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Background: The last national estimates of US ICU physician staffing are 25 years old and lack information about interprofessional teams., Research Question: How are US adult ICUs currently staffed?, Study Design and Methods: We conducted a cross-sectional survey (May 4, 2022-February 2, 2023) of adult ICU clinicians (targeting nurse/physician leadership) contacted using 2020 American Hospital Association (AHA) database information and, secondarily, through professional organizations. The survey included questions about interprofessional ICU staffing availability and roles at steady state (pre-COVID-19). We linked survey data to hospital data in the AHA database to create weighted national estimates by extrapolating ICU staffing data to nonrespondent hospitals based on hospital characteristics., Results: The cohort consisted of 596 adult ICUs (response rates: AHA contacts: 2.1%; professional organizations: unknown) with geographic diversity and size variability (median, 20 beds; interquartile range, 12-25); most cared for mixed populations (414 [69.5%]), yet medical (55 [9.2%]), surgical (70 [11.7%]), and specialty (57 [9.6%]) ICUs were well represented. A total of 554 (93.0%) had intensivists available, with intensivists covering all patients in 75.6% of these and onsite 24 h/d in one-half (53.3% weekdays; 51.8% weekends). Of all ICUs, 69.8% had physicians-in-training and 77.7% had nurse practitioners/physician assistants. For patients on mechanical ventilation, nurse to patient ratios were 1:2 in 89.6% of ICUs. Clinical pharmacists were available in 92.6%, and respiratory therapists were available in 98.8%. We estimated 85.1% (95% CI, 85.7%-84.5%) of hospitals nationally had ICUs with intensivists, 51.6% (95% CI, 50.6%-52.5%) had physicians-in-training, 72.1% (95% CI, 71.3%-72.9%) had nurse practitioners/physician assistants, 98.5% (95% CI, 98.4%-98.7%) had respiratory therapists, and 86.9% (95% CI, 86.4%-87.4%) had clinical pharmacists. For patients on mechanical ventilation, 86.4% (95% CI, 85.8%-87.0%) used 1:2 nurses/patients., Interpretation: We found that intensivist presence in adult US ICUs has greatly increased over 25 years. Intensivists, respiratory therapists, and clinical pharmacists are commonly available, and each nurse usually provides care for two patients on mechanical ventilation. However, team composition and workload vary., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: H. B. G. received funds from Gilead Sciences, Inc to serve as a scientific advisor regarding COVID-19 therapeutics and is currently Editor in Chief of CHEST Critical Care. None declared (A. G., D. K. C., A. L. D., R. F., A. A. K., V. X. L., D. L., D. C. S., H. W.)., (Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Objective: Predictive models developed for use in ICUs have been based on retrospectively collected data, which does not take into account the challenges associated with live, clinical data. This study sought to determine if a previously constructed predictive model of ICU mortality (ViSIG) is robust when using data collected prospectively in near real-time., Design: Prospectively collected data were aggregated and transformed to evaluate a previously developed rolling predictor of ICU mortality., Setting: Five adult ICUs at Robert Wood Johnson-Barnabas University Hospital and one adult ICU at Stamford Hospital., Patients: One thousand eight hundred and ten admissions from August to December 2020., Measurements and Main Results: The ViSIG Score, comprised of severity weights for heart rate, respiratory rate, oxygen saturation, mean arterial pressure, mechanical ventilation, and values for OBS Medical's Visensia Index. This information was collected prospectively, whereas data on discharge disposition was collected retrospectively to measure the ViSIG Score's accuracy. The distribution of patients' maximum ViSIG Score was compared with ICU mortality rate, and cut points determined where changes in mortality probability were greatest. The ViSIG Score was validated on new admissions. The ViSIG Score was able to stratify patients into three groups: 0-37 (low risk), 38-58 (moderate risk), and 59-100 (high risk), with mortality of 1.7%, 12.0%, and 39.8%, respectively ( p < 0.001). The sensitivity and specificity of the model to predict mortality for the high-risk group were 51% and 91%. Performance on the validation dataset remained high. There were similar increases across risk groups for length of stay, estimated costs, and readmission., Conclusions: Using prospectively collected data, the ViSIG Score produced risk groups for mortality with good sensitivity and excellent specificity. A future study will evaluate making the ViSIG Score visible to clinicians to determine whether this metric can influence clinician behavior to reduce adverse outcomes., Competing Interests: Dr. Lissauer’s institution received funding from Prescient Healthcare Consulting; he received funding from Leon Piechta Esq, Lacava, Jacobs and Goodis, Saltz Mongeluzzi and Bendesky, Rugers University, and Hartford Healthcare Medical Group. The remaining authors have not disclosed any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Objectives: The high cost of critical care has engendered research into identifying influential factors. However, existing studies have not considered patient vital status at ICU discharge. This study sought to determine the effect of mortality upon the total cost of an ICU stay., Design: Retrospective cohort study., Setting: Twenty-six ICUs at 13 hospitals in the United States., Patients: 58,344 admissions from January 1, 2012, to June 30, 2016, obtained from a commercial ICU database., Interventions: None., Measurements and Main Results: The median observed cost of a unit stay was $9,619 (mean = $16,353). A multivariable regression model was developed on the log of total costs for a unit stay, using severity of illness, unit admitting diagnosis, mortality in the unit, daily unit occupancy (occupying a bed at midnight), and length of mechanical ventilation. This model had an r of 0.67 and a median difference between observed and expected costs of $437. The first few days of care and the first day receiving mechanical ventilation had the largest effect on total costs. Patients dying before unit discharge had 12.4% greater costs than survivors (p < 0.01; 99% CI = 9.3-15.5%) after multivariable adjustment. This effect was most pronounced for patients with an extended ICU stay who were receiving mechanical ventilation., Conclusions: While the largest drivers of ICU costs at the patient level are day 1 room occupancy and day 1 mechanical ventilation, mortality before unit discharge is associated with substantially higher costs. The increase was most evident for patients with an extended ICU stay who were receiving mechanical ventilation. Studies evaluating costs among ICUs need to take mortality into account.

Objective: Reintubation after liberation from mechanical ventilation is viewed as an adverse event in ICUs. We sought to describe the frequency of reintubations across U.S. ICUs and to propose a standard, appropriate time cutoff for reporting of reintubation events., Design and Setting: We conducted a cohort study using data from the Project IMPACT database of 185 diverse ICUs in the United States., Patients: We included patients who received mechanical ventilation and excluded patients who received a tracheostomy, had a do-not-resuscitate order placed, or died prior to first extubation., Measurements and Main Results: We assessed the percentage of patients extubated who were reintubated; the cumulative probability of reintubation, with death and do-not-resuscitate orders after extubation modeled as competing risks, and time to reintubation. Among 98,367 patients who received mechanical ventilation without death or tracheostomy prior to extubation, 9,907 (10.1%) were reintubated, with a cumulative probability of 10.0%. Median time to reintubation was 15 hours (interquartile range, 2-45 hr). Of patients who required reintubation in the ICU, 90% did so within the first 96 hours after initial extubation; this was consistent across various patient subtypes (89.3% for electives surgical patients up to 94.8% for trauma patients) and ICU subtypes (88.6% for cardiothoracic ICUs to 93.5% for medical ICUs)., Conclusions: The reintubation rate for ICU patients liberated from mechanical ventilation in U.S. ICUs is approximately 10%. We propose a time cutoff of 96 hours for reintubation definitions and benchmarking efforts, as it captures 90% of ICU reintubation events. Reintubation rates can be reported as simple percentages, without regard for deaths or changes in goals of care that might occur.

Objectives: To examine ICU performance based on the Simplified Acute Physiology Score 3 using 30-day, 90-day, or 180-day mortality as outcome measures and compare results with 30-day mortality as reference., Design: Retrospective cohort study of ICU admissions from 2010 to 2014., Setting: Sixty-three Swedish ICUs that submitted data to the Swedish Intensive Care Registry., Patients: The development cohort was first admissions to ICU during 2011-2012 (n = 53,546), and the validation cohort was first admissions to ICU during 2013-2014 (n = 57,729)., Interventions: None., Measurements and Main Results: Logistic regression was used to develop predictive models based on a first level recalibration of the original Simplified Acute Physiology Score 3 model but with 30-day, 90-day, or 180-day mortality as measures of outcome. Discrimination and calibration were excellent for the development dataset. Validation in the more recent 2013-2014 database showed good discrimination (C-statistic: 0.85, 0.84, and 0.83 for the 30-, 90-, and 180-d models, respectively), and good calibration (standardized mortality ratio: 0.99, 0.99, and 1.00; Hosmer-Lemeshow goodness of fit H-statistic: 66.4, 63.7, and 81.4 for the 30-, 90-, and 180-d models, respectively). There were modest changes in an ICU's standardized mortality ratio grouping (< 1.00, not significant, > 1.00) when follow-up was extended from 30 to 90 days and 180 days, respectively; about 11-13% of all ICUs., Conclusions: The recalibrated Simplified Acute Physiology Score 3 hospital outcome prediction model performed well on long-term outcomes. Evaluation of ICU performance using standardized mortality ratio was only modestly sensitive to the follow-up time. Our results suggest that 30-day mortality may be a good benchmark of ICU performance. However, the duration of follow-up must balance between what is most relevant for patients, most affected by ICU care, least affected by administrative policies and practically feasible for caregivers.

Objectives: To develop a model that predicts the duration of mechanical ventilation and then to use this model to compare observed versus expected duration of mechanical ventilation across ICUs., Design: Retrospective cohort analysis., Setting: Eighty-six eligible ICUs at 48 U.S. hospitals., Patients: ICU patients receiving mechanical ventilation on day 1 (n = 56,336) admitted from January 2013 to September 2014., Interventions: None., Measurements and Main Results: We developed and validated a multivariable logistic regression model for predicting duration of mechanical ventilation using ICU day 1 patient characteristics. Mean observed minus expected duration of mechanical ventilation was then obtained across patients and for each ICU. The accuracy of the model was assessed using R. We defined better performing units as ICUs that had an observed minus expected duration of mechanical ventilation less than -0.5 days and a p value of less than 0.01; and poorer performing units as ICUs with an observed minus expected duration of mechanical ventilation greater than +0.5 days and a p value of less than 0.01. The factors accounting for the majority of the model's explanatory power were diagnosis (71%) and physiologic abnormalities (24%). For individual patients, the difference between observed and mean predicted duration of mechanical ventilation was 3.3 hours (95% CI, 2.8-3.9) with R equal to 21.6%. The mean observed minus expected duration of mechanical ventilation across ICUs was 3.8 hours (95% CI, 2.1-5.5), with R equal to 69.9%. Among the 86 ICUs, 66 (76.7%) had an observed mean mechanical ventilation duration that was within 0.5 days of predicted. Five ICUs had significantly (p < 0.01) poorer performance (observed minus expected duration of mechanical ventilation, > 0.5 d) and 14 ICUs significantly (p < 0.01) better performance (observed minus expected duration of mechanical ventilation, < -0.5 d)., Conclusions: Comparison of observed and case-mix-adjusted predicted duration of mechanical ventilation can accurately assess and compare duration of mechanical ventilation across ICUs, but cannot accurately predict an individual patient's mechanical ventilation duration. There are substantial differences in duration of mechanical ventilation across ICU and their association with unit practices and processes of care warrants examination.

Importance: Little is known about the deadoption of ineffective or harmful clinical practices. A large clinical trial (the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation [NICE-SUGAR] trial) demonstrated that strict blood glucose control (tight glycemic control) in patients admitted to adult intensive care units (ICUs) should be deadopted; however, it is unknown whether deadoption occurred and how it compared with the initial adoption., Objective: To evaluate glycemic control in critically ill patients before and after the publication of clinical trials that initially suggested that tight glycemic control reduced mortality (Leuven I) but subsequently demonstrated that it increased mortality (NICE-SUGAR)., Design, Setting, and Participants: Interrupted time-series analysis of 353,464 patients admitted to 113 adult ICUs from January 1, 2001, through December 31, 2012, in the United States using data from the Acute Physiology and Chronic Health Evaluation database., Main Outcomes and Measures: The physiologically most extreme blood glucose level on day 1 of ICU admission defined glycemic control as tight control (glucose level, 80-110 mg/dL; to convert to millimoles per liter, multiply by 0.0555), hypoglycemia (glucose level, <70 mg/dL), and hyperglycemia (glucose level, ≥180 mg/dL). Temporal changes in each marker were examined using mixed-effects segmented linear regression., Results: Before the publication of Leuven I, 17.2% (95% CI, 16.2%-18.2%) of ICU admissions had tight glycemic control, 3.0% (95% CI, 2.6%-3.5%) had hypoglycemia, and 40.2% (95% CI, 38.8%-41.5%) had hyperglycemia. After the publication of Leuven I, there were significant increases in the relative proportion of admissions with tight glycemic control (1.7% per quarter; 95% CI, 1.2%-2.3%; P<.001) and hypoglycemia (2.5% per quarter; 95% CI, 1.9%-3.2%; P<.001) and decreases in those with hyperglycemia (0.6% per quarter; 95% CI, 0.4%-0.9%; P<.001). Following the publication of NICE-SUGAR, there was no change in the proportion of patients with tight glycemic control or hyperglycemia. There was an immediate decrease in the relative proportion of patients with hypoglycemia (22.4%; 95% CI, 13.2%-30.1%; P<.001) but no subsequent change over time., Conclusions and Relevance: Among patients admitted to adult ICUs in the United States, there was a slow steady adoption of tight glycemic control following publication of a clinical trial that suggested benefit, with little to no deadoption following a subsequent trial that demonstrated harm. There is an urgent need to understand and promote the deadoption of ineffective clinical practices.

Objectives: To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences., Design: Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort., Setting: Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013., Patients: Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions., Interventions: None., Measurements and Main Results: We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more severely ill patients and those with a higher percentage of patients on mechanical ventilation had the most discordant standardized mortality ratios between the two predictive models., Conclusions: Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models yield different ICU performance assessments due to differences in case-mix adjustment. Given the growing role of outcomes in driving prospective payment patient referral and public reporting, performance should be assessed by models with fewer exclusions, superior accuracy, and better case-mix adjustment.

Purpose of Review: There are few first-hand accounts that describe the history of outcome prediction in critical care. This review summarizes the authors' personal perspectives about the development and evolution of Acute Physiology and Chronic Health Evaluation over the past 35 years., Recent Findings: We emphasize what we have learned in the past and more recently our perspectives about the current status of outcome prediction, and speculate about the future of outcome prediction., Summary: There is increasing evidence that superior accuracy in outcome prediction requires complex modeling with detailed adjustment for diagnosis and physiologic abnormalities. Thus, an automated electronic system is recommended for gathering data and generating predictions. Support, either public or private, is required to assist users and to update and improve models. Current outcome prediction models have increasingly focused on benchmarks for resource use, a trend that seems likely to increase in the future.

Objective: To examine the accuracy of the original Mortality Probability Admission Model III, ICU Outcomes Model/National Quality Forum modification of Mortality Probability Admission Model III, and Acute Physiology and Chronic Health Evaluation IVa models for comparing observed and risk-adjusted hospital mortality predictions., Design: Retrospective paired analyses of day 1 hospital mortality predictions using three prognostic models., Setting: Fifty-five ICUs at 38 U.S. hospitals from January 2008 to December 2012., Patients: Among 174,001 intensive care admissions, 109,926 met model inclusion criteria and 55,304 had data for mortality prediction using all three models., Interventions: None., Measurements and Main Results: We compared patient exclusions and the discrimination, calibration, and accuracy for each model. Acute Physiology and Chronic Health Evaluation IVa excluded 10.7% of all patients, ICU Outcomes Model/National Quality Forum 20.1%, and Mortality Probability Admission Model III 24.1%. Discrimination of Acute Physiology and Chronic Health Evaluation IVa was superior with area under receiver operating curve (0.88) compared with Mortality Probability Admission Model III (0.81) and ICU Outcomes Model/National Quality Forum (0.80). Acute Physiology and Chronic Health Evaluation IVa was better calibrated (lowest Hosmer-Lemeshow statistic). The accuracy of Acute Physiology and Chronic Health Evaluation IVa was superior (adjusted Brier score = 31.0%) to that for Mortality Probability Admission Model III (16.1%) and ICU Outcomes Model/National Quality Forum (17.8%). Compared with observed mortality, Acute Physiology and Chronic Health Evaluation IVa overpredicted mortality by 1.5% and Mortality Probability Admission Model III by 3.1%; ICU Outcomes Model/National Quality Forum underpredicted mortality by 1.2%. Calibration curves showed that Acute Physiology and Chronic Health Evaluation performed well over the entire risk range, unlike the Mortality Probability Admission Model and ICU Outcomes Model/National Quality Forum models. Acute Physiology and Chronic Health Evaluation IVa had better accuracy within patient subgroups and for specific admission diagnoses., Conclusions: Acute Physiology and Chronic Health Evaluation IVa offered the best discrimination and calibration on a large common dataset and excluded fewer patients than Mortality Probability Admission Model III or ICU Outcomes Model/National Quality Forum. The choice of ICU performance benchmarks should be based on a comparison of model accuracy using data for identical patients.

Objectives: Detailed data on occupancy and use of mechanical ventilators in U. S. ICU over time and across unit types are lacking. We sought to describe the hourly bed occupancy and use of ventilators in U.S. ICUs to improve future planning of both the routine and disaster provision of intensive care., Design: Retrospective cohort study. We calculated mean hourly bed occupancy in each ICU and hourly bed occupancy for patients on mechanical ventilators. We assessed trends in overall occupancy over the 3 years. We also assessed occupancy and mechanical ventilation rates across different types and sizes of ICUs., Setting: Ninety-seven U.S. ICUs participating in Project IMPACT from 2005 to 2007., Patients: A total of 226,942 consecutive admissions to ICUs., Interventions: None., Measurements and Main Results: Over the 3 years studied, total ICU occupancy ranged from 57.4% to 82.1% and the number of beds filled with mechanically ventilated patients ranged from 20.7% to 38.9%. There was no change in occupancy across years and no increase in occupancy during influenza seasons. Mean hourly occupancy across ICUs was 68.2% ± 21.3% (SD) and was substantially higher in ICUs with fewer beds (mean, 75.8% ± 16.5% for 5-14 beds vs 60.9% ± 22.1% for 20+ beds, p = 0.001) and in academic hospitals (78.7% ± 15.9% vs 65.3% ± 21.3% for community not-for-profit hospitals, p < 0.001). More than half of ICUs (53.6%) had 4+ beds available more than half the time. The mean percentage of ICU patients receiving mechanical ventilation in any given hour was 39.5% (± 15.2%), and a mean of 29.0% (± 15.9%) of ICU beds were filled with a patient on a ventilator., Conclusions: Occupancy of U.S. ICUs was stable over time, but there is uneven distribution across different types and sizes of units. Only three of 10 beds were filled at any time with mechanically ventilated patients, suggesting substantial surge capacity throughout the system to care for acutely critically ill patients.

Objectives: Severity of illness scores have gained considerable interest for their use in predicting outcomes such as mortality and length of stay. The most sophisticated scoring systems require the collection of numerous physiologic measurements, making their use in real-time difficult. A severity of illness score based on a few parameters that can be captured electronically would be of great benefit. Using a machine-learning technique known as particle swarm optimization, we attempted to reduce the number of physiologic parameters collected in the Acute Physiology, Age, and Chronic Health Evaluation IV system without losing predictive accuracy., Design: Retrospective cohort study of ICU admissions from 2007 to 2011., Setting: Eighty-six ICUs at 49 U.S. hospitals where an Acute Physiology, Age, and Chronic Health Evaluation IV system had been installed., Patients: 81,087 admissions, of which 72,474 did not have any missing values., Interventions: None., Measurements and Main Results: Machine-learning algorithms were used to come up with the minimal set of variables that were capable of yielding an accurate severity of illness score: the Oxford Acute Severity of Illness Score. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score were developed on admissions during 2007-2009 and validated on admissions during 2010-2011. The most parsimonious Oxford Acute Severity of Illness Score consisted of seven physiologic measurements, elective surgery, age, and prior length of stay. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score achieved an area under the receiver operating characteristic curve of 0.88 and calibrated well., Conclusions: A reduced severity of illness score had discrimination and calibration equivalent to more complex existing models. This was accomplished in large part using machine-learning algorithms, which can effectively account for the nonlinear associations between physiologic parameters and outcome.

Introduction: A decrease in disease-specific mortality over the last twenty years has been reported for patients admitted to United States (US) hospitals, but data for intensive care patients are lacking. The aim of this study was to describe changes in hospital mortality and case-mix using clinical data for patients admitted to multiple US ICUs over the last 24 years., Methods: We carried out a retrospective time series analysis of hospital mortality using clinical data collected from 1988 to 2012. We also examined the impact of ICU admission diagnosis and other clinical characteristics on mortality over time. The potential impact of hospital discharge destination on mortality was also assessed using data from 2001 to 2012., Results: For 482,601 ICU admissions there was a 35% relative decrease in mortality from 1988 to 2012 despite an increase in age and severity of illness. This decrease varied greatly by diagnosis. Mortality fell by >60% for patients with chronic obstructive pulmonary disease, seizures and surgery for aortic dissection and subarachnoid hemorrhage. Mortality fell by 51% to 59% for six diagnoses, 41% to 50% for seven diagnoses, and 10% to 40% for seven diagnoses. The decrease in mortality from 2001 to 2012 was accompanied by an increase in discharge to post-acute care facilities and a decrease in discharge to home., Conclusions: Hospital mortality for patients admitted to US ICUs has decreased significantly over the past two decades despite an increase in the severity of illness. Decreases in mortality were diagnosis specific and appear attributable to improvements in the quality of care, but changes in discharge destination and other confounders may also be responsible.

Objective: To examine the association between ICU readmission rates and case-mix-adjusted outcomes., Design: Retrospective cohort study of ICU admissions from 2002 to 2010., Setting: One hundred five ICUs at 46 United States hospitals., Patients: Of 369,129 admissions, 263,082 were first admissions that were alive at ICU discharge and candidates for readmission., Interventions: None., Measurements and Main Results: The median unit readmission rate was 5.9% (intraquartile range 5.1%-7.0%). Across all admissions, hospital mortality for patients with and without readmission was 21.3% vs. 3.6%, mean ICU stay 4.9 days vs. 3.4 days, and hospital stay 13.3 days vs. 4.5 days, respectively. We stratified ICUs according to their readmission rate: high (>7%), moderate (5%-7%), and low (<5%) rates. Observed and case-mix-adjusted hospital mortality, ICU and hospital lengths of stay were examined by readmission rate strata. Observed outcomes were much worse in the high readmission rate units. But after adjusting for patient and institutional differences, there was no association between level of unit readmission rate and case-mix-adjusted mortality. The difference between observed and predicted mortality was -0.4%, 0.4%, and -1.1%, for the high, medium, and low readmission rate strata, respectively. Additionally, the difference between observed and expected ICU length of stay was approximately zero for the three strata., Conclusions: Patients readmitted to ICUs have increased hospital mortality and lengths of stay. After case-mix adjustment, there were no significant differences in standardized mortality or case-mix-adjusted lengths of stay between units with high readmission rates compared to units with moderate or low rates. The use of readmission as a quality measure should only be implemented if patient case-mix is taken into account.

Objective: The classification of the comatose patient has been greatly improved with the use of coma scales. The Full Outline of Unresponsiveness score has emerged as an alternative to the Glasgow Coma Scale in that it incorporates essential information needed to assess the depth of coma. One set of patients for which the Full Outline of Unresponsiveness score could be particularly beneficial is those admitted to an intensive care unit, where approximately 30%-35% of all patients are intubated or ventilated. This manuscript reports on a study that examined the inter-rater reliability of the Full Outline of Unresponsiveness score in five intensive care units., Setting: Seven intensive care units at five U.S. hospitals partici-pated., Subjects: Patients admitted during parts of 2010 and 2011 had their Full Outline of Unresponsiveness score assessed independently by two nurses within 1 hr of admission., Design: We evaluated the weighted kappa statistic of the Full Outline of Unresponsiveness score over all patients and stratified by mechanical ventilation status. Finally, we looked for evidence of heterogeneity in Full Outline of Unresponsiveness score agreement across hospitals., Measurements and Main Results: A total of 907 adult critically ill patients had Full Outline of Unresponsiveness score assessments by two evaluators. The overall weighted kappa statistic was 0.92, and this did not differ by whether or not a patient was on a ventilator. Among hospitals there was modest heterogeneity for the weighted kappa; however, all of the values were >0.80., Conclusions: The Full Outline of Unresponsiveness score showed excellent inter-rater agreement overall and at each of the five hospitals. This demonstrates that the Full Outline of Unresponsiveness score can be utilized reliably in critically ill patients.

Background: Hospitals are increasingly adopting 24-hour intensivist physician staffing as a strategy to improve intensive care unit (ICU) outcomes. However, the degree to which nighttime intensivists are associated with improvements in the quality of ICU care is unknown., Methods: We conducted a retrospective cohort study involving ICUs that participated in the Acute Physiology and Chronic Health Evaluation (APACHE) clinical information system from 2009 through 2010, linking a survey of ICU staffing practices with patient-level outcomes data from adult ICU admissions. Multivariate models were used to assess the relationship between nighttime intensivist staffing and in-hospital mortality among ICU patients, with adjustment for daytime intensivist staffing, severity of illness, and case mix. We conducted a confirmatory analysis in a second, population-based cohort of hospitals in Pennsylvania from which less detailed data were available., Results: The analysis with the use of the APACHE database included 65,752 patients admitted to 49 ICUs in 25 hospitals. In ICUs with low-intensity daytime staffing, nighttime intensivist staffing was associated with a reduction in risk-adjusted in-hospital mortality (adjusted odds ratio for death, 0.62; P=0.04). Among ICUs with high-intensity daytime staffing, nighttime intensivist staffing conferred no benefit with respect to risk-adjusted in-hospital mortality (odds ratio, 1.08; P=0.78). In the verification cohort, there was a similar relationship among daytime staffing, nighttime staffing, and in-hospital mortality. The interaction between nighttime staffing and daytime staffing was not significant (P=0.18), yet the direction of the findings were similar to those in the APACHE cohort., Conclusions: The addition of nighttime intensivist staffing to a low-intensity daytime staffing model was associated with reduced mortality. However, a reduction in mortality was not seen in ICUs with high-intensity daytime staffing. (Funded by the National Heart, Lung, and Blood Institute.).

Objective: To examine which patient characteristics increase the risk for intensive care unit readmission and assess the association of readmission with case-mix adjusted mortality and resource use., Design: : Retrospective cohort study., Setting: Ninety-seven intensive and cardiac care units at 35 hospitals in the United States., Patients: A total of 229,375 initial intensive care unit admissions during 2001 through 2009 who met inclusion criteria., Interventions: None., Measurements and Main Results: For patients who were discharged alive and candidates for readmission, we compared the characteristics of those with and without a readmission. A multivariable logistic regression analysis was used to identify potential patient-level characteristics that increase the risk for subsequent readmission. We also evaluated case-mix adjusted outcomes by comparing observed and predicted values of mortality and length of stay for patients with and without intensive care unit readmission. Among 229,375 first admissions that met inclusion criteria, 13,980 (6.1%) were eventually readmitted. Risk factors associated with the highest multivariate odds ratio for unit readmission included location before intensive care unit admission, age, comorbid conditions, diagnosis, intensive care unit length of stay, physiologic abnormalities at intensive care discharge, and discharge to a step-down unit. After adjustment for risk factors, patients who were readmitted had a four-fold greater probability of hospital mortality and a 2.5-fold increase in hospital stay compared to patients without readmission., Conclusions: Intensive care readmission is associated with patient factors that reflect a greater severity and complexity of illness, resulting in a higher risk for hospital mortality and a longer hospital stay. To improve patient safety, physicians should consider these risk factors when making intensive care discharge decisions. Because intensive care unit readmission correlates with more complex and severe illness, readmission rates require case-mix adjustment before they can be properly interpreted as quality measures.

Objectives: To assess variations in case-mix-adjusted hospital and intensive care unit length of stay and to examine the relationship between intensive care unit and hospital stay., Design: Retrospective cohort study., Setting: Sixty-nine intensive and cardiac care units in 23 U.S. hospitals during 2002 to 2008., Patients: Intensive care unit admissions (202,300) who met inclusion criteria., Interventions: None., Measurements and Main Results: We obtained hospital and intensive care unit characteristics and patient demographic, clinical, diagnostic, and physiologic variables, mortality, and lengths of stay. We developed and validated a model to assess case-mix-adjusted hospital stay and modified and updated a previously validated model to assess adjusted intensive care unit stay. We used these models to compare observed and expected hospital and intensive care unit stay for each patient by calculating the observed minus expected length of stay. Mean observed intensive care unit stay was 4.33 days and mean predicted intensive care unit stay was 4.09 days (5.9-hr difference); mean observed hospital stay was 9.93 days and mean predicted hospital stay was 9.52 days (9.7-hr difference). Observed minus expected intensive care unit and hospital length of stay were significantly shorter (p < .01) at one intensive care unit and significantly longer (p < .01) at nine intensive care units. There was a correlation between hospital and intensive care unit observed minus expected length of stay across individuals (R2 = .40), which was much stronger across units (R2 = .76)., Conclusions: Case-mix-adjusted benchmarks for hospital and intensive care unit stays reveal substantial differences in unit efficiency. Hospital and intensive care unit stays are strongly correlated at the patient and unit level, suggesting that there are causal factors common to both.

Background: A recent update of the Mortality Probability Model (MPM)-III found 14% of intensive care patients had age as their only MPM risk factor for hospital mortality. This subgroup had a low mortality rate (2% vs 14% overall), and pronounced differences were noted among elderly patients. This article is an expanded analysis of age-related mortality rates in patients in the ICU., Methods: Project IMPACT data from 135 ICUs for 124,885 patients treated from 2001 to 2004 were analyzed. Patients were stratified as elective surgical, emergency/unscheduled surgical, and medical and then further stratified by age and whether additional MPM risk factors were present or absent., Results: Mortality rose with advancing age within all patient categories. Elective surgical patients without other risk factors were the least likely to die at all ages (0.4% for patients aged 18-29 years to 9.2% for patients aged ≥ 90 years), whereas medical patients with one or more additional risk factors had the highest mortality rate (12.1% for patients aged 18-29 years to 36.0% for patients aged ≥ 90 years). In these two subsets, mortality rates approximately doubled in the elective surgical group among patients aged in their 70s (2.4%), 80s (4.3%), and 90s (9.2%) but rose less dramatically in the medical group (27.0%, 30.7%, and 36.0%, respectively)., Conclusions: Although mortality increased with age, the risk differed significantly by patient subset, even among elderly patients, which may reflect a selection bias. Advanced age alone does not preclude successful surgical and ICU interventions, although the presence of serious comorbidities decreases the likelihood of survival to discharge for all age groups.

Objective: To examine variations in the frequency of discharge of elderly patients to postacute care facilities across multiple intensive care units and identify the influence of institutional and patient factors on the frequency of postacute care discharge., Design: Observational cohort study., Setting: Consecutive admissions from 65 intensive and coronary care units in 24 US hospitals during 2002-2008. Each hospital had a clinical information system in place., Patients: A total of 13,370 admissions in patients aged≥65 yrs who were alive at hospital discharge and met inclusion criteria., Interventions: None., Measurements and Main Results: Demographic, clinical, diagnostic, and physiological variables were obtained on all patients. In addition, information for each hospital and intensive care unit was recorded. Among hospital survivors, 46.2% were discharged to postacute care facilities with a range of 8.8-77.8%. A multivariable logistic regression model was developed that predicted discharge to a postacute care facility. The major variables affecting postacute care discharge were diagnosis, day 5 physiology, and day 5 therapy; these variables accounted for 61% of the model's explanatory power. Patient age, hospital bed size, teaching status, and intensive care unit type also affected postacute care discharge. Physiology and therapy on day 1 had little impact on postacute care discharge. The model accounted for only 31% of the variation in rates across intensive care units, indicating that unmeasured factors play a part in dictating discharge location., Conclusion: Discharge of elderly intensive care unit patients to postacute care facilities varies widely by institution. These variations are only partially explained by differences in patient and institutional characteristics and are affected more by diagnosis and physiology on day 5, respectively. Unmeasured factors such as admission from a postacute care facility, postacute care availability, patient preferences, and socioeconomic factors may account for unexplained variations in postacute care discharge.

Background: Dexmedetomidine is a novel sedative agent that causes anxiolysis without respiratory depression in critically ill patients. We sought to examine patient and hospital variation in dexmedetomidine use and adoption patterns of dexmedetomidine over time., Methods: We performed a retrospective cohort study of all patients who received intravenous infusion sedation in 174 intensive care units contributing data to Project IMPACT from 2001 through 2007. Sedation use was defined as having received an intravenous sedative infusion (dexmedetomidine, midazolam, lorazepam, or propofol) for any period during the intensive care stay. The primary outcome was use of dexmedetomidine in the intensive care unit., Results: Of 58,391 patients who received intravenous infusion sedation, 2,535 (4.3%, 95% confidence interval [CI], 4.2-4.5) received dexmedetomidine. Overall use was highest in cardiac surgery patients (11.7%, 10.8-12.7) and was similar in other surgical patients (4.3%, 4.0-4.6) and medical patients (3.4%, 3.2-3.6, P < 0.001). Use of dexmedetomidine increased from 2.0% (1.6-2.4) of patients receiving intravenous infusion sedation in 2001 to 7.2% (6.6-7.9) in 2007 (P < 0.001), primarily because of an increase in use in cardiac surgery patients (1.4%, 0.0-2.8, in 2001 vs. 20.2%, 17.6-22.8 in 2007, P < 0.001). Of the patients who received dexmedetomidine, 31.5% (29.6-33.3) received the infusion for more than 1 day, and 10.9% were not mechanically ventilated., Conclusion: Use of dexmedetomidine in critically ill patients has increased over time, primarily as a result of an increase in use among cardiac surgery patients. A substantial portion of dexmedetomidine was administered outside of the regulatory approval guidelines at the time.

Purpose: This study presents a new model for identifying patients who might be too well to benefit from intensive care unit (ICU) care., Patients and Methods: Intensive care unit admissions in 2002 to 2003 were used to develop a model to predict whether patients monitored on day one would receive one or more of 33 subsequent active life-supporting treatments. Accuracy was assessed by testing the model in a subsequent cohort of admissions in 2004 to 2006. We then assessed the frequency of active treatment among monitor patients at a low (<10%) risk for active life-supporting therapy on ICU day 1., Results: Among 28 847 ICU monitor admissions in 2004 to 2006, 3153 patients (11.0%) were predicted to receive active treatment; 3296 (11.5%) actually did. There were 17 720 admissions with a low (<10%) risk for receiving subsequent active life-supporting treatment; 1238 (7.0%) received subsequent active treatment. Hospital mortality (2.5%) and mean ICU stay (1.8 days) suggests that most of these patients did not require ICU care., Conclusions: The outcome for low-risk monitor patients suggest they may be too well to benefit from intensive care. The frequency of low-risk monitor admissions provides a measure of ICU resource use. Improved resource use and reduced costs might be achieved by strategies to provide care for these patients on floors or intermediate care units., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Background: Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay., Methods: We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model., Results: The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO2: FiO2 ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r2 was 20.2% across individuals and 44.3% across units., Conclusions: A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.

Objectives: : To examine the association of the resident work-hours reform with mortality for patients in medical and surgical intensive care units. The United States instituted restrictions on resident work-hours in July 2003. The clinical impact of this reform on critically ill patients is unknown., Design: : A retrospective cohort study, comparing mortality trends before and after July 1, 2003, in teaching and nonteaching hospitals., Setting and Patients: : The study included 230,151 adult patients admitted to 104 different intensive care units at 40 hospitals participating in the Acute Physiology and Chronic Health Evaluation IV clinical information system from July 1, 2001, to June 30, 2005., Interventions: : None., Measurements and Main Results: : The primary exposure was the date of admission, relative to the implementation of the work-hours regulations. The primary outcome was in-hospital mortality; a secondary outcome was intensive care unit mortality. The analysis included 79,377 patients in 12 academic hospitals; 73,580 patients in 12 community hospitals with residents; and 77,194 patients in 16 nonteaching hospitals. Risk-adjusted mortality improved in hospitals of all teaching levels during the study period. There were no significant differences in the mortality trends between hospitals of different teaching intensities, as demonstrated by nonsignificant interaction between time and teaching status (global test of interaction, p = .56)., Conclusions: : There was a decrease in in-hospital mortality in intensive care unit patients during the years of observation. This decrease was not associated with hospital teaching status, suggesting no net positive or negative association of the resident work-hours regulations with a major patient-centered outcome.

Objectives: To examine the sensitivity of the performance of the latest Mortality Probability Model at intensive care unit admission (MPM0-III) to case-mix variations and to determine how specialized models for these subgroups would affect intensive care unit performance assessment. MPM0-III is an important benchmarking tool for intensive care units in Project IMPACT. Overall, MPM0-III has excellent discrimination and calibration but its performance varies on six common patient subsets., Design: A total of 124,171 patients in six subgroups (complex cardiovascular, trauma, elective surgery, medical, neurosurgery, and emergency surgery) were divided randomly into development (60%) and validation (40%) groups. A logistic regression model was developed to predict hospital mortality for each subgroup, using MPM0-III variables. Model performance was evaluated on the validation sets, using Hosmer-Lemeshow and receiver operating characteristic statistics. Intensive care unit standardized mortality ratios, using the subgroup models and MPM0-III, were compared. A sensitivity analysis was used to identify the occurrence of each subgroup associated with degraded MPM0-III performance., Setting: One hundred thirty-five intensive care units at 98 hospitals participating in Project IMPACT between 2001 and 2004. ICUs with <100 patient records were excluded., Patients: Consecutive intensive care unit patients in the Project IMPACT database who were eligible for MPM0-II scoring., Interventions: None., Measurements and Main Results: Hospital mortality and standardized mortality ratio values by intensive care unit. All six subgroup models had good performance on their validation sets. Intensive care unit standardized mortality ratios calculated with MPM0-III and the subgroup models were nearly identical, with MPM0-III identifying 33 of 135 as significant standardized mortality ratio outliers and the subgroup models identifying 35 of 135, with 33 overlapping. Sensitivity analysis indicated that MPM0-III calibration degraded substantially only when patient mix varied significantly from that of the data set on which MPM0-III was based., Conclusion: We recommend users of MPM make MPM0-III their primary model. Subgroup models may have utility when evaluating highly specialized intensive care units or in research on specific, homogeneous populations.

Background: Hospital performance measures rely on aggregate outcomes. For patients receiving mechanical ventilation (MV), outcomes depend on severity of illness, hospital MV volume, and case mix. Patients requiring prolonged acute MV (PAMV) [MV for >or= 96 h] comprise a resource-intensive group, but the impact of its volume on aggregate outcomes is unknown. We investigated whether observed outcomes differed from those predicted by APACHE (acute physiology and chronic health evaluation) IV risk adjustment and the relationship between hospital MV volume and outcomes among patients receiving PAMV., Methods: We conducted a retrospective cohort study using the APACHE IV database between the years 2001 and 2005., Results: Of the 94,553 patients receiving MV at 45 hospitals, 24,366 (25.8%) were receiving PAMV. Unadjusted mortality was 32.3% for patients receiving PAMV and 22.9% for patients receiving short-term MV (STMV) [< 96 h]. Although mortality predictions were accurate in both groups, the length-of-stay (LOS) predictions underestimated duration of MV, ICU LOS, and hospital LOS by 5.2, 4.6, and 5.4 days, respectively, in the PAMV group. After stratifying the PAMV group by hospital MV volume, except for quintile 1, the standardized mortality ratio (SMR) was found to be inversely related to the volume quintile. The difference between actual and predicted MV durations, however, exhibited a consistent direct relationship with the MV volume., Conclusions: In patients requiring PAMV, the SMR is inversely proportional to hospital MV volume. Conversely, the PAMV group had a disproportionate effect on durations of MV, ICU LOS, and hospital LOS, and these marginal excesses increased with the hospital MV volume quintile. Development of specific predictive equations for patients receiving PAMV is recommended. Benchmarking measures must consider the case mix of patients receiving STMV vs those receiving PAMV.

Objective: To validate performance characteristics of the intensive care unit (ICU) admission mortality probability model, version III (MPM0-III) on Project IMPACT data submitted in 2004 and 2005. This data set was external from the MPM0-III developmental and internal validation data collected between 2001 and 2004., Design: Retrospective analysis of clinical data collected concurrently with care., Setting: One hundred three (103) adult ICUs in North America that voluntarily collect and submit data to Project IMPACT., Subjects: A total of 55,459 patients who were eligible for MPM scoring (age >or=18; first ICU admission for hospitalization, excludes burns, coronary care, and cardiac surgical patients)., Interventions: None., Measurements: Prevalence of MPM risk factors and their relationship to hospital mortality; calibration and discrimination of MPM0-III model applied to new data., Main Results: Seventy-eight ICUs contributed data to both this study and the original development set. Fifty-six ICUs from the original MPM0-III study were replaced by 25 new ICUs in this external validation set. Patient characteristics (type of patient, risk factors, and resuscitation status) were similar to the original 2001-2004 cohort, except for slightly more patients on mechanical ventilation at admission (32% vs. 27%, p < 0.01) and the percentage of patients having no MPM0-III risk factors except age (11% vs. 14%, p < 0.01). Observed deaths were 7331 (13.2%) vs. 7456 predicted, yielding a standardized mortality ratio of 0.983, 95% CI (0.963-1.001)., Conclusions: MPM0-III calibrates on a new population of 55,459 North American patients who include many patients from new ICUs, which helps confirm that the model is robust and was not overfitted to the development sample. Although Project IMPACT participants change over time, 2004-2005 patient risk factors and their relationship to hospital mortality have not significantly changed. The increase in mechanically ventilated patients and reduction in admissions with no risk factors are trends worth following.

Principle: Although intensive care units (ICUs) with higher overall patient volume may achieve better outcomes than lower volume ICUs, there are few data on the effects of increasing patient loads on patients within the ICU., Objectives: To examine the association of ICU occupancy with the patient outcomes within the same ICU., Methods: We examined 200,499 patients in 108 ICUs using the Acute Physiology and Chronic Health Evaluation IV database in 2002-2005. Daily census on the day of admission was determined for each patient and defined in relation to the mean census. We used conditional logistic regression to compare inpatient outcomes of patients admitted on high census days to those admitted in the same ICU on low census days. We controlled for severity of illness at the patient level using data on clinical, demographic, and physiologic variables on admission to the ICU., Measurements and Main Results: Patients admitted on high census days had the same odds of inpatient mortality or transfer to another hospital as patients admitted on average or on low census days. These findings were robust to multiple alternative definitions of day of admission census and were confirmed in several subgroup analyses., Conclusions: The ICUs in this data are able to function as high reliability organizations. They are able to scale up their operations to meet the needs of a wide range of operating conditions while maintaining consistent patient mortality outcomes.

Rationale: General intensive care units (ICUs) provide care across a wide range of diagnoses, whereas specialty ICUs provide diagnosis-specific care. Risk-adjusted outcome differences across such units are unknown., Objectives: To determine the association between specialty ICU care and the outcome of critical illness., Methods: We conducted a retrospective cohort study design analyzing patients admitted to 124 ICUs participating in the Acute Physiology and Chronic Health Evaluation IV from January 2002 to December 2005. We examined 84,182 patients admitted to specialty and general ICUs with an admitting diagnosis or procedure of acute coronary syndrome, ischemic stroke, intracranial hemorrhage, pneumonia, abdominal surgery, or coronary-artery bypass graft surgery. ICU type was determined by a local data coordinator at each site. Patients were classified by admission to a general ICU, a diagnosis-appropriate ("ideal") specialty ICU, or a diagnosis-inappropriate ("non-ideal") specialty ICU. The primary outcomes were in-hospital mortality and ICU length of stay., Measurements and Main Results: After adjusting for important confounders, there were no significant differences in risk-adjusted mortality between general versus ideal specialty ICUs for all conditions other than pneumonia. Risk-adjusted mortality was significantly greater for patients admitted to non-ideal specialty ICUs. There was no consistent effect of specialization on length of stay for all patients or for ICU survivors., Conclusions: Ideal specialty ICU care appears to offer no survival benefit over general ICU care for select common diagnoses. Non-ideal specialty ICU care (i.e., "boarding") is associated with increased risk-adjusted mortality.

Most performance assessments of cardiac surgery programs use models based on preoperative risk factors. Models that were primarily developed to assess performance in general intensive care unit (ICU) populations have also been used to evaluate the quality of surgical, anesthetic, and ICU management after cardiac surgery. Although there are currently 5 models for evaluating general ICU populations, only the Acute Physiology and Chronic Health Evaluation (APACHE) system has been independently validated for cardiac surgery patients. This review describes the evolution, rationale, and accuracy of APACHE models that are specific for cardiac surgery patients as well as for patients who have had vascular and thoracic procedures. In addition to performance comparisons based on observed and predicted mortality, APACHE provides similar comparisons of ICU and hospital lengths of stay and duration of mechanical ventilation. However, the low mortality incidence of many cardiac outcomes means that very large numbers of patients must be obtained to get good predictive models. Thus, the equations are not designed for predicting individual patients' outcome but have proven useful in performance comparisons and for quality improvement initiatives.

Objective: To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data., Design: Retrospective analysis of data from 124,855 patients admitted to 135 ICUs at 98 hospitals participating in Project IMPACT between 2001 and 2004. Independent variables considered were 15 MPM0-II variables, time before ICU admission, and code status. Univariate analysis and multivariate logistic regression were used to identify risk factors associated with hospital mortality., Setting: One hundred thirty-five ICUs at 98 hospitals., Patients: Patients in the Project IMPACT database eligible for MPM0-II scoring., Interventions: None., Measurements and Main Results: Hospital mortality rate in the current data set was 13.8% vs. 20.8% in the MPM0-II cohort. All MPM0-II variables remained associated with mortality. Clinical conditions with high relative risks in MPM0-II also had high relative risks in MPM0-III. Gastrointestinal bleeding is now associated with lower mortality risk. Two factors have been added to MPM0-III: "full code" resuscitation status at ICU admission, and "zero factor" (absence of all MPM0-II risk factors except age). Seven two-way interactions between MPM0-II variables and age were included and reflect the declining marginal contribution of acute and chronic medical conditions to mortality risk with increasing age. Lead time before ICU admission and pre-ICU location influenced individual outcomes but did not improve model discrimination or calibration. MPM0-III calibrates well by graphic comparison of actual vs. expected mortality, overall standardized mortality ratio (1.018; 95% confidence interval, 0.996-1.040) and a low Hosmer-Lemeshow goodness-of-fit statistic (11.62; p = .31). The area under the receiver operating characteristic curve was 0.823., Conclusions: MPM0-II risk factors remain relevant in predicting ICU outcome, but the 1993 model significantly overpredicts mortality in contemporary practice. With the advantage of a much larger sample size and the addition of new variables and interaction effects, MPM0-III provides more accurate comparisons of actual vs. expected ICU outcomes.

Background: Transferring critically ill patients to other acute care hospitals may artificially impact benchmarking measures. We sought to quantify the effect of out-of-hospital transfers on the standardized mortality ratio (SMR), an outcome-based measure of ICU performance., Methods: We performed a cohort study and Monte Carlo simulation using data from 85 ICUs participating in the acute physiology and chronic health evaluation (APACHE) clinical information system from 2002 to 2003. The SMR (observed divided by expected hospital mortality) was calculated for each ICU using APACHE IV risk adjustment. A set number of patients was randomly assigned to be transferred out alive rather than experience their original outcome. The SMR was recalculated, and the mean simulated SMR was compared to the original., Results: The mean (+/- SD) baseline SMR was 1.06 +/- 0.19. In the simulation, increasing the number of transfers by 2% and 6% over baseline decreased the SMR by 0.10 +/- 0.03 and 0.14 +/- 0.03, respectively. At a 2% increase, 27 ICUs had a decrease in SMR of > 0.10, and two ICUs had a decrease in SMR of > 0.20. Transferring only one additional patient per month was enough to create a bias of > 0.1 in 27 ICUs., Conclusions: Increasing the number of acute care transfers by a small amount can significantly bias the SMR, leading to incorrect inference about ICU quality. Sensitivity to the variation in hospital discharge practices greatly limits the use of the SMR as a quality measure.

Objective: To revise and update the Acute Physiology and Chronic Health Evaluation (APACHE) model for predicting intensive care unit (ICU) length of stay., Design: Observational cohort study., Setting: One hundred and four ICUs in 45 U.S. hospitals., Patients: Patients included 131,618 consecutive ICU admissions during 2002 and 2003, of which 116,209 met inclusion criteria., Interventions: None., Measurements and Main Results: The APACHE IV model for predicting ICU length of stay was developed using ICU day 1 patient data and a multivariate linear regression procedure to estimate the precise ICU stay for randomly selected patients who comprised 60% of the database. New variables were added to the previous APACHE III model, and advanced statistical modeling techniques were used. Accuracy was assessed by comparing mean observed and mean predicted ICU stay for the excluded 40% of patients. Aggregate mean observed ICU stay was 3.86 days and mean predicted 3.78 days (p < .001), a difference of 1.9 hrs. For 108 (93%) of 116 diagnoses, there was no significant difference between mean observed and mean predicted ICU stay. The model accounted for 21.5% of the variation in ICU stay across individual patients and 62% across ICUs. Correspondence between mean observed and mean predicted length of stay was reduced for patients with a short (< or =1.7 days) or long (> or =9.4 days) ICU stay and a low (<20%) or high (>80%) risk of death on ICU day 1., Conclusions: The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients. APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay.

Objective: To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models., Design: : Observational cohort study., Setting: A total of 104 intensive care units (ICUs) in 45 U.S. hospitals., Patients: A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data., Interventions: None., Measurements and Main Results: We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy., Conclusions: APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.

The authors of a recent paper have described an updated simplified acute physiology score (SAPS) II mortality model developed on patient data from 1998 to 1999. Hospital mortality models have a limited range of applicability. SAPS II, Acute Physiology, Age, and Chronic Health Evaluation (APACHE) III, and mortality probability model (MPM)-II, which were developed in the early 1990s, have shown a decline in predictive accuracy as the models age. The deterioration in accuracy is manifested by a decline in the models' calibration. In particular, mortality tends to get over predicted when older models are applied to more contemporary data, which in turn leads to 'grade inflation' when benchmarking intensive care unit (ICU) performance. Although the authors claim that their updated SAPS II can be used for benchmarking ICU performance, it seems likely that this model might already be out of calibration for patient data collected in 2005 and beyond. Thus, the updated SAPS II model may be interesting for historical purposes, but it is doubtful that it can be an accurate tool for benchmarking data from contemporary populations.