Repositori URV

Identifier: imarina:9138867

Handle: https://hdl.handle.net/20.500.11797/imarina9138867

Authors:
Clua-Espuny JLMuria-Subirats EBallesta-Ors JLorman-Carbo BClua-Queralt JPalà ELechuga-Duran IGentille-Lorente DBustamante AMuñoz MÁMontaner J

Abstract:
© 2020 Clua-Espuny et al. Purpose: To evaluate a model for calculating the risk of AF and its relationship with the incidence of ischemic stroke and prevalence of cognitive decline. Materials and Methods: It was a multicenter, observational, retrospective, community-based study of a cohort of general population ≥6ct 35 years, between 01/01/2016 and 31/12/2018. Setting: Primary Care. Participants: 46,706 people ≥65 years with an active medical history in any of the primary care teams of the territory, information accessible through shared history and without previous known AF. Interventions: The model to stratify the risk of AF (PI) has been previously published and included the variables sex, age, mean heart rate, mean weight and CHA2DS2VASc score. Main measurements: For each risk group, the incidence density/1000 person/years of AF and stroke, number of cases required to detect a new AF, the prevalence of cognitive decline, Kendall correlation, and ROC curve were calculated. Results: The prognostic index was obtained in 37,731 cases (80.8%) from lowest (Q1) to highest risk (Q4). A total of 1244 new AFs and 234 stroke episodes were diagnosed. Q3-4 included 53.8% of all AF and 69.5% of strokes in men; 84.2% of all AF and 85.4% of strokes in women; and 77.4% of cases of cognitive impairment. There was a significant linear correlation between the risk-AF score and the Rankin score (p < 0.001), the Pfeiffer score (p < 0.001), but not NIHSS score (p 0.150). The overall NNS was 1/19. Conclusion: Risk stratification allows identifying high-risk individuals in whom to inter-vene on modifiable risk factors, prioritizing the diagnosis of AF and investigating cognitive status.