Nomogram-Based Approach for Predicting Complication Risks following Prepectoral Direct-to-Implant Breast Reconstruction

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Abstract
Background: Despite the recent steep rise in the use of prepectoral direct-to-implant (DTI) breast reconstruction, concerns remain regarding the potential risk of complications, resulting in the selective application of the technique; however, the selection process was empirically based on the operator’s decision. Using patient- and operation-related factors, this study aimed to develop a nomogram for predicting postoperative complications following prepectoral DTI reconstruction. Methods: Between August of 2019 and March of 2023, immediate prepectoral DTI was performed for all patients deemed suitable for 1-stage implant-based reconstruction. A retrospective analysis of the complications was conducted for this cohort. The cohort was randomly divided into the training and the validation data sets. A nomogram was developed using least absolute shrinkage and selection operator logistic regression and Firth bias-reduced logistic regression. Results: The authors analyzed 433 breasts (362 patients). Complications developed in 131 patients (33.5%), including early complications within 90 days postoperatively (26.1%), infection (1.8%), wound revision (9.7%), and reconstructive failure (3.5%). Increased age and body mass index, therapeutic mastectomy, reduction pattern mastectomy, implant size, and projection, and radiotherapy history were associated with early complications. For infection and reconstructive failure, increased age and body mass index, heavier mastectomy specimen weight, implant projection, and previous and adjuvant radiotherapy showed association. The internal validation of each model demonstrated areas under the receiver operating characteristic curve of 68.9%, 68.0%, 84.9%, and 79.0% for early complications, delayed wound healing, infection, and reconstructive failure, respectively. Conclusion: A nomogram-based approach for predicting complications in prepectoral DTI reconstruction may enhance clinical decision-making, leading to optimized outcomes. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

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