Zhang Xiaoting, Wei Meng, Xue Pengjie, Lu Yanmei, Tang Baopeng
Department of Cardiac Pacing and Electrophysiology, The First Affiliated Hospital of Xinjiang Medical University, 830000 Urumqi, Xinjiang, China.
Xinjiang Key Laboratory of Cardiac Electrophysiology and Cardiac Remodeling, The First Affiliated Hospital of Xinjiang Medical University, 830000 Urumqi, Xinjiang, China.
Rev Cardiovasc Med. 2025 Jun 25;26(6):36467. doi: 10.31083/RCM36467. eCollection 2025 Jun.
The high prevalence and mortality rate of combined atrial fibrillation (AF) and obstructive sleep apnea syndrome (OSAS) impose a significant disease burden on public healthcare systems. However, there is currently a lack of risk-assessment tools for all-cause mortality in patients with both AF and OSAS. Therefore, this study utilized clinical data from patients at the First Affiliated Hospital of Xinjiang Medical University to establish a predictive model and address this gap.
This study included 408 patients with AF and OSAS, randomly divided into a training set (n = 285) and a validation set (n = 123). Subsequently, the training set was split into deceased and surviving groups to analyze in-hospital indicators.
A total 10 variables were selected from an initial 64 variables in patients with AF and OSAS identified through Lasso regression screening, including hypoxemia, catheter ablation (CA), red blood cell count (RBC), lymphocyte count, basophil granulocyte count, total bile acids, D-dimer, free triiodothyronine, N-terminal pro-brain natriuretic peptide (NT-proBNP), and chronic obstructive pulmonary disease. Variables identified as significant in the univariate logistic regression analysis were included in the multivariable logistic regression analysis, which revealed that CA (odds ratio (OR) = 0.21) was an independent protective factor. In contrast, moderate-to-severe hypoxemia (OR = 11.11), RBC <3.8 × 10/L (OR = 20.70), and D-dimer ≥280 ng/mL (OR = 7.07) were independent risk factors. Based on this, receiver operating characteristic (ROC) curves were plotted, showing area under the curve (AUC) values of 0.96 for the training set and 0.91 for the validation set, indicating the model exhibited good predictive ability. A risk-scoring system was developed to assess the overall mortality risk of patients with AF and OSAS. The percentage bar chart demonstrated an increase in mortality rate and a decrease in survival rate as the risk level increased.
The predictive model and risk scoring system developed in this study exhibit good predictive abilities in evaluating all-cause mortality in patients with AF and OSAS, providing valuable clinical guidance and reference.
合并心房颤动（AF）和阻塞性睡眠呼吸暂停综合征（OSAS）的高患病率和死亡率给公共卫生保健系统带来了巨大的疾病负担。然而，目前缺乏针对AF和OSAS患者全因死亡率的风险评估工具。因此，本研究利用新疆医科大学第一附属医院患者的临床数据建立了一个预测模型，以填补这一空白。
本研究纳入408例AF和OSAS患者，随机分为训练集（n = 285）和验证集（n = 123）。随后，将训练集分为死亡组和存活组，分析住院指标。
通过Lasso回归筛选，从AF和OSAS患者最初的64个变量中总共选择了10个变量，包括低氧血症、导管消融（CA）、红细胞计数（RBC）、淋巴细胞计数、嗜碱性粒细胞计数、总胆汁酸、D-二聚体、游离三碘甲状腺原氨酸、N末端脑钠肽前体（NT-proBNP）和慢性阻塞性肺疾病。单因素逻辑回归分析中被确定为显著的变量被纳入多因素逻辑回归分析，结果显示CA（比值比（OR）= 0.21）是一个独立的保护因素。相比之下，中度至重度低氧血症（OR = 11.11）、RBC <3.8×10/L（OR = 20.70）和D-二聚体≥280 ng/mL（OR = 7.07）是独立的危险因素。基于此，绘制了受试者工作特征（ROC）曲线，训练集的曲线下面积（AUC）值为0.96，验证集为0.91，表明该模型具有良好预测能力。开发了一个风险评分系统来评估AF和OSAS患者的总体死亡风险。百分比柱状图显示，随着风险水平的增加，死亡率上升，生存率下降。
本研究开发的预测模型和风险评分系统在评估AF和OSAS患者全因死亡率方面具有良好的预测能力，提供了有价值的临床指导和参考。
