机构地区:[1]中南大学湘雅二医院全科医学科,湖南省长沙市410001 [2]中南大学湘雅二医院老年医学科,湖南省长沙市410001
出 处:《中国全科医学》2023年第20期2459-2468,共10页Chinese General Practice
基 金:湖南省自然科学基金资助项目(2021JJ30944)。
摘 要:背景阻塞性睡眠呼吸暂停低通气综合征(OSAHS)在高血压患者中患病率高,但诊断率低,其中心率变异性(HRV)和血压变异性(BPV)都是心血管事件相关预测因子,但目前关于OSAHS与高血压患者BPV和HRV内在联系的相关研究较少。目的本研究旨在探讨OSAHS对高血压患者HRV、BPV的影响,并开发和内、外部验证一种通过HRV和BPV相关指标预测高血压患者OSAHS患病风险的列线图。方法选取2018年1月—2020年12月在中南大学湘雅二医院收治的228例高血压患者作为研究对象,根据OSAHS诊断标准分为单纯高血压组(n=114)和高血压合并OSAHS组(n=114);另外收集2021年1—2月住院的34例高血压伴或不伴OSAHS患者作为独立的外部验证组。收集研究对象的一般资料(年龄、性别、BMI等)、平均血压水平〔夜间收缩压(nSBP)等〕、BPV相关指标〔夜间收缩压标准差(nSSD)、夜间舒张压标准差(nDSD)、24 h舒张压标准差(24 hDSD)等〕、血压昼夜节律、HRV相关指标〔RR间期平均值标准差(SDANN)、低频带(LF)等〕、多导睡眠监测(PSG)参数〔氧减指数(ODI)、睡眠呼吸暂停低通气指数(AHI)、最低血氧饱和度(MinSpO_(2))等〕。采用多元线性回归分析探究HRV和BPV相关影响因素;并绘制限制性立方样条图检验高血压患者平均血压水平、BPV和HRV相关指标与OSAHS患病风险的相关性;通过多因素Logistic回归分析高血压患者患OSAHS的影响因素,构建列线图预测模型,采用Bootstrap方法在检验内、外部组验证组在列线图模型中的性能;采用受试者工作特征(ROC)曲线评估内、外部验证组列线图对高血压患者OSAHS患病风险的预测价值,计算ROC曲线下面积(AUC)等指标。结果多元线性回归分析结果显示:BMI、ODI、MinSpO_(2)是高血压合并OSAHS组患者nSSD、nDSD水平和HRV相关指标的独立影响因素(P<0.05);限制性立方样条模型结果显示BPV、HRV相关指标与发生OSAHS存在非线性相关Background Obstructive sleep apnea-hypopnea syndrome(OSAHS)is highly prevalent but is underdiagnosed in hypertensive patients.There are few studies on the internal association of OSAHS with two predictors of cardiovascular events,namely heart rate variability(HRV)and blood pressure variability(BPV),in hypertensive patients.Objective To explore the influence of OSAHS on HRV and BPV in hypertension patients,and to develop and validate a nomogram for predicting the risk of OSAHS in these patients using HRV and BRV related indicators.Methods Two hundred and twenty-eight hypertensive patients〔including 114 without OSAHS(simple hypertension subgroup)and 114 with OSAHS(hypertension with OSAHS subgroup)assessed by the diagnostic criteria of OSAHS〕were selected as internal validation group from the Second Xiangya Hospital of Central South University from January 2018 to December 2020,and other 34 hypertensive patients with or without OSAHS who hospitalized in the same hospital during January to February 2021 were selected as an independent external verification group.General information(age,gender,BMI,etc.),average blood pressure level〔nighttime systolic blood pressure(nSBP),etc.〕,BPV related indices〔nighttime systolic blood pressure standard deviation(nSSD),nighttime diastolic blood pressure standard deviation(nDSD),24-hour diastolic blood pressure standard deviations(24 hDSD),etc〕,blood pressure circadian rhythm,HRV related parameters〔standard deviation of the mean RR intervals(SDANN),low frequency(LF),etc.〕,polysomnography parameters〔oxygen desaturation index(ODI),apnea hypopnea index(AHI),minimum oxygen saturation(MinSpO_(2)),etc.〕.Multiple linear regression analysis were used to explore the influencing factors of HRV and BPV.Restricted cubic splines were used to test the correlation of the average blood pressure level,BPV and HRV related indicators with the risk of OSAHS.Multivariate Logistic regression model was used to analyze the influencing factors of OSAHS,and the screened factors were used t
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