Assessing the Impact of Morphological Parameters on the Mechanical Behavior of Synthetic Meshes. A Multivariate Regression Approach.

The impact of morphological and mechanical parameters of surgical meshes on the healing processes and patient comfort after abdominal repair surgery is widely accepted. However, how the structure of the knitted pattern of synthetic meshes affects the mechanical behavior remains primarily theoretical. The objective of this study was therefore to assess the correlation between these key factors, identifying the most crucial morphological parameters able to support the design of new meshes. In this perspective, morphological parameters related to pore size, shape, and orientation were computed based on high-resolution images using the poreScanner app and the Matlab Image Processing toolbox. Additional parameters such as weight and thickness were measured through high-precision instruments. Concurrently, 12 mechanical parameters were assessed by executing a comprehensive testing protocol. Multivariate regression models were implemented, each using one to five morphological parameters as independent variables and one of the 12 mechanical parameters as dependent variables. A leave-one-out (LOO) validation algorithm was then employed to estimate the models' performance, robustness, and accuracy for potential future predictions. Regression models showed high coefficients of determination (R ≥ 0.8), except for uniaxial strains (0.59 < R < 0.71). The LOO validation reveals good predictive capabilities (R > 0.65) for 5 out of 12 mechanical parameters, whereas moderate predictive capabilities (R > 0.55) for one model. Promising results demonstrate a quantifiable relationship between pore characteristics and mechanical behavior. Thanks to further validation using different meshes, the models could be beneficial for all stakeholders involved in this field, from patients to manufacturers.