Xiaowu Sun

The Early Prediction of Mortality in Acute Pancreatitis: A Large Population-based Study

Background: Identification of patients at risk for mortality early in the course of acute pancreatitis (AP) is an important step in improving outcome. Methods: Using Classification and Regression Tree (CART) analysis, a clinical scoring system was developed for prediction of in-hospital mortality in AP. The scoring system was derived on data collected from 17 992 cases of AP from 212 hospitals in 2000–2001. The new scoring system was validated on data collected from 18 256 AP cases from 177 hospitals in 2004–2005. The accuracy of the scoring system for prediction of mortality was measured by the area under the receiver operating characteristic curve (AUC). The performance of the new scoring system was further validated by comparing its predictive accuracy with that of Acute Physiology and Chronic Health Examination (APACHE) II. Results: CART analysis identified five variables for prediction of in-hospital mortality. One point is assigned for the presence of each of the following during the first 24 h: blood urea nitrogen (BUN) >25 mg/dl; impaired mental status; systemic inflammatory response syndrome (SIRS); age >60 years; or the presence of a pleural effusion (BISAP). Mortality ranged from >20% in the highest risk group to <1% in the lowest risk group. In the validation cohort, the BISAP AUC was 0.82 (95% CI 0.79 to 0.84) versus APACHE II AUC of 0.83 (95% CI 0.80 to 0.85). Conclusions: A new mortality-based prognostic scoring system for use in AP has been derived and validated. The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality.

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.

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.

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 mm3. 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.

Mortality and Need for Mechanical Ventilation in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Development and Validation of a Simple Risk Score

BACKGROUND Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) often require hospitalization, may necessitate mechanical ventilation, and can be fatal. We sought to develop a simple risk score to determine its severity. METHODS We analyzed 88,074 subjects admitted with an AECOPD between 2004 and 2006. We used recursive partition to create risk classifications for in-hospital mortality. Need for mechanical ventilation served as a secondary end point. We internally validated the model via 1000 bootstrapping on half of patients and externally validated it on the remaining patients. We assessed predictive ability using the area under the receiver operating curve (AUROC). RESULTS The in-hospital mortality rate was 2%. Three variables had high discrimination of outcomes: serum urea nitrogen level greater than 25 mg/dL (to convert to millimoles per liter, multiply by 0.357); acute mental status change, and pulse greater than 109/min. For those without any of the 3 factors, age 65 years or younger further differentiated the lowest-risk group. In those with all 3 factors, the mortality rates were 13.1% (131 in 1000) and 14.6% (146 in 1000) in the derivation and validation cohorts, respectively, compared with 0.3% (3 in 1000) in both cohorts among patients without any of the 3 factors and age 65 years or younger (P < .001). The AUROC for mortality in the 2 cohorts were 0.72 (95% confidence interval [CI], 0.70-0.74) and 0.71 (95% CI, 0.70-0.73), respectively. For mechanical ventilation, the AUROCs were 0.77 (95% CI, 0.75-0.79) for both cohorts. CONCLUSIONS A simple risk class based on clinical variables easily obtained at presentation predicts mortality and need for mechanical ventilation. It may facilitate the triage and care of patients with AECOPD.

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 (

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

12,617; 95% confidence Interval,

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

10,755–

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

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.

Development and Validation of a Disease-Specific Risk Adjustment System Using Automated Clinical Data

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],