Person:

Johannes, Richard Scott

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.

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.

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.