Validation of a post operative complication risk prediction algorithm in a non-head and neck squamous cell carcinoma cohort.

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.