Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study.
Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study.
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DOI：
10.1136/bmj.j2099
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发表时间：
2017-05-23
期刊：
影响因子：
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通讯作者：
Brindle P
中科院分区：
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作者：
Hippisley-Cox J;Coupland C;Brindle P
目的：开发和验证更新的QRISK3预测算法，以估计考虑潜在新危险因素的女性和男性心血管疾病的10年风险。设计：前瞻性开放队列研究。为QResearch数据库提供数据的英国的一般做法。英国1309项QResearch一般实践的参与者：981项实践被用来制定分数，另一组328项实践被用来验证分数。789万名年龄在25-84岁的患者属于衍生队列，267万名患者属于验证队列。患者在基线时没有心血管疾病，也没有服用他汀类药物。方法利用派生队列中的COX比例风险模型推导出男性和女性的独立风险方程，并在10年时进行评估。考虑的危险因素包括QRISK2中已经存在的危险因素(年龄、种族、贫困、收缩压、体重指数、总胆固醇：高密度脂蛋白胆固醇比率、吸烟、60岁以下一级亲属的冠心病家族史、1型糖尿病、2型糖尿病、高血压、类风湿性关节炎、房颤、慢性肾病(4或5期))和新的危险因素(慢性肾病(3、4或5期)、收缩压变异性的测量(重复测量的标准差)、偏头痛、皮质类固醇激素、系统性红斑狼疮(SLE)、非典型抗精神病药物、严重精神病、和艾滋病毒/艾滋病)。我们还考虑了男性勃起功能障碍的诊断或治疗。在验证队列中分别确定了男性和女性的校准和歧视措施，并根据年龄组、种族和基线疾病状况确定了个别亚组的校准和歧视措施。主要结果衡量记录在以下三个相关数据源中的任何一个上的心血管疾病事件：一般实践、死亡率或住院记录。结果在5080万人年的随访中，在派生队列中发现了363例 565例心血管疾病事件。除艾滋病毒/艾滋病外，所考虑的所有新风险因素都符合纳入标准，这在统计上并不显著。模型具有良好的校准性和较高的可解释性差异和区分性。在女性中，该算法解释了59.6%的心血管疾病诊断时间变异(R2值越高，表明变异越大)，D统计量为2.48，Harrell‘s C统计量为0.88(这两个辨别度衡量标准，值越高表明辨别力越好)。男性的相应数值分别为54.8%、2.26和0.86。更新后的QRISK3算法的整体性能与QRISK2算法相似。结论建立了更新的QRISK3风险预测模型，并进行了验证。QRISK3(慢性肾脏疾病，一种测量收缩压变异性(重复测量的标准差)、偏头痛、皮质类固醇、系统性红斑狼疮、非典型抗精神病药物、严重精神疾病和勃起功能障碍)的额外临床变量的纳入，可以帮助医生识别心脏病和中风的最高风险人群。
Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors. Design Prospective open cohort study. Setting General practices in England providing data for the QResearch database. Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline. Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status. Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records. Results 363 565 incident cases of cardiovascular disease were identified in the derivation cohort during follow-up arising from 50.8 million person years of observation. All new risk factors considered met the model inclusion criteria except for HIV/AIDS, which was not statistically significant. The models had good calibration and high levels of explained variation and discrimination. In women, the algorithm explained 59.6% of the variation in time to diagnosis of cardiovascular disease (R2, with higher values indicating more variation), and the D statistic was 2.48 and Harrell’s C statistic was 0.88 (both measures of discrimination, with higher values indicating better discrimination). The corresponding values for men were 54.8%, 2.26, and 0.86. Overall performance of the updated QRISK3 algorithms was similar to the QRISK2 algorithms. Conclusion Updated QRISK3 risk prediction models were developed and validated. The inclusion of additional clinical variables in QRISK3 (chronic kidney disease, a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, SLE, atypical antipsychotics, severe mental illness, and erectile dysfunction) can help enable doctors to identify those at most risk of heart disease and stroke.