Prediction of subpectoral direct-to-implant breast reconstruction failure based on random forest and logistic regression algorithms: A multicenter study in Chinese population.

Background: Few studies have been conducted on direct-to-implant (DTI) breast reconstruction failure, and consistent conclusions are lacking. Thus, this study aimed to comprehensively analyze the risk factors of reconstruction failure.

Methods: Patients who underwent DTI breast reconstruction after mastectomy at a single center between July 18, 2014, and January 13, 2020, were retrospectively included in this study. Two algorithms, random forest and logistic regression, were employed to construct models that analyzed the complications and risk factors of reconstruction failure. Subsequently, a multicenter external validation was performed for both models.

Results: There were 538 patients in the model construction group and 91 patients in the multicenter external validation group, with 23 and 5 reconstruction failure outcomes, respectively. Random forest analysis revealed that infection and wound dehiscence were the most significant factors leading to reconstruction failure. Multivariate logistic regression analysis indicated that body mass index (BMI), infection, and wound dehiscence were correlated with reconstruction failure. The risk of failure was 3.35% higher in overweight (BMI > 24 kg/m²) patients, 9.6% higher in patients with infection, and 42.5% higher in patients with wound dehiscence than that in the control group. The internal validation receiver operating characteristic (ROC) value for the random forest model was 0.990, whereas the external validation ROC was 0.736. The internal and external validation ROC values for the logistic regression model were 0.995 and 0.826, respectively.

Conclusion: Wound dehiscence and infection were the most significant risk factors for DTI breast reconstruction failure, and preoperative weight control was also important.