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OBJECTIVEDiabetic 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 METHODSUsing 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.RESULTSAmong 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.CONCLUSIONSUsing 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.

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.

Objective. To develop and validate a disease-specific automated inpatient mortality risk adjustment system primarily using computerized numerical laboratory data and supplementing them with administrative data. To assess the values of additional manually abstracted data. Methods. Using 1,271,663 discharges in 2000-2001, we derived 39 disease-specific automated clinical models with demographics, laboratory findings on admission, ICD-9 principal diagnosis subgroups, and secondary diagnosis-based chronic conditions. We then added manually abstracted clinical data to the automated clinical models (manual clinical models). We compared model discrimination, calibration, and relative contribution of each group of variables. We validated these 39 models using 1,178,561 discharges in 2004-2005. Results. The overall mortality was 4.6 percent (n 5 58,300) and 4.0 percent (n 5 47,279) for derivation and validation cohorts, respectively. Common mortality predictors included age, albumin, blood urea nitrogen or creatinine, arterial pH, white blood counts, glucose, sodium, hemoglobin, and metastatic cancer. The average c-statistic for the automated clinical models was 0.83. Adding manually abstracted variables increased the average c-statistic to 0.85 with better calibration. Laboratory results displayed the highest relative contribution in predicting mortality. Conclusions. A small number of numerical laboratory results and administrative data provided excellent risk adjustment for inpatient mortality for a wide range of clinical conditions.

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