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Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction

  • Reconstructive Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

A Correction to this article was published on 01 November 2021

This article has been updated

Abstract

Background

Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. However, as flap failure is a rare event, identification of risk factors is statistically challenging. Machine learning is a form of artificial intelligence that automates analytical model building. It has been proposed that machine learning can build superior prediction models when the outcome of interest is rare.

Methods

In this study we evaluate machine learning resampling and decision-tree classification models for the prediction of flap failure in a large retrospective cohort of microvascular breast reconstructions.

Results

A total of 1012 patients were included in the study. Twelve patients (1.1%) experienced flap failure. The ROSE informed oversampling technique and decision-tree classification resulted in a strong prediction model (AUC 0.95) with high sensitivity and specificity. In the testing cohort, the model maintained acceptable specificity and predictive power (AUC 0.67), but sensitivity was reduced. The model identified four high-risk patient groups. Obesity, comorbidities and smoking were found to contribute to flap loss. The flap failure rate in high-risk patients was 7.8% compared with 0.44% in the low-risk cohort (p = 0.001).

Conclusions

This machine-learning risk prediction model suggests that flap failure may not be a random event. The algorithm indicates that flap failure is multifactorial and identifies a number of potential contributing factors that warrant further investigation.

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Disclosures

Anne C. O’Neill, Dongyang Yang, Melissa Roy, Stephanie Sebastiampillai, Stefan O.P. Hofer and Wei Xu declare no conflicts of interest.

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Authors and Affiliations

  1. Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada

    Anne C. O’Neill MBBCh, PhD, Melissa Roy MD, MSc, Stephanie Sebastiampillai BSc & Stefan O.P. Hofer MD, PhD

  2. Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

    Dongyang Yang PhD & Wei Xu PhD

Authors
  1. Anne C. O’Neill MBBCh, PhD
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  2. Dongyang Yang PhD
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  3. Melissa Roy MD, MSc
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  4. Stephanie Sebastiampillai BSc
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  6. Wei Xu PhD
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Correspondence to Anne C. O’Neill MBBCh, PhD.

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The original online version of this article was revised: Dongyang Yang’s given name was corrected.

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O’Neill, A.C., Yang, D., Roy, M. et al. Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction. Ann Surg Oncol 27, 3466–3475 (2020). https://doi.org/10.1245/s10434-020-08307-x

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  • DOI: https://doi.org/10.1245/s10434-020-08307-x

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