Journal Article

Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones

Hiroki Ito, Takahiko Watanabe, Fukashi Yamamichi, Junichi Matsuzaki, Takaaki Inoue, Tadashi Tabei, Kazuki Kobayashi, Tetsuo Fukuda, Kentaro Sakamaki

2023

Overview

To establish a safer and more efficient treatment strategy with mini-endoscopic combined intrarenal surgery (ECIRS), the present study aimed to develop models to predict the outcomes of mini-ECIRS in patients with renal and/or ureteral stones. We retrospectively analysed consecutive patients with renal and/or ureteral stones who underwent mini-ECIRS at three Japanese tertiary institutions. Final treatment outcome was evaluated by CT imaging at 1 month postoperatively and stone free (SF) was defined as completely no residual stone or residual stone fragments ≤ 2 mm. Three prognostic models (multiple logistic regression, classification tree analysis, and machine learning-based random forest) were developed to predict surgical outcomes using preoperative clinical factors. Clinical data from 1432 ECIRS were pooled from a database registered at three institutions, and 996 single sessions of mini-ECIRS were analysed in this study. The overall SF rate was 62.3%. The multiple logistic regression model consisted of stone burden ( P  < 0.001), number of involved calyces ( P  < 0.001), nephrostomy prior to mini-ECIRS ( P  = 0.091), and ECOG-PS ( P  = 0.110), wherein the area under the curve (AUC) was 70.7%. The classification tree analysis consisted of the number of involved calyces with an AUC of 61.7%. The random forest model showed that the top predictive variable was the number of calyces involved, with an AUC of 91.9%. Internal validation revealed that the AUCs for the multiple logistic regression model, classification tree analysis and random forest models were 70.4, 69.6 and 85.9%, respectively. The number of involved calyces, and a smaller stone burden implied a SF outcome. The machine learning-based model showed remarkably high accuracy and may be a promising tool for physicians and patients to obtain proper consent, avoid inefficient surgery, and decide preoperatively on the most efficient treatment strategies, including staged mini-ECIRS.

• Publisher: Springer Science and Business Media LLC,Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 692/308 / 692/4025 / 692/499 / Classification / Endoscopy / Humanities and Social Sciences / Kidney stones / Kidneys / Learning algorithms / Machine learning / Medicine / multidisciplinary / Patients / Q / R / Regression analysis / Science / Science (multidisciplinary) / Surgery / Surgical outcomes