Jorg Tobias, Halfmann Moritz C, Rölz Niklas, Mager René, Pinto Dos Santos Daniel, Düber Christoph, Mildenberger Peter, Müller Lukas
Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany.
Department of Urology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
Abdom Radiol (NY). 2023 Nov;48(11):3520-3529. doi: 10.1007/s00261-023-04006-9. Epub 2023 Jul 19.
To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports.
The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department's structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated.
The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36-62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy.
SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt.
通过对结构化放射学报告进行数据挖掘，研究尿路结石的流行病学及疾病特征分布。
从科室的结构化报告（SR）平台提取2028例尿路结石CT的结构化放射学报告内容。所研究队列代表了三级医疗中心的全部病例，包括大多数有症状的门诊患者和住院患者。计算尿路结石总体患病率以及肾结石和输尿管结石的患病率。计算年龄、性别、结石大小、密度和位置的分布情况，以及输尿管和肾结石的数量。对于输尿管结石，计算结石特征对可能的梗阻性肾病程度的影响。
在所研究队列中，尿路结石患病率为72%。其中，25%患有肾结石，40%患有输尿管结石，35%患有肾结石合并输尿管结石。性别分布为2.3:1（男:女）。患者年龄中位数为50岁（四分位间距36 - 62岁）。每位患者结石数量中位数为1个。结石大小中位数为4mm，密度中位数为734HU。患有输尿管结石的患者中，81%出现梗阻性肾病，其中二级肾病最为常见。结石特征对梗阻性肾病程度无影响。
基于SR的数据挖掘是一种简单方法，可用于获取所研究尿路结石患者队列的流行病学数据和疾病特征分布。这些额外信息可用于多种目的，如临床质量保证、辐射防护以及科学或经济研究。为从中受益，必须持续使用SR。然而，在临床常规工作中，SR的使用可能较为复杂，需要放射科医生进行调整。
