用于预测连续性阻塞性睡眠呼吸暂停低通气综合征患者高血压的临床模型的开发与验证：一项基于医院的研究及列线图分析
Development and validation of a clinical model to predict hypertension in consecutive patients with obstructive sleep apnea hypopnea syndrome: a hospital-based study and nomogram analysis.
作者信息
Zeng Xiangxia, Ma Danjie, Wu Kang, Yang Qifeng, Zhang Sun, Luo Yateng, Wang Donghao, Ren Yingying, Zhang Nuofu
机构信息
State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University Guangzhou 510120, Guangdong, China.
Medical Record Management Department, The First Affiliated Hospital of Guangzhou Medical University Guangzhou 510120, Guangdong, China.
出版信息
Am J Transl Res. 2022 Feb 15;14(2):819-830. eCollection 2022.
BACKGROUND
To screen for risk predictors of hypertension in patients with Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) and develop and validate a clinical model for individualized prediction of hypertension in consecutive patients with OSAHS.
METHODS
114 consecutive patients with OSAHS confirmed by PSG monitoring participated in this study. Those individuals were divided into two sets at a ratio of 7:3, using computer-generated random numbers: 82 individuals were assigned to the training set and 32 to the validation set. Important risk predictors of hypertension in individuals with OSAHS were confirmed using the LASSO method and a clinical nomogram constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot.
RESULTS
Univariate and multivariate regression analysis identified BMI, REM-AHI, REM-MSpO and T90% as predictive risk factors of OSAHS. Those risk factors were used to construct a clinical predictive nomogram. The calibration curves for hypertension in patients with OSAHS risk revealed excellent accuracy of the predictive nomogram model, internally and externally. The unadjusted concordance index (C-index) for the training and validation set was 0.897 [95% CI 0.795-0.912] and 0.894 [95% CI 0.788-0.820] respectively. The AUC of the training and validation set was 0.8175882 and 0.8031522, respectively. Decision curve analysis showed that the predictive model could be applied clinically when the threshold probability was 20 to 80%.
CONCLUSION
We constructed and validated a clinical nomogram to individually predict the occurrence of hypertension in patients with OSAHS. We determined that BMI, REM-AHI, REM-MSpO and T90% were independent risk predictors for hypertension in patients with OSAHS. This practical prognostic nomogram may help improve clinical decision making.
背景
筛选阻塞性睡眠呼吸暂停低通气综合征（OSAHS）患者高血压的风险预测因素，并建立和验证一个用于连续OSAHS患者高血压个体化预测的临床模型。
方法
114例经多导睡眠图（PSG）监测确诊的连续OSAHS患者参与本研究。利用计算机生成的随机数，将这些个体按7:3的比例分为两组：82例个体被分配到训练集，32例被分配到验证集。使用LASSO方法确认OSAHS个体中高血压的重要风险预测因素，并构建临床列线图。通过未调整的一致性指数（C指数）和校准图评估预测准确性。
结果
单因素和多因素回归分析确定体重指数（BMI）、快速眼动期呼吸暂停低通气指数（REM-AHI）、快速眼动期平均血氧饱和度（REM-MSpO）和T90%为OSAHS的预测风险因素。这些风险因素被用于构建临床预测列线图。OSAHS风险患者高血压的校准曲线显示，预测列线图模型在内部和外部均具有出色的准确性。训练集和验证集的未调整一致性指数（C指数）分别为0.897[95%可信区间（CI）0.795 - 0.912]和0.894[95%CI 0.788 - 0.820]。训练集和验证集的曲线下面积（AUC）分别为0.8175882和0.8031522。决策曲线分析表明，当阈值概率为20%至80%时，该预测模型可应用于临床。
结论
我们构建并验证了一个临床列线图，用于个体化预测OSAHS患者高血压的发生。我们确定BMI、REM-AHI、REM-MSpO和T90%是OSAHS患者高血压的独立风险预测因素。这个实用的预后列线图可能有助于改善临床决策。
Zotero 插件