Vermeulen Robin Jeanna, Andersson Vebjørn, Banken Jimmy, Hannink Gerjon, Govers Tim Martin, Rovers Maroeska Mariet, Rikkert Marcel Gerardus Maria Olde
Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, The Netherlands.
Department of Neurology, Oslo University Hospital, Oslo, Norway.
Alzheimers Dement. 2025 Apr;21(4):e70069. doi: 10.1002/alz.70069.
Prediction models have been developed to identify mild cognitive impairment (MCI) cases likely to convert to dementia. This systematic review summarizes multi-source prediction models for MCI to dementia conversion. PubMed and Embase were searched for model development and validation studies from inception up to January 18 2024. Models were assessed for included predictors, predictive performance, risk of bias, and generalizability. 62 studies were included: 41 machine learning models, 11 regression models, and 5 disease state indexes. The number of predictors in the models ranged from 2 to 60; magnetic resonance imaging (MRI) and cognitive scores were the most common sources. Performance measures indicate reasonable predictive capabilities (area under the curve [AUC] range: 0.58-0.98, accuracy range: 66.1-96.3%); however, most studies are at high risk of bias and 47 studies lack external validation. Currently, no highly valid prediction model is available for MCI to dementia conversion risk due to limited generalizability and high risk of bias in most studies. HIGHLIGHTS: Numerous models have been developed to predict the likelihood of conversion to dementia in individuals with MCI. Prediction models seem to have a reasonably good performance in predicting conversion to dementia, however, external validation and generalizability is often lacking. There is no prediction model available with a low risk for bias and that has been externally validated to accurately predict the risk of MCI to dementia conversion. For MCI to dementia conversion prediction models, more emphasis should be directed towards external validation, generalizability, and clinical applicability.
已经开发出预测模型来识别可能转化为痴呆症的轻度认知障碍（MCI）病例。本系统评价总结了用于MCI向痴呆症转化的多源预测模型。检索了PubMed和Embase数据库，查找从数据库建立至2024年1月18日的模型开发和验证研究。对模型的纳入预测因素、预测性能、偏倚风险和可推广性进行了评估。纳入了62项研究：41个机器学习模型、11个回归模型和5个疾病状态指数。模型中的预测因素数量从2到60不等；磁共振成像（MRI）和认知评分是最常见的来源。性能指标表明具有合理的预测能力（曲线下面积[AUC]范围：0.58 - 0.98，准确率范围：66.1 - 96.3%）；然而，大多数研究存在高偏倚风险，47项研究缺乏外部验证。目前，由于大多数研究的可推广性有限和高偏倚风险，尚无用于MCI向痴呆症转化风险的高度有效的预测模型。要点：已经开发了许多模型来预测MCI个体转化为痴呆症的可能性。预测模型在预测向痴呆症的转化方面似乎具有相当不错的性能，然而，往往缺乏外部验证和可推广性。没有一个偏倚风险低且经过外部验证能准确预测MCI向痴呆症转化风险的预测模型。对于MCI向痴呆症转化预测模型，应更加注重外部验证、可推广性和临床适用性。
