Reuters Health Information: Atrial fibrillation has four distinct clinical phenotypes
Atrial fibrillation has four distinct clinical phenotypes
Last Updated: 2017-11-15
By Will Boggs MD
NEW YORK (Reuters Health) - Atrial fibrillation (AF) can be classified into four clinical phenotypes with distinct outcomes, researchers report.
"The most surprising finding was that the clusters really do represent common patient groups we see in clinic and everyday practice," Dr. Jonathan P. Piccini from Duke University Medical Center, Durham, North Carolina, told Reuters Health by email. "Another important (and surprising) finding was that the characteristics we often use to classify AF, including the type or pattern of AF (paroxysmal versus persistent, etc.) and left atrial size did not vary significantly across the phenotype clusters."
Dr. Piccini and colleagues used cluster analysis of data from the ORBIT-AF prospective, nationwide registry of 9,749 patients with AF to identify people who share similar phenotypes and to evaluate whether these clusters differ in their treatment patterns and outcomes. The findings were published online November 12 in JAMA Cardiology.
The largest cluster, the low comorbidity cluster (48% of patients), included younger people with considerably lower rates of risk factors and comorbidities than the other three clusters. Their median left ventricular ejection fraction (LVEF), at 60%, was higher than in the other clusters - but their thromboembolism and bleeding-risk scores were among the lowest.
The smallest cluster, the younger/behavioral disorder cluster (10% of patients), was characterized by younger age; higher rates of liver disease, alcohol abuse, drug abuse, and current smoking; and high body-mass index. This cluster's thromboembolism and bleeding-risk scores were also lower than those in the next two groups.
The device implementation cluster (17% of patients) included a predominance of people with tachycardia-bradycardia whose prevalence of device implantation resulted from sinus node dysfunction or atrioventricular node ablation. These patients had the highest symptom scores and among the highest thromboembolism and bleeding-risk scores.
The atherosclerotic-comorbid cluster (25% of patients) included older people, most with a history of coronary artery disease. This group had the lowest LVEF (median, 55%), the highest rates of heart failure, and the highest prevalence of most other risk factors and comorbidities. They had the highest thromboembolism risk score and among the highest bleeding-risk scores.
Rates of major adverse cardiovascular and neurological events (MACNEs), per 100-patient years, differed across the four clusters: low comorbidity cluster, 2.58; younger/behavioral disorder cluster, 3.97; device implantation cluster, 5.10; and atherosclerotic-comorbid AF, 6.12. These differences persisted after adjustment for thromboembolism risk score, medications, and provider subspecialty.
After adjustment for bleeding score, medications, and physician subspecialty, compared with the low comorbidity cluster, the other three clusters had generally higher risks of major bleeding and bleeding hospitalization events. Patients in the device implantation cluster had the worst bleeding-risk profile.
"AF is a highly heterogeneous disease, and comorbidities are often very important distinguishing characteristics that also have important prognostic significance," Dr. Piccini said. "The phenotypes we identified in this analysis may help us begin to design studies that can begin to test personalized treatment strategies based upon these or other clinically relevant clusters."
"The next step is to see if these phenotypes are preserved in other populations," he said. "Future work should also investigate how we might use these phenotypes to tailor and improve clinical care."
JAMA Cardiol 2017.