Ensayos clínicos en atención primaria. La experiencia de la red AGICAP en Cataluña

Bargalló, E. Vinyoles; Ribó, Cristina

doi:10.1016/s0212-6567(04)79449-4

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Survey questions, design and administration are described elsewhere 6. Briefly, GPs from the IDIAPJGol Agency of Clinical Research Management in Primary Care (AGICAP) network20 were invited to participate. Those who agreed to participate were sent the online survey in January 2018 and given 1 month to evaluate a consecutive series of their patients with a dementia diagnosis recorded in the EHR 6.…”

Section: Methodsmentioning

confidence: 99%

<p>How well can electronic health records from primary care identify Alzheimer’s disease cases?</p>

Ponjoan¹,

Garré-Olmo²,

Blanch³

et al. 2019

CLEP

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Background Electronic health records (EHR) from primary care are emerging in Alzheimer’s disease (AD) research, but their accuracy is a concern. We aimed to validate AD diagnoses from primary care using additional information provided by general practitioners (GPs), and a register of dementias. Patients and methods This retrospective observational study obtained data from the System for the Development of Research in Primary Care (SIDIAP). Three algorithms combined International Statistical Classification of Diseases (ICD-10) and Anatomical Therapeutic Chemical codes to identify AD cases in SIDIAP. GPs evaluated dementia diagnoses by means of an online survey. We linked data from the Register of Dementias of Girona and from SIDIAP. We estimated the positive predictive value (PPV) and sensitivity and provided results stratified by age, sex and severity. Results Using survey data from the GPs, PPV of AD diagnosis was 89.8% (95% CI: 84.7–94.9). Using the dataset linkage, PPV was 74.8 (95% CI: 73.1–76.4) for algorithm A1 (AD diagnoses), and 72.3 (95% CI: 70.7–73.9) for algorithm A3 (diagnosed or treated patients without previous conditions); sensitivity was 71.4 (95% CI: 69.6–73.0) and 83.3 (95% CI: 81.8–84.6) for algorithms A1 (AD diagnoses) and A3, respectively. Stratified results did not differ by age, but PPV and sensitivity estimates decreased amongst men and severe patients, respectively. Conclusions PPV estimates differed depending on the gold standard. The development of algorithms integrating diagnoses and treatment of dementia improved the AD case ascertainment. PPV and sensitivity estimates were high and indicated that AD codes recorded in a large primary care database were sufficiently accurate for research purposes.

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Section: Methodsmentioning

confidence: 99%