Christensen Daniel Mølager, Phelps Matthew, Gerds Thomas, Malmborg Morten, Schjerning Anne-Marie, Strange Jarl Emanuel, El-Chouli Mohamad, Larsen Lars Bruun, Fosbøl Emil, Køber Lars, Torp-Pedersen Christian, Mehta Suneela, Jackson Rod, Gislason Gunnar
The Danish Heart Foundation, Vognmagergade 7, 3rd Floor, Copenhagen 1120, Denmark.
Department of Biostatistics, University of Copenhagen, Øster Farimagsgade 5, Copenhagen 1014, Denmark.
Eur Heart J Open. 2021 Aug 2;1(2):oeab015. doi: 10.1093/ehjopen/oeab015. eCollection 2021 Sep.
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.
本研究旨在建立并验证一个覆盖全国范围的风险预测模型，以预测心血管疾病（CVD）的个体和群体风险。
2014年1月1日纳入了所有298万年龄在30 - 85岁且无心血管疾病的丹麦居民，并利用全国性行政医疗保健登记系统对其进行随访，直至2018年12月31日。预先确定了模型预测因子和结局指标。预测因子包括年龄、性别、教育程度、使用抗血栓药物、降压药物、降糖药物或降脂药物，以及用戒烟药物使用情况或慢性阻塞性肺疾病作为吸烟替代指标。结局指标为首次发生心血管疾病事件的5年风险，该事件是缺血性心脏病、心力衰竭、外周动脉疾病、中风或心血管死亡的综合情况。使用特定病因的Cox回归模型计算预测值。在完整数据中拟合的最终模型在丹麦的每个地区进行了内部 - 外部验证。该模型在所有地区均校准良好。受试者工作特征曲线下面积（AUC）和Brier评分范围分别为76.3%至79.6%以及3.3至4.4。该模型优于年龄 - 性别基准模型，AUC和Brier评分差异范围分别为1.2%至1.5%以及 - 0.02至 - 0.03。丹麦每个直辖市的平均预测风险范围为2.8%至5.9%。一名66岁老人的预测风险范围为2.6%至25.3%。30 - 85岁人群的个性化预测风险通过在线计算器（https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/）呈现。
仅基于全国行政登记数据的心血管疾病风险预测模型，能准确预测丹麦人群个体和群体层面首次发生心血管疾病事件的5年风险。这可为临床和公共卫生一级预防工作提供参考。
