Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer.

[OBJECTIVES/HYPOTHESIS] Machine learning (ML) is a type of artificial intelligence wherein a computer learns patterns and associations between variables to correctly predict outcomes. The objectives of this study were to 1) use a ML platform to identify factors important in predicting surgical complications in patients undergoing head and neck free tissue transfer, and 2) compare ML outputs to traditionally employed logistic regression models.

[STUDY DESIGN] Retrospective cohort study.

[METHODS] Using a dataset of 364 consecutive patients who underwent head and neck microvascular free tissue transfer at a single institution, 14 clinicopathologic characteristics were analyzed using a supervised ML algorithm of ensemble decision trees to predict surgical complications. The relative importance values of each variable in the ML analysis were then compared to logistic regression models.

[RESULTS] There were 166 surgical complications, which included bleeding or hematoma in 30 patients (8.2%), fistulae in 25 patients (6.9%), and infection or dehiscence in 52 patients (14.4%). There were 59 take-backs (16.2%), and six total (1.6%) and five partial (1.4%) flap failures. ML models were able to correctly classify outcomes with an accuracy of 65% to 75%. Factors that were identified in ML analyses as most important for predicting complications included institutional experience, flap ischemia time, age, and smoking pack-years. In contrast, the significant factors most frequently identified in traditional logistic regression analyses were patient age (P = .03), flap type (P = .03), and primary site of reconstruction (P = .06).

[CONCLUSIONS] In this single-institution dataset, ML algorithms identified factors for predicting complications after free tissue transfer that were distinct from traditional regression models.

[LEVEL OF EVIDENCE] 2c Laryngoscope, 2020.