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    A novel model for early prediction of in-hospital mortality in seawater drowning: the SNOP score
    (Bmc, 2025) Oncu, Kivanc; Ozcan, Ozhan; Kara, Seyma Simsirgil; Parmaksiz, Ayhan; Ersen, Teoman
    Background : Drowning is a leading cause of preventable mortality worldwide; however, early in-hospital risk stratification remains limited. Although tools such as the Szpilman score assist in early severity assessment, they may not fully capture the evolving clinical status after admission. This study aimed to develop a simplified and objective model based on readily available parameters to predict in-hospital mortality following seawater drowning. Methods : This retrospective study was conducted at a referral emergency department (ED) in northern Turkey between July 1, 2011, and December 31, 2024. Of 190 patients initially included, 166 with complete clinical and laboratory data were analyzed. Data were obtained from institutional and national health information systems. Clinical, physiological, and biochemical variables were assessed. Predictors of in-hospital mortality were identified using receiver operating characteristic (ROC) analysis and multivariable logistic regression. Variables with near-perfect discrimination (e.g., GCS, pH, Szpilman score) were excluded to avoid overfitting. Results : Among the 166 patients, 34 (20.5%) died during hospitalization. CPR and endotracheal intubation rates were significantly higher among non-survivors (CPR: 97.1% vs. 0%; intubation: 97.1% vs. 2.3%; both p < 0.001). Non-survivors also presented with lower GCS (median 3 vs. 15), lower arterial pH, and higher Szpilman scores (all p < 0.001). ROC analysis identified four potential predictors with AUC values between 0.90 and 0.95-pCO(2), lactate, SpO(2), and sodium-all showing significant discriminatory capacity (p < 0.001). These variables were entered into a binary logistic regression model, from which serum sodium (OR = 2.110; 95% CI: 1.310-3.401; p = 0.002) and SpO(2) (OR = 0.902; 95% CI: 0.847-0.961; p = 0.001) emerged as independent predictors. These formed the basis of the SNOP score (Saturation and Natremia-based Outcome Predictor), a two-parameter logistic model demonstrating excellent performance: AUC = 0.996, sensitivity = 99.0%, specificity = 96.2%, and overall accuracy = 98.4%. Conclusion: The SNOP score is a simple, ED-specific tool for early prediction of in-hospital mortality in seawater drowning. It complements existing assessment systems by incorporating objective, admission-based parameters. Prospective multicenter validation is warranted to confirm its clinical applicability and support broader implementation.
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    Öğe
    Developing The Menstrual Migraine Symptoms Scale
    (Ataturk Universitesi, 2025) Özyer Güvener, Yasemin; Erdoğan Acar, Meryem; Parmaksiz, Ayhan
    Objective: The objective of this study was to develop the Menstrual Migraine Symptoms Scale (MMSS). Methods: The study utilized a robust methodological design with a sample size of 582 participants. The data underwent a comprehensive analysis employing various statistical techniques, including item analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Cronbach's alpha internal consistency coefficient, and the Intraclass Correlation Coefficient (ICC) for test‐retest reliability. Results: The two sub‐dimensions of the scale, consisting of 19 items, demonstrated excellent internal consistency, with Cronbach's alpha coefficients ranging from 0.932 to 0.970. Furthermore, the total scale exhibited a high level of internal consistency, with a Cronbach's alpha coefficient of 0.976. The item correlation values within the scale ranged from 0.741 to 0.921, indicating strong relationships between the items. Conclusion: The study findings conclusively demonstrated the validity and reliability of the MMSS as a robust measurement tool specifically designed for assessing female individuals. © 2025, Ataturk Universitesi. All rights reserved.