The Effect of a Piperacillin/Tazobactam Shortage on Antimicrobial Prescribing and Clostridium difficile Risk in 88 US Medical Centers.

BACKGROUND: Anti-infective shortages are a pervasive problem in the United States. The objective of this study was to identify any associations between changes in prescribing of antibiotics that have a high risk for CDI during a piperacillin/tazobactam (PIP/TAZO) shortage and hospital-onset Clostridium difficile infection (HO-CDI) risk in 88 US medical centers. METHODS: We analyzed electronically captured microbiology and antibiotic use data from a network of US hospitals from July 2014 through June 2016. The primary outcome was HO-CDI rate and the secondary outcome was changes in antibiotic usage. We fit a Poisson model to estimate the risk of HO-CDI associated with PIP/TAZO shortage that were associated with increased high-risk antibiotic use while controlling for hospital characteristics. RESULTS: A total of 88 hospitals experienced PIP/TAZO shortage and 72 of them experienced a shift toward increased use of high-risk antibiotics during the shortage period. The adjusted relative risk (RR) of HO-CDI for hospitals experiencing a PIP/TAZO shortage was 1.03 (95% confidence interval [CI], .85-1.26; P = .73). The adjusted RR of HO-CDI for hospitals that both experienced a shortage and also showed a shift toward increased use of high-risk antibiotics was 1.30 (95% CI, 1.03-1.64; P < .05). CONCLUSIONS: Hospitals that experienced a PIP/TAZO shortage and responded to that shortage by shifting antibiotic usage toward antibiotics traditionally known to place patients at greater risk for CDI experienced greater HO-CDI rates; this highlights an important adverse effect of the PIP/TAZO shortage and the importance of antibiotic stewardship when mitigating drug shortages.

Diagnosis of Chronic Pancreatitis Incorporating Endosonographic Features, Demographics, and Behavioral Risk.

OBJECTIVES: Diagnosing chronic pancreatitis remains challenging. Endoscopic ultrasound (EUS) is utilized to evaluate pancreatic disease. Abnormal pancreas function test is considered the "nonhistologic" criterion standard for chronic pancreatitis. We derived a prediction model for abnormal endoscopic pancreatic function test (ePFT) by enriching EUS findings with patient demographic and pancreatitis behavioral risk characteristics. METHODS: Demographics, behavioral risk characteristics, EUS findings, and peak bicarbonate results were collected from patients evaluated for pancreatic disease. Abnormal ePFT was defined as peak bicarbonate of less than 75 mEq/L. We fit a logistic regression model and converted it to a risk score system. The risk score was validated using 1000 bootstrap simulations. RESULTS: A total of 176 patients were included; 61% were female with median age of 48 years (interquartile range, 38-57 years). Abnormal ePFT rate was 39.2% (69/176). Four variables formulated the risk score: alcohol or smoking status, number of parenchymal abnormalities, number of ductal abnormalities, and calcifications. Abnormal ePFT occurred in 10.7% with scores 4 or less versus 92.0% scoring 20 or greater. The model C-statistic was 0.78 (95% confidence interval, 0.71-0.85). CONCLUSIONS: Number of EUS pancreatic duct and parenchymal abnormalities, presence of calcification, and smoking/alcohol status were predictive of abnormal ePFT. This simple model has good discrimination for ePFT results.

Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score.

BACKGROUND: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. METHODS: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model. RESULTS: There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722. CONCLUSIONS: Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.

Innovative Use of Existing Public and Private Data Sources for Postmarketing Surveillance of Central Line-Associated Bloodstream Infections Associated With Intravenous Needleless Connectors.

The Centers for Medicare and Medicaid Services (CMS) Hospital Compare central line-associated bloodstream infection (CLABSI) data and private databases containing new-generation intravenous needleless connector (study NC) use at the hospital level were linked. The relative risk (RR) of CLABSI associated with the study NCs was estimated, adjusting for hospital characteristics. Among 3074 eligible hospitals in the 2013 CMS database, 758 (25%) hospitals used the study NCs. The study NC hospitals had a lower unadjusted CLABSI rate (1.03 vs 1.13 CLABSIs per 1000 central line days, P < .0001) compared with comparator hospitals. The adjusted RR for CLABSI was 0.94 (95% confidence interval: 0.86, 1.02; P = .11).

Performance characteristics and associated outcomes for an automated surveillance tool for bloodstream infection.

BACKGROUND: The objective of this study was to evaluate performance metrics and associated patient outcomes of an automated surveillance system, the blood Nosocomial Infection Marker (NIM). METHODS: We reviewed records of 237 patients with and 36,927 patients without blood NIM using the National Healthcare Safety Network (NHSN) definition for laboratory-confirmed bloodstream infection (BSI) as the gold standard. We matched cases with noncases by propensity score and estimated attributable mortality and cost of NHSN-reportable central line-associated bloodstream infections (CLABSIs) and non-NHSN-reportable BSIs. RESULTS: For patients with central lines (CL), the blood NIM had 73.2% positive predictive value (PPV), 99.9% negative predictive value (NPV), 89.2% sensitivity, and 99.7% specificity. For all patients regardless of CL status, the blood NIM had 53.6% PPV, 99.9% NPV, 84.0% sensitivity, and 99.9% specificity. For CLABSI cases compared with noncases, mortality was 17.5% versus 9.4% (P = .098), and median charge was $143,935 (interquartile range [IQR], $89,794-$257,447) versus $115,267 (IQR, $74,937-$173,053) (P < .01). For non-NHSN-reportable BSI cases compared with noncases, mortality was 23.6% versus 6.7% (P < .0001), and median charge was $86,927 (IQR, $54,728-$156,669) versus $62,929 (IQR, $36,743-$115,693) (P < .0001). CONCLUSIONS: The NIM is an effective screening tool for BSI. Both NHSN-reportable and nonreportable BSI cases were associated with increased mortality and cost.

Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score.

OBJECTIVE: To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission DESIGN: Retrospective data analysis SETTING: Six US acute care hospitals PATIENTS: Adult inpatients METHODS: We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations. RESULTS: Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76-0.81) with good calibration. Among 79% of patients with risk scores of 0-7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001). CONCLUSION: Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.

Meta-analysis on central line-associated bloodstream infections associated with a needleless intravenous connector with a new engineering design.

BACKGROUND: Intravenous needleless connectors (NCs) with a desired patient safety design may facilitate effective intravenous line care and reduce the risk for central line-associated bloodstream infection (CLA-BSI). We conducted a meta-analysis to determine the risk for CLA-BSI associated with the use of a new NC with an improved engineering design. METHODS: We reviewed MEDLINE, Cochrane Database of Systematic Reviews, Embase, ClinicalTrials.gov, and studies presented in 2010-2012 at infection control and infectious diseases meetings. Studies reporting the CLA-BSIs in patients using the positive-displacement NC (study NC) compared with negative- or neutral-displacement NCs were analyzed. We estimated the relative risk of CLA-BSIs with the study NC for the pooled effect using the random effects method. RESULTS: Seven studies met the inclusion criteria: 4 were conducted in intensive care units, 1 in a home health setting, and 2 in long-term acute care settings. In the comparator period, total central venous line (CL) days were 111,255; the CLA-BSI rate was 1.5 events per 1,000 CL days. In the study NC period, total CL days were 95,383; the CLA-BSI rate was 0.5 events per 1,000 CL days. The pooled CLA-BSI relative risk associated with the study NC was 0.37 (95% confidence interval, 0.16-0.90). CONCLUSION: The NC with an improved engineering design is associated with lower CLA-BSI risk.

Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS).

OBJECTIVE: Using numeric laboratory data and administrative data from hospital electronic health record (EHR) systems, to develop an inpatient mortality predictive model. METHODS: Using EHR data of 1,428,824 adult discharges from 70 hospitals in 2006-2007, we developed the Acute Laboratory Risk of Mortality Score (ALaRMS) using age, gender, and initial laboratory values on admission as candidate variables. We then added administrative variables using the Agency for Healthcare Research and Quality (AHRQ)'s clinical classification software (CCS) and comorbidity software (CS) as disease classification tools. We validated the model using 770,523 discharges in 2008. RESULTS: Mortality predictors with ORs >2.00 included age, deranged albumin, arterial pH, bands, blood urea nitrogen, oxygen partial pressure, platelets, pro-brain natriuretic peptide, troponin I, and white blood cell counts. The ALaRMS model c-statistic was 0.87. Adding the CCS and CS variables increased the c-statistic to 0.91. The relative contributions were 69% (ALaRMS), 25% (CCS), and 6% (CS). Furthermore, the integrated discrimination improvement statistic demonstrated a 127% (95% CI 122% to 133%) overall improvement when ALaRMS was added to CCS and CS variables. In contrast, only a 22% (CI 19% to 25%) improvement was seen when CCS and CS variables were added to ALaRMS. CONCLUSIONS: EHR data can generate clinically plausible mortality predictive models with excellent discrimination. ALaRMS uses automated laboratory data widely available on admission, providing opportunities to aid real-time decision support. Models that incorporate laboratory and AHRQ's CCS and CS variables have utility for risk adjustment in retrospective outcome studies.

Attributable burden of hospital-onset Clostridium difficile infection: a propensity score matching study.

OBJECTIVE: To determine the attributable in-hospital mortality, length of stay (LOS), and cost of hospital-onset Clostridium difficile infection (HO-CDI). DESIGN: Propensity score matching. SETTING: Six Pennsylvania hospitals (2 academic centers, 1 community teaching facility, and 3 community nonteaching facilities) contributing data to a clinical research database. PATIENTS: Adult inpatients between 2007 and 2008. METHODS: We defined HO-CDI in adult inpatients as a positive C. difficile toxin assay result from a specimen collected more than 48 hours after admission and more than 8 weeks following any previous positive result. We developed an HO-CDI propensity model and matched cases with noncases by propensity score at a 1∶3 ratio. We further restricted matching within the same hospital, within the same principal disease group, and within a similar length of lead time from admission to onset of HO-CDI. RESULTS: Among 77,257 discharges, 282 HO-CDI cases were identified. The propensity score-matched rate was 90%. Compared with matched noncases, HO-CDI patients had higher mortality (11.8% vs. 7.3%; P < .05), longer LOS (median [interquartile range (IQR)], 12 [9-21] vs. 11 [8-17] days; P < .01), and higher cost (median [IQR], $20,804 [$11,059-$38,429] vs. $16,634 [$9,413-$30,319]; P < .01). The attributable effect of HO-CDI was 4.5% (95% confidence interval [CI], 0.2%-8.7%; P < .05) for mortality, 2.3 days (95% CI, 0.9-3.8; P < .01) for LOS, and $6,117 (95% CI, $1,659-$10,574; P < .01) for cost. CONCLUSIONS: Patients with HO-CDI incur additional attributable mortality, LOS, and cost burden compared with patients with similar primary clinical condition, exposure risk, lead time of hospitalization, and baseline characteristics.

Development and validation of a mortality risk-adjustment model for patients hospitalized for exacerbations of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death. We sought to develop and validate a mortality risk-adjustment model to enhance hospital performance measurement and to support comparative effectiveness research. METHODS: Using a derivation cohort of 69,299 AECOPD admissions in 2005-2006 across 172 hospitals, we developed a logistic regression model with age, sex, laboratory results, vital signs, and secondary diagnosis-based comorbidities as covariates. We converted the model coefficients into a score system and validated it using 33,327 admissions from 2007. We used the c-statistic to assess model fit. RESULTS: In the derivation and validation cohorts, the median (interquartile range) age was 72 (range, 63-79) versus 71 (range, 62-79) years; 45.6% versus 45.9% were male; and in-hospital mortality rates were 3.2% versus 2.9%, respectively. The predicted probability of deaths for individuals ranged from 0.004 to 0.942 versus 0.001 to 0.933, respectively. The relative contribution of variables to the predictive ability of the derivation model was age (18.3%), admission laboratory results (39.9%), vital signs (14.7%), altered mental status (7.1%), and comorbidities (19.9%). The model c-statistic was 0.83 (95% CI: 0.82, 0.84) versus 0.84 (95% CI: 0.83, 0.85), respectively, with good calibration for both cohorts. CONCLUSIONS: A mortality prediction model combining clinical and administrative data that can be obtained from electronic health records demonstrated good discrimination among patients hospitalized for AECOPD. The addition of admission vital signs and laboratory results enhanced clinical validity and could be applied to future comparative effectiveness research and hospital profiling efforts.

Using enriched observational data to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients.

BACKGROUND: Growth and development in early childhood are associated with rapid physiological changes. We sought to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients using objective physiological variables on admission in addition to administrative variables. METHODS: Age-specific laboratory and vital sign variables were crafted for neonates (up to 30 d old), infants/toddlers (1-23 mo), and children (2-17 y). We fit 3 logistic regression models, 1 for each age group, using a derivation cohort comprising admissions from 2000-2001 in 215 hospitals. We validated the models with a separate validation cohort comprising admissions from 2002-2007 in 62 hospitals. We used the c statistic to assess model fit. RESULTS: The derivation cohort comprised 93,011 neonates (0.55% mortality), 46,152 infants/toddlers (0.37% mortality), and 104,010 children (0.40% mortality). The corresponding numbers of admissions (mortality rates) for the validation cohort were 162,131 (0.50%), 33,818 (0.09%), and 73,362 (0.20%), respectively. The c statistics for the 3 models were 0.94, 0.91, and 0.92, respectively, for the derivation cohort and 0.91, 0.86, and 0.93, respectively, for the validation cohort. The relative contributions of physiological versus administrative variables to the model fit were 52% versus 48% (neonates), 93% versus 7% (infants/toddlers), and 82% versus 18% (children). CONCLUSIONS: The thresholds for physiological determinants varied by age. Common physiological variables assessed on admission contributed significantly to predicting mortality for hospitalized pediatric patients. These models may have practical utility in risk adjustment for pediatric outcomes and comparative effectiveness research when physiological data are captured through the electronic medical record.

Predicting the need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: comparing the CURB-65 and BAP-65 scores.

PURPOSE: Clinicians lack a validated tool for risk stratification for need for mechanical ventilation (MV) in acute exacerbations of chronic obstructive pulmonary disease (AECOPD). We sought to compare 2 risk scores, BAP-65 and CURB-65, at predicting a need for MV in AECOPDs. MATERIALS AND METHODS: We analyzed 34,478 AECOPD admissions to 195 US hospitals (2007). We compared the rates of MV at admission and at any point during hospitalization based on the respective BAP-65 and CURB-65 scores. We compared the accuracy of the 2 scores via the area under the receiver operating characteristic curves. RESULTS: The overall MV rate at admission was 7.9%, and the rate of MV any time equaled 9.3%. Use of MV increased with escalating BAP-65 and CURB-65 scores. The area under the receiver operating characteristic curve for BAP-65 was higher than that for CURB-65 for both early MV, 0.81 (95% confidence interval [CI], 0.80-0.82) vs 0.76 (95% CI, 0.75-0.77), P < .0001, and MV any time, 0.78 (95% CI, 0.77-0.79) vs 0.74 (95% CI, 0.73-0.75), P < .0001. CONCLUSIONS: BAP-65 identifies patients with AECOPD at high risk for need of MV more accurately than does CURB-65. BAP-65 may represent a useful tool for initial MV risk stratification in AECOPD.

Severe acute hypertension among inpatients admitted from the emergency department.

BACKGROUND: Hospitalists often treat patients with severe acute hypertension (AH) presenting to the hospital. Little is known about the epidemiology of this syndrome. OBJECTIVE: To examine the prevalence of severe AH in patients admitted through the emergency department (ED) and its associated outcomes. DESIGN: A cohort study using retrospectively collected vital signs and other clinical data. PATIENTS: A total of 1,290,804 adults admitted between 2005 and 2007. SETTING: One hundred fourteen acute-care hospitals. MEASUREMENTS: Severe AH was defined as at least 1 systolic blood pressure (SBP) >180 mmHg. We used multivariable regression to estimate AH-attributable in-hospital mortality, need for mechanical ventilation (MV), and length of stay (LOS). RESULTS: Severe AH occurred in 178,131 (13.8%) patients. Disease categories with the highest prevalence were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%). The overall in-hospital mortality was 3.6%. The relationship between severe AH strata and mortality was graded for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 9.9%, 12.0%, and 19.7%, respectively (P < 0.0001). The relationship between severe AH strata and need for MV was graded in the most pronounced way in respiratory and circulatory conditions (P < 0.0001). The relationship between severe AH strata and LOS was graded in most disease categories (P < 0.0001). CONCLUSIONS: Severe AH appears common and its prevalence varies by underlying clinical condition. Severe AH is associated with excess in-hospital mortality for patients with nervous system diseases and, for most disease categories, prolongs hospitalization.

A simple risk score accurately predicts in-hospital mortality, length of stay, and cost in acute upper GI bleeding.

BACKGROUND: Although the early use of a risk stratification score in upper GI bleeding is recommended, existing risk scores are not widely used in clinical practice. OBJECTIVE: We sought to develop and validate an easily calculated bedside risk score, AIMS65, by using data routinely available at initial evaluation. DESIGN: Data from patients admitted from the emergency department with acute upper GI bleeding were extracted from a database containing information from 187 U.S. hospitals. Recursive partitioning was applied to derive a risk score for in-hospital mortality by using data from 2004 to 2005 in 29,222 patients. The score was validated by using data from 2006 to 2007 in 32,504 patients. Accuracy to predict mortality was assessed by the area under the receiver operating characteristic (AUROC) curve. MAIN OUTCOME MEASUREMENTS: Mortality, length of stay (LOS), and cost of admission. RESULTS: The 5 factors present at admission with the best discrimination were albumin less than 3.0 g/dL, international normalized ratio greater than 1.5, altered mental status, systolic blood pressure 90 mm Hg or lower, and age older than 65 years. For those with no risk factors, the mortality rate was 0.3% compared with 31.8% in patients with all 5 (P < .001). The model had a high predictive accuracy (AUROC = 0.80; 95% CI, 0.78-0.81), which was confirmed in the validation cohort (AUROC = 0.77, 95% CI, 0.75-0.79). Longer LOS and increased costs were seen with higher scores (P < .001). LIMITATIONS: Database data used does not include outcomes such as rebleeding. CONCLUSIONS: AIMS65 is a simple, accurate risk score that predicts in-hospital mortality, LOS, and cost in patients with acute upper GI bleeding.

Developing and validating a risk score for lower-extremity amputation in patients hospitalized for a diabetic foot infection.

OBJECTIVE: Diabetic foot infection is the predominant predisposing factor to nontraumatic lower-extremity amputation (LEA), but few studies have investigated which specific risk factors are most associated with LEA. We sought to develop and validate a risk score to aid in the early identification of patients hospitalized for diabetic foot infection who are at highest risk of LEA. RESEARCH DESIGN AND METHODS: Using a large, clinical research database (CareFusion), we identified patients hospitalized at 97 hospitals in the U.S. between 2003 and 2007 for culture-documented diabetic foot infection. Candidate risk factors for LEA included demographic data, clinical presentation, chronic diseases, and recent previous hospitalization. We fit a logistic regression model using 75% of the population and converted the model coefficients to a numeric risk score. We then validated the score using the remaining 25% of patients. RESULTS: Among 3,018 eligible patients, 21.4% underwent an LEA. The risk factors most highly associated with LEA (P < 0.0001) were surgical site infection, vasculopathy, previous LEA, and a white blood cell count >11,000 per mm(3). The model showed good discrimination (c-statistic 0.76) and excellent calibration (Hosmer-Lemeshow, P = 0.63). The risk score stratified patients into five groups, demonstrating a graded relation to LEA risk (P < 0.0001). The LEA rates (derivation and validation cohorts) were 0% for patients with a score of 0 and ~50% for those with a score of ≥21. CONCLUSIONS: Using a large, hospitalized population, we developed and validated a risk score that seems to accurately stratify the risk of LEA among patients hospitalized for a diabetic foot infection. This score may help to identify high-risk patients upon admission.

Using electronic health information to risk-stratify rates of Clostridium difficile infection in US hospitals.

BACKGROUND: Expanding hospitalized patients' risk stratification for Clostridium difficile infection (CDI) is important for improving patient safety. We applied definitions for hospital-onset (HO) and community-onset (CO) CDI to electronic data from 85 hospitals between January 2007 and June 2008 to identify factors associated with higher HO CDI rates. METHODS: Nonrecurrent CDI cases were identified among adult (≥ 18-year-old) inpatients by a positive C. difficile toxin assay result more than 8 weeks after any previous positive result. Case categories included HO, CO-hospital associated (CO-HA), CO-indeterminate hospital association (CO-IN), and CO-non-hospital associated (CO-NHA). C. difficile testing intensity (CDTI) was defined as the total number of C. difficile tests performed, normalized to the number of patients with at least 1 C. difficile toxin test recorded. We calculated both the incidence density and the prevalence of CDI where appropriate. We fitted a multivariable Poisson model to identify factors associated with higher HO CDI rates. RESULTS: Among 1,351,156 unique patients with 2,022,213 admissions, 9,803 cases of CDI were identified; of these, 50.6% were HO, 17.4% were CO-HA, 9.0% were CO-IN, and 23.0% were CO-NHA. The incidence density of HO was 6.3 per 10,000 patient-days. The prevalence of CO CDI on admission was, per 10,000 admissions, 8.4 for CO-HA, 4.4 for CO-IN, and 11.1 for CO-NHA. Factors associated (P < .0001) with higher HO CDI rates included older age, higher CO-NHA prevalence on admission, and increased CDTI. CONCLUSION: Electronic health information can be leveraged to risk-stratify HO CDI rates by patient age and CO-NHA prevalence on admission. Hospitals should optimize diagnostic testing to improve patient care and measured CDI rates.

Validation of a novel risk score for severity of illness in acute exacerbations of COPD.

BACKGROUND: Clinicians lack a validated tool for risk stratification in acute exacerbations of COPD (AECOPD). We sought to validate the BAP-65 (elevated BUN, altered mental status, pulse > 109 beats/min, age > 65 years) score for this purpose. METHODS: We analyzed 34,699 admissions to 177 US hospitals (2007) with either a principal diagnosis of AECOPD or acute respiratory failure with a secondary diagnosis of AECOPD. Hospital mortality and need for mechanical ventilation (MV) served as co-primary end points. Length of stay (LOS) and costs represented secondary end points. We assessed the accuracy of BAP-65 via the area under the receiver operating characteristic curve (AUROC). RESULTS: Nearly 4% of subjects died while hospitalized and approximately 9% required MV. Mortality increased with increasing BAP-65 class, ranging from < 1% in subjects in class I (score of 0) to > 25% in those meeting all BAP-65 criteria (Cochran-Armitage trend test z = -38.48, P < .001). The need for MV also increased with escalating score (2% in the lowest risk cohort vs 55% in the highest risk group, Cochran-Armitage trend test z = -58.89, P < .001). The AUROC for BAP-65 for hospital mortality and/or need for MV measured 0.79 (95% CI, 0.78-0.80). The median LOS was 4 days, and mean hospital costs equaled $5,357. These also varied linearly with increasing BAP-65 score. CONCLUSIONS: The BAP-65 system captures severity of illness and represents a simple tool to categorize patients with AECOPD as to their risk for adverse outcomes. BAP-65 also correlates with measures of resource use. BAP-65 may represent a useful adjunct in the initial assessment of AECOPDs.

Burden of sodium abnormalities in patients hospitalized for heart failure.

Hyponatremia presumably is associated with adverse clinical outcomes in patients with congestive heart failure (CHF), but risk thresholds and economic burden are less studied. The authors analyzed 115,969 patients hospitalized for CHF and grouped them by serum sodium levels (severe hyponatremia, ≤130 mEq/L; hyponatremia, 131-135 mEq/L; normonatremia, 136-145 mEq/L; hypernatremia, >145 mEq/L). Univariable and multivariable analyses on the associated clinical and economic outcomes were performed. The most common abnormality was hyponatremia (15.9%), followed by severe hyponatremia (5.3%) and hypernatremia (3.2%). Hospital mortality was highest for severe hyponatremia (7.6%), followed by hypernatremia (6.7%) and hyponatremia (4.9%) (P<.0001). Compared with normonatremia, risk-adjusted mortality was highest for severe hyponatremia (odds ratio [OR], 1.78; 95% confidence interval [CI], 1.59-1.99), followed by hypernatremia (OR, 1.55; 95% CI, 1.34-1.80) and hyponatremia (OR, 1.29; 95% CI, 1.19-1.40; all P<.0001). Risk-adjusted hospital prolongation was greater for each level of sodium abnormality than for normonatremia, ranging from 0.42 (CI, 0.26-0.60) days for hypernatremia to 1.28 (CI, 1.11-1.47) days for severe hyponatremia. Risk-adjusted attributable hospital cost increase was highest for severe hyponatremia ($1132; CI, $856-$1425; all (P<.0001). Sodium abnormalities were common in patients hospitalized for CHF. Adverse outcomes resulted not only from severe hyponatremia, but also from mild hyponatremia and hypernatremia.

Increased mortality and length of stay among patients with inflammatory bowel disease and hospital-acquired infections.

BACKGROUND & AIMS: Hospitalized patients with inflammatory bowel disease (IBD) could be at increased risk for hospital-acquired infections (HAIs). By using HAI outcome data from Pennsylvania, we examined the influence of HAIs on in-patient mortality and length of stay (LOS) in the hospital among patients with IBD. METHODS: Data were generated by linking the Clinical Research Databases from CareFusion (formerly MediQual), which includes all acute care hospitals in Pennsylvania, with publicly reported HAI data from Pennsylvania. The study population included all patients discharged in 2004 with International Classification of Diseases, 9th Clinical Modification codes of 555.x or 556.x (2324 IBD cases from 161 hospitals). Controls were selected using risk-score matching with a 5:1 ratio. Mortality and LOS end points were estimated and corroborated with regression methods. RESULTS: Among the IBD patients studied, there were 20 deaths and 22 reported cases of HAI. The mortality from HAI among patients with IBD was 13.6%, compared with 0.9% among controls (P = .0146, Fisher exact test). The odds ratio for mortality was 17.2 (95% confidence interval, 1.7-174.3). The median LOS for patients with IBD and HAI was 22 days, versus 6 days for controls (P < .001, Wilcoxon). Of the 22 cases with HAIs, 15 were urinary tract infections, 5 were blood stream infections, and 2 were from multiple sources. CONCLUSIONS: Results from a population-based data set indicate that mortality and LOS are increased among IBD patients who develop HAIs. A majority of the HAIs were from urinary sources. Although HAIs are low-frequency events, increased vigilance to avoid HAI among patients with IBD could improve outcomes.