Development and validation of prediction models for incident atrial fibrillation in heart failure
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Development and validation of prediction models for incident atrial fibrillation in heart failure. / Vinter, Nicklas; Gerds, Thomas Alexander; Cordsen, Pia; Valentin, Jan Brink; Lip, Gregory Y. H.; Benjamin, Emelia J. J.; Johnsen, Søren Paaske; Frost, Lars.
I: Open Heart, Bind 10, Nr. 1, 002169, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Development and validation of prediction models for incident atrial fibrillation in heart failure
AU - Vinter, Nicklas
AU - Gerds, Thomas Alexander
AU - Cordsen, Pia
AU - Valentin, Jan Brink
AU - Lip, Gregory Y. H.
AU - Benjamin, Emelia J. J.
AU - Johnsen, Søren Paaske
AU - Frost, Lars
PY - 2023
Y1 - 2023
N2 - ObjectivesAccurate 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.MethodsUsing 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.ResultsThe 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%).ConclusionWe 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.
AB - ObjectivesAccurate 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.MethodsUsing 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.ResultsThe 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%).ConclusionWe 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.
KW - Atrial Fibrillation
KW - HEART FAILURE
KW - Electronic Health Records
KW - RISK SCORE
KW - MORTALITY
U2 - 10.1136/openhrt-2022-002169
DO - 10.1136/openhrt-2022-002169
M3 - Journal article
C2 - 36639191
VL - 10
JO - Open Heart
JF - Open Heart
SN - 2398-595X
IS - 1
M1 - 002169
ER -
ID: 334709086