Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness

Author:

Bernstorff MartinORCID,Hansen LasseORCID,Enevoldsen KennethORCID,Damgaard JakobORCID,Hæstrup FridaORCID,Perfalk ErikORCID,Danielsen Andreas AalkjærORCID,Østergaard Søren DinesenORCID

Abstract

AbstractBackgroundType 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness.MethodsThe study was based on routinely collected data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74.880 patients with 1.59 million psychiatric service contacts were included in the analyses. We included 1343 potential predictors covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalized as HbA1c ≥48 mmol/mol, fasting plasma glucose >7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularized logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts.FindingsThe XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%).InterpretationA machine learning model can accurately predict development of T2D among patients with mental illness based on routinely collected data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.FundingThe Lundbeck Foundation, the Central Denmark Region Fund for Strengthening of Health Science and the Danish Agency for Digitisation Investment Fund for New Technologies.Research in contextEvidence before this studyWe searched Pubmed for relevant studies regardless of time of publication using the search query “predict*” AND Diabetes Mellitus, Type 2 [Mesh] AND Mental Disorders [Mesh] AND Patients [Mesh]. We did not identify any studies developing T2D prediction models for patients with mental illness.Added value of this studyTo the best of our knowledge, this study is the first to develop and validate a machine learning model for prediction of T2D among patients with mental illness. The developed model is sensitive and specific - and detects patients at high risk 2.7 years before T2D. Notably, as only routinely collected data from electronic health records were used in the training of the model training, it can be assumed to have similar predictive performance if implemented in clinical practice. This study adds value by offering a T2D prediction model tailored specifically to patients with mental illness, which may facilitate early intervention and prevention strategies.Implications of all the available evidenceThe findings of this study, combined with the absence of existing T2D prediction models for patients with mental illness in the literature, offer a new possibility for identifying and potentially preventing T2D in a high-risk population. Specifically, implementing such a system in clinical practice may inform targeted interventions, such as lifestyle modifications (e.g., exercise and diet) and pharmacological treatment, to reduce the risk of T2D.

Publisher

Cold Spring Harbor Laboratory

Reference30 articles.

1. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants - The Lancet. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)00618-8/fulltext.

2. Type 2 diabetes and quality of life;World J. Diabetes,2017

3. The High Prevalence of Poor Physical Health and Unhealthy Lifestyle Behaviours in Individuals with Severe Mental Illness

4. Pharmacological treatment of bipolar disorder and risk of diabetes mellitus: A nationwide study of 30,451 patients

5. Preventing Diabetes: Early Versus Late Preventive Interventions

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3