PURPOSE: Patients with inflammatory bowel disease are often at highest risk for surgical site infections. We sought to define the predictors of surgical site infections and to develop a risk score for predicting those at highest risk. METHODS: Patients undergoing a bowel resection for Crohn's disease or ulcerative colitis were identified from National Surgical Quality Improvement Program 2008. Univariate and multivariate analyses were conducted to identify predictors of surgical site infections. Clinically relevant prediction categories were developed and the predictive behavior of the model was validated by use of National Surgical Quality Improvement Program 2007. An integer-based scoring system risk score was created proportional to the logistic regression coefficients, grouping patients into categories of similar risk. RESULTS: We identified 271,368 patients; 3981 of these patients underwent an operation for Crohn's disease (n = 2895) or ulcerative colitis (n = 1086). Nine hundred (22.6%) patients developed surgical site infections. Predictors included weight loss, smoking, emergent surgery, wound class, operative time (minutes), and an ASA score >2. A risk score was developed by stratifying patients into low (0-5), 15.6%; medium (6-8), 25.2%; and high (>8), 36.1% risk. CONCLUSIONS: Patients with inflammatory bowel disease are at high risk for surgical site infections. Preoperative factors including weight loss, smoking, emergent surgery and an ASA score >2 are strong predictors of surgical site infections. Operative time and wound class are important intraoperative predictors. A risk score, based on pre- and intraoperative variables, can be used to identify patients at highest risk of developing surgical site infections. This may allow for appropriate process measures to be implemented to prevent and lessen the impact of surgical site infections in this high-risk population.

PURPOSE: To explore the association between the attitudes of primary care physicians toward uncertainty and risk taking, as measured by a validated survey, with resource use in a Medicare HMO. DESIGN: All primary-care internists (n=20) in a large, multi-specialty clinic were surveyed to measure their attitudes about uncertainty and risk taking using three previously developed scales. Results were linked with administrative data for 792 consecutive patients in a recently created Medicare HMO. The patients' index visits occurred between April 1, 1995, and November 30, 1995. ANALYSIS: Charges stemming from several claim types (primary care and subspecialty physician, laboratory, radiology, and ambulatory procedures) in the 30 days following the index visit were summed. The physician scales were dichotomized at the median to seek unadjusted associations with charges. Generalized estimation equations were used to account for the correlation of charges resulting from patients' being nested within physicians and adjusted for physician characteristics (age, sex, years in practice) and patient characteristics (age, sex, comorbidity). MAIN RESULTS: The physician response rate was 90%. Most physicians (90%) were male. The mean age of the patients was 74 years, and 69% were female. The mean cost (+/-SD) per patient was $621.61+/-1,737.31. From the unadjusted analysis, high "anxiety due to uncertainty" was associated with higher patient charges ($197.85 vs $158.21, p=0.01). From the multivariable analysis, each standard deviation increase in "anxiety due to uncertainty" (3.5 points) corresponded to a 17% increase in mean charges (p < 0.01) and each similar increase in "reluctance to disclose uncertainty to patients" (1.92 points) corresponded to a 12% increase (p=0.03). However, increasing "reluctance to disclose mistakes to physicians" and increasing physician risk-taking propensity were associated with decreased total charges [-10% per standard deviation (1.34 points), p=0.02, and -8% per standard deviation (3.26 points), p=0.02, respectively]. CONCLUSION: Physician attitudes toward uncertainty were significantly associated with patient charges. Further investigation may improve prediction of patient-care charges, offer insight into the medical decision-making process, and perhaps clarify the relationship between cost, uncertainty, and quality of care.