Correlation Engine 2.0
Clear Search sequence regions

Sizes of these terms reflect their relevance to your search.

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. 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. 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). 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. © 2023. Society of Surgical Oncology.


Malke Asaad, Sheng-Chieh Lu, Abbas M Hassan, Praneeth Kambhampati, David Mitchell, Edward I Chang, Peirong Yu, Matthew M Hanasono, C Sidey-Gibbons. The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction. Annals of surgical oncology. 2023 Apr;30(4):2343-2352

Expand section icon Mesh Tags

PMID: 36719569

View Full Text