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. 2017 Mar:185:74-84.
doi: 10.1016/j.ahj.2016.11.008. Epub 2016 Dec 9.

Multimorbidity and the risk of hospitalization and death in atrial fibrillation: A population-based study

Affiliations

Multimorbidity and the risk of hospitalization and death in atrial fibrillation: A population-based study

Alanna M Chamberlain et al. Am Heart J. 2017 Mar.

Abstract

Patients with atrial fibrillation (AF) have many comorbidities and excess risks of hospitalization and death. Whether the impact of comorbidities on outcomes is greater in AF than the general population is unknown.

Methods: One thousand four hundred thirty patients with AF and community controls matched 1:1 on age and sex were obtained from Olmsted County, Minnesota. Andersen-Gill and Cox regression estimated associations of 19 comorbidities with hospitalization and death, respectively.

Results: AF cases had a higher prevalence of most comorbidities. Hypertension (25.4%), coronary artery disease (17.7%), and heart failure (13.3%) had the largest attributable risk of AF; these along with obesity and smoking explained 51.4% of AF. Over a mean follow-up of 6.3 years, patients with AF experienced higher rates of hospitalization and death than did population controls. However, the impact of comorbidities on hospitalization and death was generally not greater in patients with AF compared with controls, with the exception of smoking. Ever smokers with AF experienced higher-than-expected risks of hospitalization and death, with observed vs expected (assuming additivity of effects) hazard ratios compared with never smokers without AF of 1.78 (1.56-2.02) vs 1.52 for hospitalization and 2.41 (2.02-2.87) vs 1.84 for death.

Conclusions: Patients with AF have a higher prevalence of most comorbidities; however, the impact of comorbidities on hospitalization and death is generally similar in AF and controls. Smoking is a notable exception; ever smokers with AF experienced higher-than-expected risks of hospitalization and death. Thus, interventions targeting modifiable behaviors may benefit patients with AF by reducing their risk of adverse outcomes.

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Figures

Figure 1
Figure 1
Odds ratios (95% confidence intervals) for atrial fibrillation by presence of individual comorbidities in unadjusted models (Panel A) and after adjustment for all other comorbidities (Panel B). OR, odds ratio; CI, confidence interval; AR, attributable risk; CHF, congestive heart failure; CAD, coronary heart disease; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease.
Figure 1
Figure 1
Odds ratios (95% confidence intervals) for atrial fibrillation by presence of individual comorbidities in unadjusted models (Panel A) and after adjustment for all other comorbidities (Panel B). OR, odds ratio; CI, confidence interval; AR, attributable risk; CHF, congestive heart failure; CAD, coronary heart disease; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease.
Figure 2
Figure 2
Mean cumulative number of hospitalizations (Panel A) and cumulative incidence of death (Panel B) for atrial fibrillation cases and population controls.
Figure 2
Figure 2
Mean cumulative number of hospitalizations (Panel A) and cumulative incidence of death (Panel B) for atrial fibrillation cases and population controls.
Figure 3
Figure 3
Hazard ratios (95% confidence intervals) for hospitalization (Panel A) and death (Panel B) by presence of individual comorbidities in atrial fibrillation patients, after adjustment for age, sex, and all other comorbidities modeled as time-dependent variables. HR, hazard ratio; CI, confidence interval; CHF, congestive heart failure; CAD, coronary heart disease; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease.
Figure 3
Figure 3
Hazard ratios (95% confidence intervals) for hospitalization (Panel A) and death (Panel B) by presence of individual comorbidities in atrial fibrillation patients, after adjustment for age, sex, and all other comorbidities modeled as time-dependent variables. HR, hazard ratio; CI, confidence interval; CHF, congestive heart failure; CAD, coronary heart disease; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease.
Figure 4
Figure 4
Hazard ratios (95% confidence intervals) for hospitalization (Panel A) and death (Panel B) by atrial fibrillation and comorbidity classification for significant additive and/or multiplicative interactions. Hazard ratios are adjusted for age, sex, and all other comorbidities modeled as time-dependent variables and are presented overall and among 1 year survivors. HR, hazard ratio; CI, confidence interval; CHF, congestive heart failure; CAD, coronary heart disease; CKD, chronic kidney disease; AF, atrial fibrillation. The expected hazard ratios were calculated as follows: Expected additive HR = HR10 + HR01 − 1. Expected multiplicative HR = HR10 × HR01. HR10 represents the HR for those with the comorbidity and no AF compared to those with no comorbidity/no AF; HR01 represents the HR for those without the comorbidity and AF compared to those with no comorbidity/no AF. If the observed HR is above the expected, then a synergistic effect on the risk of the outcome is observed for those with both AF and the comorbidity. Alternatively, an antagonistic effect may be observed where patients with both AF and the comorbidity experience a lower than expected risk of the outcome.
Figure 4
Figure 4
Hazard ratios (95% confidence intervals) for hospitalization (Panel A) and death (Panel B) by atrial fibrillation and comorbidity classification for significant additive and/or multiplicative interactions. Hazard ratios are adjusted for age, sex, and all other comorbidities modeled as time-dependent variables and are presented overall and among 1 year survivors. HR, hazard ratio; CI, confidence interval; CHF, congestive heart failure; CAD, coronary heart disease; CKD, chronic kidney disease; AF, atrial fibrillation. The expected hazard ratios were calculated as follows: Expected additive HR = HR10 + HR01 − 1. Expected multiplicative HR = HR10 × HR01. HR10 represents the HR for those with the comorbidity and no AF compared to those with no comorbidity/no AF; HR01 represents the HR for those without the comorbidity and AF compared to those with no comorbidity/no AF. If the observed HR is above the expected, then a synergistic effect on the risk of the outcome is observed for those with both AF and the comorbidity. Alternatively, an antagonistic effect may be observed where patients with both AF and the comorbidity experience a lower than expected risk of the outcome.

References

    1. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001;285(18):2370–2375. - PubMed
    1. Miyasaka Y, Barnes ME, Gersh BJ, Cha SS, Bailey KR, Abhayaratna WP, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation. 2006;114(2):119–125. - PubMed
    1. Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X. Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am J Cardiol. 2013;112(8):1142–1147. - PubMed
    1. Naccarelli GV, Varker H, Lin J, Schulman KL. Increasing prevalence of atrial fibrillation and flutter in the United States. Am J Cardiol. 2009;104(11):1534–1539. - PubMed
    1. Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013;2(2):e000102. - PMC - PubMed

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