Skip to main content
construction release_alert
Scholars@Duke will be undergoing maintenance April 11-15. Some features may be unavailable during this time.
cancel
Journal cover image

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

Publication ,  Journal Article
Formeister, EJ; Baum, R; Knott, PD; Seth, R; Ha, P; Ryan, W; El-Sayed, I; George, J; Larson, A; Plonowska, K; Heaton, C
Published in: Laryngoscope
December 2020

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Laryngoscope

DOI

EISSN

1531-4995

Publication Date

December 2020

Volume

130

Issue

12

Start / End Page

E843 / E849

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Prognosis
  • Postoperative Complications
  • Plastic Surgery Procedures
  • Otorhinolaryngology
  • Middle Aged
  • Microvessels
  • Male
  • Machine Learning
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Formeister, E. J., Baum, R., Knott, P. D., Seth, R., Ha, P., Ryan, W., … Heaton, C. (2020). Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer. Laryngoscope, 130(12), E843–E849. https://doi.org/10.1002/lary.28508
Formeister, Eric J., Rachel Baum, P Daniel Knott, Rahul Seth, Patrick Ha, William Ryan, Ivan El-Sayed, et al. “Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer.Laryngoscope 130, no. 12 (December 2020): E843–49. https://doi.org/10.1002/lary.28508.
Formeister EJ, Baum R, Knott PD, Seth R, Ha P, Ryan W, et al. Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer. Laryngoscope. 2020 Dec;130(12):E843–9.
Formeister, Eric J., et al. “Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer.Laryngoscope, vol. 130, no. 12, Dec. 2020, pp. E843–49. Pubmed, doi:10.1002/lary.28508.
Formeister EJ, Baum R, Knott PD, Seth R, Ha P, Ryan W, El-Sayed I, George J, Larson A, Plonowska K, Heaton C. Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer. Laryngoscope. 2020 Dec;130(12):E843–E849.
Journal cover image

Published In

Laryngoscope

DOI

EISSN

1531-4995

Publication Date

December 2020

Volume

130

Issue

12

Start / End Page

E843 / E849

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Prognosis
  • Postoperative Complications
  • Plastic Surgery Procedures
  • Otorhinolaryngology
  • Middle Aged
  • Microvessels
  • Male
  • Machine Learning
  • Humans