Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan.
Department of Advanced Transplant and Regenerative Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan.
Int J Urol. 2020 Apr;27(4):344-349. doi: 10.1111/iju.14203. Epub 2020 Mar 8.
To develop and validate a nomogram predicting the occurrence of a stone episode, given the lack of such predicting risk tools for urolithiasis.
We retrospectively analyzed 1305 patients with urolithiasis and 2800 community-dwelling individuals who underwent a comprehensive health survey. The STone Episode Prediction nomogram was created based on data from the medical records of 600 patients with urolithiasis and 1300 controls, and was validated using a different population of 705 patients with urolithiasis and 1500 controls. Logistic regression analysis was used to construct a model to predict the potential candidate for a stone episode. The predictive ability of the model was evaluated using the results of the area under the receiver operating characteristics curve (area under the curve).
Age, sex, diabetes mellitus, renal function, serum albumin, and serum uric acid were found to be significantly associated with urolithiasis in the training set and were included in the STone Episode Prediction nomogram. The optimal cut-off value for the probability of a stone episode using the nomogram was >28% with a sensitivity of 79%, a specificity of 76%, and area under the curve of 0.860. In the validation test, area under the curve for the detection of urolithiasis was 0.815 with a sensitivity of 81% and specificity of 63%.
Herein, we developed and validated the STone Episode Prediction nomogram that can predict a potential candidate for an episode of urolithiasis. This nomogram might be beneficial for the first step in stone screening in individuals with lifestyle-related diseases.
由于缺乏预测肾结石风险的工具，我们旨在开发和验证一种预测结石发作的列线图。
我们回顾性分析了 1305 例肾结石患者和 2800 例社区居民的资料。列线图是基于 600 例肾结石患者和 1300 例对照者的病历资料以及另外 705 例肾结石患者和 1500 例对照者的队列研究资料建立的，并进行验证。使用逻辑回归分析构建预测结石发作的潜在候选模型。使用受试者工作特征曲线下面积（AUC）评估模型的预测能力。
在训练集中，年龄、性别、糖尿病、肾功能、血清白蛋白和血清尿酸与肾结石显著相关，且被纳入 STone Episode Prediction 列线图。使用该列线图预测结石发作的概率的最佳截断值为>28%，其灵敏度为 79%，特异性为 76%，AUC 为 0.860。在验证试验中，检测肾结石的 AUC 为 0.815，灵敏度为 81%，特异性为 63%。
我们开发和验证了一种可预测结石发作的潜在候选者的 STone Episode Prediction 列线图。该列线图可能有助于对有生活方式相关疾病的个体进行结石筛查的第一步。
