Paynter Nina P, Cook Nancy R, Everett Brendan M, Sesso Howard D, Buring Julie E, Ridker Paul M
The Center for Cardiovascular Disease Prevention and the Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Mass. 02215, USA.
Am J Med. 2009 May;122(5):464-71. doi: 10.1016/j.amjmed.2008.10.034.
We examined whether a hypertension risk prediction model based on clinical characteristics and blood biomarkers might improve on risk prediction based on current blood pressure alone.
A prospective cohort of 14,822 normotensive women aged 45 years and older were followed over 8 years beginning in 1992 for the development of hypertension. Among a randomly selected two-thirds sample (N=9427), hypertension prediction models were developed using 52 potential predictors and compared with a model based on blood pressure alone. Each prediction model was validated in the remaining one third (N=5395).
In the development cohort, the best prediction model for incident hypertension included age, blood pressure, ethnicity, body mass index, total grain intake, apolipoprotein B, lipoprotein(a), and C-reactive protein (Bayes Information Criteria [BIC]=8788). Although this model was superior to a model based on blood pressure alone (BIC=8957), it was only marginally better than a simplified model including age, blood pressure, ethnicity, and body mass index (BIC=8820). In the validation cohort, the simplified model demonstrated adequate calibration, a c-index similar to that of the best model (0.703 vs 0.705), and when compared with the model based on blood pressure alone, reclassified 1499 participants to hypertension risk categories that proved to be closer to observed risk in all but one instance.
In this prospective cohort of initially normotensive women, a model based on readily available clinical information predicted incident hypertension better than a model based on blood pressure alone.
我们研究了基于临床特征和血液生物标志物的高血压风险预测模型是否能比仅基于当前血压的风险预测有所改进。
从1992年开始，对14822名年龄在45岁及以上的血压正常女性进行了为期8年的前瞻性队列研究，以观察高血压的发生情况。在随机抽取的三分之二样本（N = 9427）中，使用52个潜在预测因子建立高血压预测模型，并与仅基于血压的模型进行比较。每个预测模型在其余三分之一（N = 5395）的样本中进行验证。
在开发队列中，预测新发高血压的最佳模型包括年龄、血压、种族、体重指数、谷物总摄入量、载脂蛋白B、脂蛋白(a)和C反应蛋白（贝叶斯信息准则[BIC]=8788）。尽管该模型优于仅基于血压的模型（BIC = 8957），但仅略优于包含年龄、血压、种族和体重指数的简化模型（BIC = 8820）。在验证队列中，简化模型显示出良好的校准，c指数与最佳模型相似（0.703对0.705），并且与仅基于血压的模型相比，将1499名参与者重新分类到高血压风险类别，除一个实例外，所有实例的风险类别都更接近观察到的风险。
在这个最初血压正常的女性前瞻性队列中，基于易于获得的临床信息的模型比仅基于血压的模型能更好地预测新发高血压。
