Alzheimer Center Amsterdam and Department of Neurology, VU University Medical Center, Amsterdam UMC, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands.
Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081HV, The Netherlands.
Alzheimers Res Ther. 2024 Nov 26;16(1):256. doi: 10.1186/s13195-024-01614-5.
The increasing prevalence of dementia and the introduction of disease-modifying therapies (DMTs) highlight the need for efficient diagnostic pathways in memory clinics. We present a data-driven approach to efficiently guide stepwise diagnostic testing for three clinical scenarios: 1) syndrome diagnosis, 2) etiological diagnosis, and 3) eligibility for DMT.
We used data from two memory clinic cohorts (ADC, PredictND), including 504 patients with dementia (302 Alzheimer's disease, 107 frontotemporal dementia, 35 vascular dementia, 60 dementia with Lewy bodies), 191 patients with mild cognitive impairment, and 188 cognitively normal controls (CN). Tests included digital cognitive screening (cCOG), neuropsychological and functional assessment (NP), MRI with automated quantification, and CSF biomarkers. Sequential testing followed a predetermined order, guided by diagnostic certainty. Diagnostic certainty was determined using a clinical decision support system (CDSS) that generates a disease state index (DSI, 0-1), indicating the probability of the syndrome diagnosis or underlying etiology. Diagnosis was confirmed if the DSI exceeded a predefined threshold based on sensitivity/specificity cutoffs relevant to each clinical scenario. Diagnostic accuracy and the need for additional testing were assessed at each step.
Using cCOG as a prescreener for 1) syndrome diagnosis has the potential to accurately reduce the need for extensive NP (42%), resulting in syndrome diagnosis in all patients, with a diagnostic accuracy of 0.71, which was comparable to using NP alone. For 2) etiological diagnosis, stepwise testing resulted in an etiological diagnosis in 80% of patients with a diagnostic accuracy of 0.77, with MRI needed in 77%, and CSF in 37%. When 3) determining DMT eligibility, stepwise testing (100% cCOG, 83% NP, 75% MRI) selected 60% of the patients for confirmatory CSF testing and eventually identified 90% of the potentially eligible patients with AD dementia.
Different diagnostic pathways are accurate and efficient depending on the setting. As such, a data-driven tool holds promise for assisting clinicians in selecting tests of added value across different clinical contexts. This becomes especially important with DMT availability, where the need for more efficient diagnostic pathways is crucial to maintain the accessibility and affordability of dementia diagnoses.
痴呆症的患病率不断上升，以及疾病修饰疗法（DMT）的引入，突出了在记忆诊所中建立高效诊断途径的必要性。我们提出了一种数据驱动的方法，以有效地指导三种临床情况的逐步诊断测试：1）综合征诊断，2）病因诊断，3）DMT 的资格。
我们使用了来自两个记忆诊所队列（ADC、PredictND）的数据，包括 504 名痴呆症患者（302 名阿尔茨海默病患者、107 名额颞叶痴呆患者、35 名血管性痴呆患者、60 名路易体痴呆患者）、191 名轻度认知障碍患者和 188 名认知正常对照者（CN）。测试包括数字认知筛查（cCOG）、神经心理学和功能评估（NP）、带有自动量化的 MRI 以及 CSF 生物标志物。根据诊断的确定性，按照预定的顺序进行序贯测试。诊断的确定性使用临床决策支持系统（CDSS）来确定，该系统会生成疾病状态指数（DSI，0-1），表明综合征诊断或潜在病因的可能性。如果 DSI 超过基于每个临床情况相关的敏感性/特异性截止值的预定义阈值，则可确认诊断。在每个步骤中评估诊断准确性和额外测试的需求。
使用 cCOG 作为 1）综合征诊断的预筛选器，有可能准确减少对广泛的 NP 的需求（42%），从而使所有患者都能进行综合征诊断，诊断准确性为 0.71，与单独使用 NP 相当。对于 2）病因诊断，逐步测试使 80%的患者能够获得病因诊断，诊断准确性为 0.77，需要 MRI 的比例为 77%，需要 CSF 的比例为 37%。当 3）确定 DMT 的资格时，逐步测试（100%的 cCOG、83%的 NP、75%的 MRI）使 60%的患者接受了确认性 CSF 测试，并最终确定了 90%的潜在 AD 痴呆症患者有资格接受 DMT。
不同的诊断途径根据具体情况而准确和高效。因此，数据驱动的工具有望帮助临床医生在不同的临床环境下选择有附加值的测试。随着 DMT 的可用性变得越来越重要，这种方法变得尤为重要，因为需要更有效的诊断途径，以保持痴呆症诊断的可及性和可负担性。
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