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.

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.

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.

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.

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.

Early changes in blood urea nitrogen predict mortality in acute pancreatitis.

BACKGROUND & AIMS: Routine laboratory tests that reflect intravascular volume status can play an important role in the early assessment of acute pancreatitis (AP). The objective of this study was to evaluate accuracy of serial blood urea nitrogen (BUN) versus serial hemoglobin (Hgb) measurement for prediction of in-hospital mortality in AP. METHODS: We performed an observational cohort study on data from 69 US hospitals from January 2003 to December 2006. Repeated measures analysis was used to examine the relationship between early trends in BUN and Hgb with respect to mortality. Multivariate logistic regression was used to evaluate the impact of admission BUN, change in BUN, admission Hgb, and change in Hgb on mortality. Time-specific receiver operating characteristic curves and multivariable logistic regression compared accuracy of BUN, Hgb, and additional routine laboratory tests. RESULTS: BUN levels were persistently higher among nonsurvivors than survivors during the first 48 hours of hospitalization (F-test; P < .0001). No such relationship existed for Hgb (F-test; P = .33). For every 5-mg/dl increase in BUN during the first 24 hours, the age- and gender-adjusted odds ratio for mortality increased by 2.2 (95% confidence limits, 1.8, 2.7). Of the 6 routine laboratory tests examined, BUN yielded the highest area under the concentration-time curve (AUC) for predicting mortality at admission (AUC = 0.79), 24 hours (AUC = 0.89), and 48 hours (AUC = 0.90). Combining admission BUN and change in BUN at 24 hours produced an AUC of 0.91 for mortality. CONCLUSION: In a large, hospital-based cohort study, we identified serial BUN measurement as the most valuable single routine laboratory test for predicting mortality in AP.

The impact of hospital-acquired infection on outcome in acute pancreatitis.

BACKGROUND & AIMS: Little is known regarding the impact of hospital-acquired infection (HAI) in acute pancreatitis (AP). We conducted a population-based assessment of the impact of HAI on outcome in AP. METHODS: Patient data were obtained from the Cardinal Health Clinical Outcomes Research Database, a large population-based data set. Cases with principal diagnosis by International Classification of Diseases, ninth revision, clinical modification 577.0 (AP) between January 2004 and January 2005 were identified. These cases were linked with recently reported HAI data collected by the Pennsylvania Health Care Cost Containment Council. Identification of HAI was based on definitions set forth by the National Nosocomial Infection Surveillance System. We conducted a 5:1 multivariate propensity-matched cohort study to determine the independent contribution of HAI to in-hospital mortality, length of stay (LOS), and hospital charges. RESULTS: From 177 participating hospitals, there were 11,046 AP cases identified. Eighty-two (0.7%) patients developed an HAI. Mortality in the overall AP population was 1.2% vs 11.4% among 405 matched non-HAI controls vs 28.4% among patients who developed HAI (chi(2) test, P < .0001). Fifteen percent of all deaths was associated with an HAI. Both average LOS and hospital charges were significantly increased among patients with HAI compared with matched non-HAI controls. CONCLUSIONS: We determined that HAI had a major impact on mortality in AP. Patients who developed HAI also had significantly increased LOS and hospital charges. These differences were not explained by increased disease severity alone. Reducing HAI is an important step to improving outcome in AP.

A prospective evaluation of the bedside index for severity in acute pancreatitis score in assessing mortality and intermediate markers of severity in acute pancreatitis.

OBJECTIVES: Our aim was to prospectively evaluate the ability of the bedside index for severity in acute pancreatitis (BISAP) score to predict mortality as well as intermediate markers of severity in a tertiary center. METHODS: The BISAP score was evaluated among 397 consecutive cases of acute pancreatitis admitted to our institution between June 2005 and December 2007. BISAP scores were calculated on all cases using data within 24 h of presentation. The ability of the BISAP score to predict mortality was evaluated using trend and discrimination analysis. The optimal cutoff score for mortality from the receiver operating curve was used to evaluate the development of organ failure, persistent organ failure, and pancreatic necrosis. RESULTS: Among 397 cases, there were 14 (3.5%) deaths. There was a statistically significant trend for increasing mortality (P < 0.0001) with increasing BISAP score. The area under the receiver operating curve for mortality by BISAP score in the prospective cohort was 0.82 (95% confidence interval: 0.70, 0.95), which was similar to that of the previously published validation cohort. A BISAP score >or=3 was associated with an increased risk of developing organ failure (odds ratio=7.4, 95% confidence interval: 2.8, 19.5), persistent organ failure (odds ratio=12.7, 95% confidence interval: 4.7, 33.9), and pancreatic necrosis (odds ratio=3.8, 95% confidence interval: 1.8, 8.5). CONCLUSIONS: The BISAP score represents a simple way to identify patients at risk of increased mortality and the development of intermediate markers of severity within 24 h of presentation. This risk stratification capability can be utilized to improve clinical care and facilitate enrollment in clinical trials.

Burden of early-onset candidemia: analysis of culture-positive bloodstream infections from a large U.S. database.

OBJECTIVES: : To characterize the epidemiology and burden of early-onset, nonnosocomial candidemia. DESIGN: : Retrospective review of Cardinal Health Outcomes Research Database, which comprises all acute care admissions at participating hospitals. SETTING: : A total of 176 acute care hospitals. PATIENTS: : All patients admitted from 2000 through 2005 who had early-onset bloodstream infection, defined as presence of both a positive blood culture drawn within 1 day before or within 48 hrs after hospital admission and an appropriate diagnostic code for infection. INTERVENTION: : None. MEASUREMENTS AND MAIN RESULTS: : To evaluate the impact of different pathogens on clinical and economic outcomes, we performed mixed-effect logistic and linear regression analyses and controlled for potential confounding factors. Of 64,307 early-onset bloodstream infections, 738 (1.2%) were positive for Candida. The rate of early-onset candidemia nearly doubled between 2000 and 2003 (p < .001) and then stabilized. Crude in-hospital mortality was higher for candidemia than for bacterial bloodstream infection (28.3% vs. 15.0%; p < .0001). Compared with patients with bacterial bloodstream infections, patients with candidemia were more likely to have been admitted within 30 days and to have been transferred from another healthcare facility. Compared with Gram-negative bacterial bloodstream infection and after controlling for other risk factors, candidemia was associated with increased mortality risk (odds ratio, 2.38; 95% confidence interval, 1.94-2.91; p < .0001), longer attributable hospital stay (4.8 days; 95% confidence interval, 4.1-5.5; p < .0001), and higher attributable hospital costs ($12,617; 95% confidence Interval, $10,755-$14,479; p < .0001). CONCLUSIONS: : Early-onset candidemia seems to be a distinct entity, which is increasing in frequency and is associated with increased mortality risk, longer hospital stay, and higher hospital costs relative to bacterial bloodstream infection.

Early hemoconcentration predicts increased mortality only among transferred patients with acute pancreatitis.

BACKGROUND/AIMS: The prognostic utility of hemoconcentration in acute pancreatitis (AP) remains controversial. METHODS: We examined the relationship between early hemoconcentration and in-hospital mortality in an observational cohort study. Data was collected from 177 US hospitals from January 2004 to September 2005. Early hemoconcentration was defined as hemoglobin > or =14.6 mg/dl (hematocrit approximately 44%) at any point during the first 24 h of initial hospitalization. For transferred cases, we linked clinical data from the first hospitalization to outcomes from the second hospitalization. We then examined the impact of hospital transfer status on the prognostic utility of hemoconcentration. RESULTS: We identified 388 (2.2%) cases as interhospital transfers. Of these, we successfully linked 198 (51.0%) to their initial hospitalization. Early hemoconcentration was associated with increased mortality among transferred cases (OR 7.4, 95% CI 1.6, 35.4). However, no such relationship existed among non-transferred cases (OR 0.9, 95% CI 0.7, 1.2). Differences in outcome between transferred vs. nontransferred cases were not explained by extent of comorbid illness or initial disease severity (either APACHE II or organ failure). CONCLUSIONS: Early hemoconcentration predicted increased risk of mortality only among transferred cases despite similar levels of initial disease severity. These findings may help explain discordant results from prior studies of hemoconcentration in AP.

Candidemia on presentation to the hospital: development and validation of a risk score.

INTRODUCTION: Candidemia results in substantial morbidity and mortality, especially if initial antifungal therapy is delayed or is inappropriate; however, candidemia is difficult to diagnose because of its nonspecific presentation. METHODS: To develop a risk score for identifying hospitalized patients with candidemia, we performed a retrospective analysis of a large database of 176 acute-care hospitals in the United States. We studied 64,019 patients with bloodstream infection (BSI) on presentation from 2000 through 2005 (derivation cohort) and 24,685 from 2006 to 2007 (validation cohort). We used recursive partitioning (RPART) to identify the best discriminators for Candida as the cause of BSI. We compared three sets of models (equal-weight, unequal-weight, vs full model with additional variables from logistic regression model) for sensitivity analysis. RESULTS: The RPART identified 6 variables as the best discriminators: age < 65 years, temperature 0.10, indicating predicted and observed candidemia rates did not differ significant across the 7 risk stratus). The full model with 16 risk factors had slightly higher AUROCs (0.74 versus 0.73 for derivation versus validation); however, 7 variables were no longer significant in the recalibrated model for the validation cohort, indicating that the additional items did not materially enhance the model. CONCLUSIONS: A simple equal-weight risk score differentiated patients' risk for candidemia in a graded fashion upon hospital presentation.

Physician reminders to promote surveillance colonoscopy for colorectal adenomas: a randomized controlled trial.

BACKGROUND: Most colorectal cancers develop from adenomatous polyps. National guidelines recommend surveillance colonoscopy within 5 years after such polyps are removed. OBJECTIVE: To determine whether surveillance colonoscopy can be increased among overdue patients by reminders to their primary physicians. DESIGN: Randomized, controlled trial of patient-specific reminders mailed to 141 physicians in 2 Massachusetts primary care networks during April, 2006. PATIENTS: Seven hundred seventeen patients who had colorectal adenomas removed during 1995 through 2000 and no follow-up colonoscopy identified via automated review of electronic records through March, 2006. MEASUREMENTS AND MAIN RESULTS: The use of colonoscopy and detection of new adenomas or cancer were assessed at 6 months by a blinded medical record review in all patients. Among 358 patients whose physicians received reminders, 33 (9.2%) patients underwent colonoscopy within 6 months, compared with 16 (4.5%) of 359 patients whose physicians did not receive reminders (P = 0.009). In prespecified subgroups, this effect did not differ statistically between 2 primary care networks, elderly and nonelderly patients, or women and men (all P > 0.60 by Breslow-Day test). New adenomas or cancer were detected in 14 (3.9%) intervention patients and 6 (1.7%) control patients (P = 0.06), representing 42.4% and 37.5% of patients who underwent colonoscopy in each group, respectively. Despite using advanced electronic health records to identify eligible patients, 22.5% of enrolled patients had a prior follow-up colonoscopy ascertained only by visual record review, and physicians reported 27.9% of intervention patients were no longer active in their practice. CONCLUSIONS: Among patients with prior colorectal adenomas, physician reminders increased the use of surveillance colonoscopy, but better systems are needed to identify eligible patients (ClinicalTrials.gov ID number NCT00397969).

Reductions in mortality associated with intensive public reporting of hospital outcomes.

It is unclear whether public reporting of hospital and physician performance has improved outcomes for the conditions being reported. We studied the effect of intensive public reporting on hospital mortality for 6 high-frequency, high-mortality medical conditions. Patients in Pennsylvania were matched to patients in other states with varying public reporting environments using propensity score methods. The effect of public reporting was estimated using a difference in differences approach. Patients treated at hospitals subjected to intensive public reporting had significantly lower odds of in-hospital mortality when compared with similar patients treated at hospitals in environments with no public reporting or only limited reporting. Overall, the 2000-2003 in-hospital mortality odds ratio for Pennsylvania patients versus non-Pennsylvania patients ranged from 0.59 to 0.79 across 6 clinical conditions (all P < .0001). For the same comparison using the 1997-1999 period, odds ratios ranged from 0.72 to 0.90, suggesting improvement when intensive public reporting occurred.

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.

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.

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).

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.

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.