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.

Background: Candidozyma auris (C. auris) is an emerging multidrug-resistant yeast associated with healthcare-related transmission, difficult laboratory identification, antifungal resistance, and high mortality in critically ill patients. This study aimed to describe the clinical and microbiological characteristics of early institutional C. auris cases detected in a tertiary hospital in Istanbul, Türkiye. Methods: Adult patients hospitalized between January 2021 and December 2022 with C. auris isolation were retrospectively reviewed. Demographic data, underlying conditions, intensive care unit (ICU) exposure, invasive procedures, antimicrobial and antifungal use, culture sources, antifungal susceptibility testing (AFST) results, and clinical outcomes were evaluated. C. auris isolates were identified using VITEK MS v.3.2, and AFST was performed using C. auris isolates were identified using VITEK MS v.3.2, and AFST was performed using Sensititre YeastOne YO10 (Thermo Fisher Scientific, Waltham, MA, USA) panels. Results were interpreted according to CDC interim breakpoints. Multiple isolates from an individual patient were retained when they represented different specimen types or sampling events. Results: Thirty-three adult patients and 62 clinical isolates were included. The mean age was 67.8 years, and 20 patients (60.6%) were aged 65 years or older. Most patients had prior ICU admission (93.9%), urinary catheterization (97.0%), central venous catheterization (90.9%), mechanical ventilation (81.8%), and broad-spectrum antimicrobial exposure. Blood cultures represented the most frequent source of isolation (56.5%), followed by urine cultures (27.4%). Resistance rates were 98.4% for amphotericin B, 88.7% for fluconazole, 6.6% for micafungin, and 6.5% for caspofungin. Overall mortality was 72.7%. While mortality was numerically higher among patients aged 65 years or older and those requiring inotropic support, only the need for mechanical ventilation was statistically significantly associated with mortality. The cohort-level APACHE II-based expected mortality was 48.0%, whereas observed mortality was 72.7%; this comparison was descriptive and no formal calibration analysis was performed. Conclusions: Early institutional C. auris cases in this tertiary hospital occurred predominantly among elderly, critically ill patients with prolonged healthcare exposure, invasive devices, and extensive antimicrobial use. The findings support the need for early laboratory recognition, cautious interpretation of commercial AFST results, particularly given the propensity of Sensititre YeastOne to overestimate amphotericin B resistance, strengthened infection control practices, and multicenter studies incorporating molecular typing to clarify transmission dynamics and antifungal resistance patterns.

Background/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.