Predicting Surgical Outcome Using Bayesian Analysis

Surgical outcome analysis is best performed using Bayesian statistics. The ability of this type of analysis to take into consideration multiple parameters affecting surgical outcome is a marked improvement over single-condition probabilities that ignore the many degrees of freedom in the dynamics of a surgical intervention. To illustrate the power of a Bayesian analysis a surgical population of 1017 patients undergoing cholecystectomy, colon resection, and appendectomy was developed. Each patient was assigned to a mutually exclusive outcome group (D1, survival;D2, survival with complications;D3, nonsurvival). A priori outcome probabilities for the population wereD1= 0.917,D2= 0.066, andD3= 0.017. A conditional probability matrix (CPM) was then developed for 59 patient parameters (Sj) that may have affected outcome. The CPM contained the conditional probability that a parameter was present given the known outcomeP(Sj/Di). Once the CPM was matured Bayesian analysis allowed one to predict the surgical outcome given any set or combination of patient parametersP(Di/Sj). Posterior probabilities generated by the Bayes analysis allowed one to investigate the effect of a single parameter or any group of parameters on outcome. Criterion based validity testing based on comparison of Bayesian outcomes versus the surgeons perception of outcomes for computer simulated surgery on 15 artificial patients suggests that this type of analysis provides insightful and educational data to the operating surgeons (V-mortality = 0.547, SEE = 24.46; V-morbidity = 0.319, SEE = 25.86). Objective outcome analysis or surgical peer review cannot be fairly accomplished unless the statistical methodology takes into consideration all of the parameters affecting outcome. This study concludes that Bayes Theorem provides the ideal statistical framework for performing an outcome analysis that considers the many parameters affecting the results of a surgical intervention.