Tully Phillip J, Qchiqach Sarah, Pereira Edwige, Debette Stephanie, Mazoyer Bernard, Tzourio Christophe
Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, F-33000 Bordeaux, France.
UMR5293, Groupe d'Imagerie Neurofonctionnelle, University Bordeaux, Institut des Maladies Neurodégénératives, Bordeaux, France.
BMJ Open. 2017 Dec 29;7(12):e018328. doi: 10.1136/bmjopen-2017-018328.
The objective was to develop and validate a risk model for the likelihood of extensive white matter lesions (extWML) to inform clinicians on whether to proceed with or forgo diagnostic MRI.
Population-based cohort study and multivariable prediction model.
Two representative samples from France.
Persons aged 60-80 years without dementia or stroke. Derivation sample n=1714; validation sample n=789.
Volume of extWML (log cm) was obtained from T2-weighted images in a 1.5 T scanner. 20 candidate risk factors for extWML were evaluated with the C-statistic. Secondary outcomes in validation included incident stroke over 12 years follow-up.
The multivariable prediction model included six clinical risk factors (C-statistic=0.61). A cut-off of 7 points on the multivariable prediction model yielded the optimum balance in sensitivity 63.7% and specificity 54.0% and the negative predictive value was high (81.8%), but the positive predictive value was low (31.5%). In further validation, incident stroke risk was associated with continuous scores on the multivariable prediction model (HR 1.02; 95% CI 1.01 to 1.04, P=0.02) and dichotomised scores from the multivariable prediction model (HR 1.28; 95% CI 1.02 to 1.60, P=0.03).
A simple clinical risk equation for WML constituted by six variables can inform decisions whether to proceed with or forgo brain MRI. The high-negative predictive value demonstrates potential to reduce unnecessary MRI in the population aged 60-80 years.
开发并验证一个用于预测广泛脑白质病变（extWML）可能性的风险模型，以便为临床医生提供信息，辅助其决定是否进行诊断性磁共振成像（MRI）检查。
基于人群的队列研究及多变量预测模型。
来自法国的两个代表性样本。
年龄在60 - 80岁之间、无痴呆或中风的人群。推导样本n = 1714；验证样本n = 789。
通过1.5T扫描仪的T2加权图像获取extWML的体积（log cm）。使用C统计量评估20个extWML的候选风险因素。验证中的次要结局包括12年随访期内的中风发生率。
多变量预测模型包含六个临床风险因素（C统计量 = 0.61）。多变量预测模型中7分的截断值在敏感性（63.7%）和特异性（54.0%）之间产生了最佳平衡，阴性预测值较高（81.8%），但阳性预测值较低（31.5%）。在进一步验证中，中风发生风险与多变量预测模型的连续评分相关（风险比1.02；95%置信区间1.01至1.04，P = 0.02），也与多变量预测模型的二分评分相关（风险比1.28；95%置信区间1.02至1.60，P = 0.03）。
一个由六个变量构成的简单的WML临床风险方程可为是否进行脑部MRI检查的决策提供参考。较高的阴性预测值表明该模型有潜力减少60 - 80岁人群中不必要的MRI检查。
