Anne C O'Neill1, Donyang Yang2, Melissa Roy3, Stephanie Sebastiampillai3, Stefan O P Hofer3, Wei Xu2. 1. Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada. Anne.O'Neill@uhn.ca. 2. Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. 3. Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada.
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.
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.