Scientific journal
Научное обозрение. Медицинские науки
ISSN 2500-0780
ПИ №ФС77-57452

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THE PREDICTION OF THE PROBABILITY OF SEROMA DEVELOPMENT IN CASE OF ENDOPROSTHESIS REPLACEMENT OF VENTRAL HERNIAS

Vlasov A.V. 1
1 Nizhny Novgorod State Medical Academy
The purpose. To suggest the way of the prediction of the probability of seroma development due to some risk factors in case of endoprosthesis replacement of ventral hernias. Materials and methods. The research included 224 patients, who were operated by “onlay” (220 patients) and “inlay” method (4 patients). To prevent the wound complications in the main group (n= 122) subcutaneous tissue was sewed along and fixed with the vertical P - stitches to the prosthesis and the wound bottom. The wound drainage was performed in 4 patients (3,3 %). In the control group (n=102) there was carried out the drainage and wound layerwise stitch without wide taking and fixation of subcutaneous tissue. Drainage was made in 83 patients (81,4 %). To predict the risks of the seroma development with the dependence model construction there was used extrapolation method – binary logistic regression. Results. In the main group the formation of clinically significant seromas was observed in 9 (7,4 %) patients; in the control group – in 28 (27,5 %) patients (p<0,001). The most appropriate model for the prediction of the risk of seroma development is the cooperation of three factors – the combination of the median and lateral localization of hernia, the presence of cardiovascular diseases and the application of the developed P-stitches in case of wound stitching. Conclusion. The concomitant cardiovascular diseases and the combination of the median and lateral localization of hernia are the significant risk factors for seroma development in case of endoprosthesis development. The effectiveness of vertical P-stiches in case of wound stitching in the prophylaxis of wound complications has been proved not only by the statistic analysis, based on the comparability and comparison of 2 groups, but also by multivariance statistic analysis – binary logistic regression.