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

IF 5.3 2区 医学 Q1 PSYCHIATRY
Martin Bernstorff, Lasse Hansen, Kenneth Enevoldsen, Jakob Damgaard, Frida Hæstrup, Erik Perfalk, Andreas Aalkjær Danielsen, Søren Dinesen Østergaard
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Abstract

BackgroundType 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 routine clinical 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 created 1343 potential predictors from 51 source variables, covering patient‐level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised 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 regularised 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.ResultsThe 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%).ConclusionA machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical 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.
开发和验证用于预测精神病患者 2 型糖尿病的机器学习模型
背景2型糖尿病(T2D)在精神病患者中的发病率约为普通人群的两倍,但可以通过早期干预生活方式、饮食或药物来预防。这种预防有赖于对高危人群的识别(预测)。本研究的目的是开发并验证一种机器学习模型,用于预测精神疾病患者的 T2D。共有 74880 名患者和 159 万次精神科服务接触被纳入分析。我们从 51 个源变量中创建了 1343 个潜在预测因子,涵盖患者层面的人口统计学、诊断、药物治疗和实验室结果等信息。T2D是指HbA1c≥48 mmol/mol、空腹血浆葡萄糖≥7.0 mmol/mol、口服葡萄糖耐量试验≥11.1 mmol/mol或随机血浆葡萄糖≥11.1 mmol/mol。基于 85% 的纳入联系人,训练了两个机器学习模型(XGBoost 和正则逻辑回归)来预测 T2D。结果XGBoost模型能在T2D发生前2.7年检测出高风险患者,接收者操作特征曲线下面积为0.84。在测试集中的 996 名罹患 T2D 的患者中,该模型至少对 305 人(31%)做出了阳性预测。基于该模型的决策支持系统可为预防这类高危人群患上终末期糖尿病的措施提供参考。
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来源期刊
Acta Psychiatrica Scandinavica
Acta Psychiatrica Scandinavica 医学-精神病学
CiteScore
11.20
自引率
3.00%
发文量
135
审稿时长
6-12 weeks
期刊介绍: Acta Psychiatrica Scandinavica acts as an international forum for the dissemination of information advancing the science and practice of psychiatry. In particular we focus on communicating frontline research to clinical psychiatrists and psychiatric researchers. Acta Psychiatrica Scandinavica has traditionally been and remains a journal focusing predominantly on clinical psychiatry, but translational psychiatry is a topic of growing importance to our readers. Therefore, the journal welcomes submission of manuscripts based on both clinical- and more translational (e.g. preclinical and epidemiological) research. When preparing manuscripts based on translational studies for submission to Acta Psychiatrica Scandinavica, the authors should place emphasis on the clinical significance of the research question and the findings. Manuscripts based solely on preclinical research (e.g. animal models) are normally not considered for publication in the Journal.
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