Yang Siyu, Zhang Xintong, Du Xinyu, Yan Peng, Zhang Jing, Wang Wei, Wang Jing, Zhang Lei, Sun Huaiqing, Liu Yin, Xu Xinran, Di Yaxuan, Zhong Jin, Wu Caiyun, Reinhardt Jan D, Zheng Yu, Wu Ting
Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
Department of Neurology, Nanjing Second Hospital, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210003, China.
Alzheimers Res Ther. 2025 Feb 13;17(1):41. doi: 10.1186/s13195-025-01686-x.
Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-assignment decisions to more intensive therapies where needed.
Longitudinal data including five variable sets, i.e. demographics, medical history, neuropsychological outcomes, laboratory and neuroimaging results, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were analyzed. We first developed a deep learning model to predicted cognitive conversion using all five variable sets. We then gradually removed variable sets to obtained parsimonious models for four different years of forecasting after baseline within acceptable frames of reduction in overall model fit (AUC remaining > 0.8).
A total of 607 individuals were included at baseline, of whom 538 participants were followed up at 12 months, 482 at 24 months, 268 at 36 months and 280 at 48 months. Predictive performance was excellent with AUCs ranging from 0.87 to 0.92 when all variable sets were considered. Parsimonious prediction models that still had a good performance with AUC 0.80-0.84 were established, each only including two variable sets. Neuropsychological outcomes were included in all parsimonious models. In addition, biomarker was included at year 1 and year 2, imaging data at year 3 and demographics at year 4. Under our pre-set threshold, the rate of upgrade to more intensive therapies according to predicted cognitive conversion was always higher than according to actual cognitive conversion so as to decrease the false positive rate, indicating the proportion of patients who would have missed upgraded treatment based on prognostic models although they actually needed it.
Neurophysiological tests combined with other indicator sets that vary along the AD continuum can improve can provide aid for clinical treatment decisions leading to improved management of the disease.
ClinicalTrials.gov Identifier: NCT00106899 (Registration Date: 31 March 2005).
阿尔茨海默病（AD）认知功能衰退的早期诊断和准确预后对于及时采用最佳治疗模式至关重要。我们旨在开发一种深度学习模型来预测认知转换，以指导在需要时重新分配至更强化的治疗方法。
分析了来自阿尔茨海默病神经影像倡议（ADNI）队列的纵向数据，包括五组变量，即人口统计学、病史、神经心理学结果、实验室检查和神经影像学结果。我们首先使用所有五组变量开发了一个深度学习模型来预测认知转换。然后，我们逐渐去除变量组，以在总体模型拟合度降低的可接受范围内（AUC保持>0.8）获得用于基线后四个不同年份预测的简约模型。
共有607名个体纳入基线，其中538名参与者在12个月时接受随访，482名在24个月时接受随访，268名在36个月时接受随访，280名在48个月时接受随访。当考虑所有变量组时，预测性能极佳，AUC范围为0.87至0.92。建立了仍具有良好性能（AUC为0.80 - 0.84）的简约预测模型，每个模型仅包括两组变量。所有简约模型均包含神经心理学结果。此外，第1年和第2年纳入生物标志物，第3年纳入影像数据，第4年纳入人口统计学数据。在我们预先设定的阈值下，根据预测的认知转换升级至更强化治疗的比例始终高于根据实际认知转换的比例，从而降低了假阳性率，即基于预后模型会错过升级治疗但实际上需要治疗的患者比例。
神经生理学测试与其他随AD病程变化的指标集相结合，可以改善为临床治疗决策提供帮助，从而改善疾病管理。
ClinicalTrials.gov标识符：NCT00106899（注册日期：2005年3月31日）。
