Tailored oral anticoagulant prescription in patients with atrial fibrillation: Use and misuse of clinical risk prediction scores

Proietti, Marco; Vitolo, Marco; Boriani, Giuseppe

doi:10.1177/2047487319877469

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Recent cohort studies have re-affirmed the importance of using stroke risk assessment tool, such as CHA 2 DS 2 -VASc, to guide anticoagulation decisions and not to withhold this treatment based on high baseline bleeding risk alone. 49 , 50 However, the relatively poor performance of CHA 2 DS 2 -VASc for predicting either AF prevalence or incidence hampers the possibility of using a single score for prediction of AF diagnosis and risk stratification of outcomes, such as stroke or systemic thromboembolism. Using CHA 2 DS 2 -VASc for selection of cases was recently applied by the REHEARSE-AF trial, a randomized controlled trial of AF screening using the AliveCor Kardia smartphone device in people with a CHA 2 DS 2 -VASc score ≥2.…”

Section: Discussionmentioning

confidence: 99%

Utility of risk prediction models to detect atrial fibrillation in screened participants

Poorthuis

Jones

Sherliker

et al. 2020

European Journal of Preventive Cardiology

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Aims Atrial fibrillation (AF) is associated with higher risk of stroke. While the prevalence of AF is low in the general population, risk prediction models might identify individuals for selective screening of AF. We aimed to systematically identify and compare the utility of established models to predict prevalent AF. Methods and results Systematic search of PubMed and EMBASE for risk prediction models for AF. We adapted established risk prediction models and assessed their predictive performance using data from 2.5M individuals who attended vascular screening clinics in the USA and the UK and in the subset of 1.2M individuals with CHA2DS2-VASc ≥2. We assessed discrimination using area under the receiver operating characteristic (AUROC) curves and agreement between observed and predicted cases using calibration plots. After screening 6959 studies, 14 risk prediction models were identified. In our cohort, 10 464 (0.41%) participants had AF. For discrimination, six prediction model had AUROC curves of 0.70 or above in all individuals and those with CHA2DS2-VASc ≥2. In these models, calibration plots showed very good concordance between predicted and observed risks of AF. The two models with the highest observed prevalence in the highest decile of predicted risk, CHARGE-AF and MHS, showed an observed prevalence of AF of 1.6% with a number needed to screen of 63. Selective screening of the 10% highest risk identified 39% of cases with AF. Conclusion Prediction models can reliably identify individuals at high risk of AF. The best performing models showed an almost fourfold higher prevalence of AF by selective screening of individuals in the highest decile of risk compared with systematic screening of all cases. Registration This systematic review was registered (PROSPERO CRD42019123847).

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Section: Discussionmentioning

confidence: 99%

Utility of risk prediction models to detect atrial fibrillation in screened participants

Poorthuis

Jones

Sherliker

et al. 2020

European Journal of Preventive Cardiology

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Editors’ introduction: focus on cardiac arrhythmias

Halász

Piepoli

2021

European Journal of Preventive Cardiology

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Impact of anthropometric factors on outcomes in atrial fibrillation patients: analysis on 10 220 patients from the European Society of Cardiology (ESC)-European Heart Rhythm Association (EHRA) EurObservational Research Programme on Atrial Fibrillation (EORP-AF) general long-term registry

Boriani

Vitolo

Malavasi

et al. 2022

European Journal of Preventive Cardiology

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Aim To investigate the association of anthropometric parameters [height, weight, body mass index (BMI), body surface area (BSA) and lean body mass (LBM)] with outcomes in atrial fibrillation (AF). Methods and results 10220 patients were enrolled [40.3% females, median age 70 (62-77) years, followed for 728 (IQR 653-745) days]. Sex-specific tertiles were considered for the 5 anthropometric variables. At the end of follow-up survival free from all-cause death was worse in the lowest tertiles for all the anthropometric variables analyzed. On multivariable Cox regression analysis, an independent association with all-cause death was found for the lowest vs. middle tertile when body weight (hazard ratio [HR] 1.66, 95%CI 1.23-2.23), BMI (HR 1.65, 95%CI 1.23-2.21), BSA (HR 1.49, 95%CI 1.11-2.01) were analysed in female sex, as well as for body weight in male patients (HR 1.61, 95%CI 1.25-2 .07). Conversely, the risk of MACE was lower for the highest tertile (vs. middle tertile) of BSA and LBM in males and for the highest tertile of weight and BSA in female patients. A higher occurrence of hemorrhagic events was found for female patients in the lowest tertile of height [odds ratio (OR) 1.90, 95%CI 1.23-2.94] and LBM (OR 2.13, 95%CI 1.40-3.26). Conclusions In AF patients height, weight, BMI, BSA and LBM were associated with clinical outcomes, with all-cause death being higher for patients presenting lower values of these variables, i.e in the lowest tertiles of distribution. The anthropometric variables independently associated with other outcomes were also different between male and female subjects.

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Tailored oral anticoagulant prescription in patients with atrial fibrillation: Use and misuse of clinical risk prediction scores

Cited by 3 publications

References 11 publications

Utility of risk prediction models to detect atrial fibrillation in screened participants

Utility of risk prediction models to detect atrial fibrillation in screened participants

Editors’ introduction: focus on cardiac arrhythmias

Impact of anthropometric factors on outcomes in atrial fibrillation patients: analysis on 10 220 patients from the European Society of Cardiology (ESC)-European Heart Rhythm Association (EHRA) EurObservational Research Programme on Atrial Fibrillation (EORP-AF) general long-term registry

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