Developing a risk stratification tool for audit of outcome after surgery for head and neck squamous cell carcinoma. Issue 7 (29th March 2017) - Tighe, David F.; Thomas, Alan J.; Sassoon, Isabel; Kinsman, Robin; McGurk, Mark
Abstract: Background: Patients treated surgically for head and neck squamous cell carcinoma (HNSCC) represent a heterogeneous group. Adjusting for patient case mix and complexity of surgery is essential if reporting outcomes represent surgical performance and quality of care. Methods: A case note audit totaling 1075 patients receiving 1218 operations done for HNSCC in 4 cancer networks was completed. Logistic regression, decision tree analysis, an artificial neural network, and Naïve Bayes Classifier were used to adjust for patient case‐mix using pertinent preoperative variables. Results: Thirty‐day complication rates varied widely (34%‐51%; P < .015) between units. The predictive models allowed risk stratification. The artificial neural network demonstrated the best predictive performance (area under the curve [AUC] 0.85). Conclusion: Early postoperative complications are a measurable outcome that can be used to benchmark surgical performance and quality of care. Surgical outcome reporting in national clinical audits should be taking account of the patient case mix.