To develop a predictive model for the risk of complications after thyroid and parathyroid surgery.Study Design
Case series with planned chart review of patients undergoing surgery, 2007-2013.Setting
Kaiser Permanente Northern California and Kaiser Permanente Southern California.Subjects and Methods
Patients (N = 16,458) undergoing thyroid and parathyroid procedures were randomly assigned to model development and validation groups. We used univariate analysis to assess relationships between each of 28 predictor variables and 30-day complication rates. We subsequently entered all variables into a recursive partitioning decision tree analysis, with P < .05 as the basis for branching.Results
Among patients undergoing thyroidectomies, the most important predictor variable was thyroid cancer. For patients with thyroid cancer, additional risk predictors included coronary artery disease and central neck dissection. For patients without thyroid cancer, additional predictors included coronary artery disease, dyspnea, complete thyroidectomy, and lobe size. Among patients undergoing parathyroidectomies, the most important predictor variable was coronary artery disease, followed by cerebrovascular disease and chronic kidney disease. The model performed similarly in the validation groups.Conclusion
For patients undergoing thyroid surgery, 7 of 28 predictor variables accounted for statistically significant differences in the risk of 30-day complications; for patients undergoing parathyroid surgery, 3 variables accounted for significant differences in risk. This study forms the foundation of a parsimonious model to predict the risk of complications among patients undergoing thyroid and parathyroid surgery.