Mortality analysis among sepsis patients in and out of intensive care units using the Japanese nationwide medical claims database: a study by the Japan Sepsis Alliance study group

BACKGROUND

A substantial number of sepsis patients require specialized care, including multidisciplinary care, close monitoring, and artificial organ support in the intensive care unit (ICU). However, the efficacy of ICU management on clinical outcomes remains insufficiently researched. Therefore, we tested the hypothesis that ICU admission would increase the survival rate among sepsis patients.

METHODS

We conducted a retrospective study using the nationwide medical claims database of sepsis patients in Japan from 2010 to 2017 with propensity score matching to adjust for baseline imbalances. Patients aged over 20 years, with a combined diagnosis of presumed serious infection and organ failure, were included in this study. The primary outcome studied was the in-hospital mortality among non-ICU and ICU patients. In addition to propensity score matching, we performed a multivariable logistic regression analysis for the primary outcome. As the treatment policy was not extracted from the database, we performed sensitivity analyses to determine mortality differences in adults (20 ≤ age ≤ 64), independent patients, patients without malignant tumors, based on the assumption that treatment intensity is likely to increase in those population.

RESULTS

Among 1,167,901 sepsis patients (974,289 in non-ICU and 193,612 in ICU settings), the unadjusted in-hospital mortality was 22.5% among non-ICU patients and 26.2% among ICU patients (3.7% [95% CI 3.5-3.9]). After propensity score matching, the in-hospital mortality was 29.2% among non-ICU patients and 25.8% among ICU patients ([Formula: see text] 3.4% [95% CI [Formula: see text] 3.7 to [Formula: see text] 3.1]). In-hospital mortality with a multivariable regression analysis ([Formula: see text] 5.0% [95% CI [Formula: see text] 5.2 to [Formula: see text] 4.8]) was comparable with the results of the propensity score matching analysis. In the sensitivity analyses, the mortality differences between non-ICU and ICU in adults, independent patients, and patients without malignant tumors were [Formula: see text] 2.7% [95% CI [Formula: see text] 3.3 to [Formula: see text] 2.2], [Formula: see text] 5.8% [95% CI [Formula: see text] 6.4 to [Formula: see text] 5.2], and [Formula: see text] 1.3% [95% CI [Formula: see text] 1.7 to [Formula: see text] 1.0], respectively.

CONCLUSIONS

Herein, using the nationwide medical claims database, we demonstrated that ICU admission was potentially associated with decreasing in-hospital mortality among sepsis patients. Further investigations are warranted to validate these results and elucidate the mechanisms favoring ICU management on clinical outcomes.

Variation in Bed-to-Physician Ratios During Weekday Daytime Hours in ICUs in Australia and New Zealand

OBJECTIVES

To determine common "bed-to-physician" ratios during weekday hours across ICUs and assess factors associated with variability in this ratio.

DESIGN

Retrospective cohort study.

SETTING

All ICUs in Australia/New Zealand that participated in a staffing survey administered in 2017-2018.

PATIENTS

ICU admissions from 2016 to 2018.

METHODS

We linked survey data with patient-level data. We defined: 1) bed-to-intensivist ratio as the number of usually available ICU beds divided by the number of onsite weekday daytime intensivists; and 2) bed-to-physician ratio as the number of available ICU beds divided by the total number of physicians (intensivists + nonintensivists, including trainees). We calculated the median and interquartile range (IQR) of bed-to-intensivist ratio and bed-to-physician ratios during weekday hours. We assessed variability in each by type of hospital and ICU and by severity of illness of patients, defined by the predicted hospital mortality.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the 123 (87.2%) of Australia/New Zealand ICUs that returned staffing surveys, 114 (92.7%) had an intensivist present during weekday daytime hours, and 116 (94.3%) reported at least one nonintensivist physician. The median bed-to-intensivist ratio was 8.0 (IQR, 6.0-11.4), which decreased to a bed-to-physician ratio of 3.0 (IQR, 2.2-4.9). These ratios varied with mean severity of illness of the patients in the unit. The median bed-to-intensivist ratio was highest (13.5) for ICUs with a mean predicted mortality > 2-4%, and the median bed-to-physician ratio was highest (5.7) for ICUs with a mean predicted mortality of > 4-6%. Both ratios decreased and plateaued in ICUs with a mean predicted mortality for patients greater than 8% (median bed-to-intensivist ratio range, 6.8-8.0, and bed-to-physician ratio range of 2.4-2.7).

CONCLUSIONS

Weekday bed-to-physician ratios in Australia/New Zealand ICUs are lower than the bed-to-intensivist ratios and have a relatively fixed ratio of less than 3 for units taking care of patients with a higher average severity of illness. These relationships may be different in other countries or healthcare systems.

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.

Impact of ICU strain on outcomes

PURPOSE OF REVIEW

Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes.

RECENT FINDINGS

This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions.

SUMMARY

An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.

Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

SWEAT ICU-An Observational Study of Physician Workload and the Association of Physician Outcomes in Academic ICUs

The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality.

DESIGN

Cross-sectional observational study.

SETTING

Fourteen academic centers in the United States from August 2020 to July 2021.

SUBJECTS

We enrolled ICU physicians and collected data on adult ICU patients under the physician's care on the single physician-selected study day for each physician.

MEASUREMENTS and MAIN RESULTS

The primary exposure was workload (self-reported number of patients' physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10-14), and the overall prevalence of burnout was 26.4% (n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24-2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92-1.91).

CONCLUSIONS

In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.

Association of Anesthesiologist Staffing Ratio With Surgical Patient Morbidity and Mortality

Importance

Recent studies have investigated the effect of overlapping surgeon responsibilities or nurse to patient staffing ratios on patient outcomes, but the association of overlapping anesthesiologist responsibilities with patient outcomes remains unexplored to our knowledge.

Objective

To examine the association between different levels of anesthesiologist staffing ratios and surgical patient morbidity and mortality.

Design, Setting, and Participants

A retrospective, matched cohort study consisting of major noncardiac inpatient surgical procedures performed from January 1, 2010, to October 31, 2017, was conducted in 23 US academic and private hospitals. A total of 866 453 adult patients (aged ≥18 years) undergoing major inpatient surgery within the Multicenter Perioperative Outcomes Group electronic health record registry were included. Anesthesiologist sign-in and sign-out times were used to calculate a continuous time-weighted average staffing ratio variable for each operation. Propensity score-matching methods were applied to create balanced sample groups with respect to patient-, operative-, and hospital-level confounders and resulted in 4 groups based on anesthesiologist staffing ratio. Groups consisted of patients receiving care from an anesthesiologist covering 1 operation (group 1), more than 1 to no more than 2 overlapping operations (group 1-2), more than 2 to no more than 3 overlapping operations (group 2-3), and more than 3 to no more than 4 overlapping operations (group 3-4). Data analysis was performed from October 2019 to October 2021.

Exposure

Undergoing a major inpatient surgical operation that involved an anesthesiologist providing care for up to 4 overlapping operations.

Main Outcomes and Measures

The primary composite outcome was 30-day mortality and 6 major surgical morbidities (cardiac, respiratory, gastrointestinal, urinary, bleeding, and infectious complications) derived from International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision discharge diagnosis codes.

Results

In all, 578 815 adult patients (mean [SD] age, 55.7 [16.2] years; 55.1% female) were analyzed. After matching operations according to anesthesiologist staffing ratio, 48 555 patients were in group 1; 247 057, group 1-2; 216 193, group 2-3; and 67 010, group 3-4. Increasing anesthesiologist coverage responsibilities was associated with an increase in risk-adjusted surgical patient morbidity and mortality. Compared with patients in group 1-2, those in group 2-3 had a 4% relative increase in risk-adjusted mortality and morbidity (5.06% vs 5.25%; adjusted odds ratio [AOR], 1.04; 95% CI, 1.01-1.08; P = .02) and those in group 3-4 had a 14% increase in risk-adjusted mortality and morbidity (5.06% vs 5.75%; AOR, 1.15; 95% CI, 1.09-1.21; P < .001).

Conclusions and Relevance

This study's findings suggest that increasing overlapping coverage by anesthesiologists is associated with increased surgical patient morbidity and mortality. Therefore, the potential effects of staffing ratios in perioperative team models should be considered in clinical coverage efforts.

Outcomes in Temporary ICUs Versus Conventional ICUs: An Observational Cohort of Mechanically Ventilated Patients With COVID-19–Induced Acute Respiratory Distress Syndrome

Throughout the COVID-19 pandemic, thousands of temporary ICUs have been established worldwide. The outcomes and management of mechanically ventilated patients in these areas remain unknown.

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

OBJECTIVES

To determine whether patients admitted to an ICU during times of unprecedented ICU capacity strain, during the COVID-19 pandemic in the United Kingdom, experienced a higher risk of death.

DESIGN

Multicenter, observational cohort study using routine clinical audit data.

SETTING

Adult general ICUs participating the Intensive Care National Audit & Research Centre Case Mix Programme in England, Wales, and Northern Ireland.

PATIENTS

One-hundred thirty-thousand six-hundred eighty-nine patients admitted to 210 adult general ICUs in 207 hospitals.

INTERVENTIONS

Multilevel, mixed effects, logistic regression models were used to examine the relationship between levels of ICU capacity strain on the day of admission (typical low, typical, typical high, pandemic high, and pandemic extreme) and risk-adjusted hospital mortality.

MEASUREMENTS AND MAIN RESULTS

In adjusted analyses, compared with patients admitted during periods of typical ICU capacity strain, we found that COVID-19 patients admitted during periods of pandemic high or pandemic extreme ICU capacity strain during the first wave had no difference in hospital mortality, whereas those admitted during the pandemic high or pandemic extreme ICU capacity strain in the second wave had a 17% (odds ratio [OR], 1.17; 95% CI, 1.05-1.30) and 15% (OR, 1.15; 95% CI, 1.00-1.31) higher odds of hospital mortality, respectively. For non-COVID-19 patients, there was little difference in trend between waves, with those admitted during periods of pandemic high and pandemic extreme ICU capacity strain having 16% (OR, 1.16; 95% CI, 1.08-1.25) and 30% (OR, 1.30; 95% CI, 1.14-1.48) higher overall odds of acute hospital mortality, respectively.

CONCLUSIONS

For patients admitted to ICU during the pandemic, unprecedented levels of ICU capacity strain were significantly associated with higher acute hospital mortality, after accounting for differences in baseline characteristics. Further study into possible differences in the provision of care and outcome for COVID-19 and non-COVID-19 patients is needed.

Similarities in extracorporeal membrane oxygenation management across intensive care unit types in the United States: An analysis of the Extracorporeal Life Support Organization Registry

BACKGROUND

Extracorporeal membrane oxygenation (ECMO) use in the United States occurs often in cardiothoracic ICUs (CTICU). It is unknown how it varies across ICU types.

METHODS

We identified 10,893 ECMO runs from the Extracorporeal Life Support Organization (ELSO) Registry across 2018 and 2019. Primary outcome was ECMO case volume by ICU type (CTICU vs. non-CTICU). Adjusting for pre-ECMO characteristics and case mix, secondary outcomes were on-ECMO physiologic variables by ICU location stratified by support type.

RESULTS

CTICU ECMO occurred in 65.1% and 55.1% (2018 and 2019) of total runs. A minority of total runs related to cardiac surgery procedures (CTICU: 21.7% [2018], 18% [2019]; non-CTICU: 11.2% [2018], 13% [2019]). After multivariate adjustment, non-CTICU ECMO for cardiac support associated with lower 4- and 24-hour circuit flow (3.9 liters per minute [LPM] vs. 4.1 LPM, p<0.0001; 4.1 LPM vs. 4.3 LPM, p<0.0001); for respiratory support, lower on-ECMO mean fraction of inspired oxygen ([F_i O₂ ], 67% versus 69%, p=0.02) and lower respiratory rate (14 versus 15, p<0.0001); and, for extracorporeal cardiopulmonary resuscitation (ECPR), lower ECMO flow rates at 24 hours (3.5 liters per minute [LPM] versus 3.7 LPM, p=0.01).

CONCLUSIONS

ECMO mostly remains in CTICUs though a minority is associated with cardiac surgery. Statistically significant but clinically minor differences in on-ECMO metrics were observed across ICU types.

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.

Comparison of Health Outcomes Among Patients Admitted on Busy vs Less Busy Days for Hospitalists

This cohort study uses Medicare claims data to analyze health outcomes of Medicare patients admitted to the hospital and being treated by hospitalists on busy vs less busy days.

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.

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.

Intensive care unit versus high-dependency care unit for mechanically ventilated patients with pneumonia: a nationwide comparative effectiveness study

Background

Many mechanically ventilated patients in Japan are treated in high-dependency care units (HDUs) rather than intensive care units (ICUs). HDUs can provide intermediate-level care with reduced costs; however, there is limited evidence on whether mechanically ventilated patients should be treated in the ICU or HDU.

Methods

This was a comparative effectiveness study using a nationwide administrative database in Japan. We identified mechanically ventilated patients with pneumonia in ICU or HDU on the day of admission in the Japanese Diagnosis Procedure Combination inpatient database from April 2014 to March 2019. The primary outcome was 30-day in-hospital mortality. Propensity score matching analysis was performed to compare this outcome between patients treated in the ICU and HDU. The robustness of the analyses was evaluated with multivariable regression, overlap weighting, and instrumental variable analyses.

Findings

Of 14,859 mechanically ventilated patients with pneumonia, 7,528 (51%) were treated in the ICU and 7,331 (49%) were treated in the HDU. After propensity score matching, patients treated in the ICU had significantly lower 30-day in-hospital mortality than did those treated in the HDU (24.0% vs. 31.2%; difference, −7.2%; 95% confidence interval, −10.0% to −4.4%). The multivariable regression, overlap weighting, and instrumental variable analyses showed a similar direction and magnitude of association.

Interpretation

Critical care for mechanically ventilated patients with pneumonia in the ICU was associated with a 7.2% decrease in 30-day in-hospital mortality vs. care in the HDU. Residual confounding may still play a role in the effect estimates.

Funding

This study received funding from Ministry of Health, Labour and Welfare, Japan, and Ministry of Education, Culture, Sports, Science and Technology, Japan.

The Mayo Cardiac Intensive Care Unit Admission Risk Score is Associated with Medical Resource Utilization During Hospitalization

Objective

To determine whether the Mayo Cardiac Intensive Care Unit (CICU) Admission Risk Score (M-CARS) is associated with CICU resource utilization.

Patients and Methods

Adult patients admitted to our CICU from 2007 to 2018 were retrospectively reviewed, and M-CARS was calculated from admission data. Groups were compared using Wilcoxon test for continuous variables and χ² test for categorical variables.

Results

We included 12,428 patients with a mean age of 67±15 years (37% female patients). The mean M-CARS was 2.1±2.1, including 5890 (47.4%) patients with M-CARS less than 2 and 644 (5.2%) patients with M-CARS greater than 6. Critical care restricted therapies were frequently used, including mechanical ventilation in 28.0%, vasoactive medications in 25.5%, and dialysis in 4.8%. A higher M-CARS was associated with greater use of critical-care therapies and longer CICU and hospital length of stay. The low-risk cohort with M-CARS less than 2 was less likely to require critical-care–restricted therapies, including invasive or noninvasive mechanical ventilation (8.0% vs 46.1%), vasoactive medications (10.1% vs 38.8%), or dialysis (1.0% vs 8.2%), compared with patients with M-CARS greater than or equal to 2 (all P<.001).

Conclusion

Patients with M-CARS less than 2 infrequently require critical-care resources and have extremely low mortality, suggesting that the M-CARS could be used to facilitate the triage of critically ill cardiac patients.

Targeted Minimal Staff-to-Patient Ratios Are Unachievable - A Nationwide Survey in German ICUs During the COVID-19 Pandemic

Introduction

Adequate staffing in the intensive care units (ICUs) is the most important factor to provide optimal care and ensure favorable outcomes in critically ill patients. Recently, the need for ICU beds has reached unprecedented levels and the management and treatment of critically ill patients has been in focus. The aim of the study was to assess the targeted and actual nurse-to-patient (NPR) and physician-to-patient ratios (PPR) regarding patients with and without COVID-19.

Methods

We conducted a nationwide online survey assessing the standard of care in German ICUs treating patients with COVID-19. We asked questions regarding targeted PPR and NPR and their implementation in daily clinical practice to heads of German ICU departments.

Results

We received 244 responses of which 171 were eligible for final analysis. Targeted median PPR ratio was 8 [interquartile range (IQR) = 4] and targeted NPR was 2 (IQR = 1). For COVID-19 patients, the median targeted PPR was 6 (IQR = 2) and the median targeted NPR was 2 (IQR = 0). Targeted PPRs were rarely met by 15.2% and never met by 3.5% of responding institutions. Targeted NPRs were rarely met in 32.2% and never in 5.3% of responding institutions.

Conclusion

In contrast to PPR, targeted NPRs were largely unattainable in German ICUs. Our results raise concern in view of studies linking worse outcomes in critically ill patients to suboptimal NPRs. This warrants further health policy efforts regarding optimal staffing in the ICU.

The arrival of COVID-19 in Brazil and the impact on coronary artery bypass surgery

BACKGROUND AND AIM OF THE STUDY

This study analyzed the arrival of coronavirus disease 2019 (COVID-19) in Brazil and its impact on coronary artery bypass grafting (CABG) surgery.

METHODS

Patients undergoing isolated CABG in six hospitals in Brazil were divided into two periods: pre-COVID-19 (March-May 2019, N = 468) and COVID-19 era (March-May 2020, N = 182). Perioperative data were included on a dedicated REDCap platform. Patients with clinical and tomographic criteria and/or PCR (+) for severe acute respiratory syndrome coronavirus 2 infection were considered COVID-19 (+). Logistic regression analysis was performed to create a multiple predictive model for mortality after CABG in COVID-19 era.

RESULTS

Compared to 2019, in 2020, CABG surgeries had a 2.8-fold increased mortality risk (95% confidence interval [CI]: 1-7.6, p = .041), patients who evolved with COVID-19 had a 11-fold increased mortality risk (95% CI: 2.2-54.9, p < .003), rates of morbidities and readmission to the intensive care unit. The surgical volume was decreased by 60%. The model to predict mortality after CABG in the COVID-19 era was validated with good calibration (Hosmer-Lemeshow = 1.43) and discrimination (receiver operating characteristic = 0.78).

CONCLUSION

The COVID-19 pandemic had an adverse impact on mortality, morbidity and volume of patients undergoing CABG.

Adverse Events After Transition From ICU to Hospital Ward: A Multicenter Cohort Study

OBJECTIVES

To examine adverse events and associated factors and outcomes during transition from ICU to hospital ward (after ICU discharge).

DESIGN

Multicenter cohort study.

SETTING

Ten adult medical-surgical Canadian ICUs.

PATIENTS

Patients were those admitted to one of the 10 ICUs from July 2014 to January 2016.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Two ICU physicians independently reviewed progress and consultation notes documented in the medical record within 7 days of patient's ICU discharge date to identify and classify adverse events. The adverse event data were linked to patient characteristics and ICU and ward physician surveys collected during the larger prospective cohort study. Analyses were conducted using multivariable logistic regression. Of the 451 patients included in the study, 84 (19%) experienced an adverse event, the majority (62%) within 3 days of transfer from ICU to hospital ward. Most adverse events resulted only in symptoms (77%) and 36% were judged to be preventable. Patients with adverse events were more likely to be readmitted to the ICU (odds ratio, 5.5; 95% CI, 2.4-13.0), have a longer hospital stay (mean difference, 16.1 d; 95% CI, 8.4-23.7) or die in hospital (odds ratio, 4.6; 95% CI, 1.8-11.8) than those without an adverse event. ICU and ward physician predictions at the time of ICU discharge had low sensitivity and specificity for predicting adverse events, ICU readmissions, and hospital death.

CONCLUSIONS

Adverse events are common after ICU discharge to hospital ward and are associated with ICU readmission, increased hospital length of stay and death and are not predicted by ICU or ward physicians.

Impact of Intensivist and Nursing Staff on Critically Ill Patient Mortality: A Retrospective Analysis of the Korean NHIS Cohort Data, 2011-2015

PURPOSE

Critical care medicine continues to evolve. However, critical care cases require increasing amount of medical resources. Intensive care unit (ICU) mortality significantly impacts the overall efficiency of healthcare resources within a system of limited medical resources. This study investigated the factors related to ICU mortality using long-term nationwide cohort data in South Korea.

MATERIALS AND METHODS

This retrospective cohort study used data of 14905721 patients who submitted reimbursement claims to the Korean Health Insurance Service between January 1, 2011 and December 31, 2015. A total of 1498102 patients who were admitted to all ICU types, except neonatal and long-term acute care hospitals, were enrolled.

RESULTS

Of the total 1498102 participants, 861397 (57.5%) were male and 636705 (42.5%) were female. The mean age at admission was 63.4±18.2 years; most of the subjects were aged over 60 years. During the 5-year period, in-hospital mortality rate was 12.9%. In Cox analysis, both in-hospital and 28-day mortality rates were significantly higher in male patients and those of lower socioeconomic status. As age increased and the number of nursing staff decreased, the mortality risk increased significantly by two or three times. The mortality risk was lower in patients admitted to an ICU of a tertiary university hospital and an ICU where intensivists worked.

CONCLUSION

The number of nursing staff and the presence of an intensivist in ICU were associated with the ICU mortality rate. Also, increasing the number of nursing staff and the presence of intensivist might reduce the mortality rate among ICU patients.

Effect on Patient Safety of a Resident Physician Schedule without 24-Hour Shifts

BACKGROUND

The effects on patient safety of eliminating extended-duration work shifts for resident physicians remain controversial.

METHODS

We conducted a multicenter, cluster-randomized, crossover trial comparing two schedules for pediatric resident physicians during their intensive care unit (ICU) rotations: extended-duration work schedules that included shifts of 24 hours or more (control schedules) and schedules that eliminated extended shifts and cycled resident physicians through day and night shifts of 16 hours or less (intervention schedules). The primary outcome was serious medical errors made by resident physicians, assessed by intensive surveillance, including direct observation and chart review.

RESULTS

The characteristics of ICU patients during the two work schedules were similar, but resident physician workload, described as the mean (±SD) number of ICU patients per resident physician, was higher during the intervention schedules than during the control schedules (8.8±2.8 vs. 6.7±2.2). Resident physicians made more serious errors during the intervention schedules than during the control schedules (97.1 vs. 79.0 per 1000 patient-days; relative risk, 1.53; 95% confidence interval [CI], 1.37 to 1.72; P<0.001). The number of serious errors unitwide were likewise higher during the intervention schedules (181.3 vs. 131.5 per 1000 patient-days; relative risk, 1.56; 95% CI, 1.43 to 1.71). There was wide variability among sites, however; errors were lower during intervention schedules than during control schedules at one site, rates were similar during the two schedules at two sites, and rates were higher during intervention schedules than during control schedules at three sites. In a secondary analysis that was adjusted for the number of patients per resident physician as a potential confounder, intervention schedules were no longer associated with an increase in errors.

CONCLUSIONS

Contrary to our hypothesis, resident physicians who were randomly assigned to schedules that eliminated extended shifts made more serious errors than resident physicians assigned to schedules with extended shifts, although the effect varied by site. The number of ICU patients cared for by each resident physician was higher during schedules that eliminated extended shifts. (Funded by the National Heart, Lung, and Blood Institute; ROSTERS ClinicalTrials.gov number, NCT02134847.).

Multicenter validation of a novel medication-regimen complexity scoring tool

BACKGROUND

The MRC-ICU, a novel regimen complexity scoring tool, provides an objective measure of medication regimen complexity in critically ill patients. The MRC-ICU may have the ability to evaluate the impact of critical care pharmacists on patient outcomes but requires further validation. The objective of this study was to confirm the external validity of the MRC-ICU scoring tool at multiple institutions and intensive care unit (ICU) settings.

METHODS

This was a multicenter, prospective, observational study. The electronic medical record was reviewed to collect patient demographics and patient outcomes, and the medication administration record was reviewed to collect MRC-ICU scores at 24 hours, 48 hours, and ICU discharge. Validation was performed by assessing convergent and divergent validity of the score. Spearman rank-order correlation was used to determine correlation.

RESULTS

A total of 230 patients were evaluated across both centers in both medical ICUs and surgical ICUs. Differences between the original center and the new site included that total number of orders (29 vs 126; P < 0.001) and total number of medication orders (17 vs 36; P < 0.001) were higher at the new site, whereas the original site had higher overall MRC-ICU scores (14 vs 11; P = 0.004). The MRC-ICU showed appropriate convergent validity with number of orders and medication orders (all P < 0.001) and appropriate divergent validity with no significant correlation found between age, weight, or gender (all P > 0.05).

CONCLUSIONS

External validity of the MRC-ICU has been confirmed through evaluation at an external site and in the surgical ICU population. The MRC-ICU scoring tool requires prospective evaluation to provide objective data regarding optimal pharmacist use.

Intensive care medicine in smaller hospitals: here to stay

Intensive care medicine is a relatively new specialty. In developing standards of care, it became apparent that some aspects were not achievable by smaller units. Within the intensive care community, there has been a gradual acceptance that smaller hospitals cannot necessarily implement structures that are used in large hospitals, and that outcomes can be comparable with larger units despite this. The Faculty of Intensive Care Medicine set up a Smaller and Specialist Units Advisory Group to explore this area, and this article initially explains the background and work of the faculty to support and sustain these units. We then move on to look at critical care in the context of the recent emergence of wider work on remote and rural healthcare. Finally, we explore our future horizons and look in detail at the areas where further developments will transform the care of critically ill patients within the smaller hospitals of the next 20 years.

Intensivists in U.S. Acute Care Hospitals

OBJECTIVES

To determine the total numbers of privileged and full-time equivalent intensivists in acute care hospitals with intensivists and compare the characteristics of hospitals with and without intensivists.

DESIGN

Retrospective analysis of the American Hospital Association Annual Survey Database (Fiscal Year 2015).

SETTING

Two-thousand eight-hundred fourteen acute care hospitals with ICU beds.

PATIENTS

None.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the 2,814 acute care hospitals studied, 1,469 (52%) had intensivists and 1,345 (48%) had no intensivists. There were 28,808 privileged and 19,996 full-time equivalent intensivists in the 1,469 hospitals with intensivists. In these hospitals, the median (25-75th percentile) numbers of privileged and full-time equivalent intensivists were 11 (5-24) and 7 (2-17), respectively. Compared with hospitals without intensivists, hospitals with privileged intensivists were primarily located in metropolitan areas (91% vs 50%; p < 0.001) and at the aggregate level had nearly thrice the number of hospital beds (403,522 [75%] vs 137,146 [25%]), 3.6 times the number of ICU beds (74,222 [78%] vs 20,615 [22%]), and almost twice as many ICUs (3,383 [65%] vs 1,846 [35%]). At the hospital level, hospitals with privileged intensivists had significantly more hospital beds (median, 213 vs 68; p < 0.0001), ICU beds (median, 32 vs 8; p < 0.0001), a higher ratio of ICU to hospital beds (15.6% vs 12.6%; p < 0.0001), and a higher number of ICUs per hospital (2 vs 1; p < 0.0001) than hospitals without intensivists.

CONCLUSIONS

Analyzing the intensivist section of the American Hospital Association Annual Survey database is a novel approach to estimating the numbers of privileged and full-time equivalent intensivists in acute care hospitals with ICU beds in the United States. This methodology opens the door to an enhanced understanding of the current supply and distribution of intensivists as well as future research into the intensivist workforce.

ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis

PURPOSE

To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.

METHODS

The following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certified intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defined using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression.

RESULTS

Analysis included data from 129,680 patients admitted to 93 ICUs (2014-2015). Three clusters were identified. The features distinguishing between the clusters were: the presence of board-certified intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confidence interval (CI), 0.87-0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22-1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54-1.69)]. Cluster 1 had the worst outcomes.

CONCLUSION

Patients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care.

Near-simultaneous intensive care unit (ICU) admissions and all-cause mortality: a cohort study

PURPOSE

Prior studies have reported the adverse effects of strain on patient outcomes. There is a paucity of literature about a type of strain that may be caused by near-simultaneous intensive care unit (ICU) admissions. We hypothesized that when multiple admissions arrive nearly at the same time, the ICU teams are excessively strained, and this leads to unfavorable patient outcomes.

METHODS

This is a retrospective cohort study of consecutive adult patients admitted to an academic medical ICU of a tertiary referral center over five consecutive years. Primary outcomes were the all-cause hospital and ICU mortality.

RESULTS

We enrolled 13,234 consecutive ICU admissions during the study period. One-fourth of the admissions had an elapsed time since the last admission (ETLA) of < 55 min. Near-simultaneous admissions (NSA) had on average, a higher unadjusted odds ratio (OR) of ICU death of 1.16 (95% CI 1-1.35, P = 0.05), adjusted 1.23 (95% CI 1.04-1.44, P = 0.01), unadjusted hospital death of 1.11 (95% CI 0.99-1.24, P = 0.06), adjusted 1.20 (95% 1.05-1.35, P = 0.004), and a lower adjusted OR of home discharge of 0.91 (95% CI 0.84-0.99, P = 0.04). NSA was associated with 0.16 (95% CI 0.04-0.29, P = 0.01) added days in the ICU. For each incremental unit increase of the logarithmic transformation of ETLA [log (ETLA in minutes)], the average adjusted hospital mortality OR incrementally decreased by an added average OR of 0.93 (95% CI 0.89‒0.97, P = 0.001).

CONCLUSION

Our results suggest that near-simultaneous ICU admissions (NSA) are frequent and are associated with a dose-dependent effect on mortality, length of stay, and odds of home versus nursing facility discharge.

Availability of resources to treat sepsis in Brazil: a random sample of Brazilian institutions

OBJECTIVE

To characterize resource availability from a nationally representative random sample of intensive care units in Brazil.

METHODS

A structured online survey of participating units in the Sepsis PREvalence Assessment Database (SPREAD) study, a nationwide 1-day point prevalence survey to assess the burden of sepsis in Brazil, was sent to the medical director of each unit.

RESULTS

A representative sample of 277 of the 317 invited units responded to the resources survey. Most of the hospitals had fewer than 500 beds (94.6%) with a median of 14 beds in the intensive care unit. Providing care for public-insured patients was the main source of income in two-thirds of the surveyed units. Own microbiology laboratory was not available for 26.8% of the surveyed intensive care units, and 10.5% did not always have access to blood cultures. Broad spectrum antibiotics were not always available in 10.5% of surveyed units, and 21.3% could not always measure lactate within three hours. Those institutions with a high resource availability (158 units, 57%) were usually larger and preferentially served patients from the private health system compared to institutions without high resource availability. Otherwise, those without high resource availability did not always have broad-spectrum antibiotics (24.4%), vasopressors (4.2%) or crystalloids (7.6%).

CONCLUSION

Our study indicates that a relevant number of units cannot perform basic monitoring and therapeutic interventions in septic patients. Our results highlight major opportunities for improvement to adhere to simple but effective interventions in Brazil.

Organizational factors associated with target sedation on the first 48 h of mechanical ventilation: an analysis of checklist-ICU database

Background

Although light sedation levels are associated with several beneficial outcomes for critically ill patients on mechanical ventilation, the majority of patients are still deeply sedated. Organizational factors may play a role on adherence to light sedation levels. We aimed to identify organizational factors associated with a moderate to light sedation target on the first 48 h of mechanical ventilation, as well as the association between early achievement of within-target sedation and mortality.

Methods

This study is a secondary analysis of a multicenter two-phase study (prospective cohort followed by a cluster-randomized controlled trial) performed in 118 Brazilian ICUs. We included all critically ill patients who were on mechanical ventilation 48 h after ICU admission.

A moderate to light level of sedation or being alert and calm (i.e., the Richmond Agitation-Sedation Scale of − 3 to 0) was the target for all patients on mechanical ventilation during the study period. We collected data on the type of hospital (public, private, profit and private, nonprofit), hospital teaching status, nursing and physician staffing, and presence of sedation, analgesia, and weaning protocols. We used multivariate random-effects regression with ICU and study phase as random-effects and correction for patients’ Simplified Acute Physiology Score 3 and Sequential Organ Failure Assessment. We also performed a mediation analysis to explore whether sedation level was just a mediator of the association between organizational factors and mortality.

Results

We included 5719 patients. Only 1710 (29.9%) were on target sedation levels on day 2. Board-certified intensivists on the morning and afternoon shifts were associated with an adequate sedation level on day 2 (OR = 2.43; CI 95%, 1.09–5.38). Target sedation levels were associated with reduced hospital mortality (OR = 0.63; CI 95%, 0.55–0.72). Mediation analysis also suggested such an association, but did not suggest a relationship between the physician staffing model and hospital mortality.

Conclusions

Board-certified intensivists on morning and afternoon shifts were associated with an increased number of patients achieving lighter sedation goals. These findings reinforce the importance of organizational factors, such as intensivists’ presence, as a modifiable quality improvement target.

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2323-y) contains supplementary material, which is available to authorized users.

Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume

IMPORTANCE

Forecasting the volume of hospital discharges has important implications for resource allocation and represents an opportunity to improve patient safety at periods of elevated risk.

OBJECTIVE

To determine the performance of a new time-series machine learning method for forecasting hospital discharge volume compared with simpler methods.

DESIGN

A retrospective cohort study of daily hospital discharge volumes at 2 large, New England academic medical centers between January 1, 2005, and December 31, 2014 (hospital 1), or January 1, 2005, and December 31, 2010 (hospital 2), comparing time-series forecasting methods for prediction was performed. Data analysis was conducted from February 28, 2017, to August 30, 2018. Group-level data for all discharges from inpatient units were included. In addition to conventional methods, a technique originally developed for allocating data center resources, and comparison strategies for incorporating prior data and frequency of model updates, was conducted to identify the model application that optimized forecast accuracy.

MAIN OUTCOMES AND MEASURES

Model calibration as measured by R2 and, secondarily, number of days with errors greater than 1 SD of daily volume.

RESULTS

During the forecasted year, hospital 1 had 54 411 discharges (daily mean, 149) and hospital 2 had 47 456 discharges (daily mean, 130). The machine learning method was well calibrated at both sites (R2, 0.843 and 0.726, respectively) and made errors greater than 1 SD of daily volume on only 13 and 22 days, respectively, of the forecast year at the 2 sites. Last-value-carried-forward models performed somewhat less well (calibration R2, 0.781 and 0.596, respectively) with 13 and 46 errors of 1 SD or greater, respectively. More frequent retraining and training sets of longer than 1 year had minimal effects on the machine learning method's performance.

CONCLUSIONS AND RELEVANCE

Volume of hospital discharges can perhaps be reliably forecasted using simple carry-forward models as well as methods drawn from machine learning. The benefit of the latter does not appear to be dependent on extensive training data and may enable forecasts up to 1 year in advance with superior absolute accuracy to carry-forward models.

Beyond Pain: Nurses' Assessment of Patient Suffering, Dignity, and Dying in the Intensive Care Unit

CONTEXT

Deaths in the intensive care unit (ICU) are increasingly common in the U.S., yet little is known about patients' experiences at the end of life in the ICU.

OBJECTIVES

The objective of this study was to determine nurse assessment of symptoms experienced, and care received by ICU patients in their final week, and their associations with nurse-perceived suffering and dignity.

METHODS

From September 2015 to March 2017, nurses who cared for 200 ICU patients who died were interviewed about physical and psychosocial dimensions of patients' experiences. Medical chart abstraction was used to document baseline patient characteristics and care.

RESULTS

The patient sample was 61% males, 70.2% whites, and on average 66.9 (SD 15.1) years old. Nurses reported that 40.9% of patients suffered severely and 33.1% experienced severe loss of dignity. The most common symptoms perceived to contribute to suffering and loss of dignity included trouble breathing (44.0%), edema (41.9%), and loss of control of limbs (36.1%). Most (n = 9) remained significantly (P < 0.05) associated with suffering, after adjusting for physical pain, including fever/chills, fatigue, and edema. Most patients received vasopressors and mechanical ventilation. Renal replacement therapy was significantly (<0.05) associated with severe suffering (adjusted odds ratio [AOR] 2.53) and loss of dignity (AOR 3.15). Use of feeding tube was associated with severe loss of dignity (AOR 3.12).

CONCLUSION

Dying ICU patients are perceived by nurses to experience extreme indignities and suffer beyond physical pain. Attention to symptoms such as dyspnea and edema may improve the quality of death in the ICU.