Health Economics Group, College of Medicine and Health, University of Exeter Medical School, Exeter, UK.
Exeter Test Group, College of Medicine and Health, University of Exeter Medical School, Exeter, UK.
Health Technol Assess. 2020 Nov;24(66):1-332. doi: 10.3310/hta24660.
Tools based on diagnostic prediction models are available to help general practitioners diagnose cancer. It is unclear whether or not tools expedite diagnosis or affect patient quality of life and/or survival.
The objectives were to evaluate the evidence on the validation, clinical effectiveness, cost-effectiveness, and availability and use of cancer diagnostic tools in primary care.
Two systematic reviews were conducted to examine the clinical effectiveness (review 1) and the development, validation and accuracy (review 2) of diagnostic prediction models for aiding general practitioners in cancer diagnosis. Bibliographic searches were conducted on MEDLINE, MEDLINE In-Process, EMBASE, Cochrane Library and Web of Science) in May 2017, with updated searches conducted in November 2018. A decision-analytic model explored the tools' clinical effectiveness and cost-effectiveness in colorectal cancer. The model compared patient outcomes and costs between strategies that included the use of the tools and those that did not, using the NHS perspective. We surveyed 4600 general practitioners in randomly selected UK practices to determine the proportions of general practices and general practitioners with access to, and using, cancer decision support tools. Association between access to these tools and practice-level cancer diagnostic indicators was explored.
Systematic review 1 - five studies, of different design and quality, reporting on three diagnostic tools, were included. We found no evidence that using the tools was associated with better outcomes. Systematic review 2 - 43 studies were included, reporting on prediction models, in various stages of development, for 14 cancer sites (including multiple cancers). Most studies relate to QCancer (ClinRisk Ltd, Leeds, UK) and risk assessment tools.
In the absence of studies reporting their clinical outcomes, QCancer and risk assessment tools were evaluated against faecal immunochemical testing. A linked data approach was used, which translates diagnostic accuracy into time to diagnosis and treatment, and stage at diagnosis. Given the current lack of evidence, the model showed that the cost-effectiveness of diagnostic tools in colorectal cancer relies on demonstrating patient survival benefits. Sensitivity of faecal immunochemical testing and specificity of QCancer and risk assessment tools in a low-risk population were the key uncertain parameters.
Practitioner- and practice-level response rates were 10.3% (476/4600) and 23.3% (227/975), respectively. Cancer decision support tools were available in 83 out of 227 practices (36.6%, 95% confidence interval 30.3% to 43.1%), and were likely to be used in 38 out of 227 practices (16.7%, 95% confidence interval 12.1% to 22.2%). The mean 2-week-wait referral rate did not differ between practices that do and practices that do not have access to QCancer or risk assessment tools (mean difference of 1.8 referrals per 100,000 referrals, 95% confidence interval -6.7 to 10.3 referrals per 100,000 referrals).
There is little good-quality evidence on the clinical effectiveness and cost-effectiveness of diagnostic tools. Many diagnostic prediction models are limited by a lack of external validation. There are limited data on current UK practice and clinical outcomes of diagnostic strategies, and there is no evidence on the quality-of-life outcomes of diagnostic results. The survey was limited by low response rates.
The evidence base on the tools is limited. Research on how general practitioners interact with the tools may help to identify barriers to implementation and uptake, and the potential for clinical effectiveness.
Continued model validation is recommended, especially for risk assessment tools. Assessment of the tools' impact on time to diagnosis and treatment, stage at diagnosis, and health outcomes is also recommended, as is further work to understand how tools are used in general practitioner consultations.
This study is registered as PROSPERO CRD42017068373 and CRD42017068375.
This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in ; Vol. 24, No. 66. See the NIHR Journals Library website for further project information.
已有基于诊断预测模型的工具可帮助全科医生诊断癌症。但目前尚不清楚这些工具是否能加快诊断速度,或者是否会影响患者的生活质量和/或生存。
评估初级保健中癌症诊断工具的验证、临床效果、成本效益、可及性和使用情况的证据。
进行了两项系统评价,以检查临床效果(综述 1)和诊断预测模型的开发、验证和准确性(综述 2),以帮助全科医生进行癌症诊断。2017 年 5 月在 MEDLINE、MEDLINE In-Process、EMBASE、Cochrane 图书馆和 Web of Science 上进行了文献检索,并于 2018 年 11 月进行了更新检索。决策分析模型探讨了这些工具在结直肠癌中的临床效果和成本效益。该模型比较了使用和不使用这些工具的策略下患者的结局和成本,使用了 NHS 视角。我们对随机选择的英国实践中的 4600 名全科医生进行了调查,以确定具有和不具有癌症决策支持工具的实践比例和全科医生比例。并探讨了获得这些工具与实践级癌症诊断指标之间的关系。
综述 1-纳入了 5 项不同设计和质量的研究,报告了 3 种诊断工具,我们没有发现使用这些工具与更好的结局相关的证据。综述 2-纳入了 43 项研究,报告了用于 14 个癌症部位(包括多种癌症)的处于不同开发阶段的预测模型。大多数研究与 QCancer(ClinRisk Ltd,利兹,英国)和风险评估工具有关。
由于没有报告其临床结局的研究,所以对 QCancer 和风险评估工具进行了评估,评估标准是粪便免疫化学检测。使用了一种链接数据方法,将诊断准确性转化为诊断和治疗的时间以及诊断时的阶段。鉴于目前缺乏证据,该模型表明,在结直肠癌中诊断工具的成本效益取决于是否能证明患者的生存获益。粪便免疫化学检测的敏感性、QCancer 和风险评估工具在低风险人群中的特异性是关键的不确定参数。
从业者和实践的应答率分别为 10.3%(476/4600)和 23.3%(227/975)。83 个实践中有癌症决策支持工具(36.6%,95%置信区间 30.3%至 43.1%),其中 38 个实践可能会使用这些工具(16.7%,95%置信区间 12.1%至 22.2%)。有和没有 QCancer 或风险评估工具的实践之间的 2 周等待转诊率没有差异(每 100000 例转诊相差 1.8 例转诊,95%置信区间每 100000 例转诊相差-6.7 至 10.3 例转诊)。
关于诊断工具的临床效果和成本效益,证据很少。许多诊断预测模型受到缺乏外部验证的限制。目前关于英国实践和诊断策略临床结局的数据有限,并且没有关于诊断结果生活质量结局的证据。调查的应答率较低。
该工具的证据基础有限。研究全科医生如何与这些工具相互作用,可能有助于确定实施和采用的障碍,以及潜在的临床效果。
建议继续对模型进行验证,特别是对风险评估工具。还建议评估这些工具对诊断和治疗时间、诊断时的阶段以及健康结局的影响,进一步研究工具在全科医生咨询中的使用情况。
本研究在 PROSPERO 注册,注册号为 CRD42017068373 和 CRD42017068375。
本项目由英国国家卫生研究院(NIHR)健康技术计划资助,全文将在《英国医学杂志》系列杂志上发表。