Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study.
Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study.
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使用丹麦行政医疗保健数据预测290万人的首次心血管疾病事件：一项全国性的基于注册的推导和验证研究
DOI：
10.1093/ehjopen/oeab015
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Gislason, Gunnar
中科院分区：
文献类型：
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作者：
Christensen, Daniel Molager;Phelps, Matthew;Gerds, Thomas;Malmborg, Morten;Schjerning, Anne-Marie;Strange, Jarl Emanuel;El-Chouli, Mohamad;Larsen, Lars Bruun;Fosbol, Emil;Kober, Lars;Torp-Pedersen, Christian;Mehta, Suneela;Jackson, Rod;Gislason, Gunnar
本研究的目的是推导并验证一个风险预测模型，该模型具有全国范围的覆盖率，可以预测个人和人群水平的心血管疾病（CVD）风险。2014年1月1日，所有298万年龄在30-85岁之间的无心血管疾病的丹麦居民都被纳入，并使用全国行政医疗保健登记处随访至2018年12月31日。预先指定模型预测因子和结局。预测因素包括年龄、性别、教育程度、抗血栓药物、降血压、降糖或降脂药物的使用，以及戒烟药物使用或慢性阻塞性肺疾病的吸烟替代物。结局为首次CVD事件、缺血性心脏病、心力衰竭、外周动脉疾病、卒中或心血管死亡的5年风险。使用原因特异性考克斯回归模型计算预测值。最终模型拟合的完整数据进行了内部和外部验证，在每个丹麦地区。该模型在所有区域都得到了很好的校准。受试者工作特征曲线下面积（AUC）和Brier评分范围为76.3%至79.6%和3.3至4.4。该模型上级年龄-性别基准模型，AUC和Brier评分的差异范围为1.2%至1.5%和-0.02至-0.03。丹麦每个城市的平均预测风险范围为2.8%至5.9%。66岁患者的预测风险范围为2.6%至25.3%。30-85岁之间的个性化预测风险显示在在线计算器中（https：//hjerteforeningen.shinyapps.io/cvd-risk-clampt/）。仅基于全国行政登记数据的CVD风险预测模型提供了丹麦人群中个人和人群水平5年首次CVD事件风险的准确预测。这可以为临床和公共卫生一级预防工作提供信息。
The aim of this study was to derive and validate a risk prediction model with nationwide coverage to predict the individual and population-level risk of cardiovascular disease (CVD). All 2.98 million Danish residents aged 30–85 years free of CVD were included on 1 January 2014 and followed through 31 December 2018 using nationwide administrative healthcare registries. Model predictors and outcome were pre-specified. Predictors were age, sex, education, use of antithrombotic, blood pressure-lowering, glucose-lowering, or lipid-lowering drugs, and a smoking proxy of smoking-cessation drug use or chronic obstructive pulmonary disease. Outcome was 5-year risk of first CVD event, a combination of ischaemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death. Predictions were computed using cause-specific Cox regression models. The final model fitted in the full data was internally-externally validated in each Danish Region. The model was well-calibrated in all regions. Area under the receiver operating characteristic curve (AUC) and Brier scores ranged from 76.3% to 79.6% and 3.3 to 4.4. The model was superior to an age-sex benchmark model with differences in AUC and Brier scores ranging from 1.2% to 1.5% and −0.02 to −0.03. Average predicted risks in each Danish municipality ranged from 2.8% to 5.9%. Predicted risks for a 66-year old ranged from 2.6% to 25.3%. Personalized predicted risks across ages 30–85 were presented in an online calculator (https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/). A CVD risk prediction model based solely on nationwide administrative registry data provided accurate prediction of personal and population-level 5-year first CVD event risk in the Danish population. This may inform clinical and public health primary prevention efforts.