Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm

IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS
Prof Rhian Daniel PhD , Hywel Jones PGDip , Prof John W Gregory MD , Ambika Shetty MD , Prof Nick Francis PhD , Prof Shantini Paranjothy PhD , Julia Townson PhD
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引用次数: 0

Abstract

Background

Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with diabetic ketoacidosis, a proportion unchanged over 25 years. Our aim was to investigate whether a machine-learning algorithm could lead to earlier detection of type 1 diabetes in primary care.

Methods

We developed the predictive algorithm using Welsh primary care electronic health records (EHRs) linked to the Brecon Dataset, a register of children newly diagnosed with type 1 diabetes. Children were included from their first primary care record within the study period of Jan 1, 2000, to Dec 31, 2016, until either type 1 diabetes diagnosis, they turned 15 years of age, or study end. We developed an ensemble learner (SuperLearner) using 26 potential predictors. Validation of the algorithm was done in English EHRs from the Clinical Practice Research Datalink (primary care) and Hospital Episode Statistics, focusing on the ability of the algorithm to identify children who went on to develop type 1 diabetes and the time by which diagnosis could be anticipated.

Findings

The development dataset comprised 34 754 400 primary care contacts, relating to 952 402 children, and the validation dataset comprised 43 089 103 primary care contacts, relating to 1 493 328 children. Of these, 1829 (0·19%) children younger than 15 years in the development dataset, and 1516 (0·10%) in the validation dataset had a reliable date of type 1 diabetes diagnosis. If set to give an alert in 10% of contacts, an estimated 71·6% (95% CI 68·8–74·4) of the children with type 1 diabetes would receive an alert by the algorithm in the 90 days before diagnosis, with diagnosis anticipated, on average, by an estimated 9·34 days (95% CI 7·77–10·9).

Interpretation

If implemented into primary care settings, this predictive algorithm could substantially reduce the proportion of patients with new-onset type 1 diabetes presenting in diabetic ketoacidosis. Acceptability of alert thresholds should be explored in primary care.

Funding

Diabetes UK.

利用英国初级医疗电子健康记录预测儿童 1 型糖尿病:机器学习算法的开发与验证
背景在初级医疗机构就诊的疑似 1 型糖尿病患儿应立即转诊至二级医疗机构,以避免发生危及生命的糖尿病酮症酸中毒。然而,早期识别儿童 1 型糖尿病患者具有挑战性。儿童可能没有典型的症状,或者症状可能被归因于更常见的疾病。四分之一的儿童会出现糖尿病酮症酸中毒,这一比例在 25 年间没有变化。我们的目的是研究机器学习算法是否能在初级医疗中更早地发现 1 型糖尿病。方法我们利用威尔士初级医疗电子健康记录(EHR)与布雷肯数据集(Brecon Dataset)(新诊断为 1 型糖尿病的儿童登记册)的链接开发了预测算法。从 2000 年 1 月 1 日到 2016 年 12 月 31 日的研究期间内的第一份初级保健记录开始纳入儿童,直到确诊为 1 型糖尿病、年满 15 岁或研究结束。我们使用 26 个潜在预测因子开发了一个集合学习器(SuperLearner)。我们在临床实践研究数据链(初级保健)和医院病历统计的英文电子病历中对该算法进行了验证,重点关注该算法识别儿童发展为1型糖尿病的能力,以及预计诊断的时间。研究结果开发数据集包括34 754 400个初级保健接触,涉及952 402名儿童;验证数据集包括43 089 103个初级保健接触,涉及1 493 328名儿童。其中,开发数据集中有 1829 名(0-19%)小于 15 岁的儿童,验证数据集中有 1516 名(0-10%)儿童有可靠的 1 型糖尿病诊断日期。如果设定在10%的接触中发出警报,估计71-6%(95% CI 68-8-74-4)的1型糖尿病患儿会在诊断前90天收到该算法发出的警报,平均预计诊断时间为9-34天(95% CI 7-77-10-9)。应在初级保健中探讨警报阈值的可接受性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
41.20
自引率
1.60%
发文量
232
审稿时长
13 weeks
期刊介绍: The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health. The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health. We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.
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