ABSTRACT
BACKGROUND: Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction. METHODS: We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss. RESULTS: We identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50). CONCLUSIONS: ML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.
Subject(s)
Free Tissue Flaps , Head and Neck Neoplasms , Plastic Surgery Procedures , Humans , Head and Neck Neoplasms/surgery , Retrospective Studies , Plastic Surgery Procedures/adverse effects , Neck/surgery , Postoperative Complications/etiology , Postoperative Complications/surgery , Machine Learning , Free Tissue Flaps/adverse effects , Free Tissue Flaps/surgeryABSTRACT
BACKGROUND: EQ-5D health state utilities (HSU) are commonly used in health economics to compute quality-adjusted life years (QALYs). The EQ-5D, which is country-specific, can be derived directly or by mapping from self-reported health-related quality of life (HRQoL) scales such as the PROMIS-29 profile. The PROMIS-29 from the Patient Reported Outcome Measures Information System is a comprehensive assessment of self-reported health with excellent psychometric properties. We sought to find optimal models predicting the EQ-5D-5L crosswalk from the PROMIS-29 in the United Kingdom, France, and Germany and compared the prediction performances with that of a US model. METHODS: We collected EQ-5D-5L and PROMIS-29 profiles and three samples representative of the general populations in the UK (n = 1509), France (n = 1501), and Germany (n = 1502). We used stepwise regression with backward selection to find the best models to predict the EQ-5D-5L crosswalk from all seven PROMIS-29 domains. We investigated the agreement between the observed and predicted EQ-5D-5L crosswalk in all three countries using various indices for the prediction performance, including Bland-Altman plots to examine the performance along the HSU continuum. RESULTS: The EQ-5D-5L crosswalk was best predicted in France (nRMSEFRA = 0.075, nMAEFRA = 0.052), followed by the UK (nRMSEUK = 0.076, nMAEUK = 0.053) and Germany (nRMSEGER = 0.079, nMAEGER = 0.051). The Bland-Altman plots show that the inclusion of higher-order effects reduced the overprediction of low HSU scores. CONCLUSIONS: Our models provide a valid method to predict the EQ-5D-5L crosswalk from the PROMIS-29 for the UK, France, and Germany.
