WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6

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  • Article
    Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tolan, H.K.; Aydın, İ.; Tanyildizi-Kökkülünk, H.; Karakuş, M.; Akkaya, Y.; Kaya, O.; Işman, F.K.
    Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020–2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among β-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance. © 2025 by the authors.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Prevalence of Colistin-Resistant Klebsiella Pneumoniae Isolates in Turkey Over a 20-Year Period: a Systematic Review and Meta-Analysis
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Kahraman Kilbas, E.P.; Kilbas, I.; Ciftci, I.H.; Kilbas, Elmas Pinar Kahraman
    Klebsiella pneumoniae is one of the leading causes of healthcare-associated infections and poses challenges in its treatment owing to its high antibiotic resistance. The development of resistance to colistin, which is used as a last resort, has become a major public health problem worldwide. This study was planned according to the PRISMA guidelines and included studies reporting the prevalence of colistin-resistant K. pneumoniae in Turkey between 2004 and 2024 through a systematic literature review. A total of 28 original research articles were included in the meta-analysis. Data were analyzed using the SPSS and CMA software. The pooled colistin resistance of a total of 8916 K. pneumoniae strains from 28 studies included in this meta-analysis was found to be 1.63% (95% CI: 1.51–3.12). Colistin resistance increased significantly over time. A higher resistance rate was detected in the strains tested using the EUCAST guidelines and broth microdilution method. The year of the study and validation methods contributed to the heterogeneity observed in the studies. This meta-analysis reveals that colistin-resistant K. pneumoniae strains have increased over time in Turkey. Current data show that colistin resistance is not only a laboratory finding but has become a crisis, requiring urgent action in terms of hospital infection management and patient safety. Regional and global measures should be taken to ensure the appropriate use of antibiotics to control the development of resistance. © 2025 by the authors.