Associations between Present-on-Admission Do-Not-Resuscitate Orders and Short-Term Outcomes in Patients with Pneumonia.

OBJECTIVES: Do-not-resuscitate (DNR) orders are used to express patient preferences for cardiopulmonary resuscitation. This study examined whether early DNR orders are associated with differences in treatments and outcomes among patients hospitalized with pneumonia. METHODS: This is a retrospective cohort study of 768,015 adult patients hospitalized with pneumonia from 2010 to 2015 in 646 US hospitals. The exposure was DNR orders present on admission. Secondary analyses stratified patients by predicted in-hospital mortality. Main outcomes included in-hospital mortality, length of stay, cost, intensive care admission, invasive mechanical ventilation, noninvasive ventilation, vasopressors, and dialysis initiation. RESULTS: Of 768,015 patients, 94,155 (12.3%) had an early DNR order. Compared with those without, patients with DNR orders were older (mean age 80.1 ± 10.6 years vs 67.8 ± 16.4 years), with higher comorbidity burden, intensive care use (31.6% vs 30.6%), and in-hospital mortality (28.2% vs 8.5%). After adjustment via propensity score weighting, these patients had higher mortality (odds ratio [OR] 2.39, 95% confidence interval [CI] 2.33-2.45) and lower use of intensive therapies such as vasopressors (OR 0.83, 95% CI 0.81-0.85) and invasive mechanical ventilation (OR 0.68, 95% CI 0.66-0.70). Although there was little relationship between predicted mortality and DNR orders, among those with highest predicted mortality, DNR orders were associated with lower intensive care use compared with those without (66.7% vs 80.8%). CONCLUSIONS: Patients with early DNR orders have higher in-hospital mortality rates than those without, but often receive intensive care. These orders have the most impact on the care of patients with the highest mortality risk.

Benchmarking Inpatient Mortality Using Electronic Medical Record Data: A Retrospective, Multicenter Analytical Observational Study.

OBJECTIVES: To develop a model to benchmark mortality in hospitalized patients using accessible electronic medical record data. DESIGN: Univariate analysis and multivariable logistic regression were used to identify variables collected during the first 24 hours following admission to test for risk factors associated with the end point of hospital mortality. Models were built using specific diagnosis (International Classification of Diseases, 9th Edition or International Classification of Diseases, 10th Edition) captured at discharge, rather than admission diagnosis, which may be discordant. Variables were selected based, in part, on prior the Acute Physiology and Chronic Health Evaluation methodology and included primary diagnosis information plus three aggregated indices (physiology, comorbidity, and support). A Physiology Index was created using parsimonious nonlinear modeling of heart rate, mean arterial pressure, temperature, respiratory rate, hematocrit, platelet counts, and serum sodium. A Comorbidity Index incorporates new or ongoing diagnoses captured by the electronic medical record during the preceding year. A Support Index considered 10 interventions such as mechanical ventilation, selected IV drugs, and hemodialysis. Accuracy was determined using area under the receiver operating curve for discrimination, calibration curves, and modified Brier score for calibration. SETTING AND PATIENTS: We used deidentified electronic medical record data from 74,434 adult inpatients (ICU and ward) at 15 hospitals from 2010 to 2013 to develop the mortality model and validated using data for additional 49,752 patients from the same 15 hospitals. A second revalidation was accomplished using data on 83,684 patients receiving care at six hospitals between 2014 and 2016. The model was also validated on a subset of patients with an ICU stay on day 1. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: This model uses physiology, comorbidity, and support indices, primary diagnosis, age, lowest Glasgow Coma Score, and elapsed time since hospital admission to predict hospital mortality. In the initial validation cohort, observed mortality was 4.04% versus predicted mortality 4.12% (Student t test, p = 0.37). In the revalidation using a different set of hospitals, predicted and observed mortality were 2.66% and 2.99%, respectively. Area under the receiver operating curve were 0.902 (0.895-0.909) and 0.884 (0.877-0.891), respectively, and calibration curves show a close relationship of observed and predicted mortalities. In the evaluation of the subset of ICU patients on day1, the area under the receiver operating curve was 0.87, with an observed mortality of 8.78% versus predicted mortality of 8.93% (Student t test, p = 0.52) and a standardized mortality ratio of 0.98 (0.932-1.034). CONCLUSIONS: Variables considered by traditional ICU prognostic models accurately benchmark patient mortality for patients receiving care in multiple hospital locations, not only the ICU. Unlike Acute Physiology and Chronic Health Evaluation, this model relies on electronic medical record data alone and does not require personnel to collect the independent predictor variables. Assessing the model's utility for benchmarking hospital performance will require prospective testing in a larger representative sample of hospitals.

Coronavirus Disease 2019 ICU Patients Have Higher-Than-Expected Acute Physiology and Chronic Health Evaluation-Adjusted Mortality and Length of Stay Than Viral Pneumonia ICU Patients.

OBJECTIVES: To compare Acute Physiology and Chronic Health Evaluation-IV-adjusted mortality and length of stay outcomes of adult ICU patients who tested positive for coronavirus disease 2019 with patients admitted to ICU with other viral pneumonias including a subgroup with viral pneumonia and concurrent acute respiratory distress syndrome (viral pneumonia-acute respiratory distress syndrome). DESIGN: Retrospective review of Acute Physiology and Chronic Health Evaluation data collected from routine clinical care. SETTING: Forty-three hospitals contributing coronavirus disease 2019 patient data between March 14, and June 17, 2020, and 132 hospitals in the United States contributing data on viral pneumonia patients to the Acute Physiology and Chronic Health Evaluation database between January 1, 2014, and December 31, 2019. PATIENTS AND MEASUREMENTS: One thousand four hundred ninety-one patients with diagnosis of coronavirus disease 2019 infection and 4,200 patients with a primary (n = 2,544) or secondary (n = 1,656) admitting diagnosis of noncoronavirus disease viral pneumonia receiving ICU care. A subset of 202 viral pneumonia patients with concurrent acute respiratory distress syndrome was examined separately. INTERVENTIONS: None. MAIN RESULTS: Mean age was 63.4 for coronavirus disease (p = 0.064) versus 64.1 for viral pneumonia. Acute Physiology and Chronic Health Evaluation-IV scores were similar at 56.7 and 55.0, respectively (p = 0.060), but gender and ethnic distributions differed, as did Pao2 to Fio2 ratio and WBC count at admission. The hospital standardized mortality ratio (95% CI) was 1.52 (1.35-1.68) for coronavirus disease patients and 0.82 (0.75-0.90) for viral pneumonia patients. In the coronavirus disease group, ICU and hospital length of stay were 3.1 and 3.0 days longer than in viral pneumonia patients. Standardized ICU and hospital length of stay ratios were 1.13 and 1.46 in the coronavirus disease group versus 0.95 and 0.94 in viral pneumonia patients. Forty-seven percent of coronavirus disease patients received invasive or noninvasive ventilatory support on their first ICU day versus 65% with viral pneumonia. Ventilator days in survivors were longer in coronavirus disease (10.4 d) than in viral pneumonia (4.3 d) patients, except in the viral pneumonia-acute respiratory distress syndrome subgroup (10.2 d). CONCLUSIONS: Severity-adjusted mortality and length of stay are higher for coronavirus disease 2019 patients than for viral pneumonia patients admitted to ICU. Coronavirus disease patients also have longer time on ventilator and ICU length of stay, comparable with the subset of viral pneumonia patients with concurrent acute respiratory distress syndrome. Mortality and length of stay increase with age and higher scores in both populations, but observed to predicted mortality and length of stay are higher than expected with coronavirus disease patients across all severity of illness levels. These findings have implications for benchmarking ICU outcomes during the coronavirus disease 2019 pandemic.

The dangers of extreme body mass index values in patients with Clostridium difficile.

PURPOSE: To examine the association between body mass index (BMI) and in-hospital mortality in patients presenting with Clostridium difficile infections in emergency department visits (ED) in the USA. Infected patients with extreme BMIs may have an elevated mortality risk, but prior studies examining this question have been too small to reach definitive conclusions. METHODS: Data were from the Nationwide Emergency Department Sample (NEDS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality during 2012. NEDS records emergency department (ED) visits across the USA and provides statistical sampling weights to approximate a nationally representative sample of US hospital-based EDs. Inclusion criteria were adults age 18 or older with an ICD-9 code for C. difficile infection (008.45) and a documented body mass index ICD-9 V code (V85.x). Logistic regression was used to predict mortality after adjusting for demographic variables and chronic comorbidities defined by Elixhauser. RESULTS: A weighted sample of 22,937 ED visits met all inclusion criteria. The cohort's mean age was 66. 64.6% were female. The unadjusted mortality rate was 6.5%. Patients with a BMI < 19 kg/m2 had an adjusted odds ratio of 2.73; 95% CI (1.80, 4.16), p < 0.001 compared to patients with a BMI of 19.0-4.9 kg/m2 (the referent category). In obese patients, only BMI values >40 kg/m2 were associated with significantly greater mortality risk. CONCLUSION: Being underweight (BMI < 19) or morbidly obese (BMI > 40) was associated with increased risk of in-hospital mortality in patients presenting with C. difficile infections.

Association Between Initial Route of Fluoroquinolone Administration and Outcomes in Patients Hospitalized for Community-acquired Pneumonia.

BACKGROUND: Fluoroquinolones have equivalent oral and intravenous bioavailability, but hospitalized patients with community-acquired pneumonia (CAP) generally are treated intravenously. Our objectives were to compare outcomes of hospitalized CAP patients initially receiving intravenous vs oral respiratory fluoroquinolones. METHODS: This was a retrospective cohort study utilizing data from 340 hospitals involving CAP patients admitted to a non-intensive care unit (ICU) setting from 2007 to 2010, who received intravenous or oral levofloxacin or moxifloxacin. The primary outcome was in-hospital mortality. Secondary outcomes included clinical deterioration (transfer to ICU, initiation of vasopressors, or invasive mechanical ventilation [IMV] initiated after the second hospital day), antibiotic escalation, length of stay (LOS), and cost. RESULTS: Of 36 405 patients who met inclusion criteria, 34 200 (94%) initially received intravenous treatment and 2205 (6%) received oral treatment. Patients who received oral fluoroquinolones had lower unadjusted mortality (1.4% vs 2.5%; P = .002), and shorter mean LOS (5.0 vs 5.3; P < .001). Multivariable models using stabilized inverse propensity treatment weighting revealed lower rates of antibiotic escalation for oral vs intravenous therapy (odds ratio [OR], 0.84; 95% confidence interval [CI], .74-.96) but no differences in hospital mortality (OR, 0.82; 95% CI, .58-1.15), LOS (difference in days 0.03; 95% CI, -.09-.15), cost (difference in $-7.7; 95% CI, -197.4-182.0), late ICU admission (OR, 1.04; 95% CI, .80-1.36), late IMV (OR, 1.17; 95% CI, .87-1.56), or late vasopressor use (OR, 0.94; 95% CI, .68-1.30). CONCLUSIONS: Among hospitalized patients who received fluoroquinolones for CAP, there was no association between initial route of administration and outcomes. More patients may be treated orally without worsening outcomes.

Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

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.

Comparative Effectiveness of Noninvasive and Invasive Ventilation in Critically Ill Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

OBJECTIVES: To compare the characteristics and hospital outcomes of patients with an acute exacerbation of chronic obstructive pulmonary disease treated in the ICU with initial noninvasive ventilation or invasive mechanical ventilation. DESIGN: Retrospective, multicenter cohort study of prospectively collected data. We used propensity matching to compare the outcomes of patients treated with noninvasive ventilation to those treated with invasive mechanical ventilation. We also assessed predictors for noninvasive ventilation failure. SETTING: Thirty-eight hospitals participating in the Acute Physiology and Chronic Health Evaluation database from 2008 through 2012. SUBJECTS: A total of 3,520 patients with a diagnosis of chronic obstructive pulmonary disease exacerbation including 27.7% who received noninvasive ventilation and 45.5% who received invasive mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Noninvasive ventilation failure was recorded in 13.7% from patients ventilated noninvasively. Hospital mortality was 7.4% for patients treated with noninvasive ventilation; 16.1% for those treated with invasive mechanical ventilation; and 22.5% for those who failed noninvasive ventilation. In the propensity-matched analysis, patients initially treated with noninvasive ventilation had a 41% lower risk of death compared with those treated with invasive mechanical ventilation (relative risk, 0.59; 95% CI, 0.36-0.97). Factors that were independently associated with noninvasive ventilation failure were Simplified Acute Physiology Score II (relative risk = 1.04 per point increase; 95% CI, 1.03-1.04) and the presence of cancer (2.29; 95% CI, 0.96-5.45). CONCLUSIONS: Among critically ill adults with chronic obstructive pulmonary disease exacerbation, the receipt of noninvasive ventilation was associated with a lower risk of in-hospital mortality compared with that of invasive mechanical ventilation; noninvasive ventilation failure was associated with the worst outcomes. These results support the use of noninvasive ventilation as a first-line therapy in appropriately selected critically ill patients with chronic obstructive pulmonary disease while also highlighting the risks associated with noninvasive ventilation failure and the need to be cautious in the face of severe disease.

Association of guideline-based antimicrobial therapy and outcomes in healthcare-associated pneumonia.

OBJECTIVES: Guidelines for treatment of healthcare-associated pneumonia (HCAP) recommend empirical therapy with broad-spectrum antimicrobials. Our objective was to examine the association between guideline-based therapy (GBT) and outcomes for patients with HCAP. PATIENTS AND METHODS: We conducted a pharmacoepidemiological cohort study at 346 US hospitals. We included adults hospitalized between July 2007 and June 2010 for HCAP, defined as patients admitted from a nursing home, with end-stage renal disease or immunosuppression, or discharged from a hospital in the previous 90 days. Outcome measures included in-hospital mortality, length of stay and costs. RESULTS: Of 85 097 patients at 346 hospitals, 31 949 (37.5%) received GBT (one agent against MRSA and at least one against Pseudomonas). Compared with patients who received non-GBT, those who received GBT had a heavier burden of chronic disease and more severe pneumonia. GBT was associated with higher mortality (17.1% versus 7.7%, P < 0.001). Adjustment for demographics, comorbidities, propensity for treatment with GBT and initial severity of disease decreased, but did not eliminate, the association (OR 1.39, 95% CI 1.32-1.47). Using an adaptation of an instrumental variable analysis, GBT was not associated with higher mortality (OR 0.93, 95% CI 0.75-1.16). Adjusted length of stay and costs were also higher with GBT. CONCLUSIONS: Among patients who met HCAP criteria, GBT was not associated with lower adjusted mortality, length of stay or costs in any analyses. Better criteria are needed to identify patients at risk for MDR infections who might benefit from broad-spectrum antimicrobial coverage.

Comparison of the Mortality Probability Admission Model III, National Quality Forum, and Acute Physiology and Chronic Health Evaluation IV hospital mortality models: implications for national benchmarking*.

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.

The association between ICU readmission rate and patient outcomes.

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.

Sepsis and scientific revolutions.

HYPOTHESIS: The therapeutic approach to sepsis is following an evolutionary process of scientific discovery as articulated in the landmark work by Kuhn, The Structure of Scientific Revolutions, first published 50 years ago. BACKGROUND: Incremental advances, beginning with the introduction of antimicrobials and most recently highlighted by revised, evidence-based guidelines for the management of sepsis, have been accompanied by episodic paradigm shifts. Although some of these have shown success, there are numerous, noteworthy failures, largely involving immune- and coagulation-based therapeutic strategies. CONCLUSION: A sustained paradigm shift in the approach to treating sepsis has yet to emerge, but recent data suggest that an open-minded posture informed by novel pathobiologic findings may eventually bear fruit.

Derivation and validation of a risk assessment model for drug-resistant pathogens in hospitalized patients with community-acquired pneumonia.

OBJECTIVE: To derive and validate a model for risk of resistance to first-line community-acquired pneumonia (CAP) therapy. DESIGN: We developed a logistic regression prediction model from a large multihospital discharge database and validated it versus the Drug Resistance in Pneumonia (DRIP) score in a holdout sample and another hospital system outside that database. Resistance to first-line CAP therapy (quinolone or third generation cephalosporin plus macrolide) was based on blood or respiratory cultures. SETTING: This study was conducted using data from 177 Premier Healthcare database hospitals and 11 Cleveland Clinic hospitals. PARTICIPANTS: Adults hospitalized for CAP. EXPOSURE: Risk factors for resistant infection. RESULTS: Among 138,762 eligible patients in the Premier database, 12,181 (8.8%) had positive cultures and 5,200 (3.8%) had organisms resistant to CAP therapy. Infection with a resistant organism in the previous year was the strongest predictor of resistance; markers of acute illness (eg, receipt of mechanical ventilation or vasopressors) and chronic illness (eg, pressure ulcer, paralysis) were also associated with resistant infections. Our model outperformed the DRIP score with a C-statistic of 0.71 versus 0.63 for the DRIP score (P < .001) in the Premier holdout sample, and 0.65 versus 0.58 (P < .001) in Cleveland Clinic hospitals. Clinicians at Premier facilities used broad-spectrum antibiotics for 20%-30% of patients. In discriminating between patients with and without resistant infections, physician judgment slightly outperformed the DRIP instrument but not our model. CONCLUSIONS: Our model predicting infection with a resistant pathogen outperformed both the DRIP score and physician practice in an external validation set. Its integration into practice could reduce unnecessary use of broad-spectrum antibiotics.

Intensive care unit readmissions in U.S. hospitals: patient characteristics, risk factors, and outcomes.

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.

A risk model to identify Legionella among patients admitted with community-acquired pneumonia: A retrospective cohort study.

BACKGROUND: Guidelines recommend testing hospitalized patients with community-acquired pneumonia (CAP) for Legionella pneumophila only if the infection is severe or risk factors are present. There are no validated models for predicting Legionella. OBJECTIVE: To derive and externally validate a model to predict a positive Legionella test. DESIGN, SETTING AND PARTICIPANTS: Diagnostic study of adult inpatients with pneumonia using data from 177 US hospitals in the Premier Healthcare Database (training and hold-out validation sets) and 12 Cleveland Clinic Health System (CCHS) hospitals (external validation set). We used multiple logistic regression to predict positive Legionella tests in the training set, and evaluated performance in both validation sets. MAIN OUTCOME AND MEASURES: The outcome was a positive Legionella test. Potential predictors included demographics and co-morbidities, disease severity indicators, season, region, and presence of a local outbreak. RESULTS: Of 166,689 patients hospitalized for pneumonia, 43,070 were tested for Legionella and 642 (1.5%) tested positive. The strongest predictors of a positive test were a local outbreak (odds ratio [OR], 3.4), June-October occurrence (OR, 3.4), hyponatremia (OR, 3.3), smoking (OR, 2.4), and diarrhea (OR, 2.0); prior admission within 6 months (OR, 0.27) and chronic pulmonary disease (OR, 0.49) were associated with a negative test. Model c-statistics were 0.79 in the Premier and 0.77 in the CCHS validation samples. High-risk patients were only slightly more likely to have been tested than lower-risk patients. Compared to actual practice, the model-based testing strategy detected twice as many cases. CONCLUSIONS: Although Legionella is an uncommon cause of pneumonia, patient characteristics can identify individuals at high risk, allowing for more efficient testing.

Development and validation of a model that uses enhanced administrative data to predict mortality in patients with sepsis.

OBJECTIVE: We aimed to determine whether a sepsis risk-adjustment model that uses only administrative data could be used when other intensive care unit risk-adjustment methods are unavailable. DESIGN: Cohort study with development and validation cohorts. PATIENTS: The development cohort included 166,931 patients at 309 hospitals that cared for at least 100 patients with sepsis between 2004 and 2006. The validation cohort included 357 adult sepsis patients who were enrolled in Project IMPACT, 2002-2009. MEASUREMENTS AND MAIN RESULTS: We developed a multilevel mixed-effects logistic regression model to predict mortality at the patient level. Predictors included patient demographics (age, sex, race, insurance type), site and source of sepsis, presence of 25 individual comorbidities, treatment (within the first 2 days of hospitalization) with mechanical ventilation and/or vasopressors, and/or admission to the intensive care unit (within 2 days of hospitalization). We validated this model in 357 sepsis patients who were admitted to the intensive care unit at a single academic medical center and who had a valid Acute Physiology and Chronic Health Evaluation II score, a valid Simplified Acute Physiology Score II, and a valid Mortality Probability Model III score. Overall, 33,192 patients (19.9%) died in the hospital. In the development cohort, the predicted mortality ranged from 0.002 to 0.938 with a mean of 0.199. The model's area under the receiver operating characteristic curve was 0.78. In the validation cohort, all models had modest discriminatory ability and the areas under the receiver operating characteristic curves of all models were statistically similar (Acute Physiology and Chronic Health Evaluation II, 0.71; Simplified Acute Physiology Score II, 0.74; Mortality Probability Model III, 0.69; administrative model, 0.69; p value that the areas under the receiver operating characteristic curves are different, .35). The Hosmer-Lemeshow statistic was significant (p < .01) for Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II, and Mortality Probability Model III but was nonsignificant (p = .11) for the administrative model. CONCLUSIONS: A sepsis mortality model using detailed administrative data has discrimination similar to and calibration superior to those of existing severity scores that require chart review. This model may be a useful alternative method of severity adjustment for benchmarking purposes or for conducting large, retrospective epidemiologic studies of sepsis patients.