Unsupervised Clinical Phenotyping Identifies Distinct Risk Profiles in Incisional Hernia Repair

Background and Objectives: Patients undergoing incisional hernia repair constitute a clinically heterogeneous population with variable postoperative outcomes. Conventional risk models based on isolated risk factors may inadequately capture this complexity. This study aimed to identify data-driven clinical phenotypes and evaluate their association with surgical outcomes. Methods and Materials: A retrospective cohort of 1262 patients undergoing retromuscular incisional hernia repair (Rives-Stoppa technique) was analyzed. Unsupervised clinical phenotyping was performed using latent class analysis based on seven preoperative variables. Model selection was guided by Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and clinical interpretability. Postoperative outcomes were compared across phenotypes. Results: Three distinct phenotypes were identified: metabolic (34.6%), structural (33.9%), and frailty (31.5%). The structural phenotype showed the highest complication (22.7%) and recurrence rates (8.6%), while the frailty phenotype had the lowest complication burden (14.6%). The metabolic phenotype was characterized by obesity and diabetes, consistent with increased wound-related morbidity. Cluster robustness was supported by internal validation metrics and sensitivity analyses. Conclusions: In this retrospective single-center cohort, distinct clinical phenotypes with different outcome profiles were identified among patients undergoing incisional hernia repair, supporting the concept that this population comprises clinically heterogeneous subgroups with distinct patterns of vulnerability. These findings should be considered preliminary and hypothesis-generating. Further external validation and prospective studies are required to determine the clinical utility of phenotype-based risk stratification.