Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE.
Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE.
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DOI：
10.1111/dom.14208
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
DEVOTE Study Group
中科院分区：
文献类型：
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作者：
Heller S;Lingvay I;Marso SP;Philis-Tsimikas A;Pieber TR;Poulter NR;Pratley RE;Hachmann-Nielsen E;Kvist K;Lange M;Moses AC;Trock Andresen M;Buse JB;DEVOTE Study Group
区分严重低血糖高风险的2型糖尿病患者人群的能力可能会影响临床决策。本研究的目的是使用患者特征开发一种风险评分，该评分可以区分心血管疾病风险增加的个体中重度低血糖2年风险较高和较低的人群。基于DEVOTE心血管结局试验的数据，为风险评分开发了两个模型。第一个是数据驱动的机器学习模型，使用双向消除逐步回归来识别重度低血糖的风险因素。第二种是基于从数据驱动模型中确定的临床实践中可获得的已知临床风险因素的风险评分，包括：胰岛素治疗方案;糖尿病病程;性别;年龄;和糖化血红蛋白，均在基线时。使用来自DEVOTE的数据对数据驱动模型和简单风险评分进行了区分、校准和概括性评价，并根据外部LEADER心血管结局试验数据集进行了验证。数据驱动模型和简单风险评分均区分了低血糖风险较高和较低的患者，并且基于2年时间范围内的时间依赖性曲线下面积指数（分别为0.63和0.66）表现相似。数据驱动模型和简单低血糖风险评分均能够区分重度低血糖风险较高和较低的患者，后者使用易于获得的临床数据进行区分。这种工具（http：//www.hyporiskscore.com/）的实施可能有助于改善对重度低血糖风险的识别和教育，从而可能改善患者护理。
The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2‐year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data‐driven machine‐learning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data‐driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data‐driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset. Both the data‐driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time‐dependent area under the curve index (0.63 and 0.66, respectively) over a 2‐year time horizon. Both the data‐driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care.
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DOI：
10.1056/nejmoa1603827
发表时间：
2016-07-28
期刊：
The New England journal of medicine
影响因子：
--
作者：
Marso SP;Daniels GH;Brown-Frandsen K;Kristensen P;Mann JF;Nauck MA;Nissen SE;Pocock S;Poulter NR;Ravn LS;Steinberg WM;Stockner M;Zinman B;Bergenstal RM;Buse JB;LEADER Steering Committee;LEADER Trial Investigators
通讯作者：
LEADER Trial Investigators
影响因子：
4.8
作者：
Marso, Steven P.;McGuire, Darren K.;Buse, John B.
通讯作者：
Buse, John B.
影响因子：
39.3
作者：
Mellbin, Linda G.;Ryden, Lars;Gerstein, Hertzel C.
通讯作者：
Gerstein, Hertzel C.
影响因子：
16.2
作者：
通讯作者：
--
基线特征，血糖治疗方法和糖化血红蛋白浓度对严重低血糖风险的影响：协定研究事后流行病学分析。
DOI：
10.1136/bmj.b5444
发表时间：
2010-01-08
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Miller ME;Bonds DE;Gerstein HC;Seaquist ER;Bergenstal RM;Calles-Escandon J;Childress RD;Craven TE;Cuddihy RM;Dailey G;Feinglos MN;Ismail-Beigi F;Largay JF;O'Connor PJ;Paul T;Savage PJ;Schubart UK;Sood A;Genuth S;ACCORD Investigators
通讯作者：
ACCORD Investigators