Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones
Abstract 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 patient...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50022-6 |
| _version_ | 1797377085350608896 |
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| author | Hiroki Ito Kentaro Sakamaki Tetsuo Fukuda Fukashi Yamamichi Takahiko Watanabe Tadashi Tabei Takaaki Inoue Junichi Matsuzaki Kazuki Kobayashi |
| author_facet | Hiroki Ito Kentaro Sakamaki Tetsuo Fukuda Fukashi Yamamichi Takahiko Watanabe Tadashi Tabei Takaaki Inoue Junichi Matsuzaki Kazuki Kobayashi |
| author_sort | Hiroki Ito |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T19:47:55Z |
| format | Article |
| id | doaj.art-fc1f0af933db4b7db5070cea08df2e5a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:55Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fc1f0af933db4b7db5070cea08df2e5a2023-12-24T12:17:23ZengNature PortfolioScientific Reports2045-23222023-12-011311810.1038/s41598-023-50022-6Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stonesHiroki Ito0Kentaro Sakamaki1Tetsuo Fukuda2Fukashi Yamamichi3Takahiko Watanabe4Tadashi Tabei5Takaaki Inoue6Junichi Matsuzaki7Kazuki Kobayashi8Department of Urology, Yokosuka Kyosai HospitalFaculty of Health Data Science, Juntendo UniversityDepartment of Urology, Ohguchi East General HospitalDepartment of Urology, Hara Genitourinary HospitalDepartment of Urology, Yokosuka Kyosai HospitalDepartment of Urology, Yokosuka Kyosai HospitalDepartment of Urology, Hara Genitourinary HospitalDepartment of Urology, Ohguchi East General HospitalDepartment of Urology, Yokosuka Kyosai HospitalAbstract 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.https://doi.org/10.1038/s41598-023-50022-6 |
| spellingShingle | Hiroki Ito Kentaro Sakamaki Tetsuo Fukuda Fukashi Yamamichi Takahiko Watanabe Tadashi Tabei Takaaki Inoue Junichi Matsuzaki Kazuki Kobayashi Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones Scientific Reports |
| title | Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones |
| title_full | Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones |
| title_fullStr | Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones |
| title_full_unstemmed | Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones |
| title_short | Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones |
| title_sort | models to predict the surgical outcome of mini ecirs endoscopic combined intrarenal surgery for renal and or ureteral stones |
| url | https://doi.org/10.1038/s41598-023-50022-6 |
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