Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction.
Ann Surg Oncol
; 27(9): 3466-3475, 2020 Sep.
Article
en En
| MEDLINE
| ID: mdl-32152777
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Colgajos Quirúrgicos
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Neoplasias de la Mama
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Mamoplastia
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Aprendizaje Automático
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Supervivencia de Injerto
Tipo de estudio:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Aged
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
Ann Surg Oncol
Asunto de la revista:
NEOPLASIAS
Año:
2020
Tipo del documento:
Article
País de afiliación:
Canadá