基于低氧参数的阻塞性睡眠呼吸暂停患者呼吸事件类型预测模型

Prediction models for respiratory event types in OSA patients based on hypoxic parameters

目的分析阻塞性睡眠呼吸暂停(OSA)患者低氧参数,探究其在不同类型呼吸事件中的差异及联系,并构建呼吸事件类型预测模型。方法选取经多导睡眠监测(PSG)诊断为OSA的50例患者进行回顾性分析,其中男41例、女9例,年龄18~74(45.72±13.39)岁。对患者的整夜记录数据中所有伴有脉搏血氧饱和度(SpO_(2))下降的呼吸事件,根据事件类型分为低通气(3316个)、阻塞型呼吸暂停(OA,5552个)、中枢型呼吸暂停(CA,1088个)、混合型呼吸暂停(MA,1369个),所有事件记录分别从PSG软件导出为逗号分隔变量(.csv)文件,使用内部构建的Matlab软件导入和分析。比较四组间SpO_(2)最低值(e-minSpO_(2))、SpO_(2)下降幅度(ΔSpO_(2))、SpO_(2)下降回升持续时间(DSpO_(2))、SpO_(2)下降持续时间(d.DSpO_(2))、SpO_(2)回升持续时间(r.DSpO_(2))、SpO_(2)<90%持续时间(T90)、下降时SpO_(2)<90%持续时间(d.T90)、回升时SpO_(2)<90%持续时间(r.T90)、SpO_(2)<90%曲线下面积(ST90)、下降时SpO_(2)<90%曲线下面积(d.ST90)、回升时SpO_(2)<90%曲线下面积(r.ST90)、氧降速率(ODR)、复氧速率(ORR)共13个低氧参数差异,并分别构建低通气模型(H)、OA模型(O)、CA模型(C)及MA模型(M);采用单因素分析及Kruskal-Wallis H检验比较组间各低氧参数的差异。针对不同呼吸事件类型,运用二元logistic回归方法确定进入方程模型的变量,绘制受试者工作特征(ROC)曲线,比较4个模型的敏感度、特异度、阳性预测值、阴性预测值,评估预测模型的准确性。结果各类型呼吸事件的ΔSpO_(2)、ODR、ORR、T90、d.T90、r.T90、ST90、d.ST90、r.ST90呈MA>OA>CA>低通气,e-minSpO_(2)呈MA<OA<CA<低通气;logistic回归显示e-minSpO_(2)、ΔSpO_(2)、d.DSpO_(2)、r.DSpO_(2)、ODR、ORR、d.T90、r.T90、d.ST90、r.ST90为低通气的独立预测因子,ΔSpO_(2)、d.DSpO_(2)、r.DSpO_(2)、ORR、d.T90、r.T90、d.ST90、r.ST90为OA的独立预测因子,ΔSpO_(2)、d.DSpO_(2)、r.DSpO_(2)、ODR、ORR、r.T90、d.ST90、r.ST90为CA的独立预测因子,e-minSpO_(2)、ΔSpO_(2)、d.DSpO_(2)、r.T90、d.ST90、r.ST90为MA的独立预测因子;模型H、O、C、M的ROC曲线下面积(AUC)分别为0.875、0.751、0.755、0.749,且所有模型特异度(分别为0.865、0.722、1.000、0.993)、阴性预测值均较高(分别为0.871、0.692、0.904、0.881)。结论基于低氧参数可建立4种呼吸事件类型预测模型,为应用夜间SpO_(2)自动识别呼吸事件类型提供了一种可行的新型工具。

Objective To analyze the hypoxic parameters in patients with obstructive sleep apnea(OSA),to explore the difference and association between different types of respiratory events and to construct predictive models for respiratory event types.Methods Fifty patients[including 41 males and 9 females with age 18-74(45.72±13.39)years]with OSA diagnosed by polysomnography(PSG)were selected for retrospective analysis,and all respiratory events with pulse oximetry(SpO_(2))desaturation in the recorded overnight data were divided into hypopnea group(Hyp,3316),obstructive apnea group(OA,5552),central apnea group(CA,1088)and mixed apnea group(MA,1369)according to the type of events,and all event records were exported separately from the PSG software as comma-separated variable(.csv)files,which were imported and analyzed using the in-house built Matlab software.A total of 13 hypoxic parameter differences were compared among the four groups,including minimum oxygen saturation of events(e-minSpO_(2)),the depth of desaturation(ΔSpO_(2)),the duration of desaturation and resaturation(DSpO_(2)),the duration of desaturation(d.DSpO_(2)),duration of resaturation(r.DSpO_(2)),duration of SpO_(2)<90%(T90),duration of SpO_(2)<90%during desaturation(d.T90),duration of SpO_(2)<90%during resaturation(r.T90),area under the curve of SpO_(2)<90%(ST90),area under the curve of SpO_(2)<90%during desaturation(d.ST90),area under the curve of SpO_(2)<90%during resaturation(r.ST90),oxygen desaturation rate(ODR)and oxygen resaturation rate(ORR).Hyp model(H),OA model(O),CA model(C)and MA model(M)were constructed respectively;group differences for the different hypoxia parameters were assessed using single factor analysis and Kruskal-Wallis H test.For different categories of respiratory events,binary logistic regression was used to identify the variables included in the regression model.Receiver operating characteristic(ROC)curves were generated to assess and compare the sensitivity,specificity,positive predictive value and negative predictive value of the four models,thereby gauging the predictive precision of each model.ResultsΔSpO_(2),ODR,ORR,T90,d.T90,r.T90,ST90,d.ST90 and r.ST90 for each type of respiratory events showed MA>OA>CA>Hyp,and e-minSpO_(2) showed MA<OA<CA<Hyp.Logistic regression showed that e-minSpO_(2),ΔSpO_(2),d.DSpO_(2),r.DSpO_(2),ODR,ORR,d.T90,r.T90,d.ST90 and r.ST90 were independent predictors for Hyp,ΔSpO_(2),d.DSpO_(2),r.DSpO_(2),ORR,d.T90,r.T90,d.ST90 and r.ST90 were independent predictors for OA,ΔSpO_(2),d.DSpO_(2),r.DSpO_(2),ODR,ORR,r.T90,d.ST90 and r.ST90 were independent predictors for CA,while e-minSpO_(2),ΔSpO_(2),d.DSpO_(2),r.T90,d.ST90 and r.ST90 were independent predictors for MA.The area under the ROC curve(AUC)of the H,O,C,and M models were 0.875,0.751,0.755,and 0.749,respectively,and all models had high specificity(0.865,0.722,1.000,and 0.993,respectively)and negative predictive values(0.871,0.692,0.904,and 0.881,respectively).Conclusions Four predictive models for respiratory event types can be constructed based on hypoxic parameters,providing a feasible novel tool for applying nocturnal SpO_(2) to automatically identify respiratory event types.