Regrouping Afib: 4 categories of patients defined

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Takeaway

  • When patients with Afib are analyzed based on features that group them together, they cluster into 4 clinical subtypes that fall outside conventional Afib categories.
  • These 4 types are clinically relevant, the study authors say, and differ in comorbidities and cardiovascular outcomes.
  • These novel clusters suggest that conventional classifications involve considerable patient heterogeneity.

Why this matters

  • Using individual-level data to define patient groupings based on comorbidities and other features might yield more targetable endpoints than using conventional parameters does. 
  • The analysis used here, called “cluster analysis,” has been used in other diseases, and the authors say that it enhances identification of subtypes.

Study design

  • Cluster analysis of 9749 patients with Afib.
  • Patients classified into low comorbidity (LC; n=4673), younger/behavioral disorder (YBD; n=963), device implantation (DI; n=1651), and atherosclerotic-comorbid (A-Co; n=2462) clusters.
  • Funding: Agency of Healthcare Research and Quality.

Key results

  • Risk for major adverse cardiovascular/neurological events (MACNE) was lowest in LC cluster (2.58 events/100 patient-years; P<.001).
  • Compared with LC cluster, risk for MACNE was significantly higher in YBD (aHR, 1.49; 95% CI, 1.10-2.00); DI (aHR, 1.39; 95% CI, 1.15-1.68), and A-Co (aHR, 1.59; 95% CI, 1.31-1.92) clusters.
  • Similar clusters were identified in external validation cohort.

Limitations

  • Limited generalizability.

Coauthored with Antara Ghosh, PhD