Mbanya Vivian, Hussain Akhtar, Kengne Andre Pascal
Department of Community Medicine, University of Oslo, Oslo, Norway; Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1, Yaoundé, Cameroon.
Department of Community Medicine, University of Oslo, Oslo, Norway.
Prim Care Diabetes. 2015 Oct;9(5):317-29. doi: 10.1016/j.pcd.2015.04.004. Epub 2015 May 11.
Prediction algorithms are increasingly advocated in diabetes screening strategies, particularly in developing countries. We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa.
systematic review data sources A systematic search of English literatures in Medline via PubMed from 1999 until June, 2014. Study selection Included studies had to report on the development, validation or implementation of a model that was primarily constructed to predict prevalent undiagnosed diabetes using non-laboratory based predictors.
Data were extracted on the type of statistical model, type and range of predictors in the model, performance measures in both internal and external validation, and whether the model was developed from, validated or implemented in an African population.
Twenty-three studies reporting on non-invasive prevalent diabetes models were identified. Ten from Europe (some with multiethnic populations), nine models were developed among Asian population, two from the USA and two from the Middle-East. The c-statistics for these models ranged from 0.65 to 0.88 in the development studies, and from 0.63 to 0.80 in the validation studies. Twenty models were validated, and none in Africa. Among predictors commonly included in models, parental/family history of diabetes and personal history of hypertension appear to be more prone to measurement errors in the African context.
Existing prevalent diabetes prediction models have not been applied to African populations, and issues with the measurement of key predictors make their applicability likely inaccurate.
预测算法在糖尿病筛查策略中越来越受到提倡，尤其是在发展中国家。我们进行了一项系统评价，以评估现有的非侵入性糖尿病患病风险模型在非洲人群中的应用及适用性。
系统评价
通过PubMed对Medline中1999年至2014年6月的英文文献进行系统检索。
纳入的研究必须报告主要使用非基于实验室的预测指标构建的用于预测未诊断糖尿病患病率的模型的开发、验证或实施情况。
提取的数据包括统计模型类型、模型中预测指标的类型和范围、内部和外部验证中的性能指标，以及该模型是否在非洲人群中开发、验证或实施。
共识别出23项报告非侵入性糖尿病患病模型的研究。其中10项来自欧洲（部分涉及多民族人群），9项模型在亚洲人群中开发，2项来自美国，2项来自中东。在开发研究中，这些模型的c统计量范围为0.65至0.88，在验证研究中为0.63至0.80。20项模型得到验证，但均未在非洲进行验证。在模型中通常包含的预测指标中，糖尿病的父母/家族史和高血压个人史在非洲背景下似乎更容易出现测量误差。
现有的糖尿病患病预测模型尚未应用于非洲人群，关键预测指标的测量问题可能使其适用性不准确。
