Aydın, İrfan
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Doktor Öğretim
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irfan.aydin@fbu.edu.tr
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Eczane Hizmetleri Bölümü
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3 results
Scholarly Output Search Results
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Article Investigation of the Susceptibility Rates to Ceftazidime-Avibactam and Colistin, Clonal Relationships, and Clinical Data of Patients with Carbapenem-Resistant Klebsiella Pneumoniae Isolates Detected in the ICUs of a Hospital in İstanbul(K Faisal Special Hospital Research Centre, 2026) Akkaya, Yuksel; Aydin, Irfan; Harmankaya, Sebile; Karakul, Mehmet; Aydin, Mehtap; Erdin, Begum Nalca; Kilic, Ibrahim HalilBACKGROUND: The increase in carbapenem-resistant K. pneumoniae (CR-Kp) in intensive care units (ICUs) causes treatment difficulties and increases risk in mortality. OBJECTIVES: The aim of this study was to investigate the susceptibility rates of CR-Kp isolates obtained from ICUs to ceftazidime-avibactam (CAZ-AVI) and colistin, carbapenem resistance genes, clonal relationships and clinical characteristics of the patients. DESIGN: Retrospective cohort SETTING: Single-center, University of Health Sciences, & Uuml;mraniye Training and Research Hospital MATERIALS AND METHODS: This study was conducted between June 2023 and December 2024. Isolates were identified using VITEK MS v.3.2, and antibiotic susceptibility testing was performed using the VITEK 2 Compact system. CAZ-AVI susceptibility was determined using disk diffusion, and colistin susceptibility was determined using broth microdilution to determine minimum inhibitory concentration (MIC) values. Carbapenem resistance genes were determined using multiplex real-time polymerase chain reaction (RT-PCR) and clonal relationship arbitrarily primed-polymerase chain reaction (AP-PCR). MAIN OUTCOME MEASURES: Resistance genes of CR-Kp isolates, clonal relationships, CAZ-AVI and colistin resistance, and clinical characteristics of patients SAMPLE SIZE: Ninety-seven isolates from 76 patients RESULTS: Among patients with CR-Kp isolates, central venous catheter use was detected in 59 cases (78%), ventilator-associated pneumonia in 44 cases (58%), and bacteremia in 39 cases (51%), respectively. It was determined that 53 of the patients (70%) died. Using the AP-PCR method, 60 different genotypes were identified among 97 isolates, and clustering was determined in 42 of the isolates (46%). It was determined that 36 (37%) of the isolates were resistant to colistin and 42 (45%) were resistant to CAZ-AVI. NDM+OXA-48, OXA-48, KPC, KPC+NDM, and NDM genes were detected in 40 (43%), 32 (35%), 10 (11%), 2 (2%), and 3 (3%) isolates, respectively. It was determined that 30 (75%) of the isolates with NDM+OXA-48 and only 4 (12%) of the isolates with OXA-48 were resistant to CAZ-AVI. CONCLUSION: In addition to OXA-48, an increase in the frequency of CR-Kp isolates containing the NDM, NDM+OXA-48, KPC+NDM, and OXA-48+KPC genes were also detected. It was also determined that resistance to colistin and CAZ-AVI is increasing. The AP-PCR method can also be used to investigate infections. LIMITATIONS: Single center,Pulsed Field Gel Electrophoresis (PFGE) could not be performed together with AP-PCRArticle 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 Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates(MDPI, 2026) Akkaya, Yuksel; Aydin, Irfan; Tanyildizi-Kokkulunk, Handan; Erturk, Ayse; Kilic, Ibrahim HalilBackground/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.
