ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1528834
Predicting Day-One Mobility in Partial Nephrectomy Patients Using Preoperative and Intraoperative Clinical Parameters
Provisionally accepted- Sun Yat-sen Memorial Hospital, Guangzhou, China
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To identify key factors influencing early postoperative ambulation in patients undergoing partial nephrectomy for renal tumors and to construct a predictive model for day-one ambulation based on these factors.This retrospective study analyzed 137 patients who underwent partial nephrectomy for renal tumors at the Department of Urology, Sun Yat-sen Memorial Hospital, between October 2020 and June 2023. Patients were randomly divided into a training set (n=97) and a test set (n=40) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted to evaluate potential risk factors influencing postoperative ambulation.Of the 137 patients, 116 were able to ambulate on the first postoperative day. Significant factors associated with early postoperative ambulation included age, hypertension, tumor size, serum cystatin C, blood urea nitrogen, renal artery clamping time, and intraoperative blood loss. A predictive model was constructed based on age, tumor size, and intraoperative blood loss, demonstrating strong accuracy with areas under the receiver operating characteristic (ROC) curve of 0.902 in the training set and 0.975 in the test set. Bootstrap calibration curves confirmed the model's predictive accuracy, and decision curve analysis (DCA) demonstrated a substantial clinical benefit.Age, tumor size, and intraoperative blood loss are key predictors of day-one ambulation in patients undergoing partial nephrectomy. This predictive model provides clinicians with a reliable tool for assessing early postoperative mobility, supporting enhanced recovery protocols and improving patient outcomes.
Keywords: Renal tumor, Partial Nephrectomy, Postoperative day-one ambulation, Enhanced recovery, predictive model
Received: 15 Nov 2024; Accepted: 22 Apr 2025.
Copyright: © 2025 Xu, Zhang, Mo, Liu, Peng and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Meijuan Xu, Sun Yat-sen Memorial Hospital, Guangzhou, China
Xuexia Ma, Sun Yat-sen Memorial Hospital, Guangzhou, China
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