基于影像的模型预测颈动脉狭窄脑血管并发症

Imaging‑based models for predicting cerebrovascular complications of carotid stenosis

作者信息Baobao Li, Xiangrong Du, Yuan Liu, Hongtao Zhang, Shitong Liu, Shengshu Wang, Xihai Zhao, Fugeng Sheng, Mingming Lu, Jianming Cai
PMID42713886
发布时间2026-09-09
DOI10.1002/14651858.CD016391

实验完整度

低

本文为Cochrane预后系统评价方案,未报告任何原始实验方法或验证结果,仅预先设定检索、数据提取与分析方法。

重点核对

索引影像模态:超声B模式/多普勒 vs CTA vs MRA 验证策略:外部验证 vs 仅内部验证 症状状态:症状性(近期同侧缺血性卒中、TIA或视网膜缺血) vs 无症状性 影像预测因子类型:手工放射组学特征 vs 深度学习衍生特征 vs 联合影像-临床预测因子 预测时间窗:主要关注中期(1至5年)

摘要

This is a protocol for a Cochrane review (prognosis). The objectives are as follows: Primary objective To systematically review and critically appraise multivariable prognostic models developed for adults (≥ 18 years) with carotid stenosis in which imaging biomarkers (e.g. plaque characteristics derived from magnetic resonance imaging (MRI), computed tomography (CT), or ultrasound) constitute the core predictors. The primary focus is to evaluate the predictive performance of these models for cerebrovascular complications - specifically ipsilateral ischaemic stroke and transient ischaemic attack (TIA) - which are the clinical outcomes to be predicted. Where feasible, we will summarise and compare the models' discrimination (C‑statistic/area under the curve (AUC)) and calibration (calibration‑in‑the‑large, calibration slope, observed‑to‑expected ratio) across studies, and assess their potential for clinical application and external validation. For the purpose of defining symptomatic carotid stenosis as an eligibility criterion and subgroup variable, we will include studies that also considered retinal ischaemia (e.g. retinal embolism, amaurosis fugax) as a qualifying event. Secondary objectives To describe the combinations of imaging markers, modelling techniques, sample sizes, and variable‑selection strategies used in the development of the included models To evaluate the performance of these models for additional secondary clinical outcomes: plaque progression or regression, incident high‑risk imaging features, and the transition from asymptomatic to symptomatic disease To explore whether predictive performance differs according to imaging modality (MRI versus CT versus contrast‑enhanced ultrasound (CEUS)) or technical protocol (e.g. 3 T versus 1.5 T, spectral CT versus conventional CT) For studies that report both cerebrovascular and broader cardiovascular outcomes (major adverse cardiovascular events, myocardial infarction, etc.), we will only extract the performance metrics relating to cerebrovascular events for the primary analysis. Performance metrics for cardiovascular outcomes will be considered exploratory and will not form part of the main synthesis.

实验结论

提炼研究问题、关键发现与证据,快速把握文章的核心贡献。

研究问题
以影像生物标志物为核心预测因子的多变量预后模型,对成人颈动脉狭窄患者同侧缺血性卒中及TIA的预测性能与校准如何?
核心机制
文中未明确说明
主要证据
文中未明确说明
研究意义
作者明确提出,本评价将评估现有基于影像的预后模型的性能与实用性,识别方法学缺口,并为颈动脉影像指南及患者选择提供依据。

研究方法

按研究目的归类文中使用的方法,便于定位所需技术。

产品清单

实验环节名称品牌货号
EndNote 21Clarivate21
Review Manager (RevMan)The Cochrane Collaboration11.8.0
GRADEpro GDTMcMaster University (developed by Evidence Prime)--

关键环节

汇总复现实验时建议重点确认的条件及原文阅读提示。

环节核对要点
研究纳入与排除
PICOTS框架:人群为成人(≥18岁)颈动脉狭窄(≥50%管腔狭窄或存在易损斑块特征);索引模型为以影像生物标志物为核心预测因子的多变量预后模型;主要结局为同侧缺血性卒中、TIA;主要预测时间窗为1至5年
阅读提示:Methods中“Criteria for considering studies for this review”及Table 1 PICOTS框架
检索策略
检索数据库:Embase、MEDLINE、CENTRAL;无发表时间或语言限制;检索策略见Supplementary material 1(草稿模板,方案发表后执行完整检索)
阅读提示:Methods中“Search methods for identification of studies”及Supplementary material 1
数据提取
提取研究特征、参与者特征、影像预测因子、临床结局、模型特征与性能指标;使用CHARMS清单设计提取表格
阅读提示:Methods中“Data extraction and management”
偏倚风险评估
使用PROBAST-AI工具从参与者与数据来源、预测因子、结局、分析四个域评估偏倚风险与适用性,每个域评为低、高或不清楚
阅读提示:Methods中“Assessment of risk of bias in included studies”
数据合成与荟萃分析
随机效应模型;τ²使用REML估计;当至少3项研究且τ²>0时使用HKSJ法计算置信区间,否则使用常规Wald型置信区间;若无法荟萃分析则采用SWiM指南进行结构化叙述性综合
阅读提示:Methods中“Data synthesis”及“Data synthesis and meta-analysis approaches”
亚组分析与异质性探究
预定义亚组:影像模态、验证策略、症状状态、影像预测因子类型、模型开发日历年;当总研究数至少10项且每个亚组至少3项研究时进行meta回归;异质性使用Cochran Q检验(P<0.10)和I²统计量评估
阅读提示:Methods中“Subgroup analysis and investigation of heterogeneity”
证据确定性评估
使用适应预测模型性能的GRADE方法,分别对区分度(C统计量/AUC)和校准进行评级(高、中、低或极低);降级考虑偏倚风险、不一致性、间接性、不精确性和发表偏倚;升级需同时满足多中心外部验证、合并C指数>0.80、预测区间下限>0.75、I²<25%、校准良好等条件
阅读提示:Methods中“Assessment of the certainty of evidence”及“Summary of findings and assessment of the certainty of the evidence”