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

Abstract

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.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

“…In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

Risk Factors for and Cost Implications of Free Flap Take‐backs: A Single Institution Review

“…22,23 Interestingly, a prior publication from our institution utilizing part (364 patients) of this dataset with machine learning algorithms found increased ischemia times to be predictive of increased complication rates. 24 Analysis of the same dataset in the same paper using multivariable regression modeling, however, found no association between ischemia times and complications. This apparent contradiction is best explained by the different assumptions made with the two different models.…”

Adverse Events Associated with Implantable Dopplers during Microvascular Surgery

“…time, and risk of flap complications secondary to increased ischemia time. [26][27][28] Users of these devices may benefit from understanding whether such intraoperative complications also occur with venous anastomotic couplers without an integrated ID. Unique complications associated with these couplers have been systematically reviewed; however, the focus was only on the postoperative setting.…”