Gross Alden L, Hassenstab Jason J, Johnson Sterling C, Clark Lindsay R, Resnick Susan M, Kitner-Triolo Melissa, Masters Colin L, Maruff Paul, Morris John C, Soldan Anja, Pettigrew Corinne, Albert Marilyn S
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Johns Hopkins University Center on Aging and Health, Baltimore, MD, USA.
Alzheimers Dement (Amst). 2017 May 30;8:147-155. doi: 10.1016/j.dadm.2017.05.003. eCollection 2017.
We established a method for diagnostic harmonization across multiple studies of preclinical Alzheimer's disease and validated the method by examining its relationship with clinical status and cognition.
Cognitive and clinical data were used from five studies ( = 1746). Consensus diagnoses established in each study used criteria to identify progressors from normal cognition to mild cognitive impairment. Correspondence was evaluated between these consensus diagnoses and three algorithmic classifications based on (1) objective cognitive impairment in 2+ tests only; (2) a Clinical Dementia Rating (CDR) of ≥0.5 only; and (3) both. Associations between baseline cognitive performance and cognitive change were each tested in relation to progression to algorithm-based classifications.
In each study, an algorithmic classification based on both cognitive testing cutoff scores and a CDR ≥0.5 provided optimal balance of sensitivity and specificity (areas under the curve: 0.85-0.95). Over an average 6.6 years of follow-up (up to 28 years), = 186 initially cognitively normal participants aged on average 64 years at baseline progressed (incidence rate: 15.3 people/1000 person-years). Baseline cognitive scores and cognitive change were associated with future diagnostic status using this algorithmic classification.
Both cognitive tests and CDR ratings can be combined across multiple studies to obtain a reliable algorithmic classification with high specificity and sensitivity. This approach may be applicable to large cohort studies and to clinical trials focused on preclinical Alzheimer's disease because it provides an alternative to implementation of a time-consuming adjudication panel.
我们建立了一种用于跨多项临床前阿尔茨海默病研究进行诊断协调的方法，并通过检验其与临床状态和认知的关系来验证该方法。
使用了五项研究（n = 1746）的认知和临床数据。每项研究中建立的共识诊断使用标准来识别从正常认知进展为轻度认知障碍的个体。评估这些共识诊断与基于以下三种算法分类之间的一致性：（1）仅在两项及以上测试中存在客观认知障碍；（2）仅临床痴呆评定量表（CDR）≥0.5；（3）两者兼具。分别测试基线认知表现和认知变化与基于算法分类的进展之间的关联。
在每项研究中，基于认知测试临界分数和CDR≥0.5的算法分类在敏感性和特异性之间提供了最佳平衡（曲线下面积：0.85 - 0.95）。在平均6.6年的随访期（最长28年）内，186名最初认知正常的参与者（基线时平均年龄64岁）出现了进展（发病率：15.3人/1000人年）。使用这种算法分类，基线认知分数和认知变化与未来诊断状态相关。
认知测试和CDR评分均可跨多项研究进行合并，以获得具有高特异性和敏感性的可靠算法分类。这种方法可能适用于大型队列研究以及专注于临床前阿尔茨海默病的临床试验，因为它为实施耗时的裁决小组提供了一种替代方案。
