Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients.

Abstract

Autologous breast reconstruction is composed of diverse techniques and results in a variety of outcome trajectories. We propose employing an unsupervised machine learning method to characterize such heterogeneous patterns in large-scale datasets. A retrospective cohort study of autologous breast reconstruction patients was conducted through the National Surgical Quality Improvement Program database. Patient characteristics, intraoperative variables, and occurrences of acute postoperative complications were collected. The cohort was classified into patient subgroups via the K-means clustering algorithm, a similarity-based unsupervised learning approach. The characteristics of each cluster were compared for differences from the complementary sample (p < 2 ×10^-4) and validated with a test set. A total of 14,274 female patients were included in the final study cohort. Clustering identified seven optimal subgroups, ordered by increasing rate of postoperative complication. Cluster 1 (2027 patients) featured breast reconstruction with free flaps (50%) and latissimus dorsi flaps (40%). In addition to its low rate of complications (14%, p < 2 ×10^-4), its patient population was younger and with lower comorbidities when compared with the whole cohort. In the other extreme, cluster 7 (1112 patients) almost exclusively featured breast reconstruction with free flaps (94%) and possessed the highest rates of unplanned reoperations, readmissions, and dehiscence (p < 2 ×10^-4). The reoperation profile of cluster 3 was also significantly different from the general cohort and featured lower proportions of vascular repair procedures (p < 8 ×10^-4). This study presents a novel, generalizable application of an unsupervised learning model to organize patient subgroups with associations between comorbidities, modality of breast reconstruction, and postoperative outcomes.