CERPOP, Universite de Toulouse, Inserm, UPS, Toulouse, France.
Caisse Nationale d'Assurance Maladie des Travailleurs Salaries (CNAMTS), Echelon Regional du Service Medical Midi-Pyrenees - F31000 Toulouse, France.
Curr Alzheimer Res. 2021;18(2):142-156. doi: 10.2174/1567205018666210416094639.
Administrative data are used in the field of Alzheimer's Disease and Related Syndromes (ADRS), however their performance to identify ADRS is unknown.
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.
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.
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.
Administrative data presented acceptable performance for detecting ADRS. External validation studies should be encouraged.
行政数据在阿尔茨海默病及相关综合征（ADRS）领域得到了应用，但目前尚不清楚其在识别 ADRS 方面的表现。
i）开发并验证一种用于识别法国行政数据中 ADRS 现患病例的模型（SNDS），ii）确定与漏诊相关的因素。
本研究为回顾性队列研究，纳入了 2013 年 4 月至 12 月期间在法国西南部参加记忆门诊的年龄≥65 岁的患者。ADRS 的金标准诊断是记忆门诊的专科诊断。将记忆门诊的数据与行政数据（药物报销、住院期间的诊断、昂贵慢性病的登记）相匹配。使用多变量逻辑回归模型，为行政数据的 1 年和 3 年期间开发预测模型。通过重新抽样来估计和校正整体模型性能、判别力和校准的偏倚。采用约登指数定义 ADRS 阳性，并估算敏感度、特异度、阳性预测值和阴性预测值。使用多变量逻辑回归确定漏诊的相关因素。
共纳入 3360 例患者，52%的患者经记忆门诊诊断为 ADRS。基于年龄、全因住院、ADRS 作为慢性病登记、使用抗痴呆药物的数量、住院期间提及 ADRS 的预测模型具有良好的判别能力（c 统计量：2013 年数据为 0.814，敏感度：76.0%，特异度：74.2%）。419 例漏诊（24.0%）患者更年轻，ADRS 类型更常见（除阿尔茨海默病以外的类型），为中度 ADRS，近期诊断，且合并其他合并症，而非真正的阳性病例。
行政数据在检测 ADRS 方面表现出可接受的性能。应鼓励开展外部验证研究。
