ArticleUrolithiasis2024
Establishment and application of a nomogram diagram for predicting calcium oxalate stones in patients with urinary tract stones.
Article in Urolithiasis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 2 citations in OpenAlex.
- Article
- Machine learning-driven multi-omics integration of urinary organic acids and ions enables precision risk stratification for calcium oxalate nephrolithiasis.Frontiers in medicine · 2026Article
- Identification of Key Genes Associated with Endoplasmic Reticulum Stress in Calcium Oxalate Kidney Stones.Genes · 2025Article
- Construction and validation of a urinary stone composition prediction model based on machine learning.Urolithiasis · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
This retrospective study aims to examine the correlation between calcium oxalate (CaOx) stones and common clinical tests, as well as urine ionic composition. Additionally, we aim to develop and implement a personalized model to assess the accuracy and feasibility of using charts to predict calcium oxalate stones in patients with urinary tract stones. A retrospective analysis was conducted on data from 960 patients who underwent surgery for urinary stones at the First Affiliated Hospital of Soochow University from January 1, 2010, to December 31, 2022. Among these patients, 447 were selected for further analysis based on screening criteria. Multivariate logistic regression analysis was then performed to identify the best predictive features for calcium oxalate stones from the clinical data of the selected patients. A prediction model was developed using these features and presented in the form of a nomogram graph. The performance of the prediction model was assessed using the C-index, calibration curve, and decision curve, which evaluated its discriminative power, calibration, and clinical utility, respectively. The nomogram diagram prediction model developed in this study is effective in predicting calcium oxalate stones which is helpful in screening and early identification of high-risk patients with calcium oxalate urinary tract stones, and may be a guide for urologists in making clinical treatment decisions.
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Registered trials
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