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Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.
Hou, Rui; Dou, Jingtao; Wu, Lijuan; Zhang, Xiaoyu; Li, Changwei; Wang, Weiqing; Gao, Zhengnan; Tang, Xulei; Yan, Li; Wan, Qin; Luo, Zuojie; Qin, Guijun; Chen, Lulu; Ji, Jianguang; He, Yan; Wang, Wei; Mu, Yiming; Zheng, Deqiang.
Affiliation
  • Hou R; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
  • Dou J; Beijing Center for Disease Prevention and Control, Beijing, China.
  • Wu L; Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Zhang X; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
  • Li C; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
  • Wang W; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
  • Gao Z; National Clinical Research Center for Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Chin
  • Tang X; Dalian Central Hospital, Dalian, Liaoning, China.
  • Yan L; First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Wan Q; Zhongshan University Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, China.
  • Luo Z; Southwest Medical University Affiliated Hospital, Luzhou, Sichuan, China.
  • Qin G; First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Chen L; First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Ji J; Wuhan Union Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • He Y; Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden.
  • Wang W; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
  • Mu Y; Centre for Precision Health, Edith Cowan University, Perth, Western Australia, Australia.
  • Zheng D; Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Diabetes Metab Res Rev ; 40(5): e3832, 2024 Jul.
Article in En | MEDLINE | ID: mdl-39031573
ABSTRACT

INTRODUCTION:

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.

METHODS:

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.

RESULTS:

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/.

CONCLUSIONS:

The proposed IPHDS could be a convenient and user-friendly screening tool for diabetes during health examinations in a large general population.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Blood Glucose / Machine Learning / Glucose Tolerance Test / Hyperglycemia Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: Asia Language: En Journal: Diabetes Metab Res Rev Journal subject: ENDOCRINOLOGIA / METABOLISMO Year: 2024 Type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Blood Glucose / Machine Learning / Glucose Tolerance Test / Hyperglycemia Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: Asia Language: En Journal: Diabetes Metab Res Rev Journal subject: ENDOCRINOLOGIA / METABOLISMO Year: 2024 Type: Article Affiliation country: China