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Machine learning to predict type 1 diabetes in children 用机器学习预测儿童 1 型糖尿病
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00098-0
The Lancet Digital Health
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引用次数: 0
Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study 基于国民健康数据的多癌症风险分层:一项回顾性建模和验证研究
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00062-1
Alexander W Jung PhD , Peter C Holm MSc , Kumar Gaurav PhD , Jessica Xin Hjaltelin PhD , Davide Placido PhD , Prof Laust Hvas Mortensen PhD , Prof Ewan Birney PhD , Prof S⊘ren Brunak PhD , Prof Moritz Gerstung PhD

Background

Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank.

Methods

In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16–86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16–75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50–75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance.

Findings

From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65–0·67] for cervix uteri cancer to 0·91 [0·90–0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44–0·66] for cervix uteri cancer to 0·78 [0·77–0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagn

背景医疗保健正经历着数字化进程,许多国家正在实施国家健康数据资源。虽然存在一系列癌症风险模型,但在人群层面对不同癌症类型进行风险分层的实用性尚未得到充分探讨。在这项回顾性建模和验证研究中,用于模型开发和内部验证的数据来自以下丹麦健康登记处:中央人员登记处、丹麦全国患者登记处、死亡登记处、癌症登记处,以及首都地区二级医疗记录的全文医疗记录。开发数据包括 1995 年 1 月 1 日至 2014 年 12 月 31 日期间年龄在 16-86 岁之间、既往未患恶性癌症的成年人。内部验证期为2015年1月1日至2018年4月10日,数据包括2014年12月31日年龄在16-75岁之间、既往无癌症指征的所有成年人。来自英国生物库的外部验证队列包括所有既往没有癌症指征的 50-75 岁成年人。我们根据丹麦人的综合病史建立了随时间变化的贝叶斯 Cox 危险模型。我们从现有的临床疾病轨迹、文本挖掘的基本健康因素和家族病史中提取了 1392 个协变量,用于训练 20 种主要癌症类型的预测模型。这些模型在 2015 年至 2018 年期间丹麦各地的癌症发病率和英国生物库中的个人身上进行了验证。主要结果是区分度和校准性能。研究结果我们从丹麦登记册中纳入了 6 732 553 人,涵盖 1978 年 1 月 1 日至 2018 年 4 月 10 日期间的 6000 万次医院就诊、9000 万次诊断和总计 1.93 亿生命年。2015年1月1日至2018年4月10日期间的丹麦登记数据用于内部验证风险模型,共包含4 248 491名仍有原发性恶性癌症诊断风险的个体和67 401个癌症病例记录。在外部验证中,我们评估了同一时期英国生物库中的 377 004 人和 11 486 个癌症病例。这些模型在丹麦数据上的预测性能显示出良好的区分度(一致性指数为 0-81 [SD 0-08],范围从子宫颈癌的 0-66 [95% CI 0-65-0-67] 到肝癌的 0-91 [0-90-0-92])。在控制年龄分布变化的情况下,英国生物库的直接转移结果与此相似(一致性指数为 0-66 [SD 0-08],子宫颈癌的一致性指数为 0-55 [95% CI 0-44-0-66],肺癌的一致性指数为 0-78 [0-77-0-79])。除遗传因素外,癌症风险还与一系列先前诊断和健康因素有关。消化系统癌症(食道癌、胃癌、结肠直肠癌、肝癌和胰腺癌)以及甲状腺癌、肾癌和子宫癌的整体表现最佳。模型预测在丹麦和英国的医疗保健系统之间具有通用性。随着多种癌症早期检测方法的出现,基于电子健康记录的风险模型可作为筛查工作的补充。
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引用次数: 0
Challenges of detecting childhood diabetes in primary care 在初级保健中发现儿童糖尿病的挑战
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00072-4
Katherine G Young , John M Dennis , Nicholas J M Thomas
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引用次数: 0
Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm 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
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00050-5
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

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.

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。
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引用次数: 0
Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data 通过人工智能增强心脏磁共振成像预测诊断结果和舒张期充盈压:医院数据建模研究
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00063-3
David Hermann Lehmann MSc , Bruna Gomes MD , Niklas Vetter MD , Olivia Braun MD , Ali Amr MD , Thomas Hilbel MD , Jens Müller MSc , Prof Ulrich Köthe PhD , Christoph Reich MD , Elham Kayvanpour MD , Farbod Sedaghat-Hamedani MD , Manuela Meder MD , Jan Haas PhD , Prof Euan Ashley MD , Prof Wolfgang Rottbauer MD , Dominik Felbel MD , Raffi Bekeredjian MD , Heiko Mahrholdt MD , Prof Andreas Keller PhD , Peter Ong MD , Prof Benjamin Meder MD

Background

With increasing numbers of patients and novel drugs for distinct causes of systolic and diastolic heart failure, automated assessment of cardiac function is important. We aimed to provide a non-invasive method to predict diagnosis of patients undergoing cardiac MRI (cMRI) and to obtain left ventricular end-diastolic pressure (LVEDP).

Methods

For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital's AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done.

Findings

66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77–0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77–7·95), with a Pearson correlation of 0·57 (95% CI 0·56–0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79–0·84) for ischaemic cardiomyopathy and 0·92 (0·91–0·94) for hypertrophic cardiomyopathy.

Interpretation

Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function.

Funding

Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCa

背景随着收缩性和舒张性心力衰竭患者人数的增加以及治疗不同病因的新型药物的出现,对心脏功能进行自动评估非常重要。我们的目标是提供一种无创方法来预测接受心脏核磁共振成像(cMRI)的患者的诊断结果,并获得左心室舒张末期压力(LVEDP)。在这项建模研究中,我们确定了 2004 年 7 月 15 日至 2023 年 3 月 16 日期间在海德堡大学医院(德国海德堡)接受过心导管检查的患者,以及单个左心室压力测量值。我们使用了常规心脏诊断中现有的患者数据。从这一初始组中,我们抽取了被诊断为缺血性心肌病、扩张型心肌病、肥厚型心肌病或淀粉样变性的患者,以及无结构表型的对照组患者。数据经过化名处理,仅在大学医院的人工智能基础设施内进行处理。我们利用这些数据建立了不同的模型来预测人口统计学参数(即人工智能-年龄和人工智能-性别)、诊断参数(即人工智能-冠状动脉疾病和人工智能-心肌病 [AI-CMP])或功能参数(即人工智能-LVEDP)。我们通过计算机将数据集随机分为训练数据集、验证数据集和测试数据集。AI-CMP 未与其他模型进行比较,但在前瞻性设置中进行了验证。研究结果66 936 名患者在海德堡大学医院接受了心导管检查,共测量了 183 772 个左心室压力。我们从这一初始群体中提取了 4390 名患者,其中 1131 人(25-8%)被诊断为缺血性心肌病,1064 人(24-2%)被诊断为扩张型心肌病,816 人(18-6%)被诊断为肥厚型心肌病,202 人(4-6%)被诊断为淀粉样变性,1177 人(26-7%)为无结构表型的对照组。核心队列只包括30天内进行过心脏采集和cMRI检查的患者,急诊病例不包括在内。人工智能性别能够预测患者性别,接收者操作特征曲线下面积(AUC)为 0-78(95% CI 0-77-0-78),人工智能年龄能够预测患者年龄,平均绝对误差为 7-86 岁(7-77-7-95),皮尔逊相关性为 0-57(95% CI 0-56-0-57)。缺血性心肌病分类任务的AUC值为0-82(95% CI 0-79-0-84),肥厚型心肌病的AUC值为0-92(0-91-0-94)。释义我们的人工智能模型可以很容易地集成到临床实践中,并为cMRI的信息内容提供附加值,使疾病分类和舒张功能预测成为可能。资助克劳斯-特奇拉基金会生命信息学计划、德国心血管研究中心、德国心脏病学会电子心脏病学分会和海德堡人工智能健康创新集群。
{"title":"Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data","authors":"David Hermann Lehmann MSc ,&nbsp;Bruna Gomes MD ,&nbsp;Niklas Vetter MD ,&nbsp;Olivia Braun MD ,&nbsp;Ali Amr MD ,&nbsp;Thomas Hilbel MD ,&nbsp;Jens Müller MSc ,&nbsp;Prof Ulrich Köthe PhD ,&nbsp;Christoph Reich MD ,&nbsp;Elham Kayvanpour MD ,&nbsp;Farbod Sedaghat-Hamedani MD ,&nbsp;Manuela Meder MD ,&nbsp;Jan Haas PhD ,&nbsp;Prof Euan Ashley MD ,&nbsp;Prof Wolfgang Rottbauer MD ,&nbsp;Dominik Felbel MD ,&nbsp;Raffi Bekeredjian MD ,&nbsp;Heiko Mahrholdt MD ,&nbsp;Prof Andreas Keller PhD ,&nbsp;Peter Ong MD ,&nbsp;Prof Benjamin Meder MD","doi":"10.1016/S2589-7500(24)00063-3","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00063-3","url":null,"abstract":"<div><h3>Background</h3><p>With increasing numbers of patients and novel drugs for distinct causes of systolic and diastolic heart failure, automated assessment of cardiac function is important. We aimed to provide a non-invasive method to predict diagnosis of patients undergoing cardiac MRI (cMRI) and to obtain left ventricular end-diastolic pressure (LVEDP).</p></div><div><h3>Methods</h3><p>For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital's AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done.</p></div><div><h3>Findings</h3><p>66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77–0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77–7·95), with a Pearson correlation of 0·57 (95% CI 0·56–0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79–0·84) for ischaemic cardiomyopathy and 0·92 (0·91–0·94) for hypertrophic cardiomyopathy.</p></div><div><h3>Interpretation</h3><p>Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function.</p></div><div><h3>Funding</h3><p>Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCa","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000633/pdfft?md5=aacaa15001510ec9d45a77812b597e06&pid=1-s2.0-S2589750024000633-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Africa CDC spearheading the strengthening of health information exchange in Africa 非洲疾病预防控制中心带头加强非洲的卫生信息交流
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00068-2
Bekure Tamirat , Festo Mazuguni , Moses Bamutura , Kyeng Mercy , Kofi M Nyarko , Binyam Tilahun , Kokou N Alinon , Yenew K Tebeje
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引用次数: 0
Harnessing population-wide health data to predict cancer risk 利用全民健康数据预测癌症风险
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00093-1
Mattias Johansson , Hilary A Robbins
{"title":"Harnessing population-wide health data to predict cancer risk","authors":"Mattias Johansson ,&nbsp;Hilary A Robbins","doi":"10.1016/S2589-7500(24)00093-1","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00093-1","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000931/pdfft?md5=1f966bca28fe492d78b9b4f155660ee8&pid=1-s2.0-S2589750024000931-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effectiveness of proactive video therapy for problematic alcohol use on treatment initiation, compliance, and alcohol intake: a randomised controlled trial in Denmark 针对问题性饮酒的前瞻性视频疗法对治疗启动、依从性和酒精摄入量的效果:丹麦的随机对照试验
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-05-22 DOI: 10.1016/S2589-7500(24)00067-0
Kia Kejlskov Egan MSc , Prof Ulrik Becker DMSc , Sanne Pagh M⊘ller MSc , Veronica Pisinger PhD , Prof Janne Schurmann Tolstrup PhD

Background

Few people with problematic alcohol use reach treatment and dropout is frequent. Therapy for problematic alcohol use delivered via video conference (e-alcohol therapy) might overcome treatment barriers. In this randomised study, we tested whether proactive e-alcohol therapy outperformed face-to-face alcohol therapy (standard care) regarding treatment initiation, compliance, and weekly alcohol intake at 3-month and 12-month follow-up.

Methods

In this two-arm randomised controlled trial, we recruited individuals who had problematic alcohol use, defined as a score of 8 or more on the Alcohol Use Disorders Identification Test; were 18 years or older; and had access to a personal computer, smartphone, or tablet with internet access in Denmark through online advertisements. Participants were assigned to receive alcohol therapy delivered either face-to-face or via video conference. The number, frequency, and duration of therapy sessions were individualised in both groups. Data analysis was conducted using masked data. Primary analyses were based on an intention-to-treat sample. The study is registered with ClinicalTrials.gov (NCT03116282).

Findings

Between Jan 22, 2018, and June 29, 2020, 816 individuals signed up for the trial and 502 (63%) were assessed for eligibility. We randomly assigned 379 to proactive e-alcohol therapy (n=187) or standard care (n=192), of which, 170 (48%) participants were female and 186 (52%) were male. In the intervention group, more participants initiated treatment (155 [88%] of 177 vs 96 [54%] of 179; odds ratio [OR] 6·3; 95% CI 2·8 to 13·8; p<0·0001 at 3 months; 151 [85%] of 177 vs 115 [64%] of 179; OR 3·2; 95% CI 1·6 to 6·2; p=0·0007 at 12 months) and complied with treatment (130 [73%] of 177 vs 74 [41%] of 179; OR 4·0; 95% CI 2·2 to 7·2; p<0·0001 at 3 months; 140 [79%] of 177 vs 95 [53%] of 179; OR 3·4; 95% CI 1·8 to 6·3; p=0·0002 at 12 months). Weekly alcohol intake was significantly lower in the intervention group only after 3 months (13·0 standard drinks per week vs 21·3 standard drinks per week; adjusted difference –6·7; 95% CI –12·3 to –1·0; p=0·019).

Interpretation

Proactive e-alcohol therapy was associated with increased treatment initiation and compliance and is promising as an easily accessible and effective alcohol treatment for individuals with problematic alcohol use.

Funding

TrygFonden.

背景有酗酒问题的人很少接受治疗,而且经常辍学。通过视频会议(电子酒精疗法)对问题性饮酒进行治疗可能会克服治疗障碍。在这项随机研究中,我们测试了在 3 个月和 12 个月的随访中,积极主动的电子酒精疗法在治疗启动、依从性和每周酒精摄入量方面是否优于面对面酒精疗法(标准护理)。方法在这项双臂随机对照试验中,我们通过在线广告招募了有问题酒精使用的个人,其定义是酒精使用障碍识别测试中的得分达到或超过 8 分;年龄在 18 岁或以上;在丹麦可以使用个人电脑、智能手机或平板电脑上网。参与者被分配接受面对面或通过视频会议提供的酒精治疗。两组的治疗次数、频率和持续时间都是个性化的。数据分析采用蒙面数据。主要分析基于意向治疗样本。该研究已在ClinicalTrials.gov(NCT03116282)上注册。研究结果在2018年1月22日至2020年6月29日期间,共有816人报名参加试验,其中502人(63%)通过了资格评估。我们随机分配了379人接受主动电子酒精疗法(187人)或标准护理(192人),其中170人(48%)为女性,186人(52%)为男性。在干预组中,更多参与者开始接受治疗(177 人中的 155 [88%] 对 179 人中的 96 [54%];3 个月时的几率比 [OR] 6-3;95% CI 2-8 到 13-8;p<0-0001;177 人中的 151 [85%] 对 179 人中的 115 [64%];OR 3-2;95% CI 1-6 到 6-2;12个月时,p=0-0007)和坚持治疗(177人中有130人[73%] vs 179人中有74人[41%];3个月时,OR 4-0;95% CI 2-2 to 7-2;p<0-0001;177人中有140人[79%] vs 179人中有95人[53%];12个月时,OR 3-4; 95% CI 1-8 to 6-3;p=0-0002)。只有在3个月后,干预组的每周酒精摄入量才明显降低(每周13-0标准饮品 vs 每周21-3标准饮品;调整后差异为-6-7;95% CI为-12-3至-1-0;p=0-019)。释义积极的电子酒精疗法与治疗启动率和依从性的提高有关,有望成为针对酗酒问题患者的一种方便有效的酒精治疗方法。
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引用次数: 0
Correction to Lancet Digit Health 2022; 4: e884–92 Lancet Digit Health 2022; 4: e884-92 更正。
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-04-25 DOI: 10.1016/S2589-7500(24)00089-X
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引用次数: 0
5 years of The Lancet Digital Health 柳叶刀数字健康》5 周年
IF 30.8 1区 医学 Q1 Medicine Pub Date : 2024-04-24 DOI: 10.1016/S2589-7500(24)00073-6
Rupa Sarkar, Diana Samuel, Lucy Dunbar, Gustavo Monnerat
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引用次数: 0
期刊
Lancet Digital Health
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