阻塞性睡眠呼吸暂停低通气综合征的亚型分析

发布时间：2018-09-01 14:25

【摘要】：目的分析阻塞性睡眠呼吸暂停低通气综合征(OSAHS)亚型,为其个体化治疗提供依据。方法选择OSAHS患者308例,收集患者年龄、BMI、呼吸暂停低通气指数(AHI)、最低血氧饱和度(LSa O2)、最长呼吸暂停时间(LAT)、Epworth嗜睡量表评分(ESS评分)、微觉醒次数、吸烟指数、合并症、症状评分资料,采用主成分分析法和聚类分析法对其进行亚型分类。结果 308例OSAHS患者经主成分分析得到5个主成分,累加方差贡献率为73.091%。其中,主成分1包括AHI、BMI、ESS评分、LSa O2,主成分2包括年龄、合并症,主成分3包括ESS评分、症状评分,主成分4包括LAT、吸烟指数,主成分5为微觉醒次数。经聚类分析将308例OSAHS患者分为4种亚型:亚型1为中年超重OSAHS患者,呼吸暂停时间最短,合并症最多;亚型2为中年肥胖OSAHS患者,缺氧程度最重,微觉醒次数最多;亚型3为青年肥胖OSAHS患者,微觉醒次数最少,ESS评分最高;亚型4为老年超重OSAHS患者,呼吸暂停时间最长,吸烟指数最高,症状最少。结论经主成分分析法和聚类分析法将OSAHS患者分为4个亚型,各亚型存在明显异质性,能更好地反映个体差异。
[Abstract]:Objective to analyze (OSAHS) subtypes of obstructive sleep apnea hypopnea syndrome (OSAS) and to provide evidence for individualized treatment. Methods three hundred and eighty-eight patients with OSAHS were selected to collect age, (AHI), minimum oxygen saturation (LSa O 2), longest apnea time (LAT) Epworth sleepiness scale (ESS score), times of arousal, smoking index, complication, symptom score, and apnea hypopnea index (LSa O 2). Principal component analysis and cluster analysis were used to classify its subtypes. Results five principal components were obtained by principal component analysis in 308 patients with OSAHS, and the cumulative variance contribution rate was 73.091. Principal component 1 includes AHI,BMI,ESS score, principal component 2 includes age, complications, principal component 3 includes ESS score, symptom score, principal component 4 includes LAT, smoking index, principal component 5 is the number of arousal. According to cluster analysis, 308 patients with OSAHS were divided into four subtypes: subtype 1 was middle aged and overweight OSAHS patients, apnea time was the shortest, complication was the most, subtype 2 was middle aged obese OSAHS patients, the degree of hypoxia was the most serious, and the number of arousal was the highest. Subtype 3 was the young obese OSAHS patients, and subtype 4 was the elderly overweight OSAHS patients with the longest apnea time, the highest smoking index and the least symptom. Conclusion the patients with OSAHS can be divided into four subtypes by principal component analysis and cluster analysis. Each subtype has obvious heterogeneity and can better reflect individual differences.
【作者单位】： 南昌大学第二附属医院;
【分类号】：R766
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本文编号：2217477