Background: Administrative data are used in the field of Alzheimer’s Disease and Related Syndromes (ADRS), however their performance to identify ADRS is unknown.

Objective: i) To develop and validate a model to identify ADRS prevalent cases in French administrative data (SNDS), ii) to identify factors associated with false negatives.

Methods: Retrospective cohort of subjects ≥ 65 years, living in South-Western France, who attended a memory clinic between April and December 2013. Gold standard for ADRS diagnosis was the memory clinic specialized diagnosis. Memory clinics’ data were matched to administrative data (drug reimbursements, diagnoses during hospitalizations, registration with costly chronic conditions). Prediction models were developed for 1-year and 3-year periods of administrative data using multivariable logistic regression models. Overall model performance, discrimination, and calibration were estimated and corrected for optimism by resampling. Youden index was used to define ADRS positivity and to estimate sensitivity, specificity, positive predictive and negative probabilities. Factors associated with false negatives were identified using multivariable logistic regressions.

Results: 3360 subjects were studied, 52% diagnosed with ADRS by memory clinics. Prediction model based on age, all-cause hospitalization, registration with ADRS as a chronic condition, number of anti-dementia drugs, mention of ADRS during hospitalizations had good discriminative performance (c-statistic: 0.814, sensitivity: 76.0%, specificity: 74.2% for 2013 data). 419 false negatives (24.0%) were younger, had more often ADRS types other than Alzheimer’s disease, moderate forms of ADRS, recent diagnosis, and suffered from other comorbidities than true positives.

Conclusion: Administrative data presented acceptable performance for detecting ADRS. External validation studies should be encouraged.

背景：管理数据用于阿尔茨海默病及相关综合征 (ADRS) 领域，但其识别 ADRS 的性能未知。

目标：i) 开发和验证模型以识别法国行政数据 (SNDS) 中的 ADRS 流行病例，ii) 识别与假阴性相关的因素。

方法：回顾性队列研究对象年龄≥65 岁，居住在法国西南部，在 2013 年 4 月至 12 月期间就诊于记忆诊所。ADRS 诊断的金标准是记忆诊所专科诊断。记忆诊所的数据与行政数据（药物报销、住院期间的诊断、昂贵的慢性病登记）相匹配。使用多变量逻辑回归模型为 1 年和 3 年期间的行政数据开发了预测模型。通过重新采样估计和校正整体模型性能、辨别力和校准的乐观度。Youden 指数用于定义 ADRS 阳性并估计敏感性、特异性、阳性预测和阴性概率。

结果：研究了 3360 名受试者，其中 52% 被记忆诊所诊断为 ADRS。基于年龄、全因住院、ADRS 登记为慢性病、抗痴呆药物数量、住院期间提及 ADRS 的预测模型具有良好的判别性能（c 统计量：0.814，敏感性：76.0%，特异性：74.2 % 为 2013 年数据）。419 名假阴性 (24.0%) 较年轻，患有除阿尔茨海默病以外的 ADRS 类型、中等形式的 ADRS、近期诊断以及患有除真阳性以外的其他合并症。

结论：管理数据显示了检测 ADRS 的可接受性能。应鼓励外部验证研究。