Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
Alzheimers Res Ther. 2022 Aug 5;14(1):110. doi: 10.1186/s13195-022-01053-0.
Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer's disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia.
This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell's C statistics.
We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell's C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell's C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination.
We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD.
患者和护理人员表示希望获得有关住院时间和死亡率的准确预后信息。以前的研究侧重于痴呆阶段,预测住院和死亡率。然而,阿尔茨海默病(AD)的特征是存在较长的痴呆前阶段。因此,我们开发了预测模型,以预测从认知正常到痴呆的 AD 连续体中的住院和死亡率。
这项研究包括 SCD/MCI 患者(主观认知下降(SCD)或轻度认知障碍(MCI))和来自阿姆斯特丹痴呆队列的 AD 痴呆患者。我们使用无生物标志物和有生物标志物的方法,分别为痴呆和非痴呆患者建立了内部和外部验证的预测模型。使用向后选择(p<0.10)选择决定因素。所有模型均包含年龄和性别。通过哈雷尔 C 统计量评估模型的判别性能。
我们纳入了 1418 名 SCD/MCI 患者(123 人死亡,74 人住院)和 1179 名 AD 痴呆患者(413 人死亡,453 人住院)。对于 SCD/MCI 和痴呆阶段,住院和死亡率模型均包含经过向后选择的临床特征、影像学和脑脊液(CSF)生物标志物。在 SCD/MCI 中,模型的哈雷尔 C 统计量为 0.81(无生物标志物模型:0.76)用于住院,0.79(无生物标志物模型:0.76)用于死亡。在 AD 痴呆中,模型的哈雷尔 C 统计量为 0.68(无生物标志物模型:0.67)用于住院,0.65(无生物标志物模型:0.65)用于死亡。仅基于淀粉样蛋白阳性患者数据的模型具有相似的判别能力。
我们构建了预测模型,以对 SCD/MCI 患者的住院和死亡率进行准确性高的预测,对 AD 痴呆患者的准确性中等。开发的预测模型可用于为患者及其护理人员提供 AD 认知连续体中住院和死亡率的预后信息。