Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK.
BMJ. 2011 Nov 28;343:d7163. doi: 10.1136/bmj.d7163.
To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.
Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes.
Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact.
Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.
8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.
Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.
评估 2 型糖尿病的现有风险模型和评分，并为其在实践中的选择和应用提供信息。
系统评价采用标准（定量）和现实主义（主要是定性）方法。纳入标准：用任何语言描述模型和评分开发或外部验证，或两者兼有的论文，以预测成年人患 2 型糖尿病的风险。
检索了 Medline、PreMedline、Embase 和 Cochrane 数据库。纳入的研究在 Google Scholar 中进行了引文追踪，以确定可用性或影响的后续研究。
对模型的统计特性、内部或外部验证的细节以及风险评分在开发它们的研究之外的使用情况进行了数据提取。定量数据被制成表格，以比较模型成分和统计特性。定性数据进行了主题分析，以确定使用风险模型或评分可能改善患者结局的机制。
扫描了 8864 个标题，考虑了 115 篇全文论文，并最终纳入了 43 篇论文。这些论文描述了 145 个风险预测模型和评分的前瞻性开发或验证，其中 94 个在此处进行了详细研究。它们已经在 688 万参与者身上进行了测试，随访时间长达 28 年。主要研究的异质性排除了荟萃分析。一些（但不是全部）风险模型或评分具有稳健的统计特性（例如，良好的区分度和校准），并且已经在不同的人群中进行了外部验证。遗传标记在临床和社会人口因素之外对模型没有任何贡献。大多数作者将他们的评分描述为“简单”或“易于实施”，尽管很少有人具体说明预期的用户和在什么情况下使用。确定了 10 种机制，通过测量糖尿病风险可能改善结局。作为旨在降低实际风险的干预措施的一部分应用风险评分的后续研究很少。
已经做了大量工作来开发糖尿病风险模型和评分，但由于需要常规检查以外的检查或由于开发时没有具体的用户或明确的用途，大多数模型和评分很少使用。令人鼓舞的是，最近的研究已经开始解决可用性和糖尿病风险评分的影响。进一步研究的两个有前途的领域是提示非专业人员检查自己的糖尿病风险的干预措施，以及在人群数据集上使用风险评分来确定有针对性的公共卫生干预措施的高风险“热点”。
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