OBJECTIVE:To benchmark machine-learning (ML) models for predicting postoperative infection after laparoscopic cholecystectomy (LC) in a real-world cohort. STUDY DESIGN:A descriptive study. Place and Duration of the Study: Department of General Surgery, Daqing People's Hospital, Daqing, China, from January 2016 to December 2025. METHODOLOGY:Consecutive patients undergoing LC were included. Postoperative infection was defined as a clinically diagnosed or culture-proven infection during index hospitalisation or within 30 days after surgery, using CDC/NHSN-based criteria where applicable. The cohort (n = 1,155; 186 infections) was randomly split into training (n = 816) and internal validation sets (n = 339). Four classifiers [decision tree (DT), support vector machine with a radial basis function kernel (SVM-RBF), random forest (RF), and naive Bayes (NB)] were developed. Performance was assessed using AUC, accuracy, sensitivity, and specificity at a prespecified probability threshold of 0.50, with precision-recall analysis, calibration, decision curve analysis, and permutation feature importance serving as complementary assessments. RESULTS:On validation, NB and RF showed the best discrimination (AUCs = 0.824 and 0.809, respectively), while DT and SVM-RBF performed poorly (AUCs = 0.614 and 0.582, respectively). At the 0.50 threshold, SVM-RBF predicted all validation patients as non-infected, indicating complete classification failure for clinical use. RF achieved high specificity (0.974) and accuracy (0.903), whereas NB yielded higher sensitivity (0.417). The highest-ranked RF predictors were preoperative white blood cell group, age, perioperative acute cholecystitis, the preoperative length-of-stay group, obesity, and operation duration group. CONCLUSION:ML models showed heterogeneous performance for predicting postoperative infection after LC. NB and RF performed best on internal validation and may support early risk stratification when paired with model interpretability, clinical utility assessment, external validation, and threshold optimisation. SVM-RBF was unsuitable for this dataset at the fixed 0.50 threshold. KEY WORDS:Laparoscopic cholecystectomy, Postoperative infection, Real-world cohort, Machine learning, Risk stratification.
OBJECTIVE:To evaluate the appropriateness of antibiotic dosing in patients with augmented renal clearance (ARC) admitted to the cardiovascular surgery intensive care unit (CVS-ICU) and the coronary intensive care unit (CICU). STUDY DESIGN:A descriptive study. Place and Duration of the Study: CVS-ICU and CICU, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkiye, from January 2024 to January 2025. METHODOLOGY:Adult patients (≥18 years) hospitalised for ≥24 hours, diagnosed with ARC (eGFR ≥130 mL/min/1.73 m2), and receiving antibiotics were included. Dosing appropriateness was assessed based on UpToDate® recommendations. ARC risk was stratified using the ARC scoring system. Data were analysed using descriptive statistics, chi-square test, and Mann-Whitney U test. RESULTS:Of the 3,334 screened patients, 83 (2.5%) met ARC criteria; 57 received antibiotics and were included in the analysis. At least one dosing error was identified in 84.2% of cases, mostly underdosing (73.1%). The most common antibiotics were cefazolin (42.6%), ampicillin-sulbactam (14.6%), and daptomycin (7.3%). A high ARC risk score was associated with a greater likelihood of dosing errors (90.4% vs. 66.6%; OR = 4.7, p = 0.044). Patients with dosing errors were younger (median 25.5 vs. 50 years, p = 0.004) and received more antibiotics (p = 0.038). CONCLUSION:Although ARC was uncommon, dosing errors were frequent. Early recognition of ARC and individualised dosing are critical to optimise therapy in ICU patients. KEY WORDS:Augmented renal clearance, Antibiotic dosing, Cardiovascular surgery intensive care unit, Coronary intensive care unit, Dose adjustment.
OBJECTIVE:To evaluate the distribution of cancers across different age groups among residents of Balochistan from 2021 to 2024 and compare these patterns with those observed in other provinces of Pakistan. STUDY DESIGN:An observational study. Place and Duration of the Study: Pathology-based cancer registry at the Section of Histopathology, Department of Pathology and Laboratory Medicine, Aga Khan University Hospital, Karachi, Pakistan, and Department of Pathology, Aria Institute of Medical Sciences, Quetta, Pakistan, from January 2021 to December 2024. METHODOLOGY:Newly diagnosed, biopsy-confirmed malignant cases received from Quetta and other cities of Balochistan through the Aga Khan University Hospital laboratory network and the collaborating Institute over four years from the cancer registry and one year from the other institute were analysed. Patients were grouped by age and gender into children (0-14 years), adolescents (15-19 years), and adults (≥20 years). Duplicate records were removed through systematic verification. Categorical variables were evaluated using frequencies and percentages. RESULTS:Out of 16,342 biopsy specimens, 5,145 (31.5%) were malignant. There were 52% (n = 2,659) male and 48% (n = 2,486) female cases. Adults accounted for 93% of cases, while children and adolescents accounted for 7%. Among a total of 2,438 adult males, oesophageal cancer was most frequent (425, 17.4%), followed by stomach (351, 14.4%) and colorectal cancers (225, 9.2%). Out of the total 2,351 adult females, breast cancer was the leading malignancy (526, 22.4%), followed by oesophageal (434, 18.5%) and colorectal cancers (133, 5.7%). In younger groups, brain tumours and lymphomas predominated. CONCLUSION:Marked predominance of oesophageal cancer in both genders highlights substantial regional variation. Overall, the predominance of gastrointestinal cancers suggests the presence of region-specific environmental risk factors. KEY WORDS:Cancer registry, Balochistan, Oesophageal cancer, Colorectal cancer.
OBJECTIVE:To determine the relative frequency of conditions causing different types of fibrosis [usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP)] in combined pulmonary fibrosis and emphysema (CPFE), a newly defined disease. STUDY DESIGN:A cross-sectional observational study. Place and Duration of the Study: Department of Radiology, Tokat Gaziosmanpasa University, Tokat, Turkiye, from January 2023 to January 2025. METHODOLOGY:Based on HRCT images, the visualised fibrosis types were divided into two groups: UIP (n = 48) and NSIP (n = 46). Demographic data included age, gender, smoking history, and occupational history. The Charlson Comorbidity Index (CCI) score was calculated for each patient. The Kolmogorov-Smirnov test was used to assess the distribution of variables in the analyses. Categorical data were compared using the chi-square test and numerical data by Student's t-test. RESULTS:CPFE patients were divided into two groups: UIP pattern (n = 48) and NSIP pattern (n = 46). No significant differences were found between the groups in terms of age, gender, smoking, CCI, and high-risk occupation. When examining the relationship between pulmonary hypertension and demographic data in patients with CPFE, the mean age and CCI were significantly higher in the group with systolic pulmonary artery pressure (sPAP) ≥25 mmHg, as measured by echocardiography. Furthermore, in those with sPAP ≥25, haemoglobin and haematocrit levels were significantly lower, and urea levels were significantly higher. Similarly, in those with sPAP ≥25, diffusing capacity of the lungs for carbon monoxide levels were significantly lower. CONCLUSION:Although comorbidities were found to be significantly higher in the UIP pattern compared to the NSIP pattern, the CCI was similar. As more than half of patients with CPFE had elevated sPAP, pulmonary hypertension should also be considered when evaluating these patients. KEY WORDS:Pulmonary fibrosis, Emphysema, Usual interstitial pneumonia, Carbon monoxide, Diffusion test, Pulmonary artery pressure.
