Wang Shuo, Fan Ji-Min, Xie Mian-Mian, Yang Jiao-Hong, Zeng Yi-Ming
The School of Nursing, Fujian Medical University, Fuzhou, China.
Department of Respiratory Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Front Neurol. 2024 Aug 21;15:1431127. doi: 10.3389/fneur.2024.1431127. eCollection 2024.
Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training.
A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18-60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application.
Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735-0.872], 0.849 (95% CI: 0.788-0.910), and 0.83 (95% CI: 0.763-0.896), respectively.
Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.
阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠呼吸障碍疾病，与轻度认知障碍（MCI）的加速发作有关。然而，OSA患者中未被诊断出的MCI患病率很高，这归因于MCI诊断的复杂性和专业性。及时识别和干预MCI有可能预防或延缓痴呆症的发作。本研究旨在开发适用于不同环境下医疗保健专业人员的OSA患者MCI筛查模型，且无需专门的神经学培训即可有效利用。
2021年4月至2022年9月在一家专业睡眠医学中心进行了一项前瞻性观察研究。350名连续的疑似OSA患者（年龄：18 - 60岁）接受了蒙特利尔认知评估（MoCA）和整夜多导睡眠图检查。收集了OSA患者的人口统计学和临床数据，包括多导睡眠图睡眠参数和额外的认知功能评估。数据被分为训练集（70%）和验证集（30%），并使用单变量和多变量逻辑回归分析确定MCI的预测因素。对模型的预测准确性和校准进行评估，并绘制列线图以供应用。
纳入了233名新诊断的OSA患者。MCI患者的比例为38.2%。开发了三个诊断模型，每个模型都配有一个列线图。模型1使用体重指数（BMI）和受教育年限作为预测因素。模型2在模型1的基础上纳入了N1和数字广度测试反向任务得分（DST_B）。模型3在模型1的基础上增加了数字广度测试总分（DST）。这些模型均表现出强大的辨别力和校准能力。模型1、2和3的C统计量分别为0.803 [95%置信区间（CI）：0.735 - 0.872]、0.849（95% CI：0.788 - 0.910）和0.83（95% CI：0.763 - 0.896）。
开发了三个简单的诊断模型，每个模型仅需两到四个易于获取的参数，且显示出高效性。这些模型为不同医疗环境中的医疗保健专业人员提供了一种便捷的诊断工具，有助于对有MCI风险增加的OSA患者进行及时且必要的进一步评估和干预。
