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基于临床因素、血液化学和尿液分析的全面预测结石病:结石病评分。

Comprehensive prediction of urolithiasis based on clinical factors, blood chemistry and urinalysis: UROLITHIASIS score.

机构信息

Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06509, Republic of Korea.

出版信息

Sci Rep. 2023 Sep 9;13(1):14885. doi: 10.1038/s41598-023-42208-9.

DOI:10.1038/s41598-023-42208-9
PMID:37689768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10492849/
Abstract

Comprehensive prediction of urolithiasis using available factors obtained in the emergency department may aid in patient-centered diagnostic imaging decisions. This retrospective study analyzed the clinical factors, blood chemistry and urine parameters of patients who underwent nonenhanced urinary computed tomography for suspected urolithiasis. A scoring system was developed from a logistic regression model and was tested using the area under the curve (AUC). The prevalence of urolithiasis and important possible causes in the three risk subgroups were determined. Finally, the scoring model was validated. In the derivation cohort (n = 673), 566 patients were diagnosed with urolithiasis. Age > 35 years, history of urolithiasis, pain duration < 8 h, nausea/vomiting, costovertebral angle tenderness, serum creatinine ≥ 0.92 mg/dL, erythrocytes ≥ 10/high power field, no leukocytes ≤  + , and any crystalluria were retained in the final multivariable model and became part of the score. This scoring model demonstrated good discrimination (AUC 0.808 [95% CI, 0.776-0.837]). In the validation cohort (n = 336), the performance was similar (AUC 0.803 [95% CI, 0.756-0.844]), surpassing that of the STONE score (AUC 0.654 [95% CI, 0.601-0.705], P < 0.001). This scoring model successfully stratified patients according to the probability of urolithiasis. Further validation in various settings is needed.

摘要

利用急诊科获得的现有因素综合预测尿石症有助于针对患者的诊断影像学决策。本回顾性研究分析了因疑似尿石症而行非增强尿路 CT 检查的患者的临床因素、血液化学和尿液参数。从逻辑回归模型中开发了一个评分系统,并使用曲线下面积 (AUC) 进行了测试。确定了三个风险亚组中尿石症的患病率和重要的可能病因。最后,验证了评分模型。在推导队列 (n = 673) 中,566 名患者被诊断为尿石症。年龄 > 35 岁、尿路结石史、疼痛持续时间 < 8 小时、恶心/呕吐、肋脊角压痛、血清肌酐 ≥ 0.92 mg/dL、红细胞≥ 10/高倍视野、白细胞无≤ +、任何结晶尿保留在最终多变量模型中,并成为评分的一部分。该评分模型表现出良好的区分度(AUC 0.808 [95%CI,0.776-0.837])。在验证队列 (n = 336) 中,表现相似(AUC 0.803 [95%CI,0.756-0.844]),优于 STONE 评分(AUC 0.654 [95%CI,0.601-0.705],P < 0.001)。该评分模型成功地根据尿石症的概率对患者进行了分层。需要在不同环境中进一步验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/573c286bf1f4/41598_2023_42208_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/4cc29b066e35/41598_2023_42208_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/53b832e4c729/41598_2023_42208_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/573c286bf1f4/41598_2023_42208_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/4cc29b066e35/41598_2023_42208_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/53b832e4c729/41598_2023_42208_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d6/10492849/573c286bf1f4/41598_2023_42208_Fig3_HTML.jpg

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Neural Network Analysis of Crystalluria Content to Predict Urinary Stone Type.通过神经网络分析晶体尿成分以预测尿路结石类型
Res Rep Urol. 2021 Dec 29;13:867-876. doi: 10.2147/RRU.S322580. eCollection 2021.
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Discrepancy in results between dipstick urinalysis and urine sediment microscopy.试纸法尿液分析与尿沉渣显微镜检查结果之间的差异。
Exp Ther Med. 2021 May;21(5):538. doi: 10.3892/etm.2021.9971. Epub 2021 Mar 23.
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The Impact of Diet on Urinary Risk Factors for Cystine Stone Formation.饮食对胱氨酸结石形成的尿危险因素的影响。
Nutrients. 2021 Feb 6;13(2):528. doi: 10.3390/nu13020528.
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Validity of STONE Score in Clinical Prediction of Ureteral Stone Disease.STONE评分在输尿管结石疾病临床预测中的有效性
Pak J Med Sci. 2020 Nov-Dec;36(7):1693-1697. doi: 10.12669/pjms.36.7.2625.
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The comparison of two prediction models for ureteral stones: CHOKAI and STONE scores.两种输尿管结石预测模型的比较:CHOKAI评分与STONE评分。
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