Risk prediction models for colorectal cancer in people with symptoms: a systematic review.
Risk prediction models for colorectal cancer in people with symptoms: a systematic review.
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DOI：
10.1186/s12876-016-0475-7
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发表时间：
2016-06-13
影响因子：
2.4
通讯作者：
Usher-Smith JA
中科院分区：
文献类型：
--
作者：
Williams TG;Cubiella J;Griffin SJ;Walter FM;Usher-Smith JA
结直肠癌（CRC）是欧洲和美国癌症相关死亡的第四大原因。在早期发现疾病可以改善预后。结合联合收割机多种危险因素和症状的风险预测模型有可能提高及时诊断。本综述的目的是系统地识别和比较有症状个体中预测原发性CRC风险的模型的性能。我们检索了Medline和EMBASE，以确定报告、验证或评估模型影响的主要研究。对于纳入，模型需要评估包括症状在内的风险因素的组合，提供模型性能的数据，并适用于一般人群。筛选纳入研究和数据提取由至少两名研究人员独立完成。从文献检索中识别出12808篇论文，通过引文检索识别出3篇论文。纳入了18篇描述15种风险模型的论文。9个是在初级保健人群中开发的，6个是在二级保健中开发的。其中四种在外部验证研究中具有良好的区分度（AUROC > 0.8），灵敏度和特异性范围从0.25和0.99到0.99和0.46，具体取决于所选择的截止值。在初级和二级保健人群中已开发出具有良好区分力的模型。大多数包含的变量是很容易获得的一个单一的咨询，但需要进一步的研究，以评估临床效用之前，他们被纳入实践。本文的在线版本（doi：10.1186/s12876-016-0475-7）包含补充材料，可供授权用户使用。
Colorectal cancer (CRC) is the fourth leading cause of cancer-related death in Europe and the United States. Detecting the disease at an early stage improves outcomes. Risk prediction models which combine multiple risk factors and symptoms have the potential to improve timely diagnosis. The aim of this review is to systematically identify and compare the performance of models that predict the risk of primary CRC among symptomatic individuals. We searched Medline and EMBASE to identify primary research studies reporting, validating or assessing the impact of models. For inclusion, models needed to assess a combination of risk factors that included symptoms, present data on model performance, and be applicable to the general population. Screening of studies for inclusion and data extraction were completed independently by at least two researchers. Twelve thousand eight hundred eight papers were identified from the literature search and three through citation searching. 18 papers describing 15 risk models were included. Nine were developed in primary care populations and six in secondary care. Four had good discrimination (AUROC > 0.8) in external validation studies, and sensitivity and specificity ranged from 0.25 and 0.99 to 0.99 and 0.46 depending on the cut-off chosen. Models with good discrimination have been developed in both primary and secondary care populations. Most contain variables that are easily obtainable in a single consultation, but further research is needed to assess clinical utility before they are incorporated into practice. The online version of this article (doi:10.1186/s12876-016-0475-7) contains supplementary material, which is available to authorized users.