Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting.
Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting.
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开发2型糖尿病的风险预测模型：方法论和报告的系统评价。
DOI：
10.1186/1741-7015-9-103
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发表时间：
2011-09-08
期刊：
影响因子：
9.3
通讯作者：
Yu LM
中科院分区：
文献类型：
--
作者：
Collins GS;Mallett S;Omar O;Yu LM
世界卫生组织估计，到2030年，将有大约3.5亿人患有2型糖尿病。与肾脏并发症、心脏病、中风和外周血管疾病相关，早期识别未确诊的2型糖尿病患者或患2型糖尿病风险增加的患者是一项重要挑战。我们试图系统地回顾和批判性地评估用于开发风险预测模型的方法的实施和报告，这些模型用于预测成人未诊断（流行）或未来发展（事件）2型糖尿病的风险。我们对PubMed和EMBASE数据库进行了系统性检索，以确定2011年5月之前发表的研究，这些研究描述了结合两个或多个变量预测流行或偶发2型糖尿病风险的模型的开发。我们提取了描述开发预测模型的关键信息，包括研究设计、样本量和事件数量、结局定义、风险预测因子选择和编码、缺失数据、模型构建策略和性能方面。纳入了39项研究，包括43个风险预测模型。17项研究（44%）报告了预测2型糖尿病发病的模型的发展，而15项研究（38%）描述了预测2型糖尿病流行的模型的推导。在9项研究（23%）中，每个变量的事件数量小于10，而在14项研究中，报告的信息不足以计算该指标。候选风险预测因子的数量从4到64不等，在7项研究中，不清楚有多少风险预测因子被考虑在内。在8项研究（21%）中，不推荐使用单变量筛选的统计学显著性选择纳入多变量模型的风险预测因子的方法，而在10项研究（26%）中，选择程序尚不清楚。21个风险预测模型（49%）是通过对所有连续风险预测因子进行分类而开发的。16项研究（41%）未报告缺失数据的治疗和处理。我们发现，广泛使用的不良方法可能会危及模型的开发，包括单变量预筛选变量，连续风险预测因子的分类和缺失数据的处理不当。使用差的方法会影响预测模型的可靠性，并最终损害未诊断的2型糖尿病的概率估计或发展为2型糖尿病的预测风险的准确性。此外，许多研究的特点是报告水平普遍较差，许多客观判断模型有用性的关键细节往往被忽略。
The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults. We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance. Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%). We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.
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预测糖尿病：临床，生物学和遗传方法：来自胰岛素抵抗综合征（DESIR）的流行病学研究的数据。
影响因子：
16.2
作者：
Balkau, Beverley;Lange, Celine;Fezeu, Leopold;Tichet, Jean;De Lauzon-Guillain, Blandine;Cernichow, Sebastien;Fumeron, Frederic;Froguel, Philippe;Vaxillaire, Martine;Cauchi, Stephane;Ducimetiere, Pierre;Eschwege, Eveline
通讯作者：
Eschwege, Eveline
影响因子：
2.4
作者：
Altman, Douglas G.
通讯作者：
Altman, Douglas G.
影响因子：
8.2
作者：
Chien, K.;Cai, T.;Hu, F. B.
通讯作者：
Hu, F. B.
影响因子：
16.2
作者：
Baan, CA;Ruige, JB;Feskens, EJM
通讯作者：
Feskens, EJM
影响因子：
--
作者：
Bossuyt, PM;Reitsma, JB;de Vet, HCE
通讯作者：
de Vet, HCE