医学
血压
脉冲压力
心脏病学
内科学
人口
脉动流
血流动力学
脉冲波速
心率
环境卫生
作者
Marta Gómez‐Sánchez,Leticia Gómez‐Sánchez,Carmen Patino‐Alonso,José I. Recio-Rodríguez,Rosario Alonso‐Domínguez,Natalia Sánchez‐Aguadero,Cristina Lugones‐Sánchez,Emiliano Rodríguez‐Sánchez,Luis García‐Ortiz,Manuel A. Gomez-Marcos
标识
DOI:10.1097/hjh.0000000000002916
摘要
Objectives: Central blood pressure (BP) predicts mortality independent of office brachial BP. The aim was to describe reference values for central blood pressure and pulsatile hemodynamic parameters, and their relationship with cardiovascular risk factors in an adult Spanish population without cardiovascular disease. Methods: Cross-sectional study. We included 501 participants stratified by age and sex by random sampling, with a mean age of 56 years (50.3% women). The SphygmoCor System device's pulse wave analysis software was used to perform the measurements. Results: The following values were obtained: central blood pressure median (109/76 mmHg), central pulse pressure (33 mmHg), pulse pressure amplification (8.5 mmHg), ejection duration (130 ms) and subendocardial viability ratio (163%). All parameters were greater in men, except heart rate and ejection duration. In the logistic regression analysis, controlled for age, sex and taking antihypertensive drugs, being hypertensive was associated with cSBP (OR = 1.265), cDBP (OR = 1.307), cPP (OR = 1.067), pulse wave amplification (OR = 1.034) and SEVR (OR = 0.982); being diabetic was associated with SEVR (OR = 0.982); being obese was associated with cSBP (OR = 1.028) and cDBP (OR = 1.058) and being a smoker was associated with ejection duration (OR = 0.980) and SEVR (OR = 0.984). Conclusion: This study provides reference values for central blood pressure and parameters derived from the pulse wave analysis in a random sample of the Spanish population. The only risk factor that is not associated with any of the parameters analysed is dyslipidaemia. Trial registration number: https://clinicaltrials.gov/ct2/show/NCT02623894
科研通智能强力驱动
Strongly Powered by AbleSci AI