Cardiovascular risk prediction in healthy older people.
Cardiovascular risk prediction in healthy older people.
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DOI：
10.1007/s11357-021-00486-z
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发表时间：
2022-03
期刊：
影响因子：
5.6
通讯作者：
McNeil JJ
中科院分区：
文献类型：
--
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
识别主要不良心血管事件（MACE）风险增加的个体非常重要。然而，缺乏针对老年人的算法。分析了一项随机试验的数据，该试验涉及18，548名年龄≥ 70岁（平均年龄75.4岁）的受试者，既往无心血管疾病事件、痴呆或身体残疾。MACE包括冠心病死亡、致死性或非致死性缺血性卒中或心肌梗死。测试的潜在预测因素基于先前的证据并使用机器学习方法。采用考克斯回归分析计算5年预测风险，并根据受试者工作特征曲线评价区分度。还评估了校准，并使用自举法对结果进行了内部验证。在初级保健环境中的25，138名健康老年人中进行了外部验证。在中位随访4.7年期间，发生了594起MACE。最终模型中的预测因素包括年龄、性别、吸烟、收缩压、高密度脂蛋白胆固醇（HDL-c）、非HDL-c、血清肌酐、糖尿病和抗高血压药物的摄入。在基于机器学习的变量选择中，年龄、性别和肌酐是最重要的预测因素。最终模型的曲线下面积（AUC）为68.1（95%置信区间65.9; 70.4）。该模型的AUC在内部验证中为67.5，在外部验证中为64.2。该模型的风险排序良好，但低估了外部验证队列的绝对风险。预测健康老年人发生MACE的模型包括公认的潜在可逆风险因素，特别是肾功能。在其他人群中使用时，需要进行校准。在线版本包含补充材料，可通过10.1007/s11357-021-00486-z获得。
Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations. The online version contains supplementary material available at 10.1007/s11357-021-00486-z.
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DOI：
10.1056/nejmoa1805819
发表时间：
2018-10-18
期刊：
The New England journal of medicine
影响因子：
--
作者：
McNeil JJ;Wolfe R;Woods RL;Tonkin AM;Donnan GA;Nelson MR;Reid CM;Lockery JE;Kirpach B;Storey E;Shah RC;Williamson JD;Margolis KL;Ernst ME;Abhayaratna WP;Stocks N;Fitzgerald SM;Orchard SG;Trevaks RE;Beilin LJ;Johnston CI;Ryan J;Radziszewska B;Jelinek M;Malik M;Eaton CB;Brauer D;Cloud G;Wood EM;Mahady SE;Satterfield S;Grimm R;Murray AM;ASPREE Investigator Group
通讯作者：
ASPREE Investigator Group
DOI：
10.1016/s0140-6736(20)32332-1
发表时间：
2020-11-21
期刊：
Lancet (London, England)
影响因子：
--
作者：
Gencer B;Marston NA;Im K;Cannon CP;Sever P;Keech A;Braunwald E;Giugliano RP;Sabatine MS
通讯作者：
Sabatine MS
影响因子：
168.9
作者：
Bloom, David E.;Chatterji, Somnath;Kowal, Paul;Lloyd-Sherlock, Peter;Mckee, Martin;Rechel, Bernd;Rosenberg, Larry;Smith, James P.
通讯作者：
Smith, James P.
影响因子：
6.3
作者：
Dalton, Jarrod E.;Rothberg, Michael B.;Perzynski, Adam T.
通讯作者：
Perzynski, Adam T.
影响因子：
3.5
作者：
Schofield, Deborah;Kelly, Simon;Percival, Richard
通讯作者：
Percival, Richard