A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank.
A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank.
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作者：
现代医疗数据反映了多年来收集的大量多层次和多尺度信息。大多数现有的表型分型算法使用疾病的病例对照定义。本文旨在研究疾病发作和进展的时间，并确定驱动它们的随时间变化的风险因素。我们开发了一种算法方法，通过整合来自英国生物银行（UKB）的数据源，包括初级保健电子健康记录（EHR），对疾病的发生率进行表型分析。我们专注于定义事件、事件日期及其删失时间，包括相关术语和现有表型，排除通用、罕见或语义上遥远的术语、前向映射术语术语和专家评审。在UKB研究中，我们应用我们的方法对糖尿病并发症进行表型分型，包括复合心血管疾病（CVD）结局、糖尿病肾病（DKD）和糖尿病视网膜病变（DR）。我们确定了49049名糖尿病患者。 其中1型糖尿病（T1 D）1023例，2型糖尿病（T2 D）40193例。 共有23833名糖尿病受试者有相关的初级保健记录。 分别有3237、3113和4922例患者发生CVD、DKD和DR事件。评估了每个结局的风险预测性能，我们的结果与使用队列研究的标准风险预测模型的ROC（受试者工作特征）曲线下预测面积（AUC）一致。我们的公开渠道和平台可以简化发病事件的管理，识别疾病进展的时变风险因素，并定义相关队列进行时间-事件分析。需要同时考虑这些重要步骤来研究疾病进展。
Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case–control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them. We developed an algorithmic approach to phenotyping the incidence of diseases by consolidating data sources from the UK Biobank (UKB), including primary care electronic health records (EHRs). We focused on defining events, event dates, and their censoring time, including relevant terms and existing phenotypes, excluding generic, rare, or semantically distant terms, forward-mapping terminology terms, and expert review. We applied our approach to phenotyping diabetes complications, including a composite cardiovascular disease (CVD) outcome, diabetic kidney disease (DKD), and diabetic retinopathy (DR), in the UKB study. We identified 49 049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40 193 had type 2 diabetes (T2D). A total of 23 833 diabetes subjects had linked primary care records. There were 3237, 3113, and 4922 patients with CVD, DKD, and DR events, respectively. The risk prediction performance for each outcome was assessed, and our results are consistent with the prediction area under the ROC (receiver operating characteristic) curve (AUC) of standard risk prediction models using cohort studies. Our publicly available pipeline and platform enable streamlined curation of incidence events, identification of time-varying risk factors underlying disease progression, and the definition of a relevant cohort for time-to-event analyses. These important steps need to be considered simultaneously to study disease progression.
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影响因子：
7.2
作者：
Halfon, P;Eggli, Y;Burnand, B
通讯作者：
Burnand, B
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
影响因子：
30.8
作者：
Mahajan A;Taliun D;Thurner M;Robertson NR;Torres JM;Rayner NW;Payne AJ;Steinthorsdottir V;Scott RA;Grarup N;Cook JP;Schmidt EM;Wuttke M;Sarnowski C;Mägi R;Nano J;Gieger C;Trompet S;Lecoeur C;Preuss MH;Prins BP;Guo X;Bielak LF;Below JE;Bowden DW;Chambers JC;Kim YJ;Ng MCY;Petty LE;Sim X;Zhang W;Bennett AJ;Bork-Jensen J;Brummett CM;Canouil M;Ec Kardt KU;Fischer K;Kardia SLR;Kronenberg F;Läll K;Liu CT;Locke AE;Luan J;Ntalla I;Nylander V;Schönherr S;Schurmann C;Yengo L;Bottinger EP;Brandslund I;Christensen C;Dedoussis G;Florez JC;Ford I;Franco OH;Frayling TM;Giedraitis V;Hackinger S;Hattersley AT;Herder C;Ikram MA;Ingelsson M;Jørgensen ME;Jørgensen T;Kriebel J;Kuusisto J;Ligthart S;Lindgren CM;Linneberg A;Lyssenko V;Mamakou V;Meitinger T;Mohlke KL;Morris AD;Nadkarni G;Pankow JS;Peters A;Sattar N;Stančáková A;Strauch K;Taylor KD;Thorand B;Thorleifsson G;Thorsteinsdottir U;Tuomilehto J;Witte DR;Dupuis J;Peyser PA;Zeggini E;Loos RJF;Froguel P;Ingelsson E;Lind L;Groop L;Laakso M;Collins FS;Jukema JW;Palmer CNA;Grallert H;Metspalu A;Dehghan A;Köttgen A;Abecasis GR;Meigs JB;Rotter JI;Marchini J;Pedersen O;Hansen T;Langenberg C;Wareham NJ;Stefansson K;Gloyn AL;Morris AP;Boehnke M;McCarthy MI
通讯作者：
McCarthy MI
DOI：
10.1136/amiajnl-2012-000896
发表时间：
2013-06-01
影响因子：
6.4
作者：
Newton, Katherine M.;Peissig, Peggy L.;Denny, Joshua C.
通讯作者：
Denny, Joshua C.
影响因子：
5.6
作者：
Neumann JT;Thao LTP;Callander E;Chowdhury E;Williamson JD;Nelson MR;Donnan G;Woods RL;Reid CM;Poppe KK;Jackson R;Tonkin AM;McNeil JJ
通讯作者：
McNeil JJ