由于成本高且复杂，口服糖耐量试验并未被采用作为识别糖尿病患者的筛查方法，导致对孤立性攻击后高血糖（IPH）患者，即正常空腹的患者的误诊。血糖 (<7.0 mmoL/L) 和餐后 2 小时血糖异常 (≥11.1 mmoL/L)。我们的目标是开发一个模型来区分 IPH 患者和正常人群。来自 54301 名合格参与者的数据来自中国糖尿病患者癌症风险评估：一项在中国进行的纵向 (REACTION) 研究。 37740 名参与者的数据被用来开发诊断系统。在 16561 名参与者中进行了外部验证。使用三种机器学习算法创建预测模型，并通过各种分类算法进一步评估，以建立最佳预测模型。选择十个特征来开发基于人工神经网络的IPH诊断系统（IPHDS）。在外部验证中，IPHDS的AUC为0.823（95％CI 0.811-0.836），显着高于台湾模型的AUC[0.799（0.786-0.813）]和中国糖尿病风险评分模型的AUC[0.648] (0.635-0.662)]。 IHDS模型的敏感性为75.6%，特异性为74.6%。该模型在亚组分析中优于台湾模型和 CDRS 模型。部署了一个具有即时预测功能的在线网站：https://app-iphds-e1fc405c8a69.herokuapp.com/。拟议的 IHDS 可以成为在广大普通人群的健康检查过程中方便且用户友好的糖尿病筛查工具。© 2024约翰·威利

Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (<7.0 mmoL/L) and abnormal 2-h postprandial blood glucose (≥11.1 mmoL/L). We aimed to develop a model to differentiate individuals with IPH from the normal population.Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model.Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811-0.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786-0.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635-0.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at https://app-iphds-e1fc405c8a69.herokuapp.com/.The proposed IPHDS could be a convenient and user-friendly screening tool for diabetes during health examinations in a large general population.© 2024 John Wiley & Sons Ltd.