Dataset Information

Universal Clinician Device for improving risk prediction and management of patients with atrial fibrillation: an assumed benefit analysis

ABSTRACT: Abstract

Aim

Atrial fibrillation (AF) management guidelines advise using risk tools to optimize AF treatment. This study aims to develop a dynamic and clinically applicable digital device to assess stroke and bleeding risk, and to facilitate outcome improvements in AF patients. The device will provide tailored treatment recommendations according to easily attainable individual patient data.

Methods and Results

This Universal Clinician Device (UCD) was created using the GARFIELD-AF registry using a split sample approach. The GARFIELD-AF risk tool was adapted with two modifications. First, predictors with ≥1000 missing data points were separated, allowing expected risks estimation. Second, recommendations for modifiable risk factors and associated 2-year outcome estimates were incorporated. Outcomes of interest were all-cause mortality, non-haemorrhagic stroke/systemic embolism (SE), and major bleeding. All patients were randomized to a derivation (n = 34853) and validation cohort (n = 17165). In the derivation cohort, predictors were identified using least absolute shrinkage and selection operator regression. Cox models were fitted with the selected parameters. The UCD demonstrated superior predictive power compared with CHA2DS2VASc for all-cause mortality [0.75(0.75–0.76) vs. 0.71(0.70–0.72)] and non-haemorrhagic stroke/SE [0.68(0.66–0.70) vs. 0.65(0.63–0.67)], and with HAS-BLED for major bleeding [0.69(0.67–0.71) vs. 0.64(0.62–0.65)]. Universal Clinician Device recommendations reduced all-cause mortality (8.45–5.42%) and non-haemorrhagic stroke/SE (2.58–1.50%). Patients with concomitant diabetes and chronic kidney disease benefitted further, reducing mortality risk from 13.15% to 8.67%. One-third of patients with a CHA2DS2VASc score of >1 had the lowest risk of stroke.

Conclusion

The UCD simultaneously predicts mortality, stroke, and bleeding risk in patients using easily attainable individual clinical data and guideline-based optimized treatment plans.

Clinical Trial Registration

URL: http://www.clinicaltrials.gov. Unique identifier for GARFIELD-AF: NCT01090362 Graphical Abstract Graphical Abstract The UCD uses patient data, clinical and current treatment characteristics to provide an individual risk assessment of the current risk and the risk with optimal therapy. Applying this device in clinical practice provides significant improvement potential vs. current practice.

SUBMITTER:

PROVIDER: S-EPMC9707904 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Project description:Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF. Methods: We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort. Results: A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort. Conclusions: We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.