title: Developing a risk stratification tool for audit of outcome after surgery for head and neck squamous cell carcinoma.
abstract: 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.
  
  
  
  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.
  
  
  
  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).
  
  
  
  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.
authors.affiliation: Royal Marsden NHS Foundation Trust, London, London, United Kingdom.
authors.forename: David F
authors.identifier: 0000-0002-5293-4003
authors.initials: DF
authors.lastname: Tighe
authors.affiliation: University of Brighton, School of Computer Engineering and Mathematics, Brighton, East Sussex, United Kingdom.
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Thomas
authors.affiliation: Department of Informatics, King's College London School of Medical Education, London, United Kingdom.
authors.forename: Isabel
authors.initials: I
authors.lastname: Sassoon
authors.affiliation: Dendrite Clinical Systems, Henley-on-Thames, United Kingdom.
authors.forename: Robin
authors.initials: R
authors.lastname: Kinsman
authors.affiliation: Department of Oral and Maxillofacial Surgery, Guy's and St Thomas' NHS Trust, London, United Kingdom.
authors.forename: Mark
authors.initials: M
authors.lastname: McGurk
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002294:Carcinoma, Squamous Cell / Q000401:mortality* / Q000473:pathology / Q000601:surgery*; D018572:Disease-Free Survival; D005260:Female; D006258:Head and Neck Neoplasms / Q000401:mortality* / Q000473:pathology / Q000601:surgery*; D006801:Humans; D008297:Male; D008485:Medical Audit / Q000379:methods*; D008875:Middle Aged; D009361:Neoplasm Invasiveness / Q000473:pathology; D009367:Neoplasm Staging; D017063:Outcome Assessment, Health Care; D011183:Postoperative Complications / Q000453:epidemiology / Q000503:physiopathology; D018570:Risk Assessment; D000077195:Squamous Cell Carcinoma of Head and Neck; D016019:Survival Analysis; D006113:United Kingdom
keywords: audit; complications; head and neck; outcomes; squamous cell carcinoma