New-onset atrial fibrillation prediction: the HARMS2-AF risk score

Author:

Segan Louise123ORCID,Canovas Rodrigo245,Nanayakkara Shane126ORCID,Chieng David123,Prabhu Sandeep123,Voskoboinik Aleksandr123ORCID,Sugumar Hariharan123ORCID,Ling Liang-Han123ORCID,Lee Geoff37,Morton Joseph37,LaGerche Andre123ORCID,Kaye David M126ORCID,Sanders Prashanthan89ORCID,Kalman Jonathan M37,Kistler Peter M1237ORCID

Affiliation:

1. Department of Cardiology, The Alfred Hospital , 55 Commercial Road, Melbourne, VIC 3004 , Australia

2. Department of Clinical Research, The Baker Heart and Diabetes Research Institute , 75 Commercial Rd, Melbourne, VIC 3004 , Australia

3. Department of Medicine, Nursing and Health Sciences, University of Melbourne , Parkville, Melbourne, VIC 3010 , Australia

4. Cambridge Baker Systems Genomics Initiative , Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, VIC 3004 , Australia

5. CSIRO Health and Biosecurity, Australian e-Health Research Centre , 343 Royal Parade, Parkville, Melbourne, VIC 3052 , Australia

6. Department of Medicine, Nursing and Health Sciences, Monash University , Wellington Rd, Clayton, VIC 3800 , Australia

7. Department of Cardiology, Royal Melbourne Hospital , 300 Grattan St, Parkville, Melbourne, VIC 3050 , Australia

8. Department of Cardiology, Royal Adelaide Hospital , Port Rd, Adelaide, SA 5000 , Australia

9. Centre for Heart Rhythm Disorders, University of Adelaide, Port Rd, Adelaide, SA 5000, Australia

Abstract

Abstract Aims Lifestyle risk factors are a modifiable target in atrial fibrillation (AF) management. The relative contribution of individual lifestyle risk factors to AF development has not been described. Development and validation of an AF lifestyle risk score to identify individuals at risk of AF in the general population are the aims of the study. Methods and results The UK Biobank (UKB) and Framingham Heart Study (FHS) are large prospective cohorts with outcomes measured >10 years. Incident AF was based on International Classification of Diseases version 10 coding. Prior AF was excluded. Cox proportional hazards regression identified independent AF predictors, which were evaluated in a multivariable model. A weighted score was developed in the UKB and externally validated in the FHS. Kaplan–Meier estimates ascertained the risk of AF development. Among 314 280 UKB participants, AF incidence was 5.7%, with median time to AF 7.6 years (interquartile range 4.5–10.2). Hypertension, age, body mass index, male sex, sleep apnoea, smoking, and alcohol were predictive variables (all P < 0.001); physical inactivity [hazard ratio (HR) 1.01, 95% confidence interval (CI) 0.96–1.05, P = 0.80] and diabetes (HR 1.03, 95% CI 0.97–1.09, P = 0·38) were not significant. The HARMS2-AF score had similar predictive performance [area under the curve (AUC) 0.782] to the unweighted model (AUC 0.802) in the UKB. External validation in the FHS (AF incidence 6.0% of 7171 participants) demonstrated an AUC of 0.757 (95% CI 0.735–0.779). A higher HARMS2-AF score (≥5 points) was associated with a heightened AF risk (score 5–9: HR 12.79; score 10–14: HR 38.70). The HARMS2-AF risk model outperformed the Framingham-AF (AUC 0.568) and ARIC (AUC 0.713) risk models (both P < 0.001) and was comparable to the CHARGE-AF risk score (AUC 0.754, P = 0.73). Conclusion The HARMS2-AF score is a novel lifestyle risk score which may help identify individuals at risk of AF in the general community and assist population screening.

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine

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