文献类型：期刊文献

中文题名：老年阻塞性睡眠呼吸暂停患者远期缺血性脑卒中的风险评分模型:多中心前瞻性队列研究

英文题名：A long-term ischemic stroke risk score model in patients aged 60 years and older with obstructive sleep apnea: a multicenter prospective cohort study

作者：苏小凤[1,2];韩继明[2];高莹卉[3];范利[4];何子君[2];赵哲[1,4];林俊岭[5];郭静静[6];陈开兵[7];高燕[8];刘霖[1]

第一作者：苏小凤

机构：[1]解放军总医院第二医学中心呼吸与危重症医学科、国家老年疾病临床医学研究中心,北京100853;[2]延安大学医学院,陕西延安716000;[3]北京大学国际医院睡眠中心,北京102206;[4]解放军总医院第二医学中心心血管内科、国家老年疾病临床医学研究中心,北京100853;[5]首都医科大学附属北京朝阳医院呼吸与危重症医学科,北京100020;[6]北京大学人民医院呼吸科,北京100013;[7]甘肃中医药大学附属医院,甘肃兰州730000;[8]解放军第960医院全科医学科,山东济南250031

第一机构：解放军总医院第二医学中心呼吸与危重症医学科、国家老年疾病临床医学研究中心,北京100853

年份：2022

卷号：42

期号：3

起止页码：338

中文期刊名：南方医科大学学报

外文期刊名：Journal of Southern Medical University

收录：CSTPCD;;Scopus;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；PubMed;

基金：军队保健专项科研基金(19BJZ34);国家老年疾病临床医学研究中心2018开放课题(NCRCG-PLAGH-2018008);解放军总医院军事医学青年项目(QNC19054);解放军总医院第二医学中心专项科研课题(ZXD2008)。

语种：中文

中文关键词：阻塞性睡眠呼吸暂停;老年;缺血性脑卒中;模型;风险预测

外文关键词：obstructive sleep apnea;elderly;ischemic stroke;ModeL;risk prediction

摘要：目的基于临床资料分析老年阻塞性睡眠呼吸暂停(OSA)患者远期缺血性脑卒中发生的独立危险因素,构建老年OSA患者缺血性脑卒中风险评分模型并进行验证,为相关临床治疗提供参考。方法采用多中心前瞻性队列设计。2015年1月~2017年10月,多中心连续纳入年龄≥60岁、基线无明显心脑血管病史且无重要临床指标缺失的的首次确诊为OSA的老年患者进行随访,随访结局为缺血性脑卒中的发生情况。收集所有纳入研究对象的基线人口学资料、临床特征、睡眠参数指标、实验室和超声检查结果,以3∶1的比例将其随机分为建模组856例和验证组258例。采用LASSO回归用于变量的降维和筛选,并基于Cox比例风险回归构建老年OSA相关的缺血性脑卒中风险评分预测模型。结果共入组1141例研究对象,中位随访42(41,54)月内,58例发生缺血性脑卒中,累计发病率为5.08%,其中建模组和验证组缺血性脑卒中的累计发病率分别为5.14%和4.91%(P<0.05)。多变量逐步Cox比例风险回归的变量筛选结果显示,年龄(HR=3.44,95%CI:2.38~7.77)、空腹血糖(FPG)(HR=2.13,95%CI:1.22~3.72)、升主动脉内径(HR=2.60,95%CI:1.04~4.47)、左心房前后径(HR=1.98,95%CI:1.75~2.25)和最低氧饱和度(LSpO2)(HR=1.57,95%CI:1.20~1.93)是缺血性脑卒中发生风险的独立危险因素(P<0.05,P<0.01);利用ROC曲线分析进行比变量转化,以这5个风险变量的回归系数比取整数构建老年OSA患者远期缺血性脑卒中风险评分模型。Bootstrap法(自抽样次数=500)前后,建模组队列风险评分模型的ROC曲线下面积(AUC)分别为0.84(95%CI:0.78~0.90)和0.85(95%CI:0.78~0.89),验证组队列风险评分模型的ROC曲线下面积(AUC)分别为0.83(95%CI:0.73~0.93)和0.82(95%CI:0.72~0.90),提示模型预测效能较好且稳健性高。以模型最佳临床截点的对应值进行风险分层后的生存分析结果显示,高风险组OSA患者的缺血性脑卒中累积发生率高于低风险组(P=0.021)。结论该模型有助于在老年人群中识别高风险OSA患者进行早期干预,以降低今后与OSA潜在相关的缺血性脑卒中风险。
Objective To analyze the independent risk factors of long-term ischemic stroke and establish a nomogram for predicting the long-term risks in elderly patients with obstructive sleep apnea(OSA). Methods This multicenter prospective cohort study was conducted from January, 2015 to October, 2017 among consecutive elderly patients(≥60 years) with newly diagnosed OSA without a history of cardio-cerebrovascular diseases and loss of important clinical indicators. The follow-up outcome was the occurrence of ischemic stroke. The baseline demographic and clinical data, sleep parameters, laboratory and ultrasound results were collected from all the patients, who were randomized into the modeling group(n=856) and validation group(n=258) at a 3∶1ratio. LASSO regression was used for variable reduction and dimension screening, and the risk score prediction model of ischemic stroke was established based on Cox proportional hazard regression. Results In the total of 1141 patients enrolled in this study, 58(5.08%) patients experienced ischemic stroke during the median follow-up of 42 months(range 41-54months). The cumulative incidence of ischemic stroke was 5.14% in the model group and 4.91% in the verification group(P<0.05). Age(HR=3.44, 95% CI: 2.38-7.77), fasting blood glucose(FPG)(HR=2.13, 95% CI: 1.22-3.72), internal diameter of the ascending aorta(HR=2.60, 95% CI: 1.0-4.47), left atrial anteroposterior diameter(HR=1.98, 95% CI: 1.75-2.25) and minimum oxygen saturation(LSp O2)(HR=1.57, 95% CI: 1.20-1.93)were identified as independent risk factors for ischemic stroke(P<0.05 or 0.01). A long-term ischemic stroke risk score model was constructed based the regression coefficient ratios of these 5 risk variables. Before and after the application of the Bootstrap method, the AUC of the cohort risk score model was 0.84(95% CI: 0.78-0.90) and 0.85(95% CI: 0.78-0.89) in the model group and was 0.83(95% CI: 0.73-0.93) and 0.82(95%CI: 0.72-0.90) in the verification group, respectively, suggesting a good prediction efficiency and high robustness of the model. At the best clinical cutoff point, the cumulative incidence of ischemic stroke was significantly higher in the high-risk group than in the low-risk group(P=0.021). Conclusion This model can help to identify high-risk OSA patients for early interventions of the risks of ischemic stroke associated with OSA.