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为一般人群开发和验证突发心脏病死亡预测模型

Rajat Deo ¹, Faye L Norby ², Ronit Katz ²

¹From Section of Electrophysiology, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia (R.D.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (F.L.N., A.R.F.); Kidney Research Institute (R.K., B.K., R.A.K.), Division of Cardiology (N.S., K.K.P.), University of Washington, Seattle; Division of Cardiology, Veterans Affairs Medical Center, Minneapolis, MN (S.A.); Division of Cardiology, University of Maryland School of Medicine, Baltimore (C.R.D.); Division of Nephrology, University of Washington, Seattle (B.K.); Division of Cardiology, University of Minnesota Medical School, Minneapolis (L.Y.C., S.K.); Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle (S.R.H.); Department of Biostatistics (R.A.K.), The New York Academy of Medicine, New York, NY (D.S.); General Internal Medicine Section, Veterans Affairs Medical Center, San Francisco, CA, Departments of Medicine, Epidemiology and Biostatistics, University of California, San Francisco (M.G.S.); and Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.). Rajat.Deo@uphs.upenn.edu.
²From Section of Electrophysiology, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia (R.D.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (F.L.N., A.R.F.); Kidney Research Institute (R.K., B.K., R.A.K.), Division of Cardiology (N.S., K.K.P.), University of Washington, Seattle; Division of Cardiology, Veterans Affairs Medical Center, Minneapolis, MN (S.A.); Division of Cardiology, University of Maryland School of Medicine, Baltimore (C.R.D.); Division of Nephrology, University of Washington, Seattle (B.K.); Division of Cardiology, University of Minnesota Medical School, Minneapolis (L.Y.C., S.K.); Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle (S.R.H.); Department of Biostatistics (R.A.K.), The New York Academy of Medicine, New York, NY (D.S.); General Internal Medicine Section, Veterans Affairs Medical Center, San Francisco, CA, Departments of Medicine, Epidemiology and Biostatistics, University of California, San Francisco (M.G.S.); and Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.).

⁰From Section of Electrophysiology, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia (R.D.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (F.L.N., A.R.F.); Kidney Research Institute (R.K., B.K., R.A.K.), Division of Cardiology (N.S., K.K.P.), University of Washington, Seattle; Division of Cardiology, Veterans Affairs Medical Center, Minneapolis, MN (S.A.); Division of Cardiology, University of Maryland School of Medicine, Baltimore (C.R.D.); Division of Nephrology, University of Washington, Seattle (B.K.); Division of Cardiology, University of Minnesota Medical School, Minneapolis (L.Y.C., S.K.); Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle (S.R.H.); Department of Biostatistics (R.A.K.), The New York Academy of Medicine, New York, NY (D.S.); General Internal Medicine Section, Veterans Affairs Medical Center, San Francisco, CA, Departments of Medicine, Epidemiology and Biostatistics, University of California, San Francisco (M.G.S.); and Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.). Rajat.Deo@uphs.upenn.edu.

Circulation +

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2016年八月21日

在PubMed上查看摘要

概括

一个新的预测模型确定了没有心脏病的成年人突然心脏死亡的12个风险因素. 这种模型准确地预测了10年内SCD风险,改善了现有的心血管风险方程.

科学领域

心脏病学
预防医学
流行病学

背景情况

突然心脏病死亡 (SCD) 通常发生在没有心脏病史的人群中.
在一般成年人群中开发SCD预测模型对于早期干预至关重要.

研究的目的

在没有确诊心血管疾病的美国成年人中开发和验证10年SCD风险的预测模型.
将新SCD预测模型的性能与现有的心血管风险方程进行比较.

主要方法

利用社区动脉样硬化风险研究 (ARIC) 的数据进行模型推导和心血管健康研究 (CHS) 的数据进行验证.
确定了12个独立的SCD风险因素,包括人口,临床和实验室测量.
将SCD预测准确度与非SCD和全因死亡率进行比较,并与2013年ACC/AHA合并的队列风险方程进行比较.

主要成果

一个包含12个因素的10年SCD风险模型显示出良好的差异化 (ARIC的c-统计值为0.820,CHS的c-统计值为0.745).
开发的SCD模型略高于2013年ACC/AHA合并的队列风险方程.
左心室喷射分数没有显著改善SCD预测在心声学子队列.

结论

这项研究为预测未患有心血管疾病的成年人提供了第一个可概括的风险评分.
该模型提供了对不同人群进行精确校准的绝对风险估计.
经过验证的风险评分有助于识别心脏突然死亡的风险.

关键词:

节律失常人口数量风险预测突发心脏病死亡

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