Background: Sample size determination for area under the curve (AUC) comparisons in diagnostic accuracy studies requires the consideration of multiple methodological parameters. The type of diagnostic test, the nature of the data (discrete or continuous), the correlation structure between tests, and the degree of AUC differences all influence optimal study design and planning. To address these factors, this study provides comprehensive sample size and power calculations for comparing AUCs between diagnostic tests across clinically relevant scenarios. Methods: We conducted a comprehensive evaluation of sample size and power analysis for AUC comparisons under varying correlation levels (ρ = 0.30, 0.50, 0.80), data types (discrete vs. continuous), and AUC differences (ΔAUC = 0.02–0.10). The Obuchowski–McClish method was applied for discrete data, and the Hanley–McNeil approach was applied for continuous data, assuming balanced case-control allocation and two-sided testing ( α = 0.05). Sample sizes were calculated to achieve 80% statistical power based on established methodological approaches and adjusted for a 20% anticipated attrition rate. Power curves were generated to illustrate the relationship between sample size and statistical power across the evaluated scenarios. Results: The required sample sizes varied substantially across scenarios, ranging from 36 − 3,709 participants per group to achieve 80% power. The degree of difference in the area under the curve (AUC) was the primary determinant: ΔAUC = 0.02 required 909-3,709 participants, whereas a ΔAUC = 0.10 required only 36–142 participants. Inter-test correlation also had a marked impact on efficiency, with a strong correlation (ρ = 0.8) reducing sample sizes by 49% in discrete models and 68% in continuous models compared with a weak correlation (ρ = 0.3). Continuous data models consistently outperformed discrete models, requiring 24–53% fewer participants across all the scenarios. The most demanding scenario (ΔAUC = 0.02, discrete data, ρ = 0.3) required 3,709 participants per group, whereas the most efficient scenario (ΔAUC = 0.10, continuous data, ρ = 0.8) required only 36 participants, representing a 103-fold difference. Conclusions: Methodological choices lead to substantial variations in sample size requirements for diagnostic accuracy studies. Optimal parameter selection, particularly the use of continuous data models and accounting for strong inter-test correlations, can reduce the required sample sizes by up to 68% compared with suboptimal combinations. These results provide evidence-based guidance for efficient planning of diagnostic accuracy studies and underscore the critical importance of methodological considerations in study design optimization.

Background and Objectives: Acute pancreatitis, characterized by pancreatic inflammation, poses significant morbidity and mortality worldwide, with varied etiologies including gallstones, alcohol, and certain medications. Necrotizing pancreatitis represents a severe form of parenchymal damage with considerable impact on patient quality of life. Early identification of necrotizing pancreatitis is crucial for timely intervention and improved outcomes. The aim of this study was to investigate the usability of CONUT and PNI scores as prognostic indicators. Materials and Methods: We conducted a retrospective observational study involving patients presenting to the emergency department with acute pancreatitis between January 2020 and October 2023. The Controlling Nutritional Status (CONUT) score and Prognostic Nutrition Index (PNI) were calculated from serum biomarkers to assess nutritional status. Patients were categorized into necrotizing and nonnecrotizing pancreatitis groups, and the utility of CONUT and PNI scores in predicting necrotizing pancreatitis was evaluated. Results: A total of 339 patients were included, with 8.26% diagnosed with necrotizing pancreatitis. CONUT and PNI scores significantly differed between necrotizing and nonnecrotizing groups, with higher CONUT scores and lower PNI scores observed in the necrotizing group. Receiver operating characteristic (ROC) curve analysis revealed significant predictive value of CONUT and PNI scores for necrotizing pancreatitis, with cutoff values of >5 and ≤34, respectively. Conclusions: CONUT and PNI scores demonstrate promise in predicting necrotizing pancreatitis in patients admitted to the emergency department with acute pancreatitis. Additionally, these scores may serve as prognostic indicators for mortality in acute pancreatitis patients. Early identification using CONUT and PNI scores could facilitate timely intervention, potentially reducing mortality and morbidity in this patient population.

Background and Objectives: Spontaneous bacterial peritonitis (SBP) is a life-threatening disease that requires early diagnosis and treatment. It is known that a positive culture result for SBP, which is a common reason for admission to the emergency department, is related to the severity and prognosis of the disease. However, as it is not possible to determine the culture result in the early stage of the disease, different methods are required to predict prognosis in the emergency department. This study was conducted to evaluate the success of the SII, SIRI, NLR and PLR in predicting culture results, intensive care needs and mortality in patients with SBP admitted to the emergency department. Materials and Methods: This study was a retrospective, observational study. Patients with SBP who applied to the emergency department were included in this study. Pregnant women, patients with a malignancy, patients with another infection and patients with liver failure were excluded from this study. Data were analyzed in terms of culture results, the need for intensive care and mortality development. Analyses were performed using SPSS version 26. Results are presented with a 95% confidence interval. A p value less than 0.05 was considered statistically significant. Participant data were analyzed using the independent samples t-test or the Mann–Whitney U test based on normality, and ROC analyses were conducted to assess test accuracies and determine cut-off values. Results: A total of 275 patients were included in this study. Although the culture results of 183 patients were positive, 92 were negative. The SII, NLR and PLR were found to be significantly higher in culture-positive patients (p < 0.001, p = 0.013 and p = 0.002, respectively). The SII and NLR were found to be significantly higher in patients with high mortality (p < 0.001 and p = 0.017, respectively). Conclusions: This study showed that the SII, NLR and PLR may be useful in predicting culture positivity and prognosis in SBP patients in the emergency department.

Walled-off pancreatic necrosis (WOPN) is one of the complications of acute pancreatitis (AP) with high mortality. A method to predict the development of WOPN in AP patients admitted to the emergency department may guide life-saving practices such as early initiation of antibiotic therapy and, when necessary, referral of the patient to a center where necrosectomy can be performed. This study is a prospective observational study. One hundred eleven AP patients who applied to the emergency department were included in the study. The mean of QT interval (QT) dispersion, systemic immune–inflammation Index (SII), multi-inflammatory index-I (MII-1), multi-inflammatory index-II (MII-2), and multi-inflammatory index-III (MII-3) were compared between patients who developed WOPN and patients who did not develop WOPN during their hospitalization. In the study, the mean of QT dispersion, SII, MII-1, MII-2, and MII-3 were significantly lower in the patient group who developed WOPN compared to those who did not develop WOPN. In the receiver operating characteristic analysis, all methods except SII were found to be successful in predicting WOPN. QT dispersion, SII, MII-1, MII-2, and MII-3 are valuable tools that provide rapid results and successfully predict the development of WOPN in AP. However, MII-2 and QT dispersion appears to be slightly more successful than the others.

Purpose: The purpose of this study is to describe long COVID-19 symptoms in patients receiving outpatient treatment. Materials and Methods: This prospective, observational, descriptive study was conducted in the COVID-19 clinic of a university hospital in February-May 2022. Patients who applied to the outpatient clinic and were diagnosed with COVID-19 were included in the study. Patients were follow up for prolonged COVID-19 symptoms for a 12-week period. It was investigated whether there was a relationship between long COVID-19 symptoms frequency and gender, age and blood type. Results: At least one long COVID symptom was observed in 23% of 7139 patients included in the study. Myalgia was the most common symptom detected in 4.33% of all patients. Cough and loss of taste/smell were the most common symptoms in women, and dizziness in men. The frequency of 26 of 29 symptoms varied with age. While the symptoms most affected by the Body Mass Index were peripheral neuropathy and tinnitus, the least affected symptoms were loss of taste/smell, anxiety and depression. Conclusion: Long COVID symptoms were widely detected in COVID-19 outpatients. Age, gender and BMI may be factors affecting long COVID symptoms.