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폐경 후 부인암 환자의 수술 전 노쇠와 수술 후 건강 결과: 후향적 단면 조사 연구. (Korean)

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  • معلومة اضافية
    • Alternate Title:
      Preoperative frailty and postoperative health outcomes in postmenopausal patients undergoing gynecological cancer surgery: a retrospective observational study. (English)
    • نبذة مختصرة :
      Purpose: This study aimed to assess preoperative frailty using the Frailty Index–Laboratory (FI-LAB) in postmenopausal patients undergoing gynecological cancer surgery and to investigate the association between frailty level and postoperative health outcomes. Methods: A retrospective review was conducted of medical records from 198 postmenopausal women who underwent gynecologic cancer surgery at Gyeongsang National University Hospital between January 2018 and December 2024. Frailty was evaluated using the FI-LAB, and postoperative outcomes included recovery room stay, hospital stay, transfusion, and complications. Data were analyzed using analysis of variance and the chi-square test. Results: Higher frailty levels were associated with older age (p = .030), cancer type (p < .001) and stage (p = .001), American Society of Anesthesiologists Physical Status Classification class (p = .036), and longer anesthesia (p = .023) and surgery time (p = .026). Among health outcomes, patients with higher frailty scores had longer hospital stays (F = 4.26, p = .015) and higher transfusion rates (χ² = 11.78, p = .005). Conclusion: Preoperative frailty assessed by FI-LAB may serve as a useful predictor of postoperative outcomes in patients undergoing gynecologic cancer surgery. Early identification of frailty could enable targeted perioperative interventions to improve recovery and support the integration of frailty screening into routine preoperative evaluation. [ABSTRACT FROM AUTHOR]
    • نبذة مختصرة :
      Copyright of Journal of Korean Biological Nursing Science is the property of Korean Society of Biological Nursing Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)