Personalized approach in choosing tactics for abdominal hernia treatment

Abstract

Objective: to develop objective criteria for planning surgical treatment of abdominal hernias, taking into account the individual characteristics of patients.

Materials and methods. The study was conducted in two phases. First, a retrospective analysis of 2514 hernia repair cases was performed, and logistic regression was used to identify key predictors for mesh implantation. Based on this, a tripartite algorithm was developed, incorporating clinical factors (Block A), complication risk from fat mass metrics (Block B), and physiological reserve from active tissue parameters (Block C). Subsequently, a prospective cohort study was undertaken with 120 patients, randomized into a Control group (standard care, n = 60) and a Study group (algorithm-guided treatment, n = 60). Outcomes were assessed via ROC-analysis, Clavien-Dindo complication rates, and serial SF-36 quality of life surveys.

Results. The regression model confirmed hernia defect > 4 cm, BMI ≥ 30 kg/m², history of abdominal surgery, and age > 60 years as significant predictors for mesh use. The algorithm, using a threshold score of 3 in Block A, demonstrated excellent predictive performance in the Study group, with 90 % sensitivity and 95 % specificity (AUC = 0.916). Algorithm-guided management led to a non-significant but notable reduction in infectious complications (0 % vs. 8.3 % in Controls) and yielded a statistically significant acceleration in the recovery of physical and psychosocial quality of life metrics within the first six postoperative months.

Conclusion. The integration of bioimpedance analysis with established clinical factors into a personalized decision-making algorithm proves highly effective. It standardizes surgical strategy, reduces unwarranted mesh use, and enhances early postoperative recovery and quality of life, offering a refined tool for personalized hernia care.