BACKGROUNDThe history from a relative or caregiver is an important tool for differentiating neurodegenerative disease. We characterized patterns of caregiver questionnaire responses, at diagnosis and follow‐up, on the Cambridge Behavioural Inventory (CBI).METHODSData‐driven multivariate analysis (n = 4952 questionnaires) was undertaken for participants (n = 2481) with Alzheimer's disease (typical/amnestic n = 543, language n = 50, and posterior cortical n = 50 presentations), Parkinson's disease (n = 740), dementia with Lewy bodies (n = 55), multiple system atrophy (n = 55), progressive supranuclear palsy (n = 422), corticobasal syndrome (n = 176), behavioral variant frontotemporal dementia (n = 218), semantic (n = 125) and non‐fluent variant progressive aphasia (n = 88), and motor neuron disease (n = 12).RESULTSItem‐level support vector machine learning gave high diagnostic accuracy between diseases (area under the curve mean 0.83), despite transdiagnostic changes in memory, behavior, and everyday function. There was progression in CBI subscores over time, which varied by diagnosis.DISCUSSIONOur results highlight the differential diagnostic information for a wide range of neurodegenerative diseases contained in a simple, structured collateral history.Highlights We analyzed 4952 questionnaires from caregivers of 2481 participants with neurodegenerative disease. Behavioral and neuropsychiatric manifestations of neurodegenerative disease had overlapping diagnostic boundaries. Simple questionnaire response patterns were sufficient for accurate diagnosis of each disease. We reinforce the value of a collateral history to support a diagnosis of dementia. The Cambridge Behavioural Inventory is sensitive to change over time and suitable as an outcome measure in clinical trials.

中文翻译：

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照护者的观点有助于准确诊断神经退行性疾病


背景亲属或照护者的病史是鉴别神经退行性疾病的重要工具。我们在剑桥行为量表 （CBI） 上描述了诊断和随访时照顾者问卷回答的模式。方法对患有阿尔茨海默病（典型/遗忘性 n = 543，语言 n = 50，后皮质 n = 50 次表现）、帕金森病 （n = 740）、路易体痴呆 （n = 55）、多系统萎缩 （n = 55）、进行性核上性麻痹 （n = 422）、皮质基底综合征 （n = 176）、行为变异额颞叶痴呆 （n = 218）、 语义 （n = 125） 和非流利变异进行性失语症 （n = 88） 和运动神经元病 （n = 12）。结果尽管记忆、行为和日常功能发生了跨诊断变化，但蛋白质水平支持向量机器学习在疾病之间提供了很高的诊断准确性（曲线下面积平均值 0.83）。CBI 子评分随着时间的推移而进展，这因诊断而异。讨论我们的结果突出了简单、结构化的旁支病史中包含的各种神经退行性疾病的鉴别诊断信息。亮点 我们分析了 4952 名神经退行性疾病参与者的护理人员的 2481 份问卷。神经退行性疾病的行为和神经精神病学表现具有重叠的诊断界限。简单的问卷回答模式足以准确诊断每种疾病。我们加强了旁证病史的价值，以支持痴呆的诊断。剑桥行为量表对随时间的变化很敏感，适合作为临床试验的结局测量。