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Background The COVID-19 pandemic placed considerable strain on critical care resources. How US hospitals responded to this crisis is unknown. Research Question What actions did US hospitals take to prepare for a potential surge in demand for critical care services in the context of the COVID-19 pandemic? Study Design and Methods From September to November 2020, the chief nursing officers of a representative sample of US hospitals were surveyed regarding organizational actions taken to increase or maintain critical care capacity during the COVID-19 pandemic. Weighted proportions of hospitals for each potential action were calculated to create estimates across the entire population of US hospitals, accounting for both the sampling strategy and nonresponse. Also examined was whether the types of actions taken varied according to the cumulative regional incidence of COVID-19 cases. Results Responses were received from 169 of 540 surveyed US hospitals (response rate, 31.3%). Almost all hospitals canceled or postponed elective surgeries (96.7%) and nonsurgical procedures (94.8%). Few hospitals created new medical units in areas not typically dedicated to health care (12.9%), and almost none adopted triage protocols (5.6%) or protocols to connect multiple patients to a single ventilator (4.8%). Actions to increase or preserve ICU staff, including use of ICU telemedicine, were highly variable, without any single dominant strategy. Hospitals experiencing a higher incidence of COVID-19 did not consistently take different actions compared with hospitals facing lower incidence. Interpretation Responses of hospitals to the mass need for critical care services due to the COVID-19 pandemic were highly variable. Most hospitals canceled procedures to preserve ICU capacity and scaled up ICU capacity using existing clinical space and staffing. Future research linking hospital response to patient outcomes can inform planning for additional surges of this pandemic or other events in the future.

Purpose of review Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic. Recent findings Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic. Summary The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.

The intensive care unit (ICU) can be a stressful environment for patients and families, with well-established long-term consequences. The impact that this unique environment can have on healthcare professionals is being increasingly recognized. Challenging ethical situations, exposure to high patient mortality and difficult daily workloads can lead to excessive stress for those caring for critically ill patients. A growing body of literature suggests that this excessive stress and resultant moral distress can lead to burnout syndrome. In this state-of-the art review, we focus on the epidemiology of burnout syndrome in the ICU and the impact it can have on clinicians, patients, and the health service. Risk factors for burnout syndrome, alongside potential strategies to mitigate burnout and optimize fulfillment, are also discussed. We conclude that burnout is a threat to the profession of critical care medicine, with high prevalence rates across critical care provider disciplines. However, with a robust community response to the call to action, the opportunity exists to mitigate burnout and optimize fulfillment among critical care professionals to ensure that caring, compassionate, high-quality critical care is delivered to all critically ill patients.

Rationale: Increasing intensive care unit (ICU) beds and the critical care workforce are often advocated to address an aging and increasingly medically complex population. However, reducing potenti...

Objectives As ICUs are increasingly a site of end-of-life care, many have adopted end-of-life care resources. We sought to determine the association of such resources with outcomes of ICU patients. Design Retrospective cohort study. Setting Pennsylvania ICUs. Patients Medicare fee-for-service beneficiaries. Interventions Availability of any of one hospital-based resource (palliative care consultants) or four ICU-based resources (protocol for withdrawal of life-sustaining therapy, triggers for automated palliative care consultation, protocol for family meetings, and palliative care clinicians embedded in ICU rounds). Measurements and main results In mixed-effects regression analyses, admission to a hospital with end-of-life resources was not associated with mortality, length of stay, or treatment intensity (mechanical ventilation, hemodialysis, tracheostomy, gastrostomy, artificial nutrition, or cardiopulmonary resuscitation); however, it was associated with a higher likelihood of discharge to hospice (odds ratio, 1.58; 95% CI, 1.11-2.24), an effect that was driven by ICU-based resources (odds ratio, 1.37; 95% CI, 1.04-1.81) rather than hospital-based resources (odds ratio, 1.19; 95% CI, 0.83-1.71). Instrumental variable analysis using differential distance (defined as the additional travel distance beyond the hospital closest to a patient's home needed to reach a hospital with end-of-life resources) demonstrated that among those for whom differential distance would influence receipt of end-of-life resources, admission to a hospital with such resources was not associated with any outcome. Conclusions ICU-based end-of-life care resources do not appear to change mortality but are associated with increased hospice utilization. Given that this finding was not confirmed by the instrumental variable analysis, future studies should attempt to verify this finding, and identify specific resources or processes of care that impact the care of ICU patients at the end of life.

OBJECTIVES: To compare sleep, work hours, and behavioral alertness in faculty and fellows during a randomized trial of nighttime in-hospital intensivist staffing compared to a standard daytime intensivist model. DESIGN: Prospective observational study. SETTING: Medical Intensive Care Unit (MICU) of a tertiary care academic medical center during a randomized controlled trial of in-hospital nighttime intensivist staffing. PATIENTS: 20 faculty and 13 fellows assigned to rotations in the MICU during 2012. INTERVENTIONS: As part of the parent study, there was weekly randomization of staffing model, stratified by 2-week faculty rotation. During the standard staffing model, there were in-hospital residents, with a fellow and faculty member available at nighttime by phone. In the intervention, there were in-hospital residents with an in-hospital nighttime intensivist. Fellows and faculty completed diaries detailing their sleep, work, and well-being; wore actigraphs; and performed psychomotor vigilance testing daily. MEASUREMENTS AND MAIN RESULTS: Daily sleep time (mean hours (standard deviation)) was increased for fellows and faculty in the intervention vs control—6.7 (0.3) vs 6.0 (0.2), p

Use of intensive care unit (ICU) resources in the USA far outpaces that of other countries. This increased use is not accompanied by superior clinical outcomes and is at times discordant with patient desires. This Series paper seeks to identify major drivers of ICU resource use in the USA, and to offer steps towards better aligning ICU resource use with clinical needs and patient preferences. After considering several factors, such as organisational, ethical, and economic factors, we suggest that there are four intersecting drivers of irresponsible use of ICU resources in the USA: first, excess ICU bed capacity and a scarcity of data to understand which patients that truly benefit from ICU compared with ward care; second, clinicians misinterpreting the goals and means of patient autonomy; third, an extreme fear of rationing by the general public; and fourth, fee-for-service driven use of advanced medical technologies and procedures that beget ICU expansion.

Summary: Background: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. Methods: CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. Findings: In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89–0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88–0·96), sensitivity 83·0% (95% CI 74·0–91·1), and specificity 88·3% (95% CI 83·1–92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85–0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92–0·95). Interpretation: A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. Funding: National Institutes of Health, Department of Defense, and Department of Veterans Affairs.

Supplemental Digital Content is available in the text., Objectives: Examine well-being, measured as burnout and professional fulfillment, across critical care healthcare professionals, ICUs, and hospitals within a health system; examine the impact of the coronavirus disease 2019 pandemic. Design: To complement a longitudinal survey administered to medical critical care physicians at the end of an ICU rotation, which began in May 2018, we conducted a cross-sectional survey among critical care professionals across four hospitals in December 2018 to January 2019. We report the results of the cross-sectional survey and, to examine the impact of the coronavirus disease 2019 pandemic, the longitudinal survey results from July 2019 to May 2020. Setting: Academic medical center. Subjects: Four-hundred eighty-one critical care professionals, including 353 critical care nurses, 58 advanced practice providers, 57 physicians, and 13 pharmacists, participated in the cross-sectional survey; 15 medical critical care physicians participated in the longitudinal survey through the coronavirus disease 2019 pandemic. Interventions: None. Measurements and Main Results: Burnout was present in 50% of ICU clinicians, ranging from 42% for critical care physicians to 55% for advanced practice providers. Professional fulfillment was less common at 37%, with significant variability across provider (p = 0.04), with a low of 23% among critical care pharmacists and a high of 53% among physicians. Well-being varied significantly at the hospital and ICU level. Workload and job demand were identified as drivers of burnout and meaning in work, culture and values of work community, control and flexibility, and social support and community at work were each identified as drivers of well-being. Between July 2019 and March 2020, burnout and professional fulfillment were present in 35% (15/43) and 58% (25/43) of medical critical care physician responses, respectively. In comparison, during the coronavirus disease 2019 pandemic, burnout and professional fulfillment were present in 57% (12/21) and 38% (8/21), respectively. Conclusions: Burnout was common across roles, yet differed across ICUs and hospitals. Professional fulfillment varied by provider role. We identified potentially modifiable factors related to clinician well-being that can inform organizational strategies at the ICU and hospital level. Longitudinal studies, designed to assess the long-term impact of the coronavirus disease 2019 pandemic on the well-being of the critical care workforce, are urgently needed.

Background Admission to high-acuity ICUs has been associated with improved outcomes compared with outcomes in low-acuity ICUs, although the mechanism for these findings is unclear. Research Question The goal of this study was to determine if high-acuity ICUs more effectively implement evidence-based processes of care that have been associated with improved clinical outcomes. Study Design and Methods This retrospective cohort study was performed in adult ICU patients admitted to 322 ICUs in 199 hospitals in the Philips ICU telemedicine database between 2010 and 2015. The primary exposure was ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles. Multivariable logistic regression was used to examine relations of ICU acuity with adherence to evidence-based VTE and stress ulcer prophylaxis, and with the avoidance of potentially harmful events. These events included hypoglycemia, sustained hyperglycemia, and liberal transfusion practices (defined as RBC transfusions prescribed for nonbleeding patients with preceding hemoglobin levels ≥ 7 g/dL). Results Among 1,058,510 ICU admissions, adherence to VTE and stress ulcer prophylaxis was high across acuity levels. In adjusted analyses, those admitted to low-acuity ICUs compared with the highest acuity ICUs were more likely to experience hypoglycemic events (adjusted OR [aOR], 1.12; 95% CI, 1.04-1.19), sustained hyperglycemia (aOR, 1.07; 95% CI, 1.04-1.10), and liberal transfusion practices (aOR, 1.55; 95% CI, 1.33-1.82). Interpretation High-acuity ICUs were associated with better adherence to several evidence-based practices, which may be a marker of high-quality care. Future research should investigate how high-acuity ICUs approach ICU organization to identify targets for improving the quality of critical care across all ICU acuity levels.

Telemedicine coverage of intensive care units is an organizational innovation that has been touted as a means to improve access to and quality of critical care. The purpose of this narrative review is to discuss the different organizational models of intensive care unit telemedicine and factors that have influenced its adoption and to review the existing literature to consider whether it has lived up to its promise. We conclude by suggesting future directions to fill in some of the existing gaps in the literature.

Physicians are increasingly being held accountable for patient outcomes, yet their specific contribution to the outcomes remains uncertain.To determine variation in outcomes of mechanically ventilated patients among intensivists, as well as associations between intensivist experience and patient outcomes.We performed a retrospective cohort study of mechanically ventilated Medicare fee-for-service patients in acute care hospitals in Pennsylvania using administrative, clinical, and physician data from Centers for Medicare and Medicaid Services and the American Medical Association from 2008 and 2009. We identified intensivists by training background, board certification, and claims for services provided to patients admitted to an intensive care unit. We assigned patients to intensivists for outcome attribution based on submitted claims for critical care and in-patient services. We estimated the physician-specific adjusted odds ratios (ORs) for 30-day mortality using a hierarchical model with a random effect for physician, adjusted for patient and hospital characteristics. We tested for independent association of physician experience with patient outcomes using mixed-effects regression for the primary outcome of 30-day mortality. We defined physician experience in two ways: years since training completion ("duration") and annual number of mechanically ventilated patients ("volume").We assigned 345 physicians to 11,268 patients. The 30-day mortality was 43% and median hospital length of stay was 11 days (interquartile range = 6-18). The physician adjusted OR varied from 0.72 to 1.64 (median = 0.99; interquartile range = 0.92-1.09). A total of 48% of physicians was outliers, with an adjusted OR significantly different from 1. However, among intensivists, physician experience was not associated with 30-day mortality (duration OR = 1.00 per additional year; 95% confidence interval = 1.00-1.01; volume OR = 1.00 per additional patient; 95% confidence interval = 1.00-1.00).Intensivists independently contribute to outcomes of Medicare patients who undergo mechanical ventilation, as evidenced by the variation in risk-adjusted mortality across intensivists. However, physician experience does not underlie this relationship between intensivists, suggesting the need to identify modifiable physician factors to improve outcomes.

Many ICU patients do not require critical care interventions. Whether aggressive care environments increase risks to low-acuity patients is unknown. We evaluated whether ICU acuity was associated with outcomes of low mortality-risk patients. We hypothesized that admission to high-acuity ICUs would be associated with worse outcomes. This hypothesis was based on two possibilities: 1) high-acuity ICUs may have a culture of aggressive therapy that could lead to potentially avoidable complications and 2) high-acuity ICUs may focus attention toward the many sicker patients and away from the fewer low-risk patients.Retrospective cohort study.Three hundred twenty-two ICUs in 199 hospitals in the Philips eICU database between 2010 and 2015.Adult ICU patients at low risk of dying, defined as an Acute Physiology and Chronic Health Evaluation-IVa-predicted mortality of 3% or less.ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles.We used generalized estimating equations to test whether ICU acuity is independently associated with a primary outcome of ICU length of stay and secondary outcomes of hospital length of stay, hospital mortality, and discharge destination. The study included 381,997 low-risk patients. Mean ICU and hospital length of stay were 1.8 ± 2.1 and 5.2 ± 5.0 days, respectively. Mean Acute Physiology and Chronic Health Evaluation IVa-predicted hospital mortality was 1.6% ± 0.8%; actual hospital mortality was 0.7%. In adjusted analyses, admission to low-acuity ICUs was associated with worse outcomes compared with higher-acuity ICUs. Specifically, compared with the highest-acuity quartile, ICU length of stay in low-acuity ICUs was increased by 0.24 days; in medium-acuity ICUs by 0.16 days; and in high-acuity ICUs by 0.09 days (all p0.001). Similar patterns existed for hospital length of stay. Patients in lower-acuity ICUs had significantly higher hospital mortality (odds ratio, 1.28 [95% CI, 1.10-1.49] for low-; 1.24 [95% CI, 1.07-1.42] for medium-, and 1.14 [95% CI, 0.99-1.31] for high-acuity ICUs) and lower likelihood of discharge home (odds ratio, 0.86 [95% CI, 0.82-0.90] for low-, 0.88 [95% CI, 0.85-0.92] for medium-, and 0.95 [95% CI, 0.92-0.99] for high-acuity ICUs).Admission to high-acuity ICUs is associated with better outcomes among low mortality-risk patients. Future research should aim to understand factors that confer benefit to patients with different risk profiles.

The coronavirus disease 2019 (COVID-19) pandemic has severely affected ICUs and critical care health-care providers (HCPs) worldwide.How do regional differences and perceived lack of ICU resources affect critical care resource use and the well-being of HCPs?Between April 23 and May 7, 2020, we electronically administered a 41-question survey to interdisciplinary HCPs caring for patients critically ill with COVID-19. The survey was distributed via critical care societies, research networks, personal contacts, and social media portals. Responses were tabulated according to World Bank region. We performed multivariate log-binomial regression to assess factors associated with three main outcomes: limiting mechanical ventilation (MV), changes in CPR practices, and emotional distress and burnout.We included 2,700 respondents from 77 countries, including physicians (41%), nurses (40%), respiratory therapists (11%), and advanced practice providers (8%). The reported lack of ICU nurses was higher than that of intensivists (32% vs 15%). Limiting MV for patients with COVID-19 was reported by 16% of respondents, was lowest in North America (10%), and was associated with reduced ventilator availability (absolute risk reduction [ARR], 2.10; 95% CI, 1.61-2.74). Overall, 66% of respondents reported changes in CPR practices. Emotional distress or burnout was high across regions (52%, highest in North America) and associated with being female (mechanical ventilation, 1.16; 95% CI, 1.01-1.33), being a nurse (ARR, 1.31; 95% CI, 1.13-1.53), reporting a shortage of ICU nurses (ARR, 1.18; 95% CI, 1.05-1.33), reporting a shortage of powered air-purifying respirators (ARR, 1.30; 95% CI, 1.09-1.55), and experiencing poor communication from supervisors (ARR, 1.30; 95% CI, 1.16-1.46).Our findings demonstrate variability in ICU resource availability and use worldwide. The high prevalence of provider burnout and its association with reported insufficient resources and poor communication from supervisors suggest a need for targeted interventions to support HCPs on the front lines.

OBJECTIVES:. Prior studies have demonstrated suboptimal adherence to lung protective ventilation among patients with acute respiratory distress syndrome. A common barrier to providing this evidence-based practice is diagnostic uncertainty. We sought to test the hypothesis that patients with acute respiratory distress syndrome due to coronavirus disease 2019, in whom acute respiratory distress syndrome is easily recognized, would be more likely to receive low tidal volume ventilation than concurrently admitted acute respiratory distress syndrome patients without coronavirus disease 2019. DESIGN:. Retrospective cohort study. SETTING:. Five hospitals of a single health system. PATIENTS:. Mechanically ventilated patients with coronavirus disease 2019 or noncoronavirus disease 2019 acute respiratory distress syndrome as identified by an automated, electronic acute respiratory distress syndrome finder in clinical use at study hospitals. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Among 333 coronavirus disease 2019 patients and 234 noncoronavirus disease 2019 acute respiratory distress syndrome patients, the average initial tidal volume was 6.4 cc/kg predicted body weight and 6.8 cc/kg predicted body weight, respectively. Patients had tidal volumes less than or equal to 6.5 cc/kg predicted body weight for a mean of 70% of the first 72 hours of mechanical ventilation in the coronavirus disease 2019 cohort, compared with 52% in the noncoronavirus disease 2019 cohort (unadjusted p < 0.001). After adjusting for height, gender, admitting hospital, and whether or not the patient was admitted to a medical specialty ICU, coronavirus disease 2019 diagnosis was associated with a 21% higher percentage of time receiving tidal volumes less than or equal to 6.5 cc/kg predicted body weight within the first 72 hours of mechanical ventilation (95% CI, 14–28%; p < 0.001). CONCLUSIONS:. Adherence to low tidal volume ventilation during the first 72 hours of mechanical ventilation is higher in patients with coronavirus disease 2019 than with acute respiratory distress syndrome without coronavirus disease 2019. This population may present an opportunity to understand facilitators of implementation of this life-saving evidence-based practice.

OBJECTIVES Without widely available physiologic data, a need exists for ICU risk adjustment methods that can be applied to administrative data. We sought to expand the generalizability of the Acute Organ Failure Score by adapting it to a commonly used administrative database. DESIGN Retrospective cohort study. SETTING One hundred fifty-one hospitals in Pennsylvania. PATIENTS A total of 90,733 ICU admissions among 77,040 unique patients between January 1, 2009, and December 1, 2009, in the Medicare Provider Analysis and Review database. MEASUREMENTS AND MAIN RESULTS We used multivariable logistic regression on a random split cohort to predict 30-day mortality, and to examine the impact of using different comorbidity measures in the model and adding historical claims data. Overall 30-day mortality was 17.6%. In the validation cohort, using the original Acute Organ Failure Score model's β coefficients resulted in poor discrimination (C-statistic, 0.644; 95% CI, 0.639-0.649). The model's C-statistic improved to 0.721 (95% CI, 0.711-0.730) when the Medicare cohort was used to recalibrate the β coefficients. Model discrimination improved further when comorbidity was expressed as the COmorbidity Point Score 2 (C-statistic, 0.737; 95% CI, 0.728-0.747; p < 0.001) or the Elixhauser index (C-statistic, 0.748; 95% CI, 0.739-0.757) instead of the Charlson index. Adding historical claims data increased the number of comorbidities identified, but did not enhance model performance. CONCLUSIONS Modification of the Acute Organ Failure Score resulted in good model discrimination among a diverse population regardless of comorbidity measure used. This study expands the use of the Acute Organ Failure Score for risk adjustment in ICU research and outcomes reporting using standard administrative data.

Describe the operating characteristics of a proposed set of revenue center codes to correctly identify ICU stays among hospitalized patients.Retrospective cohort study. We report the operating characteristics of all ICU-related revenue center codes for intensive and coronary care, excluding nursery, intermediate, and incremental care, to identify ICU stays. We use a classification and regression tree model to further refine identification of ICU stays using administrative data. The gold standard for classifying ICU admission was an electronic patient location tracking system.The University of Pennsylvania Health System in Philadelphia, PA, United States.All adult inpatient hospital admissions between July 1, 2013, and June 30, 2015.None.Among 127,680 hospital admissions, the proposed combination of revenue center codes had 94.6% sensitivity (95% CI, 94.3-94.9%) and 96.1% specificity (95% CI, 96.0-96.3%) for correctly identifying hospital admissions with an ICU stay. The classification and regression tree algorithm had 92.3% sensitivity (95% CI, 91.6-93.1%) and 97.4% specificity (95% CI, 97.2-97.6%), with an overall improved accuracy (χ = 398; p0.001).Use of the proposed combination of revenue center codes has excellent sensitivity and specificity for identifying true ICU admission. A classification and regression tree algorithm with additional administrative variables offers further improvements to accuracy.

Studies of nighttime intensivist staffing have yielded mixed results.To review the association of nighttime intensivist staffing with outcomes of intensive care unit (ICU) patients.We searched five databases (2000-2016) for studies comparing in-hospital nighttime intensivist staffing with other nighttime staffing models in adult ICUs and reporting mortality or length of stay. We abstracted data on staffing models, outcomes, and study characteristics and assessed study quality, using standardized tools. Meta-analyses used random effects models.Eighteen studies met inclusion criteria: one randomized controlled trial and 17 observational studies. Overall methodologic quality was high. Studies included academic hospitals (n = 10), community hospitals (n = 2), or both (n = 6). Baseline clinician staffing included residents (n = 9), fellows (n = 4), and nurse practitioners or physician assistants (n = 2). Studies included both general and specialty ICUs and were geographically diverse. Meta-analysis (one randomized controlled trial; three nonrandomized studies with exposure limited to nighttime intensivist staffing with adjusted estimates of effect) demonstrated no association with mortality (odds ratio, 0.99; 95% confidence interval, 0.75-1.29). Secondary analyses including studies without risk adjustment, with a composite exposure of organizational factors, stratified by intensity of daytime staffing and by ICU type, yielded similar results. Minimal or no differences were observed in ICU and hospital length of stay and several other secondary outcomes.Notwithstanding limitations of the predominantly observational evidence, our systematic review and meta-analysis suggests nighttime intensivist staffing is not associated with reduced ICU patient mortality. Other outcomes and alternative staffing models should be evaluated to further guide staffing decisions.

Rationale: Gender gaps exist in academic leadership positions in critical care. Peer-reviewed publications are crucial to career advancement, and yet little is known regarding gender differences in...

BackgroundSpecialty wards cohort hospitalised patients to improve outcomes and lower costs. When demand exceeds capacity, patients overflow and are “bedspaced” to alternate wards. Some studies have demonstrated that bedspacing among medicine service patients is associated with adverse patient-centred outcomes, however, results have been inconsistent and have primarily been performed within national health systems. The objective of this study was to assess the association of bedspacing with patient-centred outcomes among United States patients admitted to general medicine services.MethodsWe performed a retrospective cohort study of internal medicine, family medicine and geriatric service patients who were bedspaced vs cohorted for the entirety of their hospital stay within three large, urban United States hospitals (quaternary referral centre, tertiary referral centre and community hospital, with different patient demographics and case-mixes) in 2014 and 2015. We performed quantile regression to determine differences in length of stay (LOS) between bedspaced vs cohorted patients and logistic regression for in-hospital mortality and discharge to home.ResultsAmong 18 802 patients in 33 wards, 6119 (33%) patients were bedspaced. Bedspaced patients had significantly longer LOS compared with cohorted patients at the 25th (0.1 days, 95% CI: 0.05 to 0.2, p=0.001), 50th (0.2 days, 95% CI: 0.1 to 0.3, p=0.003) and 75th (0.3 days, 95% CI: 0.2 to 0.5, pConclusionBedspacing is associated with adverse patient-centred outcomes. Future work is needed to confirm these findings, understand mechanisms contributing to adverse outcomes and identify factors that mitigate these adverse effects in order to provide high-value, patient-centred care to hospitalised patients.

Mechanically ventilated patients require complex care and are at high risk for rehospitalization, but different systems of care may result in different hospital discharge practices and rates of rehospitalization.To compare lengths of hospitalization, discharge patterns, and rehospitalization rates in New York in the United States and Ontario in Canada.We conducted a retrospective cohort study of mechanically ventilated patients who survived an acute care hospitalization in New York or Ontario from 2010 to 2012, using linkable administrative healthcare data.The primary outcome was the cumulative incidence of first rehospitalization within 30 days of discharge, accounting for the competing risk of death. Of 71,063 mechanically ventilated patients in New York, and 41,875 in Ontario who survived to hospital discharge, median length of initial hospital stay was similar in New York and Ontario (15 d, interquartile range = 8-28 vs. 16 d [9-30]), but was systematically shorter in New York when stratified by patient subgroups of different illness severity. Fewer patients in New York were discharged directly home (53.6% vs. 71.4%). Of patients in New York, 15,527 (cumulative incidence 21.9%) had a first rehospitalization within 30 days versus 5,580 (cumulative incidence 13.3%) in Ontario (P 0.001). Incidence of rehospitalization was higher in New York across all subgroups assessed, with the greatest differences among patients with a tracheostomy (29.8% vs. 13.3%, P 0.001), those who received dialysis during the hospitalization (31.9% vs. 17.4%, P 0.001), and for patients not discharged directly home (27.6% vs. 13.3%, P 0.001).Care patterns for mechanically ventilated patients in New York and Ontario are very different; mechanically ventilated patients who survive to hospital discharge in New York have shorter hospital stays, with higher rehospitalization rates within 30 days compared with Ontario.

BACKGROUND: Delayed mechanical ventilation monitoring may impede recognition of life-threatening acidemia. Coordination of multidisciplinary processes can be improved by using a checklist and time-out procedure. The study objective was to evaluate process-related outcomes after implementation of a post-intubation checklist and time out. METHODS: An observational study of a 24-bed medical ICU in Philadelphia, Pennsylvania, was conducted from January to December 2011. A random sample of mechanically ventilated adults was selected from the pre-intervention ( n = 80) and post-intervention ( n = 144) periods. The primary outcome was the proportion of subjects with an arterial blood gas (ABG) result within 60 min of mechanical ventilation initiation. Secondary outcomes included rates of respiratory acidosis, moderate-severe acidemia (pH

Rationale: Telemedicine is an increasingly common care delivery strategy in the ICU. However, ICU telemedicine programs vary widely in their clinical effectiveness, with some studies showing a large mortality benefit and others showing no benefit or even harm. Objectives: To identify the organizational factors associated with ICU telemedicine effectiveness. Methods: We performed a focused ethnographic evaluation of 10 ICU telemedicine programs using site visits, interviews, and focus groups in both facilities providing remote care and the target ICUs. Programs were selected based on their change in risk-adjusted mortality after adoption (decreased mortality, no change in mortality, and increased mortality). We used a constant comparative approach to guide data collection and analysis. Measurements and Main Results: We conducted 460 hours of direct observation, 222 interviews, and 18 focus groups across six telemedicine facilities and 10 target ICUs. Data analysis revealed three domains that influence ICU telemedicine effectiveness: 1) leadership (i.e., the decisions related to the role of the telemedicine, conflict resolution, and relationship building), 2) perceived value (i.e., expectations of availability and impact, staff satisfaction, and understanding of operations), and 3) organizational characteristics (i.e., staffing models, allowed involvement of the telemedicine unit, and new hire orientation). In the most effective telemedicine programs these factors led to services that are viewed as appropriate, integrated, responsive, and consistent. Conclusions: The effectiveness of ICU telemedicine programs may be influenced by several potentially modifiable factors within the domains of leadership, perceived value, and organizational structure.

Admission to the intensive care unit (ICU) is costly and strains health system resources (1). Accurate estimates of population-level ICU admission rates could aid in disaster planning, training a suitable critical care workforce, and targeting policy interventions to reduce low-value or preference-discordant admissions. We sought to overcome limitations of prior estimates of ICU utilization (2) by using nationally representative data and examining geographic differences in admission incidence.

Rationale: The epidemiology of post–acute care use and hospital readmission after sepsis remains largely unknown.Objectives: To examine the rate of post–acute care use and hospital readmission after sepsis and to examine risk factors and outcomes for hospital readmissions after sepsis.Methods: In an observational cohort study conducted in an academic health care system (2010–2012), we compared post–acute care use at discharge and hospital readmission after 3,620 sepsis hospitalizations with 108,958 nonsepsis hospitalizations. We used three validated, claims-based approaches to identify sepsis and severe sepsis.Measurements and Main Results: Post–acute care use at discharge was more likely after sepsis, driven by skilled care facility placement (35.4% after sepsis vs. 15.8%; P

BACKGROUND:Evidence regarding nighttime physician staffing of ICUs is suboptimal. We aimed to determine how nighttime physician staffing models influence patient outcomes. METHODS:We performed a multicenter retrospective cohort study in a multicenter registry of US ICUs. The exposure variable was the ICU's nighttime physician staffing model. The primary outcome was hospital mortality. Secondary outcomes included new limitations on life support, ICU length of stay, hospital length of stay, and duration of mechanical ventilation. Daytime physician staffing was studied as a potential effect modifier. RESULTS:The study included 270,742 patients in 143 ICUs. Compared with nighttime staffing with an attending intensivist, nighttime staffing without an attending intensivist was not associated with hospital mortality (OR, 1.03; 95% CI, 0.92-1.15; P = .65). This relationship was not modified by daytime physician staffing (interaction P = .19). When nighttime staffing was subcategorized, neither attending nonintensivist nor physician trainee staffing was associated with hospital mortality compared with attending intensivist staffing. However, nighttime staffing without any physician was associated with reduced odds of hospital mortality (OR, 0.79; 95% CI, 0.68-0.91; P = .002) and new limitations on life support (OR, 0.83; 95% CI, 0.75-0.93; P = .001). Nighttime staffing was not associated with ICU or hospital length of stay. Nighttime staffing with an attending nonintensivist was associated with a slightly longer duration of mechanical ventilation (hazard ratio, 1.05; 95% CI, 1.02-1.09; P CONCLUSIONS:We found little evidence that nighttime physician staffing models affect patient outcomes. ICUs without physicians at night may exhibit reduced hospital mortality that is possibly attributable to differences in end-of-life care practices.

Background Video recording of resuscitation from fixed camera locations has been used to assess adherence to guidelines and provide feedback on performance. However, inpatient cardiac arrests often happen in unpredictable locations and crowded rooms, making video recording of these events problematic. Objective We sought to understand the feasibility of Google Glass (GG) as a method for recording inpatient cardiac arrests and capturing salient resuscitation factors for post-event review. Methods This observational study involved recording simulated cardiac arrest events on inpatient medical wards. Each simulation was reviewed by 3 methods: in-room physician direct observation, stationary video camera (SVC), and GG. Nurse and physician specialists analyzed the videos for global visibility and audibility, as well as recording quality of predefined resuscitation events and behaviors. Resident code leaders were surveyed regarding attitudes toward GG use in the clinical emergency setting. Results Of 11 simulated cardiac arrest events, 9 were successfully recorded by all observation methods (1 GG failure, 1 SVC failure). GG was judged slightly better than SVC recording for average global visualization (3.95 versus 3.15, P = .0003) and average global audibility (4.77 versus 4.42, P = .002). Of the GG videos, 19% had limitations in overall interpretability compared with 35% of SVC recordings (P = .039). All 10 survey respondents agreed that GG was easy to use; however, 2 found it distracting and 3 were uncomfortable with future use during actual resuscitations. Conclusions GG is a feasible and acceptable method for capturing simulated resuscitation events in the inpatient setting.