Erschienen in:
12.06.2019 | Original Article
Generation and validation of algorithms to identify subjects with dementia using administrative data
verfasst von:
Jacopo C. DiFrancesco, Alessandra Pina, Giorgia Giussani, Laura Cortesi, Elisa Bianchi, Luca Cavalieri d’Oro, Emanuele Amodio, Alessandro Nobili, Lucio Tremolizzo, Valeria Isella, Ildebrando Appollonio, Carlo Ferrarese, Ettore Beghi
Erschienen in:
Neurological Sciences
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Ausgabe 10/2019
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Abstract
Objectives
To generate and validate algorithms for the identification of individuals with dementia in the community setting, by the interrogation of administrative records, an inexpensive and already available source of data.
Methods
We collected and anonymized information on demented individuals 65 years of age or older from ten general practitioners (GPs) in the district of Brianza (Northern Italy) and compared this with the administrative data of the local health protection agency (Agenzia per la Tutela della Salute). Indicators of the disease in the administrative database (diagnosis of dementia in the hospital discharge records; use of cholinesterase inhibitors/memantine; neuropsychological tests; brain CT/MRI; outpatient neurological visits) were used separately and in different combinations to generate algorithms for the detection of patients with dementia.
Results
When used individually, indicators of dementia showed good specificity, but low sensitivity. By their combination, we generated different algorithms: I-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests (specificity 97.9%, sensitivity 52.5%); II-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests or brain CT/MRI or neurological visit (sensitivity 90.8%, specificity 70.6%); III-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests or brain CT/MRIMRI and neurological visit (specificity 89.3%, sensitivity 73.3%).
Conclusions
These results show that algorithms obtained from administrative data are not sufficiently accurate in classifying patients with dementia, whichever combination of variables is used for the identification of the disease. Studies in large patient cohorts are needed to develop further strategies for identifying patients with dementia in the community setting.