基于健康管理队列的高血压风险预测模型

doi:10.6040/j.issn.1671-7554.0.2017.357

山东大学学报（医学版） ›› 2017, Vol. 55 ›› Issue (6): 61-65.doi: 10.6040/j.issn.1671-7554.0.2017.357

基于健康管理队列的高血压风险预测模型

于涛^1,2,刘焕乐³,冯新⁴,徐付印⁴,陈亚飞^1,2,薛付忠^1,2,张成琪⁵

1.山东大学公共卫生学院生物统计学系, 山东济南 250012;2.山东大学齐鲁生物医学大数据研究中心, 山东济南 250012;3.胜利油田中心医院保健科, 山东东营 257000;4.胜利油田中心医院健康管理中心, 山东东营 257000;5.山东大学附属千佛山医院健康管理中心, 山东济南 250014

收稿日期:2017-04-26 出版日期:2017-06-10 发布日期:2017-06-10
通讯作者: 刘焕乐. E-mail:zxyylhl@163.com张成琪. E-mail:chengqizhangsd@126.com E-mail:zxyylhl@163.com
基金资助:
国家自然科学基金(81273082)

A hypertension risk prediction model based on health management cohort

YU Tao^1,2, LIU Huanle³, FENG Xin⁴, XU Fuyin⁴, CHEN Yafei^1,2, XUE Fuzhong^1,2, ZHANG Chengqi⁵

1. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
2. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China;
3. Department of Health Care, Shengli Oilfield Central Hospital, Dongying 257000, Shandong, China;
4. Health Management Center, Shengli Oilfield Central Hospital, Dongying 257000, Shandong, China;
5. Health Management Center, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan 250014, Shandong, China

Received:2017-04-26 Online:2017-06-10 Published:2017-06-10

摘要/Abstract

摘要： 目的基于健康管理队列,构建高血压风险预测模型。方法依托山东多中心健康管理纵向观察队列,排除基线高血压、心脑血管疾病、血肌酐>177 μmol/L、年龄<20岁者,构建高血压研究队列(共22 177人,其中男12 044人,女10 133人),分性别采用Cox回归建立高血压预测模型,并评价模型预测效果。结果观察期间新发高血压4 571例,发病密度为62.84/1 000人年。最终男性模型中的变量包括年龄、体质量指数、收缩压、舒张压、空腹血糖和红细胞压积,ROC曲线下面积(AUC)为0.821(95%CI:0.812~0.830);女性模型中的变量包括年龄、体质量指数、收缩压、红细胞计数和高密度脂蛋白,AUC为0.818(95%CI:0.806~0.828)。十折交叉验证结果显示,男女AUC分别为0.819(95%CI:0.810~0.828)、0.814(95%CI:0.803~0.825)。结论该模型具有较好的预测能力,可用来识别高血压高危个体。

关键词: 健康管理队列, 高血压, 预测模型

Abstract: Objective To establish a prediction model for the risk of hypertension based on health management cohort. Methods Based on Shandong Multi-center Longitudinal Cohort for Health Management and after exclusion of participants with baseline hypertension, cardiovascular and cerebrovascular diseases and other related diseases, a cohort with 22 177 subjects, including 12 044 males and 10 133 females, was established. Cox regression was used to construct prediction models for hypertension in males and females. The effect of model prediction was evaluated. Results A total of 4 571 new hypertention cases were observed, resulting in a cumulative incidence of 62.84/1 000 person years. The risk factors of the model for males included age, body mass index, systolic pressure, diastolic pressure, fasting blood-glucose and hematocrit. The estimated AUC of the model was 0.821(95%CI: 0.812-0.830). In the model for females, the risk factors included age, body mass index, systolic pressure, red blood cell and high density lipoprotein cholesterol. The estimated AUC of the model was 0.818(95%CI: 0.806-0.828). Ten-fold cross validation showed that the estimated AUC of the model for males was 0.819(95%CI: 0.810-0.828)and 0.814(95%CI: 0.803-0.825) 山东大学学报 (医学版)55卷6期 -于涛,等.基于健康管理队列的高血压风险预测模型 \=-for females. Conclusion The model has good prediction ability and could be used to identify individuals with high risk of hypertension.

Key words: Health management cohort, Prediction model, Hypertension

中图分类号:

R544.1

于涛,刘焕乐,冯新,徐付印,陈亚飞,薛付忠,张成琪. 基于健康管理队列的高血压风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 61-65.

YU Tao, LIU Huanle, FENG Xin, XU Fuyin, CHEN Yafei, XUE Fuzhong, ZHANG Chengqi. A hypertension risk prediction model based on health management cohort[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 61-65.

参考文献

[1] Kearney PM, Whelton M, Reynolds K, et al. Global burden of hypertension: analysis of worldwide data[J]. Lancet, 2005, 365(9455): 217-223.
[2] 刘力生.中国高血压防治指南 2010[J]. 中华高血压杂志, 2011, 19(8): 701-708. Liu Lisheng. 2010 Chinese guidelines for the management of hypertension[J]. Chinese Journal of Hypertension, 2011, 19(8): 701-708.
[3] Wang R, Zhao Y, He X, et al. Impact of hypertension on health-related quality of life in a population-based study in Shanghai, China[J]. Public Health, 2009, 123(8): 534-539.
[4] Gao Y, Chen G, Tian H, et al. Prevalence of hypertension in China: a cross-sectional study[J]. PLoS One, 2013, 8(6): e65938. doi: 10.1371/journal.pone.0065938
[5] Wang J, Zhang L, Wang F, et al. Prevalence, awareness, treatment, and control of hypertension in China: results from a national survey[J]. Am J Hypertens, 2014, 27(11): 1355-1361.
[6] Liang Y, Liu R, Du S, et al. Trends in incidence of hypertension in Chinese adults, 1991—2009: The China Health and Nutrition Survey[J]. Int J Cardiol, 2014, 175(1): 96-101.
[7] Parikh NI, Pencina MJ, Wang TJ, et al. A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study[J]. Ann Intern Med, 2008, 148(2): 102-110.
[8] Chien KL, Hsu HC, Su TC, et al. Prediction models for the risk of new-onset hypertension in ethnic Chinese in Taiwan[J]. J Hum Hypertens, 2011, 25(5): 294-303.
[9] 李国奇, 刘静, 王薇, 等. 中国 35~64 岁人群 15 年高血压发生风险预测研究[J]. 中华高血压杂志, 2014, 22(10): 1000. LI Guoqi, LIU Jing, WANG Wei, et al. Prediction models for the 15 years risk of new-onset hypertension in Chinese people aged from 35 to 64 years old[J]. J Hypertens, 2014, 22(10): 1000.
[10] Kshirsagar AV, Chiu Y, Bomback AS, et al. A hypertension risk score for middle-aged and older adults[J]. J Clin Hypertens, 2010, 12(10): 800-808.
[11] Zhang M, Zhao Y, Sun H, et al. Effect of dynamic change in body mass index on the risk of hypertension: results from the Rural Chinese Cohort Study[J]. Int J Cardiol, 2017. doi: 10.1016/j.ijcard.2017.03.025.
[12] 陈捷, 赵秀丽, 武峰, 等. 我国 14 省市中老年人肥胖超重流行现状及其与高血压患病率的关系[J]. 中华医学杂志, 2005, 85(40): 2830-2834. CHEN Jie, ZHAO Xiuli, WU Feng, et al. Epidemiology of obesity and overweight and relation thereof to the prevalence of hypertension in 14 provinces/municipality in China[J]. Nat Med J Chi, 2005, 85(40): 2830-2834.
[13] Ferrannini E, Buzzigoli G, Bonadonna R, et al. Insulin resistance in essential hypertension[J]. N Engl J Med, 1987, 317(6): 350-357.
[14] Ceriello A, Quatraro A, Giugliano D. Diabetes mellitus and hypertension: the possible role of hyperglycaemia through oxidative stress[J]. Diabetologia, 1993, 36(3): 265-266.
[15] Title LM, Cummings PM, Giddens K, et al. Oral glucose loading acutely attenuates endothelium-dependent vasodilation in healthy adults without diabetes: an effect prevented by vitamins C and E[J]. J Am Coll Cardiol, 2000, 36(7): 2185-2191.
[16] Jae SY, Kurl S, Laukkanen JA, et al. Higher blood hematocrit predicts hypertension in men[J]. J Hypertens, 2014, 32(2): 245-250.
[17] 李玉芬, 陈燕, 戚其学. 高血压患者血液流变学变化及临床意义[J]. 中国医科大学学报, 2002, 31(4): 310-311.
[18] Devereux RB, Case DB, Alderman MH, et al. Possible role of increased blood viscosity in the hemodynamics of systemic hypertension[J]. Am J Cardiol, 2000, 85(10): 1265-1268.
[19] Emamian M, Hasanian SM, Tayefi M, et al. Association of hematocrit with blood pressure and hypertension[J]. J Clin Lab Anal, 2017. doi: 10.1002/jcla.22124.
[20] Sesso HD, Buring JE, Chown MJ, et al. A prospective study of plasma lipid levels and hypertension in women[J]. Arch Intern Med, 2005, 165(20): 2420-2427.
[21] Rader DJ, Hovingh GK. HDL and cardiovascular disease[J]. Lancet, 2014, 384(9943): 618-625.
[22] Larsen CM, McCully RB, Murphy JG, et al. Usefulness of high-density lipoprotein cholesterol to predict survival in pulmonary arterial hypertension[J]. Am J Cardiol, 2016, 118(2): 292-297.
[23] 周晓璐, 夏世勤, 王茂林, 等. 血清高密度脂蛋白水平对原发性高血压患者预后的影响[J]. 基层医学论坛, 2014, 18(4): 497-499.
[24] 吴晓强, 李豪侠, 李山平. 基于高密度脂蛋白、甘油三酯、总胆固醇水平评估老年原发性高血压患者预后情况[J]. 中国卫生检验杂志, 2016,26(15): 2204-2206. WU Xiaoqiang, LI Haoxia, LI Shanping. Assessment of prognosis in elderly patients with essential hypertension based on high density lipoprotein, triglycerides and total cholesterol levels[J]. Chinese Journal of Health Laboratory Technology, 2016, 26(15): 2204-2206.
[25] 李忠. 原发性高血压患者血清高密度脂蛋白胆固醇水平与预后的关系[J]. 中国老年学杂志, 2013, 33(18): 4593-4594.

相关文章 15

[1]	吴逸南葛志明李方贺红姜虹张运. 自发性高血压大鼠肾脏血管紧张素转换酶2的表达[J]. 山东大学学报(医学版), 2209, 47(6): 5-.
[2]	孙红林,管玉强,于培儒,李磊,杜庆霞,丁浩. 支架植入治疗儿童主动脉缩窄并高血压的可行性及近中期疗效[J]. 山东大学学报 (医学版), 2026, 64(4): 58-62.
[3]	王建民,李晓峰,由志涛,董圣杰,赵宇驰,李占菊,邹德鑫,张剑锋,孙涛,杜伟. 基于可解释机器学习的后路腰椎椎体间融合术后慢性疼痛风险预测模型构建[J]. 山东大学学报 (医学版), 2026, 64(2): 78-88.
[4]	孙爽爽,仉率杰,张伯韬,袁莹,于媛媛,薛付忠. 基于真实世界研究的18~50岁人群急性缺血性卒中影响因素[J]. 山东大学学报 (医学版), 2025, 63(9): 40-46.
[5]	申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.
[6]	王宁,郝秀梅,牛翔,黄金明,徐静雅. 中国中老年高血糖、血脂异常、高血压人群健康体检服务利用现状及影响因素[J]. 山东大学学报 (医学版), 2025, 63(7): 82-91.
[7]	王丽云,高天勤,刘雨佳,陈青,陈柳,沙凯辉. 基于机器学习产后压力性尿失禁风险预测模型的构建及验证[J]. 山东大学学报 (医学版), 2025, 63(6): 55-66.
[8]	陈瑛翼,游倩,王意,张帆,李凤,季舒铭,徐浩源,饶志勇. 办公室职员肌肉质量减少预测模型的开发与验证[J]. 山东大学学报 (医学版), 2025, 63(4): 26-35.
[9]	王宝炫,焦杰,张厚君,刘奇,于冠英. 衰弱与肌少症评估在胃肠道肿瘤术后结局预测中的应用与展望[J]. 山东大学学报 (医学版), 2025, 63(4): 51-58.
[10]	李永,崔书君,杨飞,张凡,殷晓霞. 基于增强MRI的亚区域影像组学模型可预测乳腺癌患者新辅助化疗后的病理完全反应[J]. 山东大学学报 (医学版), 2025, 63(1): 81-89.
[11]	王瑞,于康,刘燕萍. 维生素K₂与妊娠及妊娠相关疾病的研究进展[J]. 山东大学学报 (医学版), 2024, 62(8): 9-17.
[12]	孙丽娜,白红艳,牛宗格,张福帅,曲仪庆. 基于SII构建及评价预测ARDS住院死亡率的在线临床风险模型[J]. 山东大学学报 (医学版), 2024, 62(7): 10-20.
[13]	郭振江,王宁,赵光远,杜立强,崔朝勃,刘防震. 基于机器学习建立术前预测近端胃癌食管切缘阳性模型[J]. 山东大学学报 (医学版), 2024, 62(7): 78-83.
[14]	刁玉洁,林琳,李文瑄,王洲洋,江蓓,胡迎迎,刘广义. NPR预测ANCA相关血管炎不良肾脏预后及其协同多因素优化模型[J]. 山东大学学报 (医学版), 2024, 62(2): 60-68.
[15]	于晴晴,常开锋,李吉庆,张凯,付苹,刘晓雯,赵英淇,薛付忠. 中老年人群高血压在非甾体抗炎药暴露与心脑血管疾病风险关联中的中介分析[J]. 山东大学学报 (医学版), 2023, 61(8): 79-85.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于健康管理队列的高血压风险预测模型

A hypertension risk prediction model based on health management cohort

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

多维度评价

本文评价

推荐阅读 0