| Autor: |
Somakumar S; Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India., Basheer FT; Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India., K V; Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India., R VL; Department of Health technology and Informatics, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India., Bhat SN; Department of Orthopaedics, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India., Rodrigues GS; Department of General and Laparoscopic Surgery, Aster Al Raffah Hospital, Sohar, Sultanate of Oman., R GM; Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India., Raj S EA; Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India., V R; Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India. |
| Abstrakt: |
Background: Antimicrobial therapy is becoming less effective because of the rising microbial resistance. Surgical site infections (SSI) are one of the major complications that require modifications in the infection control policy for effective management. Objective/Aim: To develop a model for predicting the readmission rates post-SSI treatment and to identify prevalent microbial isolates and the respective trends in resistance patterns. Methodology: A retrospective study was carried out in a tertiary care setting in India. A total of 549 patients were diagnosed with SSI from January 1, 2016, to August 25, 2021, visiting orthopedics (n = 373), general surgery (n = 135), and neurosurgery (n = 41) departments were included in the study. Patient data and microbial isolate data were collected. Logistic regression with purposeful selection of covariates (p ≤ 0.25) was used to identify the predictors. The model fit was validated using the omnibus test. The area under the curve (AUC) was considered for the model discrimination. The resistance trend of microbial isolates was graphically represented. Results: One hundred thirty-seven (24.9%) were readmitted because of repeated infections. Readmission happened with a mean of 152 ± 32 days post-surgery was estimated. Uni-variable logistic regression showed 40 significant variables. The multi-variable logistic regression eliminated three variables because of insufficient comparator levels. Collinearity statistics further excluded two variables, i.e., reconstruction type of surgery and peripheral surgical area (variance inflation factor >10). The model showed an AUC of 0.77 and an accurate prediction of 77.8% (Akaike Information Criterion [AIC]: 568; Bayesian Information Criterion [BIC]: 722). Fifteen types of micro-organisms were isolated from 75.4% of readmitted patients. Methicillin-resistant Staphylococcus aureus (23.8%) was the primary isolate showing a resistance trend toward cloxacillin, ciprofloxacin, and ofloxacin (25.69%) equally, followed by erythromycin (18.4%) and gentamycin (6.25%). Conclusion: The current study predicted the post-SSI readmission rate and the microbial isolates along with their resistance patterns. The results of the study could serve as a tool for assessing and managing the factors leading to readmissions. |
