Combining administrative and clinical data to stratify surgical risk

Objective: To evaluate whether administrative claims data (ADM) from hospital discharges can be transformed by present-on-admission (POA) codes and readily available clinical data into a refined database that can support valid risk stratification (RS) of surgical outcomes. Summary Background Data: ADM from hospital discharges have been used for RS of medical and surgical outcomes, but results generally have been viewed with skepticism because of limited clinical information and questionable predictive accuracy. Methods: We used logistic regression analysis to choose predictor variables for RS of mortality in abdominal aortic aneurysm repair, coronary artery bypass graft surgery, and craniotomy, and for RS of 4 postoperative complications (ie, physiologic/metabolic derangement, respiratory failure, pulmonary embolism/deep vein thrombosis, and sepsis) after selected operations. RS models were developed for age only (Age model), ADM only (ADM model), ADM enhanced with POA codes for secondary diagnoses (POA-ADM model), POA-ADM supplemented with admission laboratory data (Laboratory model), Laboratory model supplemented with admission vital signs and additional laboratory data (VS model), VS model supplemented with key clinical findings abstracted from medical records (KCF model), and KCF model supplemented with composite clinical scores (Full model). Models were evaluated using c-statistics, case-based errors in predictions, and measures of hospital-based systematic bias. Results: The addition of POA codes and numerical laboratory results to ADM was associated with substantial improvements in all measures of analytic performance. In contrast, the addition of difficult-to-obtain key clinical findings resulted in only small improvements in predictions. Conclusions: Enhancement of ADM with POA codes and readily available laboratory data can efficiently support accurate risk-stratified measurements of clinical outcomes in surgical patients.