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Comparative Study between Sequential Automatic and Manual Home Respiratory Polygraphy Scoring Using a Three-Channel Device: Impact of the Manual Editing of Events to Identify Severe Obstructive Sleep Apnea

机译:使用三通道设备的顺序自动和手动家庭呼吸描记术评分之间的比较研究:手动编辑事件以识别严重阻塞性睡眠呼吸暂停的影响

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Objective. According to current guidelines, autoscoring of respiratory events in respiratory polygraphy requires manual scoring. The aim of this study was to evaluate the agreement between automatic analysis and manual scoring to identify patients with suspected OSA.Methods. This retrospective study analyzed 791 records from respiratory polygraphy (RP) performed at home. The association grade between automatic scoring and manual scoring was evaluated using Kappa coefficient and the agreement using Bland and Altman test and intraclass correlation coefficient (CCI). To determine the accuracy in the identification ofAHI≥30 eV/h, the ROC curve analysis was used.Results. The population analyzed consisted of 493 male (62.3%) and 298 female patients, with an average age of54.7±14.20years and BMI of32.7±8.21 kg/m2. There was no significant difference between automatic and manual apnea/hypopnea indexes (aAHI, mAHI): aAHI 17.25 (SD: 17.42) versus mAHI21.20±7.96(p; NS). The agreement between mAHI and aAHI toAHI≥30was 94%, with a Kappa coefficient of 0.83 (p<0.001) and a CCI of 0.83. The AUC-ROC, sensitivity, and specificity were 0.99 (CI 95%: 0.98-0.99,p<0.001), 86% (CI 95%: 78.7–91.4), and 97% (CI 95%: 96–98.3), respectively.Conclusions. We observed good agreement between automatic scoring and sequential manual scoring to identify subjects withAHI≥30 eV/h.
机译:目的。根据当前指南,在呼吸描写中对呼吸事件进行自动评分需要手动评分。这项研究的目的是评估自动分析和手动评分之间的一致性,以鉴定疑似OSA的患者。这项回顾性研究分析了在家进行的呼吸描记(RP)记录中的791条记录。使用Kappa系数评估自动评分和手动评分之间的关​​联等级,并使用Bland和Altman检验以及组内相关系数(CCI)评估一致性。为了确定AHI≥30 eV / h的准确性,使用了ROC曲线分析。分析的人群为493例男性(62.3%)和298例女性患者,平均年龄为54.7±14.20岁,BMI为32.7±8.21kg / m2。自动和手动呼吸暂停/呼吸不足指数(aAHI,mAHI):aAHI 17.25(SD:17.42)与mAHI21.20±7.96(p; NS)之间无显着差异。 mAHI和aAHI对AHI≥30的一致性为94%,Kappa系数为0.83(p <0.001),CCI为0.83。 AUC-ROC,敏感性和特异性分别为0.99(CI 95%:0.98-0.99,p <0.001),86%(CI 95%:78.7–91.4)和97%(CI 95%:96–98.3),结论。我们观察到自动评分和顺序手动评分在识别AHI≥30 eV / h的受试者之间有很好的一致性。

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