中国全科医学 ›› 2026, Vol. 29 ›› Issue (08): 997-1007.DOI: 10.12114/j.issn.1007-9572.2025.0364

• 论著 • 上一篇    

颞肌横截面积和颞肌厚度预测急性缺血性脑卒中患者肌肉衰减状态的研究

曹磊1, 刘学春2, 江伟1, 陈炎1, 严孙宏1, 杜静1,*()   

  1. 1.230601安徽省合肥市,安徽医科大学第二附属医院神经内科
    2.230011安徽省合肥市第二人民医院神经内科
  • 收稿日期:2025-10-10 修回日期:2025-11-30 出版日期:2026-03-15 发布日期:2026-02-03
  • 通讯作者: 杜静

  • 作者贡献:

    曹磊提出研究思路,设计研究方案,负责论文写作和英文修订;刘学春、江伟负责调查对象的选取、现场问卷调查、数据录入和核对、统计学处理等;陈炎、严孙宏负责影像资料的质控;杜静负责文章质量控制、审查、修订和监督管理,对论文整体负责。

  • 基金资助:
    安徽省重点研究与开发计划项目(2022e07020029)

Study on the Prediction of Muscle Attenuation in Patients with Acute Ischemic Stroke by the Cross-sectional Area and Thickness of the Temporalis Muscle

CAO Lei1, LIU Xuechun2, JIANG Wei1, CHEN Yan1, YAN Sunhong1, DU Jing1,*()   

  1. 1. Department of Neurology, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China
    2. Department of Neurology, the Second People's Hospital of Hefei, Hefei 230011, China
  • Received:2025-10-10 Revised:2025-11-30 Published:2026-03-15 Online:2026-02-03
  • Contact: DU Jing

摘要: 背景 急性缺血性脑卒中(AIS)患者合并肌肉衰减状态与临床不良预后显著相关,因此,发掘简便易行且可操作性强的临床指标辅助筛查高危人群,已成为当前卒中康复与临床营养领域的交叉研究热点。 目的 探讨颞肌横截面积(TMA)和颞肌厚度(TMT)评估AIS患者肌肉衰减状态的可行性及临床价值。 方法 纳入2022年1月—2025年8月安徽医科大学第二附属医院收治的531例AIS患者(男347例,女184例),通过颅脑CT或MRI测量双侧TMA和TMT,并根据亚洲肌肉衰减症工作组(AWGS 2019)标准诊断肌肉衰减症。采用单因素及多因素Logistic回归分析筛选独立预测因素,构建预测模型并通过受试者工作特征曲线(ROC曲线)、校准曲线及临床决策曲线分析评估其效能。 结果 AIS患者肌肉衰减症患病率为19.96%(106/531),根据诊断标准将患者分为肌肉衰减组(n=106)与无肌肉衰减组(n=425)。肌肉衰减组患者TMA、TMT均低于无肌肉衰减组(P<0.001)。多因素Logistic回归分析结果显示,年龄(OR=1.717,95%CI=1.223~2.410)、美国国立卫生研究院卒中量表(NIHSS)评分(OR=3.213,95%CI=1.829~5.643)、营养风险筛查量表(NRS 2002)评分(OR=1.337,95%CI=1.045~1.711)及TMA(OR=0.781,95%CI=0.639~0.955)为AIS出现肌肉衰减症的独立影响因素(P<0.05)。为构建并验证肌肉衰减症风险预测模型,将所有研究对象按3∶1的比例随机分为训练集(n=398)与验证集(n=133)。基于多因素Logistic回归分析构建的最终模型公式为:Logit(P)=46.221 22+0.082 11×年龄+2.078 56×(NRS 2002=1)-0.144 80×TMA+18.327 80×(NIHSS=1),并生成预测肌肉衰减症风险的列线图。预测模型在训练集中的ROC曲线下面积(AUC)为0.884(95%CI=0.782~0.947),在验证集中的AUC为0.808(95%CI=0.679~0.882)。校准曲线显示模型预测概率与实际概率一致性良好,临床决策曲线表明模型在广泛阈值概率范围内具有临床净获益。 结论 颞肌测量是评估AIS患者肌肉衰减状态的有效方法,基于年龄、NIHSS评分、NRS 2002评分和TMA构建的预测模型具有良好的判别效能与临床适用性,可作为AIS患者肌肉衰减症的早期识别的实用工具。

关键词: 缺血性脑卒中, 颞肌, 肌肉衰减症, 预测模型, 影响因素分析

Abstract:

Background

Sarcopenia in patients with acute ischemic stroke (AIS) is significantly associated with poor clinical outcomes. Consequently, there is a growing research interest in identifying simple and practical clinical indicators to screen the high-risk population, forming a key intersection between stroke rehabilitation and clinical nutrition.

Objective

This study aimed to explore the feasibility and clinical value of temporal muscle cross-sectional area (TMA) and temporal muscle thickness (TMT) in evaluating sarcopenia in AIS patients.

Methods

A total of 531 AIS patients (347 males, 184 females) admitted to the Second Affiliated Hospital of Anhui Medical University between January 2022 and August 2025 were enrolled. Bilateral TMA and TMT were measured via cranial CT or MRI. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2019 criteria. Independent predictors were identified using univariate and multivariate Logistic regression analyses. A prediction model was constructed and its performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results

The prevalence of sarcopenia among AIS patients was 19.96% (106/531). Based on the diagnostic criteria, patients were categorized into a sarcopenia group (n=106) and a non-sarcopenia group (n=425). Patients in the sarcopenia group had significantly lower TMA and TMT values compared to those in the non-sarcopenia group (P<0.001). Multivariate Logistic regression analysis identified age (OR=1.717, 95%CI=1.223-2.410), NIHSS score (OR=3.213, 95%CI=1.829-5.643), NRS 2002 score (OR=1.337, 95%CI=1.045-1.711), and TMA (OR=0.781, 95%CI=0.639-0.955) as independent influencing factors for sarcopenia in AIS (P<0.05). To construct and validate the sarcopenia risk prediction model, all subjects were randomly divided into a training set (n=398) and a validation set (n=133) according to 3∶1 ratio. The final model formula, based on multivariate logistic regression analysis, was Logit(P)=46.221 22+0.082 11×age+2.078 56×(NRS 2002=1)-0.144 80×TMA+18.327 80×(NIHSS=1). A nomogram was generated to predict the risk of sarcopenia. The area under the ROC curve (AUC) of the prediction model was 0.884 (95%CI=0.782-0.947) in the training set and 0.808 (95%CI=0.679-0.882) in the validation set. The calibration curve demonstrated good consistency between the predicted and actual probabilities. Furthermore, the DCA indicated that the model provided clinical net benefits across a wide range of threshold probabilities.

Conclusion

Temporal muscle measurement is an effective method for evaluating the muscle wasting status of patients with AIS. The prediction model based on age, NIHSS score, NRS 2002 score, and TMA exhibits good discriminatory performance and clinical applicability, providing a practical tool for early identification of sarcopenia in this patient population.

Key words: Ischemic stroke, Temporalis muscle, Sarcopenia, Predictive model, Root cause analysis

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