|
|
|
| Establishment of a TreeNet algorithm-based model for early prediction of essential hypertension |
| YU Xiaohong, QIAN Yanmei, ZHOU Chenjie, MA Yue, TANG Yanchao, ZOU Lingli
|
| The Third Sanatorium Department, Air Force Special Service Sanatorium, Hangzhou, Zhejiang 310002, China |
|
|
|
|
Abstract Objective To create a model for early prediction of essential hypertension (EH) based on the TreeNet algorithm, so as to provide a tool for early monitoring of EH. Methods The health examination data were collected from individuals receiving health examinations in Hangzhou Haiqin Health Examination Center or Shanghai Yibao Health Management Co., Ltd from 2014 to 2016, and a predictive model for EH was created based on the TreeNet algorithm. The effectiveness of the model for early prediction of EH was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), coefficient of determination (R2) and receiver operating characteristic (ROC) curve. Results A total of 4 264 healthy examination data were collected, and the prevalence of EH was 5.25% among the participants. A total of 12 variables were included in the model, and the highest contributing variable was body mass index (BMI), followed by BMI difference, two-year BMI difference, two-year triglyceride (TG) difference, two-year total cholesterol (TC) difference, high-density lipoprotein cholesterol (HDL-C) in 2014, TG in 2014, low-density lipoprotein cholesterol (LDL-C) in 2014, body weight in 2015, fasting blood glucose in 2015, TG in 2015, urea nitrogen difference and platelet in 2015. The highest predictive accuracy was 100.00%, and the lowest was 56.89%. The risk of EH significantly increased among individuals with BMI in 2015 of >25 kg/m2, two-year BMI difference of >0.5 kg/m2, two-year TG difference ranging from 1.3 to 3.3 mmol/L, TC in 2015 of 2.0 to 2.4 mmol/L and HDL-C in 2014 of <0.52 mmol/L. The model presented RMSE of 0.082, MAD of 0.064, R2 of 0.811, area under the ROC curve of 0.788 (95%CI: 0.741-0.815), sensitivity of 69.05% and specificity of 66.21% for prediction of EH. Conclusion The TreeNet algorithm-based model is effective for early monitoring of high-risk individuals for EH.
|
|
Received: 09 March 2022
Revised: 08 July 2022
Published: 08 September 2022
|
|
|
|
|
|
[1] 赵冬. 中国成人高血压流行病学现状[J].中国心血管杂志,2020,25(6):513-515.
ZHAO D.Current epidemiology of adult hypertension in China[J].Chin J Cardiovasc Med,2020,25(6):513-515.
[2] 李禄伟,黄倩,施佳成,等.基于三种统计学方法构建的超重及肥胖人群高血压发病预测模型的分析比较[J].现代预防医学,2021,48(11):2061-2065.
LI L W,HUANG Q,SHI J C,et al.Screening risk factors and interaction analysis of hypertension in overweight and obesity population based on three statistical models[J].Mod Prev Med,2021,48(11):2061-2065.
[3] 王定坤,杨杉.基于COX比例风险模型分析心力衰竭影响因素[J].电脑知识与技术(学术版),2021,17(24):33-35.
WANG D K,YANG S.Analysis of influencing factors for heart failure based on COX proportional hazard model[J].Comput Knowl Technol,2021,17(24):33-35.
[4] 付菲,彭映辉,徐肇元,等.急性心肌梗死患者心力衰竭风险预测模型研究[J].中国心血管杂志,2021,26(6):525-530.
FU F,PENG Y H,XU Z Y,et al.Study on risk prediction model of heart failure in patients with acute myocardial infarction[J].Chin J Cardiovasc Med,2021,26(6):525-530.
[5] 刘仕俊,袁寒艳,姜彩霞,等.杭州市老年高血压患者血压控制的影响因素研究[J].预防医学,2021,33(7):660-664.
LIU S J,YUAN H Y,JIANG C X,et al.Influencing factors for blood pressure control in elderly patients with hypertension in Hangzhou[J].Prev Med,2021,33(7):660-664.
[6] PADMAJA B,RAMA PRASAD V V,SUNITHA V N.TreeNet analysis of human stress behavior using socio-mobile data[J/OL].J Big Data,2016,4(1)[2022-07-08].https://doi.org/10.1186/s40537-016-0054-3.
[7] 中国高血压防治指南修订委员会,高血压联盟(中国),中华医学会心血管病学分会,等.中国高血压防治指南(2018年修订版)[J].中国心血管杂志,2019,24(1):24-56.
Writing Group of 2018 Chinese Guidelines for the Management of Hypertension,Chinese Hypertension League,Chinese Society of Cardiology,et al.2018 Chinese guidelines for the management of hypertension[J].Chin J Cardiovasc Med,2019,24(1):24-56.
[8] 张宇宁,郑浩,梁洁,等.老年人体重指数与血压水平及高血压患病率的相关性[J].中国老年学杂志,2021,41(20):4333-4335.
ZHANG Y N,ZHENG H,LIANG J,et al.Relationship between aged people body mass index and blood pressure and prevalence rate of hypertension[J].Chin J Gerontol,2021,41(20):4333-4335.
[9] 高仲淳,邹波,蓝恭赛,等.20~59岁成年人体质指数随年龄变化轨迹与高血压发病的关系研究[J].中国全科医学,2021,24(8):954-958.
GAO Z C,ZOU B,LAN G S,et al.The relationship between trajectory of body mass index based on age and the incidence of hypertension in adults aged 20 to 59 years[J].Chin Gen Pract,2021,24(8):954-958.
[10] KAMPMANN U,MATHIASSEN O N,CHRISTENSEN K L,et al.Effects of renal denervation on insulin sensitivity and inflammatory markers in nondiabetic patients with treatment-resistant hypertension[J/OL].J Diabetes Res,2017[2022-07-08].https://doi.org/10.1155/2017/6915310.
[11] D'ELIA L,STRAZZULLO P.Excess body weight,insulin resistance and isolated systolic hypertension:potential pathophysiological links[J].High Blood Press Cardiovasc Prev,2017,25(7):1377-1389.
[12] TAHERI A,MIRZABABAEI A,SETAYESH L,et al.The relationship between Dietary approaches to stop hypertension diet adherence and inflammatory factors and insulin resistance in overweight and obese women:a cross-sectional study[J/OL].Diabetes Res Clin Pract,2021,182[2022-07-08].https://doi.org/10.1016/j.diabres.2021.109128.
[13] RAJAMANI A,BORKOWSKI K,AKRE S,et al.Oxylipins in triglyceride-rich lipoproteins of dyslipidemic subjects promote endothelial inflammation following a high fat meal[J/OL].Sci Rep,2019,9(1)[2022-07-08].https://doi.org/10.1038/s41598-019-45005-5.
[14] 丁存涛,周亚群,孙希鹏,等.糖脂代谢对原发性高血压病人血管内皮功能的影响[J].首都医科大学学报,2017,38(3):401-405.
DING C T,ZHOU Y Q,SUN X P,et al.Effects of glucose and lipid metabolism on vascular endothelial function in patients with essential hypertension[J].J Cap Med Univ,2017,38(3):401-405.
[15] LANDI F,MARTONE A M,SALINI S,et al.Effects of a new combination of medical food on endothelial function and lipid profile in dyslipidemic subjects:a pilot randomized trial[J/OL].Biomed Res Int,2019(6)[2022-07-08].https://doi.org/10.1155/2019/1970878.
[16] DJINDJIĆ B,RADOVANOVIĆZ L,KOSTIĆ T,et al.The changes of oxidative stress and endothelial function biomarkers after 6 weeks of aerobic physical training in patients with stable ischemic coronary disease[J].Mil Med Pharm J Serbia,2017,74(11):1060-1065. |
|
|
|
