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Updated: May 24, 2025

A Novel Method: Super-selective Adrenal Venous Sampling
Published on: September 15, 2017
Estimation of physiologic ability and surgical stress (E-PASS) predicts postoperative complications after
Mehmet Vehbi Kayra1, Mehmet Eflatun Deniz2, Cevahir Ozer2
1Faculty of Medicine, Department of Urology, Baskent University, Dadaloglu Mh Serinevler 2591 Sk No: 4/A Yuregir, 01250, Adana, Turkey. vehbikayra@hotmail.com.
Abstract:
The Estimation of Physiologic Ability and Surgical Stress (E-PASS) score, initially developed for gastrointestinal surgery, is a validated system used to predict postoperative complications by evaluating preoperative and intraoperative factors. This study aims to assess the effectiveness of the E-PASS score in predicting postoperative complications following adrenalectomy. In this single-center retrospective study, we analyzed data from 202 patients who underwent adrenalectomy by a single surgeon between January 2017 and March 2024. 182 patients with complete data and who met the study criteria were included in the study. Demographic, clinical, intraoperative, and postoperative data were collected and analyzed, including preoperative complaints, ASA classification, ECOG performance status, presence of systemic diseases, type of surgery, and intraoperative details, such as blood loss and complications. Postoperative complications were classified using the Clavien-Dindo Classification. The mean age of the patients was 48.7 ± 13.6 years. The mean BMI was 24.1 kg/m2. Postoperative complications were observed in 26.4% of patients, categorized as Grade 1 (54.1%), Grade 2 (25%), Grade 3 (16.7%), and Grade 4 (4.2%). Multivariate logistic regression identified higher BMI (OR = 1.394) and an E-PASS CRS score > - 0.0677 (OR = 6.17) as independent risk factors for complications. ROC curve analysis determined this CRS score cut-off with an AUC of 0.866 (CI 0.808-0.923; p < 0.001). The E-PASS scoring system effectively predicts postoperative complications in adrenalectomy. Its integration into clinical practice can enhance the identification of high-risk patients, optimize perioperative management, and potentially reduce adverse outcomes.
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