Development and validation of prediction models for incident atrial fibrillation in heart failure

Nicklas Vinter; Thomas Alexander Gerds; Pia Cordsen; Jan Brink Valentin; Gregory Y.H. Lip; Emelia J. Benjamin; Søren Paaske Johnsen; Lars Frost

doi:10.1136/openhrt-2022-002169

January 1, 2023Open HeartOpen Access

Development and validation of prediction models for incident atrial fibrillation in heart failure

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Why the study?

Accurate prediction of HF patients at high risk of AF could inform shared decision-making, but no validated prediction model is currently available.

Can a clinical prediction model accurately predict the 1-year risk of incident atrial fibrillation in patients with heart failure?

Structured PICO

Can a clinical prediction model accurately predict the 1-year risk of incident atrial fibrillation in patients with heart failure?

P

Population

27,947 incident heart failure patients diagnosed from 2008 to 2018 without a history of atrial fibrillation, mean age 69 years, 34% female, from the Danish Heart Failure Registry.

I

Intervention

Clinical prediction model for 1-year risk of atrial fibrillation using preselected predictors (sex, age at HF, NYHA class, hypertension, diabetes mellitus, chronic kidney disease, obstructive sleep apnoea, chronic obstructive pulmonary disease, and myocardial infarction)

O

Outcome

Incident atrial fibrillation at 1 yearhard clinical

A newly developed clinical prediction model for incident atrial fibrillation in heart failure patients showed modest discriminative ability (AUC 65.7%) for 1-year risk.

Implication

Models for 1-year AF risk in HF were derived from Danish registry data; leaves open clinical utility pending external validation.

Abstract

OBJECTIVES: Accurate prediction of heart failure (HF) patients at high risk of atrial fibrillation (AF) represents a potentially valuable tool to inform shared decision making. No validated prediction model for AF in HF is currently available. The objective was to develop clinical prediction models for 1-year risk of AF. METHODS: Using the Danish Heart Failure Registry, we conducted a nationwide registry-based cohort study of all incident HF patients diagnosed from 2008 to 2018 and without history of AF. Administrative data sources provided the predictors. We used a cause-specific Cox regression model framework to predict 1-year risk of AF. Internal validity was examined using temporal validation. RESULTS: The population included 27 947 HF patients (mean age 69 years; 34% female). Clinical experts preselected sex, age at HF, NewYork Heart Association (NYHA) class, hypertension, diabetes mellitus, chronic kidney disease, obstructive sleep apnoea, chronic obstructive pulmonary disease and myocardial infarction. Among patients aged 70 years at HF, the predicted 1-year risk was 9.3% (95% CI 7.1% to 11.8%) for males and 6.4% (95% CI 4.9% to 8.3%) for females given all risk factors and NYHA III/IV, and 7.5% (95% CI 6.7% to 8.4%) and 5.1% (95% CI 4.5% to 5.8%), respectively, given absence of risk factors and NYHA class I. The area under the curve was 65.7% (95% CI 63.9% to 67.5%) and Brier score 7.0% (95% CI 5.2% to 8.9%). CONCLUSION: We developed a prediction model for the 1-year risk of AF. Application of the model in routine clinical settings is necessary to determine the possibility of predicting AF risk among patients with HF more accurately and if so, to quantify the clinical effects of implementing the model in practice.