Development and Assessment of Machine Learning Models for Individualized Risk Assessment of Mastectomy Skin Flap Necrosis

Abstract

Objective: To develop, validate, and evaluate ML algorithms for predicting MSFN. Background: MSFN is a devastating complication that causes significant distress to patients and physicians by prolonging recovery time, compromising surgical outcomes, and delaying adjuvant therapy. Methods: We conducted comprehensive review of all consecutive patients who underwent mastectomy and immediate implant-based reconstruction from January 2018 to December 2019. Nine supervised ML algorithms were developed to predict MSFN. Patient data were partitioned into training (80%) and testing (20%) sets. Results: We identified 694 mastectomies with immediate implant-based reconstruction in 481 patients. The patients had a mean age of 50 ± 11.5 years, years, a mean body mass index of 26.7 ± 4.8 kg/m 2 , and a median follow-up time of 16.1 (range, 11.9-23.2) months. MSFN developed in 6% (n = 40) of patients. The random forest model demonstrated the best discriminatory performance (area under curve, 0.70), achieved a mean accuracy of 89% (95% confidence interval, 83-94), and identified 10 predictors of MSFN. Decision curve analysis demonstrated that ML models have a superior net benefit regardless of the probability threshold. Higher body mass index, older age, hypertension, subpectoral device placement, nipple-sparing mastectomy, axillary nodal dissection, and no acellular dermal matrix use were all independently associated with a higher risk of MSFN. Conclusions: ML algorithms trained on readily available perioperative clinical data can accurately predict the occurrence of MSFN and aid in individualized patient counseling, preoperative optimization, and surgical planning to reduce the risk of this devastating complication.

A Surgeon’s Guide to Artificial Intelligence-Driven Predictive Models

“…14 The most notable benefits of the DT models are (1) the models’ interpretation is intuitive and widely accepted by most health care professionals and (2) the models can be easily implemented into practice due to their rule-based nature. 7

Figure 2.Decision-tree algorithms. Internal nodes in a decision tree include data variables that are processed by decision nodes and leaf nodes.

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“…15 The SVM has been widely used in many disciplines, including surgery. 5-7 In recent work, we used the SVM algorithm to predict lymph node status in breast cancer patients. 6

Figure 3.Support vector machine algorithms.

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Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications

“…17,18 AI techniques often have better predictive performance than do traditional statistical models limited to logistic regression. 17,[19][20][21] ML algorithms allow for the evaluation of a higher number of clinical variables than do traditional modeling approaches and may help identify weak predictors or interactions between variables that may improve prediction accuracy. 17,19 By developing nonlinear models that use multiple data sources, such as diagnoses, treatments, and laboratory values, ML has outperformed logistic regression for predicting postoperative outcomes.…”

“…[22][23][24][25] Recent work from our group, for example, showed that, compared with multivariable logistic regression, ML demonstrated higher predictive discriminatory performance and identified more predictors of complications in both abdominal wall reconstruction and reconstruction following mastectomy. 20,21 And in contrast to conventional statistical approaches, incremental learning enables ML to improve continuously as new data are added. 26,27 Thus, unlike traditional risk calculators, which are static, ML models are dynamic and continuously improve over time.…”

“…Recently, our team developed ML models to predict skin flap necrosis in patients undergoing mastectomy for breast cancer. 20 Using data from 694 patients, we evaluated the predictive performance of nine ML algorithms. The algorithms, which were trained on readily available perioperative data, predicted individual patients' risk of mastectomy skin flap necrosis with an AUC of .70 and 89% accuracy.…”