Premium
Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer
Author(s) -
Formeister Eric J.,
Baum Rachel,
Knott P. Daniel,
Seth Rahul,
Ha Patrick,
Ryan William,
ElSayed Ivan,
George Jonathan,
Larson Andrew,
Plonowska Karolina,
Heaton Chase
Publication year - 2020
Publication title -
the laryngoscope
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.181
H-Index - 148
eISSN - 1531-4995
pISSN - 0023-852X
DOI - 10.1002/lary.28508
Subject(s) - logistic regression , medicine , head and neck , surgery , retrospective cohort study , dehiscence , hematoma , academic institution , cohort , machine learning , computer science , library science
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