天津医药 ›› 2026, Vol. 54 ›› Issue (9): 908-912.doi: 10.11958/20260206

• 临床研究 • 上一篇    下一篇

基于炎症指标及临床特征构建OSAHS患者发生AIS的列线图预测模型

李越(), 王炜, 欧阳取平   

  1. 北京市顺义区医院神经内科(邮编 101300)
  • 收稿日期:2026-01-15 修回日期:2026-04-08 出版日期:2026-09-15 发布日期:2026-09-14
  • 作者简介:李越(1985),女,主治医师,主要从事脑血管病方面研究。E-mail:xiaoyueqingcheng@163.com
  • 基金资助:
    首都卫生发展科研专项(首发2024-3-7101)

Establishment of a nomogram prediction model for the occurrence of acute ischemic stroke in patients with obstructive sleep apnea-hypopnea syndrome based on inflammatory indicators and clinical characteristics

LI Yue(), WANG Wei, OUYANG Quping   

  1. Department of Neurology, Beijing Shunyi District Hospital, Beijing 101300, China
  • Received:2026-01-15 Revised:2026-04-08 Published:2026-09-15 Online:2026-09-14

摘要:

目的 探讨阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者并发急性缺血性脑卒中(AIS)的风险因素,并构建列线图预测模型。方法 选取278例OSAHS患者,根据是否并发AIS分为AIS组(53例)和非AIS组(225例)。收集2组患者的年龄、性别、体质量指数(BMI)、颈围、吸烟史、饮酒史、基础病史、呼吸暂停低通气指数(AHI)、血液生化指标及炎症指标,比较组间差异;采用多因素Logistic回归分析筛选OSAHS患者发生AIS的独立影响因素,并基于筛选结果构建列线图预测模型,通过受试者工作特征(ROC)曲线、校准曲线及决策曲线评估模型性能。结果 AIS组患者年龄、BMI、AHI、颈围、低密度脂蛋白胆固醇(LDL-C)、白细胞计数(WBC)、中性粒细胞与淋巴细胞比值(NLR)显著高于非AIS组,高密度脂蛋白胆固醇(HDL-C)、最低血氧饱和度(LSpO2)显著低于非AIS组(均P<0.05);AIS组炎症指标C反应蛋白(CRP)、白细胞介素(IL)-6、肿瘤坏死因子(TNF)-α水平显著高于非AIS组(均P<0.05)。多因素Logistic回归分析显示,BMI、AHI、颈围、WBC、CRP、IL-6、TNF-α是OSAHS患者发生AIS的独立影响因素(均P<0.05)。基于上述因素构建的列线图预测模型ROC曲线下面积(AUC)为0.868(95%CI:0.822~0.905),最佳截断值为3.411时,特异度为75.47%、敏感度为83.56%;校准曲线及Hosmer-Lemeshow检验显示模型预测值与实际值一致性良好(χ2=7.093,P=0.527);决策曲线提示风险阈值>0.08时进行干预,患者可获得临床净收益。结论 基于炎症指标及临床特征构建的列线图对OSAHS患者并发AIS具有良好的预测效能,可为临床早期识别高风险患者及制定干预策略提供参考。

关键词: 睡眠呼吸暂停, 阻塞性, 缺血性卒中, 炎症指标, 临床特征, 预测模型

Abstract:

Objective To explore the related risk factors of acute ischemic stroke (AIS) in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), and construct a nomogram prediction model. Methods A total of 278 OSAHS patients were selected and divided into the AIS group (n=53) and the non-AIS group (n=225) based on whether they had concurrent AIS. The patient age, gender, body mass index (BMI), neck circumference, smoking history, drinking history, underlying medical history, apnea-hypopnea index (AHI), blood biochemical indicators and inflammatory indicators were collected and compared between the two groups. Multiple Logistic regression analysis was used to screen independent risk factors for AIS in OSAHS patients, and a nomogram prediction model was constructed based on the screening results. The model performance was evaluated through the receiver operating characteristic (ROC) curve, calibration curve and decision curve. Results The patient age, BMI, AHI, neck circumference, low-density lipoprotein cholesterol (LDL-C), white blood cell count (WBC) and neutrophil to lymphocyte ratio (NLR) were significantly higher in the AIS group than those in the non-AIS group, while high-density lipoprotein cholesterol (HDL-C) and the lowest peripheral oxygen saturation (LSpO2) were significantly lower in the AIS group than those in the non-AIS group (all P<0.05). The levels of inflammatory markers C- reactive protein (CRP), interleukin (IL) -6 and tumor necrosis factor (TNF)-α were significantly higher in the AIS group than those in the non-AIS group (all P<0.05). Multivariate Logistic regression analysis showed that BMI, AHI, neck circumference, WBC, CRP, IL-6 and TNF-α were independent risk factors for AIS in OSAHS patients (all P<0.05). The area under the ROC curve (AUC) of the nomogram prediction model constructed based on the above factors was 0.868 (95% CI: 0.822-0.905). At an optimal cutoff value was 3.411, the specificity was 75.47% and the sensitivity was 83.56%. The calibration curve and Hosmer-Lemeshow test showed good consistency between the predicted values of the model (χ2=7.093,P=0.527) and the actual values. When the decision curve indicated a high-risk threshold>0.08 and intervention was carried out, patients can obtain clinical benefits. Conclusion A nomogram constructed on the basis of inflammatory markers and clinical characteristics demonstrates good predictive performance for AIS in patients with OSAHS, and can serve as a reference for the early clinical identification of high-risk patients and the formulation of intervention strategies.

Key words: sleep apnea, obstructive, ischemic stroke, inflammatory markers, clinical features, predictive model

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