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Yazar "Erkent, Murathan" seçeneğine göre listele

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    A new approach to the management of acute appendicitis: Decision tree method
    (W B Saunders Co-Elsevier Inc, 2022) Erkent, Murathan; Karakaya, Emre; Yucebas, Sait Can
    ABSTR A C T Background: It is important to distinguish between complicated acute appendicitis (CAA) and noncomplicated acute appendicitis (NCAA) because the treatment methods are different. We aimed to create an algorithm that determines the severity of acute appendicitis (AA) without the need for imaging methods, using the decision tree method. Methods: The patients were analyzed retrospectively and divided into two groups as CAA and NCAA. Age, gender, Alvarado scores, white blood cell values (WBC), neutrophil/lymphocyte ratios (NLR), C-reactive protein value (CRP), albumin value and CRP/Albumin ratios of the patients were recorded. Results: In the algorithm we created, the most important parameter in the distinction between CAA and NCAA is CRP. NLR is predictive in patients with a CRP value of <= 107.565 mg/L, and the critical value is NLR 2.165. In pa-tients with a CRP value of >107.565 mg/L, albumin is the determinant and the critical value is 2.85 g/dL. Age, gen -der, alvarado score and CRP/albumin ratio have no significance in distinguishing between CAA and NCAA. In the statistical analysis, there were significant differences between NCAA and CAA groups in terms of age (39.56 years vs 13,675 years), gender (48.1% male vs 71.4% male), WBC (13,891.10/mL vs 11,614.76/mL), CRP (27 mg/L vs 127 mg/L), albumin (3 g/dL vs 3 g/dL) and CRP/albumin (9.50 vs. 41). Conclusion: Thanks to the algorithm we created, CAA and NCAA distinction can be made quickly. In addition, by avoiding unnecessary surgical procedures in NCAA cases, patients' quality of life can be increased and morbidity rates can be minimized.(c) 2022 Elsevier Inc. All rights reserved.
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    A novel approach to distinguish complicated and non-complicated acute cholecystitis: Decision tree method
    (Lippincott Williams and Wilkins, 2023) Gojayev, Afig; Karakaya, Emre; Erkent, Murathan; Yücebaş, Sait Can; Aydın, Hüseyin Onur; Kavasoğlu, Lara; Aydoğan, Cem; Yıldırım, Sedat
    It is difficult to differentiate between non-complicated acute cholecystitis (NCAC) and complicated acute cholecystitis (CAC) preoperatively, which are two separate pathologies with different management. The aim of this study was to create an algorithm that distinguishes between CAC and NCAC using the decision tree method, which includes simple examinations. In this retrospective study, the patients were divided into 2 groups: CAC (149 patients) and NCAC (885 patients). Parameters such as patient demographic data, American Society of Anesthesiologists (ASA) score, Tokyo grade, comorbidity findings, white blood cell (WBC) count, neutrophil/lymphocyte ratio, C-reactive protein (CRP) level, albumin level, CRP/albumin ratio (CAR), and gallbladder wall thickness (GBWT) were evaluated. In this algorithm, the CRP value became a very important parameter in the distinction between NCAC and CAC. Age was an important predictive factor in patients with CRP levels >57 mg/L, and the critical value for age was 42. After the age factor, the important parameters in the decision tree were WBC and GBWT. In patients with a CRP value of ≤57 mg/L, GBWT is decisive and the critical value is 4.85 mm. Age, neutrophil/lymphocyte ratio, and WBC count were among the other important factors after GBWT. Sex, ASA score, Tokyo grade, comorbidity, CAR, and albumin value did not have an effect on the distinction between NCAC and CAC. In statistical analysis, significant differences were found groups in terms of gender (34.8% vs 51.7% male), ASA score (P < .001), Tokyo grade (P < .001), comorbidity (P < .001), albumin (4 vs 3.4 g/dL), and CAR (2.4 vs 38.4). By means of this algorithm, which includes low-cost examinations, NCAC and CAC distinction can be made easily and quickly within limited possibilities. Preoperative prediction of pathologies that are difficult to manage, such as CAC, can minimize patient morbidity and mortality.

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