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.