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Objectives: To compare outcomes for 2 weeks vs. 1 week of maximal patient-intensivist continuity in the ICU., Design: Retrospective cohort study., Setting: Two U.S. urban, teaching, medical ICUs where intensivists were scheduled for 2-week service blocks: site A was in the Midwest and site B was in the Northeast., Patients: Patients 18 years old or older admitted to a study ICU between March 1, 2017, and February 28, 2020., Interventions: None., Measurements and Main Results: We applied target trial emulation to compare admission during an intensivist's first week (as a proxy for 2 wk of maximal continuity) vs. admission during their second week (as a proxy for 1 wk of maximal continuity). Outcomes included hospital mortality, ICU length of stay, and, for mechanically ventilated patients, duration of ventilation. Exploratory outcomes included imaging, echocardiogram, and consultation orders. We used inverse probability weighting to adjust for baseline differences and random-effects meta-analysis to calculate overall effect estimates. Among 2571 patients, 1254 were admitted during an intensivist's first week and 1317 were admitted during a second week. At sites A and B, hospital mortality rates were 25.8% and 24.2%, median ICU length of stay were 4 and 2 days, and median mechanical ventilation durations were 3 and 3 days, respectively. There were no differences in adjusted mortality (odds ratio [OR], 1.01 [95% CI, 0.96-1.06]) or ICU length of stay (-0.25 d [-0.82 d to +0.32 d]) for 2 weeks vs. 1 week of maximal continuity. Among mechanically ventilated patients, there were no differences in adjusted mortality (OR, 1.00 [0.87-1.16]), ICU length of stay (+0.06 d [-0.78 d to +0.91 d]), or duration of mechanical ventilation (+0.37 d [-0.46 d to +1.21 d]) for 2 weeks vs. 1 week of maximal continuity., Conclusions: Two weeks of maximal patient-intensivist continuity was not associated with differences in clinical outcomes compared with 1 week in two medical ICUs., Competing Interests: Drs. Admon’s, Gershengorn’s, and Prescott’s institutions received funding from the National Heart, Lung, and Blood Institute. Drs. Admon, Law, and Valley received support for article research from the National Institutes of Health (NIH). Dr. Admon was funded by K08HL155407. Dr. Law’s institution received funding from the NIH. Dr. Gershengorn’s institution received funding from the Canadian Institutes for Health Research; she received funding from CHEST Critical Care, Annals of the American Thoracic Society, International Symposium on Intensive Care and Emergency Medicine, and Southeastern Critical Care Summit; and she received support from the University of Miami Hospital and Clinics Data Analytics Research Team. Dr. Prescott’s institution received funding from the Agency for Healthcare Research and Quality, Veterans Affairs Health Services Research and Development, the Centers for Disease Control and Prevention, and Blue Cross Blue Shield of Michigan; she disclosed government work. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background: Studies of nighttime intensivist staffing have yielded mixed results., Goals: To review the association of nighttime intensivist staffing with outcomes of intensive care unit (ICU) patients., Methods: 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., Results: 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., Conclusions: 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: The extent to which geographic variability in ICU admission across the United States is driven by patients with lower risk of death is unknown., Objectives: To determine whether patients at low to moderate risk of death contribute to geographic variation in ICU admission., Methods: Retrospective cohort of hospitalizations among Medicare beneficiaries (age > 64 years) admitted for ten common medical and surgical diagnoses (2004 to 2009). We examined population-adjusted rates of ICU admission per 100 hospitalizations in 304 health referral regions (HRR), and estimated the relative risk of ICU admission across strata of regional ICU and risk of death, adjusted for patient and regional characteristics., Measurement and Main Results: ICU admission rates varied nearly two-fold across HRR quartiles (quartile 1 to 4: 13.6, 17.3, 20.0, and 25.2 per 100 hospitalizations, respectively). Observed mortality for patients in regions (quartile 4) with the greatest ICU use was 17% compared to 21% in regions with lowest ICU use (quartile 1) (p<0.001). After adjusting for patient and regional characteristics, including regional differences in ICU, skilled nursing, and long-term acute care bed capacity, individuals' risk of death modified the relationship between regional ICU use and an individual's risk of ICU admission (p for interaction<0.001). Region was least important in predicting ICU admission among patients with high (quartile 4) risk of death (RR 1.27, 95% CI 1.22-1.31, for high versus low ICU use regions), and most important for patients with moderate (quartile 2; RR 1.63, 95% CI 1.53-1.72, quartile 3; RR 1.56 95% CI 1.47-1.65) and low (quartile 1) risk of death (RR 1.50, 95% CI 1.41-1.59)., Conclusions: There is wide variation in in ICU use by geography, independent of ICU beds and physician supply, for patients with low and moderate risks of death., Competing Interests: The authors have declared that no competing interests exist.

Importance: Hospital quality measures that do not account for patient do-not-resuscitate (DNR) status may penalize hospitals admitting a greater proportion of patients with limits on life-sustaining treatments., Objective: To evaluate the effect of analytic approaches accounting for DNR status on risk-adjusted hospital mortality rates and performance rankings., Design, Setting, and Participants: A retrospective, population-based cohort study was conducted among adults hospitalized with pneumonia in 303 California hospitals between January 1 and December 31, 2011. We used hierarchical logistic regression to determine associations between patient DNR status, hospital-level DNR rates, and mortality measures. Changes in hospital risk-adjusted mortality rates after accounting for patient DNR status and interhospital variation in the association between DNR status and mortality were examined. Data analysis was conducted from January 16 to September 16, 2015., Exposures: Early DNR status (within 24 hours of admission)., Main Outcomes and Measures: In-hospital mortality, determined using hierarchical logistic regression., Results: A total of 90,644 pneumonia cases (5.4% of admissions) were identified among the 303 California hospitals evaluated during 2011; mean (SD) age of the patients was 72.5 (13.7) years, 51.5% were women, and 59.3% were white. Hospital DNR rates varied (median, 15.8%; 25th-75th percentile, 8.9%-22.3%). Without accounting for patient DNR status, higher hospital-level DNR rates were associated with increased patient mortality (adjusted odds ratio [OR] for highest-quartile DNR rate vs lowest quartile, 1.17; 95% CI, 1.04-1.32), corresponding to worse hospital mortality rankings. In contrast, after accounting for patient DNR status and between-hospital variation in the association between DNR status and mortality, hospitals with higher DNR rates had lower mortality (adjusted OR for highest-quartile DNR rate vs lowest quartile, 0.79; 95% CI, 0.70-0.89), with reversal of associations between hospital mortality rankings and DNR rates. Only 14 of 27 hospitals (51.9%) characterized as low-performing outliers without accounting for DNR status remained outliers after DNR adjustment. Hospital DNR rates were not significantly associated with composite quality measures of processes of care for pneumonia (r = 0.11; P = .052); however, DNR rates were positively correlated with patient satisfaction scores (r = 0.35; P < .001)., Conclusions and Relevance: Failure to account for DNR status may confound the evaluation of hospital quality using mortality outcomes, penalizing hospitals that admit a greater proportion of patients with limits on life-sustaining treatments. Stakeholders should seek to improve methods to standardize and report DNR status in hospital discharge records to allow further assessment of implications of adjusting for DNR in quality measures.

Importance: Among patients whose need for intensive care is uncertain, the relationship of intensive care unit (ICU) admission with mortality and costs is unknown., Objective: To estimate the relationship between ICU admission and outcomes for elderly patients with pneumonia., Design, Setting, and Patients: Retrospective cohort study of Medicare beneficiaries (aged >64 years) admitted to 2988 acute care hospitals in the United States with pneumonia from 2010 to 2012., Exposures: ICU admission vs general ward admission., Main Outcomes and Measures: Primary outcome was 30-day all-cause mortality. Secondary outcomes included Medicare spending and hospital costs. Patient and hospital characteristics were adjusted to account for differences between patients with and without ICU admission. To account for unmeasured confounding, an instrumental variable was used-the differential distance to a hospital with high ICU admission (defined as any hospital in the upper 2 quintiles of ICU use)., Results: Among 1,112,394 Medicare beneficiaries with pneumonia, 328,404 (30%) were admitted to the ICU. In unadjusted analyses, patients admitted to the ICU had significantly higher 30-day mortality, Medicare spending, and hospital costs than patients admitted to a general hospital ward. Patients (n = 553,597) living closer than the median differential distance (<3.3 miles) to a hospital with high ICU admission were significantly more likely to be admitted to the ICU than patients living farther away (n = 558,797) (36% for patients living closer vs 23% for patients living farther, P < .001). In adjusted analyses, for the 13% of patients whose ICU admission decision appeared to be discretionary (dependent only on distance), ICU admission was associated with a significantly lower adjusted 30-day mortality (14.8% for ICU admission vs 20.5% for general ward admission, P = .02; absolute decrease, -5.7% [95% CI, -10.6%, -0.9%]), yet there were no significant differences in Medicare spending or hospital costs for the hospitalization., Conclusions and Relevance: Among Medicare beneficiaries hospitalized with pneumonia, ICU admission of patients for whom the decision appeared to be discretionary was associated with improved survival and no significant difference in costs. A randomized trial may be warranted to assess whether more liberal ICU admission policies improve mortality for patients with pneumonia.

Objective: Influenza is the most common vaccine-preventable disease in the United States; however, little is known about the burden of critical illness due to influenza virus infection. Our primary objective was to estimate the proportion of all critical illness hospitalizations that are attributable to seasonal influenza., Design: Retrospective cohort study., Setting: Arizona, California, and Washington from January 2003 to March 2009., Patients: All adults hospitalized with critical illness, defined by International Classification of Diseases, 9th Edition, Clinical Modification diagnosis and procedure codes for acute respiratory failure, severe sepsis, or in-hospital death., Measurements and Main Results: We combined the complete hospitalization discharge databases for three U.S. states, regional influenza virus surveillance, and state census data. Using negative binomial regression models, we estimated the incidence rates of adult influenza-associated critical illness hospitalizations and compared them with all-cause event rates. We also compared modeled outcomes to International Classification of Diseases, 9th Edition, Clinical Modification-coded influenza hospitalizations to assess potential underrecognition of severe influenza disease. During the study period, we estimated that 26,760 influenza-associated critical illness hospitalizations (95% CI, 14,541, 47,464) occurred. The population-based incidence estimate for influenza-associated critical illness was 12.0 per 100,000 person-years (95% CI, 6.6, 21.6) or 1.3% of all critical illness hospitalizations (95% CI, 0.7%, 2.3%). During the influenza season, 3.4% of all critical illness hospitalizations (95% CI, 1.9%, 5.8%) were attributable to influenza. There were only 2,612 critical illness hospitalizations with International Classification of Diseases, 9th Edition, Clinical Modification-coded influenza diagnoses, suggesting influenza is either undiagnosed or undercoded in a substantial proportion of critical illness., Conclusions: Extrapolating our data to the 2010 U.S. population, we estimate that about 28,000 adults are hospitalized for influenza-associated critical illness annually. Influenza in many of these critically ill patients may be undiagnosed. Critical care physicians should have a high index of suspicion for influenza in the ICU, particularly when influenza is known to be circulating in their communities.

Introduction: Prompt treatment of severe sepsis in the Emergency Department reduces deaths, but the role of prehospital fluid resuscitation is unknown. We sought to determine the risk-adjusted association between prehospital fluid administration and hospital mortality among emergency medical services (EMS) patients admitted with severe sepsis., Methods: We performed a prospective, observational study of patients hospitalized with severe sepsis on admission among 45,394 adult EMS encounters taken to 15 hospitals from 11/2009 to 12/2010 by a two-tier EMS system in King County, Washington. The region mandated recording of prehospital intravenous catheter and fluid administration in prehospital records, along with detailed demographic, incident, physiologic, and hospital adjustment variables. We determined the effect of prehospital intravenous catheter or fluid versus no catheter or fluid on all-cause mortality using multivariable logistic regression., Results: Of all encounters, 1,350 met criteria for severe sepsis on admission, of whom 205 (15%) died by hospital discharge, 312 (23%) received prehospital intravenous fluid, 90 (7%) received a prehospital catheter alone and 948 (70%) did not receive catheter or fluid. EMS administered a median prehospital fluid volume of 500 mL (interquartile range (IQR): 200, 1000 mL). In adjusted models, the administration of any prehospital fluid was associated with reduced hospital mortality (Odds ratio =0.46; 95% Confidence interval: 0.23, 0.88; P =0.02) compared to no prehospital fluid. The odds of hospital mortality were also lower among severe sepsis patients treated with prehospital intravenous catheter alone (Odds ratio =0.3; 95% Confidence interval: 0.17 to 0.57; P <0.01)., Conclusions: In a population-based study, the administration of prehospital fluid and placement of intravenous access were associated with decreased odds of hospital mortality compared with no prehospital catheter or fluid.

Objective: To use the natural experiment of health insurance reform in Massachusetts to study the impact of increased insurance coverage on ICU utilization and mortality., Design: Population-based cohort study., Setting: Massachusetts and four states (New York, Washington, Nebraska, and North Carolina) that did not enact reform., Patients: All nonpregnant nonelderly adults (age 18-64 yr) admitted to nonfederal acute care hospitals in one of the five states of interest were eligible, excluding patients who were not residents of a respective state at the time of admission., Measurements: We used a difference-in-differences approach to compare trends in ICU admissions and outcomes of in-hospital mortality and discharge destination for ICU patients., Main Result: Healthcare reform in Massachusetts was associated with a decrease in ICU patients without insurance from 9.3% to 5.1%. There were no significant changes in adjusted ICU admission rates, mortality, or discharge destination. In a sensitivity analysis excluding a state that enacted Medicaid reform prior to the study period, our difference-in-differences analysis demonstrated a significant increase in mortality of 0.38% per year (95% CI, 0.12-0.64%) in Massachusetts, attributable to a greater per-year decrease in mortality postreform in comparison states (-0.37%; 95% CI, -0.52% to -0.21%) compared with Massachusetts (0.01%; 95% CI, -0.20% to 0.11%)., Conclusion: Massachusetts healthcare reform increased the number of ICU patients with insurance but was not associated with significant changes in ICU use or discharge destination among ICU patients. Reform was also not associated with changed in-hospital mortality for ICU patients; however, this association was dependent on the comparison states chosen in the analysis.

Objective: To assess the relationship between volume of nonoperative mechanically ventilated patients receiving care in a specific Veterans Health Administration hospital and their mortality., Design: Retrospective cohort study., Setting: One-hundred nineteen Veterans Health Administration medical centers., Patients: We identified 5,131 hospitalizations involving mechanically ventilated patients in an intensive care unit during 2009, who did not receive surgery., Interventions: None., Measurements and Main Results: We extracted demographic and clinical data from the VA Inpatient Evaluation Center. For each hospital, we defined volume as the total number of nonsurgical admissions receiving mechanical ventilation in an intensive care unit during 2009. We examined the hospital contribution to 30-day mortality using multilevel logistic regression models with a random intercept for each hospital. We quantified the extent of interhospital variation in 30-day mortality using the intraclass correlation coefficient and median odds ratio. We used generalized estimating equations to examine the relationship between volume and 30-day mortality and risk-adjusted all models using a patient-level prognostic score derived from clinical data representing the risk of death conditional on treatment at a high-volume hospital. Mean age for the sample was 65 (SD 11) yrs, 97% were men, and 60% were white. The median VA hospital cared for 40 (interquartile range 19-62) mechanically ventilated patients in 2009. Crude 30-day mortality for these patients was 36.9%. After reliability and risk adjustment to the median patient, adjusted hospital-level mortality varied from 33.5% to 40.6%. The intraclass correlation coefficient for the hospital-level variation was 0.6% (95% confidence interval 0.1, 3.4%), with a median odds ratio of 1.15 (95% confidence interval 1.06, 1.38). The relationship between hospital volume of mechanically ventilated and 30-day mortality was not statistically significant: each 50-patient increase in volume was associated with a nonsignificant 2% decrease in the odds of death within 30 days (odds ratio 0.98, 95% confidence interval 0.87-1.10)., Conclusions: Veterans Health Administration hospitals caring for lower volumes of mechanically ventilated patients do not have worse mortality. Mechanisms underlying this finding are unclear, but, if elucidated, may offer other integrated health systems ways to overcome the disadvantages of small-volume centers in achieving good outcomes.

Purpose: Lung cancer is the leading cause of cancer-related mortality. Intensive care unit (ICU) use among patients with cancer is increasing, but data regarding ICU outcomes for patients with lung cancer are limited., Patients and Methods: We used the Surveillance, Epidemiology, and End Results (SEER) -Medicare registry (1992 to 2007) to conduct a retrospective cohort study of patients with lung cancer who were admitted to an ICU for reasons other than surgical resection of their tumor. We used logistic and Cox regression to evaluate associations of patient characteristics and hospital mortality and 6-month mortality, respectively. We calculated adjusted associations for mechanical ventilation receipt with hospital and 6-month mortality., Results: Of the 49,373 patients with lung cancer admitted to an ICU for reasons other than surgical resection, 76% of patients survived the hospitalization, and 35% of patients were alive 6 months after discharge. Receipt of mechanical ventilation was associated with increased hospital mortality (adjusted odds ratio, 6.95; 95% CI, 6.89 to 7.01; P < .001), and only 15% of these patients were alive 6 months after discharge. Of all ICU patients with lung cancer, the percentage of patients who survived 6 months from discharge was 36% for patients diagnosed in 1992 and 32% for patients diagnosed in 2005, whereas it was 16% and 11% for patients who received mechanical ventilation, respectively., Conclusion: Most patients with lung cancer enrolled in Medicare who are admitted to an ICU die within 6 months of admission. To improve patient-centered care, these results should guide shared decision making between patients with lung cancer and their clinicians before an ICU admission.

Background: Little information exists about the expected time to death after terminal withdrawal of mechanical ventilation. We sought to determine the independent predictors of time to death after withdrawal of mechanical ventilation., Methods: We conducted a secondary analysis from a cluster randomized trial of an end-of-life care intervention. We studied 1,505 adult patients in 14 hospitals in Washington State who died within or shortly after discharge from an ICU following terminal withdrawal of mechanical ventilation (August 2003 to February 2008). Time to death and its predictors were abstracted from the patients' charts and death certificates. Predictors included demographics, proxies of severity of illness, life-sustaining therapies, and International Classification of Diseases, 9th ed., Clinical Modification codes., Results: The median (interquartile range [IQR]) age of the cohort was 71 years (58-80 years), and 44% were women. The median (IQR) time to death after withdrawal of ventilation was 0.93 hours (0.25-5.5 hours). Using Cox regression, the independent predictors of a shorter time to death were nonwhite race (hazard ratio [HR], 1.17; 95% CI, 1.01-1.35), number of organ failures (per-organ HR, 1.11; 95% CI, 1.04-1.19), vasopressors (HR, 1.67; 95% CI, 1.49-1.88), IV fluids (HR, 1.16; 95% CI, 1.01-1.32), and surgical vs medical service (HR, 1.29; 95% CI, 1.06-1.56). Predictors of longer time to death were older age (per-decade HR, 0.95; 95% CI, 0.90-0.99) and female sex (HR, 0.86; 95% CI, 0.77-0.97)., Conclusions: Time to death after withdrawal of mechanical ventilation varies widely, yet the majority of patients die within 24 hours. Subsequent validation of these predictors may help to inform family counseling at the end of life.

Objective: Studies describing predictors of mortality in patients with acute lung injury were primarily derived from selected academic centers. We sought to determine the predictors of mortality in a population-based cohort of patients with acute lung injury and to characterize the performance of current severity of illness scores in this population., Design: Secondary analysis of a prospective, multicenter, population-based cohort., Setting: Twenty-one hospitals in Washington State., Patients: The cohort included 1,113 patients with acute lung injury identified during the year 1999-2000., Interventions: None., Measurements and Main Results: We evaluated physiology, comorbidities, risk factors for acute lung injury, and other variables for their association with death at hospital discharge. Bivariate predictors of death were entered into a multiple logistic regression model. We compared Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, and Simplified Acute Physiology Score II to the multivariable model using area under the receiver operating characteristic curve. The model was validated in an independent cohort of 886 patients with acute lung injury. Modified acute physiology score, age, comorbidities, arterial pH, minute ventilation, PaCO2, PaO2/FiO2 ratio, intensive care unit admission source, and intensive care unit days before onset of acute lung injury were independently predictive of in-hospital death (p < .05). The area under the receiver operating characteristic curve for the multivariable model was superior to that of APACHE III (.81 vs. .77, p < .001) but was no different after external validation (.71 vs. .70, p = .64)., Conclusions: The predictors of mortality in patients with acute lung injury are similar to those predictive of mortality in the general intensive care unit population, indicating disease heterogeneity within this cohort. Accordingly, APACHE III predicts mortality in acute lung injury as well as a model using variables selected specifically for patients with acute lung injury.