Role of Python-Based Statistical Analysis in Comparing Polypropylene and Polyester Mesh Implants in Lichtenstein Hernia Repair: A Retrospective Comparative Cohort Study.

Abstract

<h4>Introduction</h4> The choice of mesh material in Lichtenstein hernia repair remains a topic of discussion. This study aimed to compare short-term outcomes of polypropylene versus polyester mesh using statistical analysis in Python (Python Software Foundation).<h4>Methods</h4> A single-center, retrospective cohort study was conducted on 58 patients undergoing Lichtenstein repair. Patients were allocated to polypropylene (n=38) or polyester (n=20) mesh groups based on the surgeon's preference and material availability. Primary outcomes were complication rates and operative time. Statistical analysis was performed using Python (version 3.8.0) with SciPy and Pandas libraries.<h4>Results</h4> Significant baseline differences were noted; the polypropylene group was older (58.1 vs. 50.1 years, p=0.024) and had larger hernia volumes (138.5 vs. 53.9 cm³, p=0.023). Despite this, no significant differences were found in operative time (52.0 vs. 48.5 minutes, p=0.622), hospital stay (5.0 vs. 4.8 days, p=0.550), or complication rates (7.9% vs. 5.0%, p=1.000) between the polypropylene and polyester groups, respectively.<h4>Conclusions</h4> In this retrospective analysis, we observed no significant short-term differences in outcomes between mesh types. However, these findings are limited by the study's retrospective design, small sample size, and baseline imbalances. The choice of mesh may continue to be based on the surgeon's experience, but larger randomized trials are needed for definitive conclusions.