Li Chunyan, Wang Jiafeng, Han Xiaodong, Li Yuanjing, Liu Keke, Zhao Mingqing, Gong Tao, Hou Tingting, Wang Yongxiang, Cong Lin, Song Lin, Du Yifeng
Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.
Front Neurol. 2024 Jul 5;15:1388653. doi: 10.3389/fneur.2024.1388653. eCollection 2024.
Cerebral small vessel disease (CSVD) visible on MRI can be asymptomatic. We sought to develop and validate a model for detecting CSVD in rural older adults.
This study included 1,192 participants in the MRI sub-study within the Multidomain Interventions to Delay Dementia and Disability in Rural China. Total sample was randomly divided into training set and validation set. MRI markers of CSVD were assessed following the international criteria, and total CSVD burden was assessed on a scale from 0 to 4. Logistic regression analyses were used to screen risk factors and develop the diagnostic model. A nomogram was used to visualize the model. Model performance was assessed using the area under the receiver-operating characteristic curve (AUC), calibration plot, and decision curve analysis.
The model included age, high blood pressure, white blood cell count, neutrophil-to-lymphocyte ratio (NLR), and history of cerebral infarction. The AUC was 0.71 (95% CI, 0.67-0.76) in the training set and 0.69 (95% CI, 0.63-0.76) in the validation set. The model showed high coherence between predicted and observed probabilities in both the training and validation sets. The model had higher net benefits than the strategy assuming all participants either at high risk or low risk of CSVD for probability thresholds ranging 50-90% in the training set, and 65-98% in the validation set.
A model that integrates routine clinical factors could detect CSVD in older adults, with good discrimination and calibration. The model has implication for clinical decision-making.
磁共振成像（MRI）上可见的脑小血管疾病（CSVD）可能无症状。我们试图开发并验证一种用于检测农村老年人CSVD的模型。
本研究纳入了中国农村延缓痴呆和残疾多领域干预研究中MRI子研究的1192名参与者。总样本被随机分为训练集和验证集。按照国际标准评估CSVD的MRI标志物，并以0至4分的量表评估总的CSVD负担。采用逻辑回归分析筛选危险因素并建立诊断模型。使用列线图来直观展示该模型。采用受试者操作特征曲线下面积（AUC）、校准图和决策曲线分析来评估模型性能。
该模型纳入了年龄、高血压、白细胞计数、中性粒细胞与淋巴细胞比值（NLR）以及脑梗死病史。训练集中AUC为0.71（95%CI，0.67 - 0.76），验证集中AUC为0.69（95%CI，0.63 - 0.76）。该模型在训练集和验证集中预测概率与观察概率之间均显示出高度一致性。在训练集中，对于概率阈值在50%至90%之间，以及验证集中概率阈值在65%至98%之间，该模型比假设所有参与者要么处于CSVD高风险要么处于低风险的策略具有更高的净效益。
一个整合常规临床因素的模型能够检测老年人的CSVD，具有良好的区分度和校准度。该模型对临床决策具有指导意义。
