在这项研究中,我们旨在基于远程医疗管理平台确定持续气道正压通气(CPAP)用户资料的不同亚型,并确定各种依从性模式的临床和心理预测因子.在治疗期间,共有301名患者使用自动CPAP(Autosett10,ResmedInc.)。研究了四类CPAP依从性的潜在预测因子:(1)人口统计学和临床特征,(2)疾病的严重程度和合并症,(3)与睡眠有关的健康问题,(4)心理评估。然后,使用Mplus8.0进行生长混合物建模,以确定随时间变化的独特粘附轨迹.依从性数据是从远程医疗管理平台(Airview,ResmedInc.)在治疗期间。确定了三个新的亚组,并标记为“adhererrs”(53.8%的样本,截距=385,斜率=-51,高平均值,负斜率和适度下降),“改进剂”(18.6%,截距=256,斜率=50,中等平均值,正斜率和适度增长)和“非股东”(27.6%,截距=176,斜率=-31,低平均值,负斜率和轻微下降)。与OSA和呼吸暂停低通气指数(AHI)相关的合并症,这反映了疾病的客观严重程度,亚组之间没有显着差异。然而,“改良剂”显示出更高水平的白天嗜睡(8.1±6.0vs.12.1±7.0vs.8.0±6.1inSWIFT,p=0.01),白天功能降低(4.6±1.6vs.3.8±1.6vs.QSQ日间症状4.2±1.8,p=0.02),积极应对方式的特点(1.8±0.5vs.1.9±0.5vs.SCSQ积极应对指数为1.7±0.5,p=0.02)。负面情绪在“非患者”患者中更为明显(12.9±3.8vs.13.7±3.3vs.HADS凹陷维度为14.6±3.5,p=0.02;9.0±6.1vs.9.8±5.1vs.11.5±6.3,p=0.01,DS14中的负面影响,9.3±6.1与10.3±5.1vs.11.7±6.5,p=0.01,DS14中的社会抑制)。总的来说,我们的研究表明,除了传统的二元分类外,CPAP治疗随着时间的推移可能呈现不同的依从性轨迹.自我报告的睡眠健康问题(昼夜嗜睡和白天功能障碍)以及心理特征(负面情绪和应对方式)是OSA患者不同依从性亚型的预测因素。了解CPAP使用概况及其预测因素,可以识别可能需要额外干预以提高依从性并进一步增强OSA患者的治疗效果的患者。
In this study, we aim to identify the distinct subtypes of continuous positive airway pressure (CPAP) user profiles based on the telemedicine management platform and to determine clinical and psychological predictors of various patterns of adherence. A total of 301 patients used auto-CPAP (Autoset 10, Resmed Inc.) during the treatment period. Four categories of potential predictors for CPAP adherence were examined: (1) demographic and clinical characteristics, (2) disease severity and comorbidities, (3) sleep-related health issues, and (4) psychological evaluation. Then, growth mixture modeling was conducted using Mplus 8.0 to identify the unique trajectories of adherence over time. Adherence data were collected from the telemedicine management platform (Airview, Resmed Inc.) during the treatment. Three novel subgroups were identified and labeled \"adherers\" (53.8% of samples, intercept = 385, slope = -51, high mean value, negative slope and moderate decline), \"Improvers\" (18.6%, intercept = 256, slope = 50, moderate mean value, positive slope and moderate growth) and \"non-adherers\" (27.6%, intercept = 176, slope = -31, low mean value, negative slope and slight decline). The comorbidities associated with OSA and the apnea-hypopnea index (AHI), which reflects the objective severity of the disease, did not differ significantly among the subgroups. However, \"improvers\" showed higher levels of daytime sleepiness (8.1 ± 6.0 vs. 12.1 ± 7.0 vs. 8.0 ± 6.1 in SWIFT, p = 0.01), reduced daytime function (4.6 ± 1.6 vs. 3.8 ± 1.6 vs. 4.2 ± 1.8 in QSQ daytime symptoms, p = 0.02), and characteristics of positive coping style (1.8 ± 0.5 vs. 1.9 ± 0.5 vs. 1.7 ± 0.5 in SCSQ positive coping index, p = 0.02). Negative emotion was more pronounced in patients with \"non-adherers\" (12.9 ± 3.8 vs. 13.7 ± 3.3 vs. 14.6 ± 3.5, p = 0.02 in the HADS depression dimension; 9.0 ± 6.1 vs. 9.8 ± 5.1 vs. 11.5 ± 6.3, p = 0.01 with Negative Affectivity in DS14, and 9.3 ± 6.1 vs. 10.3 ± 5.1 vs. 11.7 ± 6.5, p = 0.01 with Social Inhibition in DS14). Overall, our study demonstrated that CPAP therapy may present distinct trajectories of adherence over time in addition to the traditional binary classification. Self-reported sleep health issues (diurnal sleepiness and daytime dysfunction) as well as psychological characteristics (negative emotions and coping style) were predictors of different adherence subtypes in patients with OSA. Understanding CPAP use profiles and their predictors enable the identification of those who may require additional intervention to improve adherence and further enhance the therapeutic effect in OSA patients.