The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge.
University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge.
Br J Gen Pract. 2021 Dec 31;72(714):e11-e18. doi: 10.3399/BJGP.2021.0319. Print 2022 Jan.
Timely diagnosis of bladder and kidney cancer is key to improving clinical outcomes. Given the challenges of early diagnosis, models incorporating clinical symptoms and signs may be helpful to primary care clinicians when triaging at-risk patients.
To identify and compare published models that use clinical signs and symptoms to predict the risk of undiagnosed prevalent bladder or kidney cancer.
Systematic review.
A search identified primary research reporting or validating models predicting the risk of bladder or kidney cancer in MEDLINE and EMBASE. After screening identified studies for inclusion, data were extracted onto a standardised form. The risk models were classified using TRIPOD guidelines and evaluated using the PROBAST assessment tool.
The search identified 20 661 articles. Twenty studies (29 models) were identified through screening. All the models included haematuria (visible, non-visible, or unspecified), and seven included additional signs and symptoms (such as abdominal pain). The models combined clinical features with other factors (including demographic factors and urinary biomarkers) to predict the risk of undiagnosed prevalent cancer. Several models ( = 13) with good discrimination (area under the receiver operating curve >0.8) were identified; however, only eight had been externally validated. All of the studies had either high or unclear risk of bias.
Models were identified that could be used in primary care to guide referrals, with potential to identify lower-risk patients with visible haematuria and to stratify individuals who present with non-visible haematuria. However, before application in general practice, external validations in appropriate populations are required.
及时诊断膀胱癌和肾癌对于改善临床结局至关重要。鉴于早期诊断的挑战，在对高危患者进行分诊时，纳入临床症状和体征的模型可能有助于基层医疗临床医生。
确定并比较使用临床体征和症状来预测未确诊的常见膀胱癌或肾癌风险的已发表模型。
系统评价。
检索 MEDLINE 和 EMBASE 中报告或验证预测膀胱癌或肾癌风险的原始研究。在对纳入研究进行筛选后，将数据提取到标准表格上。使用 TRIPOD 指南对风险模型进行分类，并使用 PROBAST 评估工具进行评估。
检索到 20661 篇文章。通过筛选确定了 20 项研究（29 个模型）。所有模型均纳入血尿（可见、不可见或未特指），其中 7 个模型纳入了其他体征和症状（如腹痛）。这些模型将临床特征与其他因素（包括人口统计学因素和尿液生物标志物）相结合，以预测未确诊的常见癌症风险。确定了一些具有良好区分度（接受者操作特征曲线下面积>0.8）的模型（n=13）；然而，仅有 8 个模型进行了外部验证。所有研究的偏倚风险均较高或不明确。
已经确定了一些可以在基层医疗中用于指导转诊的模型，这些模型可能有助于识别有可见性血尿的低风险患者，并对出现非可见性血尿的个体进行分层。然而，在一般实践中应用之前，需要在适当的人群中进行外部验证。
