A nomogram based on endothelial function and conventional risk factors predicts coronary artery disease in hypertensives

被引:5
|
作者
Huang, Xiao-Dong [1 ,2 ,3 ]
Lin, Ji-Yan [1 ,3 ]
Huang, Xian-Wei [1 ,3 ]
Zhou, Ting-Ting [4 ]
Xie, Liang-Di [2 ,5 ]
机构
[1] Xiamen Univ, Affiliated Hosp 1, Dept Emergency, Xiamen 361003, Peoples R China
[2] Fujian Med Univ, Affiliated Hosp 1, Fujian Hypertens Res Inst, Fuzhou 350005, Peoples R China
[3] Xiamen Univ, Affiliated Hosp 1, Xiamen Key Lab Clin Efficacy & Evidence Based Res, Xiamen 361003, Peoples R China
[4] Xiamen Haicang Hosp, Dept Cardiovasc Med, Xiamen 361026, Peoples R China
[5] Fujian Med Univ, Affiliated Hosp 1, Dept Geriatr, Fuzhou 350005, Peoples R China
关键词
Essential hypertension; Coronary artery disease; Flow-mediated dilation; Endothelial function; Nomogram; FLOW-MEDIATED VASODILATION; CARDIOVASCULAR-DISEASE; BRACHIAL-ARTERY; DILATION;
D O I
10.1186/s12872-023-03235-6
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundThere is currently a lack of a precise, concise, and practical clinical prediction model for predicting coronary artery disease (CAD) in patients with essential hypertension (EH). This study aimed to construct a nomogram to predict CAD in patients with EH based on flow-mediated dilation (FMD) of brachial artery and traditional risk factors.MethodsClinical data of 1752 patients with EH were retrospectively collected. High-resolution vascular ultrasound was used to detect FMD in all patients at the Fujian Hypertension Research Institute, China. Patients were divided into two groups, i.e. training group (n = 1204, from August 2000 to December 2013) and validation group (n = 548, from January 2014 to May 2016) according to the time of enrollment. Independent predictors of CAD were analyzed by multivariable logistic regression in the training group, and a nomogram was constructed accordingly. Finally, we evaluated the discrimination, calibration, and clinical applicability of the model using the area under curve (AUC) of receiver operating characteristic analysis, calibration curve combined with Hosmer-Lemeshow test, and decision curve, respectively.ResultsThere were 263 (21.8%) cases of EH combined with CAD in the training group. Multivariate logistic regression showed that FMD, age, duration of EH, waist circumference, and diabetes mellitus were independent influencing factors for CAD in EH patients. Smoking which was close to statistical significance (P = 0.062) was also included in the regression model to increase the accuracy. Ultimately, the nomogram for predicting CAD in EH patients was constructed according to above predictors after proper transformation. The AUC values of the training group and the validation group were 0.799 (95%CI 0.770-0.829) and 0.836 (95%CI 0.787-0.886), respectively. Calibration curve and Hosmer-Lemeshow test showed that the model had good calibration (training group: chi(2) = 0.55, P = 0.759; validation group: chi(2) = 1.62, P = 0.446). The decision curve also verified the clinical applicability of the nomogram.ConclusionThe nomogram based on FMD and traditional risk factors (age, duration of EH disease, smoking, waist circumference and diabetes mellitus) can predict CAD high-risk group among patients with EH.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Can coronary artery disease be detected by endothelial function of the brachial artery in patients with suspected coronary artery disease?
    Teragawa, H
    Kato, M
    Kurokawa, J
    Hirano, N
    Masanaga, T
    Otsu, N
    Komoto, K
    Nakashima, K
    EUROPEAN HEART JOURNAL, 2000, 21 : 81 - 81
  • [32] Conventional Risk Factors and Cardiovascular Outcomes of Patients with Inflammatory Bowel Disease with Confirmed Coronary Artery Disease
    Aggarwal, Ashish
    Atreja, Ashish
    Kapadia, Samir
    Lopez, Rocio
    Achkar, Jean-Paul
    INFLAMMATORY BOWEL DISEASES, 2014, 20 (09) : 1593 - 1601
  • [33] Homocysteine, traditional risk factors and impaired renal function in coronary artery disease
    Pizzolo, F.
    Friso, S.
    Olivieri, O.
    Martinelli, N.
    Bozzini, C.
    Guarini, P.
    Trabetti, E.
    Faccini, G.
    Corrocher, R.
    Girelli, D.
    EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2006, 36 (10) : 698 - 704
  • [34] Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease
    Cao, Xinfu
    Sun, Yi
    Chen, Yuqiao
    Tang, Chao
    Yu, Hongwen
    Li, Xiaolong
    Gu, Zhenhua
    RISK MANAGEMENT AND HEALTHCARE POLICY, 2024, 17 : 1815 - 1826
  • [35] Impact of Vascular Risk Factors on Cognitive Function in a Coronary Artery Disease Population
    Mohammmad, Dana
    Bradley, Janelle
    Herrmann, Nathan
    Lanctot, Krista
    ANNALS OF NEUROLOGY, 2017, 82 : S136 - S136
  • [36] Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score
    Truong, Buu
    Ruan, Yunfeng
    Haidermota, Sara
    Patel, Aniruddh
    Surakka, Ida
    Hornsby, Whitney
    Koyama, Satoshi
    Lee, S. Hong
    Natarajan, Pradeep
    MED, 2024, 5 (05): : 459 - 468
  • [37] Novel nomogram for predicting coronary vulnerable plaque risk in patients with coronary artery disease
    Liu, Tao
    Ji, Hanhua
    Jian, Xinwen
    Wang, Weiyi
    Fan, Zeyuan
    BIOMARKERS IN MEDICINE, 2022, 16 (16) : 1139 - 1149
  • [38] Lung Function and Coronary Artery Disease Risk
    Nowak, Christoph
    CIRCULATION-GENOMIC AND PRECISION MEDICINE, 2018, 11 (04):
  • [39] Diabetes and risk factors for coronary artery disease
    Deepa, R
    Arvind, K
    Mohan, V
    CURRENT SCIENCE, 2002, 83 (12): : 1497 - 1505
  • [40] Emerging risk factors for coronary artery disease
    Wilson, P
    FIRST US-JAPANESE DIALOGUE ON LIPID DISORDERS AND CORONARY ARTERY DISEASE: NEW ISSUES FOR THE NEXT MILLENNIUM, 1999, (238): : 17 - 25