The Dangers of Gathering Data: Surgeon-Specific Outcomes Revisited

The accuracy of risk adjustment is important in developing surgeon profiles. As surgeon profiles are obtained from observational, nonrandomized data, we hypothesized that selection bias exists in how patients are matched with surgeons and that this bias might influence surgeon profiles. We used the Society of Thoracic Surgeons risk model to calculate observed to expected (O/E) mortality ratios for each of six cardiac surgeons at a single institution. Propensity scores evaluated selection bias that might influence development of risk-adjusted mortality profiles. Six surgeons (four high and two low O/E ratios) performed 2298 coronary artery bypass grafting (CABG) operations over 4 years. Multivariate predictors of operative mortality included preoperative shock, advanced age, and renal dysfunction, but not the surgeon performing CABG. When patients were stratified into quartiles based on the propensity score for operative death, 83% of operative deaths (50 of 60) were in the highest risk quartile. There were significant differences in the number of high-risk patients operated upon by each surgeon. One surgeon had significantly more patients in the highest risk quartile and two surgeons had significantly less patients in the highest risk quartile (p < 0.05 by chi-square). Our results show that high-risk patients are preferentially shunted to certain surgeons, and away from others, for unexplained (and unmeasured) reasons. Subtle unmeasured factors undoubtedly influence how cardiac surgery patients are matched with surgeons. Problems may arise when applying national database benchmarks to local situations because of this unmeasured selection bias.