RESUMEN
Risk-adjusted algorithms in surgical audit attempt to adjust for patient case mix and complexity in order that published outcomes fairly reflect surgical performance and quality of care. Such risk-adjustment models have applied to head and neck squamous cell carcinoma (HNSCC). We test one algorithm, currently embedded in the oncology and reconstruction dataset within the Quality and Outcomes in Oral and Maxillofacial (QOMS) Audit, which is an artificial neural network, for its predictive accuracy on a surgical cohort receiving curative surgery for non-HNSCC pathology. A single centre retrospective case note audit of post operative complications between 2010 and 2020 was conducted on patients having curative surgery for non-HNSCC pathology. The observed complication rate was compared to the predicted probability of complications in order to test the performance of the complication risk-adjustment model. Of 1591 non-HNSCC patients, 58 met the inclusion criteria with a 30-day complication rate of 8/58 (13%). The artificial neural network predicted a complication rate of 20/58 (27%). Sensitivity (0.75), specificity (0.72) and overall accuracy (0.72) suggest acceptable discrimination. Hosmer-Lemershow Goodness of Fit test was good (p = 0.55) suggesting acceptable calibration though over-prediction of complication rate in the highest risk patents was observed. This external validation series suggests the algorithm can be applied to the non-HNSCC cohort, though some refinement of the algorithm is required to account for over-prediction of complications for higher-risk patients. With further analysis a robust means of risk adjusting for the non-HNSCC cohort should be possible.