Clinical management implications from 33 579 urinary stones: novel patterns in composition, comorbidities, seasonal variation, and machine learning-based urosepsis prediction
This study presents the largest comprehensive analysis of urinary stone compositions in Asia, revealing previously unreported patterns in comorbidity associations, seasonal variations, and location-specific characteristics. The novel machine learning-based prediction model for urosepsis risk represents a significant advance in clinical risk stratification for urolithiasis patients.
Authors
- Guohua Zeng (ORCID: https://orcid.org/0000-0002-3110-8933)
- Baoqing Sun (ORCID: https://orcid.org/0000-0002-1671-0723)
- Zhenglin Chang (ORCID: https://orcid.org/0000-0002-7083-6220)
- Xiaocong Pan
- Xiujing Han
- Haiyan Liu (ORCID: https://orcid.org/0000-0003-2799-5776)
- Weihong Lin
- Ming Lei (ORCID: https://orcid.org/0000-0001-9039-7774)
- Hui Huang (ORCID: https://orcid.org/0000-0001-6104-7660)
- Haojie Wu (ORCID: https://orcid.org/0000-0003-2470-9017)
- Han Zhao (ORCID: https://orcid.org/0000-0002-2743-5691)
Institutions
- Yangtze University (CN)
- Guangzhou Experimental Station (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1097/js9.0000000000002934
- Citations
- 11
- Primary Topic
- Urinary Tract Infections Management
- Type
- article
- Field-Weighted Citation Impact
- 14.90