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Aims
We aimed to analyze prevalence and predictors of NOAC off-label under-dosing in AF patients before and after the index stroke.
Methods
The post hoc analysis included 1080 patients of the investigator-initiated, multicenter prospective Berlin Atrial Fibrillation Registry, designed to analyze medical stroke prevention in AF patients after acute ischemic stroke.
Results
At stroke onset, an off-label daily dose was prescribed in 61 (25.5%) of 239 NOAC patients with known AF and CHA2DS2-VASc score ≥ 1, of which 52 (21.8%) patients were under-dosed. Under-dosing was associated with age ≥ 80 years in patients on rivaroxaban [OR 2.90, 95% CI 1.05-7.9, P = 0.04; n = 29] or apixaban [OR 3.24, 95% CI 1.04-10.1, P = 0.04; n = 22]. At hospital discharge after the index stroke, NOAC off-label dose on admission was continued in 30 (49.2%) of 61 patients. Overall, 79 (13.7%) of 708 patients prescribed a NOAC at hospital discharge received an off-label dose, of whom 75 (10.6%) patients were under-dosed. Rivaroxaban under-dosing at discharge was associated with age ≥ 80 years [OR 3.49, 95% CI 1.24-9.84, P = 0.02; n = 19]; apixaban under-dosing with body weight ≤ 60 kg [OR 0.06, 95% CI 0.01-0.47, P < 0.01; n = 56], CHA2DS2-VASc score [OR per point 1.47, 95% CI 1.08-2.00, P = 0.01], and HAS-BLED score [OR per point 1.91, 95% CI 1.28-2.84, P < 0.01].
Conclusion
At stroke onset, off-label dosing was present in one out of four, and under-dosing in one out of five NOAC patients. Under-dosing of rivaroxaban or apixaban was related to old age. In-hospital treatment after stroke reduced off-label NOAC dosing, but one out of ten NOAC patients was under-dosed at discharge.
Functional versus morphological assessment of vascular age in patients with coronary heart disease
(2021)
Communicating cardiovascular risk based on individual vascular age (VA) is a well acknowledged concept in patient education and disease prevention. VA may be derived functionally, e.g. by measurement of pulse wave velocity (PWV), or morphologically, e.g. by assessment of carotid intima-media thickness (cIMT). The purpose of this study was to investigate whether both approaches produce similar results. Within the context of the German subset of the EUROASPIRE IV survey, 501 patients with coronary heart disease underwent (a) oscillometric PWV measurement at the aortic, carotid-femoral and brachial-ankle site (PWVao, PWVcf, PWVba) and derivation of the aortic augmentation index (AIao); (b) bilateral cIMT assessment by high-resolution ultrasound at three sites (common, bulb, internal). Respective VA was calculated using published equations. According to VA derived from PWV, most patients exhibited values below chronological age indicating a counterintuitive healthier-than-anticipated vascular status: for VA(PWVao) in 68% of patients; for VA\(_{AIao}\) in 52% of patients. By contrast, VA derived from cIMT delivered opposite results: e.g. according to VA\(_{total-cIMT}\) accelerated vascular aging in 75% of patients. To strengthen the concept of VA, further efforts are needed to better standardise the current approaches to estimate VA and, thereby, to improve comparability and clinical utility.
Background
The impact of risk factors on poor outcome after ischemic stroke is well known, but estimating the amount of poor outcome attributable to single factors is challenging in presence of multimorbidity. We aim to compare population attributable risk estimates obtained from different statistical approaches regarding their consistency. We use a real-life data set from the PROSCIS study to identify predictors for mortality and functional impairment one year after first-ever ischemic stroke and quantify their contribution to poor outcome using population attributable risks.
Methods
The PROSpective Cohort with Incident Stroke (PROSCIS) is a prospective observational hospital-based cohort study of patients after first-ever stroke conducted independently in Berlin (PROSCIS-B) and Munich (PROSCIS-M). The association of baseline factors with poor outcome one year after stroke in PROSCIS-B was analysed using multiple logistic regression analysis and population attributable risks were calculated, which were estimated using sequential population attributable risk based on a multiple generalized additive regression model, doubly robust estimation, as well as using average sequential population attributable risk. Findings were reproduced in an independent validation sample from PROSCIS-M.
Results
Out of 507 patients with available outcome information after 12 months in PROSCIS-B, 20.5% suffered from poor outcome. Factors associated with poor outcome were age, pre-stroke physical disability, stroke severity (NIHSS), education, and diabetes mellitus. The order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but population attributable risk estimates varied markedly between the methods. In PROSCIS-M, incidence of poor outcome and distribution of baseline parameters were comparable. The multiple logistic regression model could be reproduced for all predictors, except pre-stroke physical disability. Similar to PROSCIS-B, the order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but magnitudes of population attributable risk differed markedly between the methods.
Conclusions
Ranking of risk factors by population impact is not affected by the different statistical approaches. Thus, for a rational decision on which risk factor to target in disease interventions, population attributable risk is a supportive tool. However, population attributable risk estimates are difficult to interpret and are not comparable when they origin from studies applying different methodology. The predictors for poor outcome identified in PROSCIS-B have a relevant impact on mortality and functional impairment one year after first-ever ischemic stroke.