Developing a risk stratification tool for audit of outcome after surgery for head and neck squamous cell carcinoma

David F. Tighe, Alan J. Thomas, Isabel Sassoon, Robin Kinsman, Mark McGurk

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1357-1363
Number of pages7
JournalHead and Neck
Volume39
Issue number7
DOIs
Publication statusPublished - 29 Mar 2017

Keywords

  • audit
  • complications
  • head and neck
  • outcomes
  • squamous cell carcinoma

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