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Intraoperative identification of cardiac patients at risk to develop postoperative atrial fibrillation

J E Lowe1, P J Hendry, S C Hendrickson

  • 1Department of Surgery, Duke University Medical Center, Durham, North Carolina 27710.

Annals of Surgery
|May 1, 1991
PubMed

Insights

A new electrophysiologic screening test can identify patients at risk for postoperative atrial fibrillation (AF) after cardiac surgery. This allows for targeted prophylactic therapy, potentially reducing complications and hospital costs.

Area of Science:

  • Cardiology
  • Cardiac Surgery
  • Electrophysiology

Background:

  • Postoperative atrial fibrillation (AF) is a common complication after cardiac surgery, increasing morbidity and healthcare costs.
  • Current methods for predicting AF risk are insufficient, leading to broad prophylactic treatment strategies.

Purpose of the Study:

  • To develop and validate a novel intraoperative electrophysiologic screening test to identify patients at high risk for developing postoperative AF.
  • To assess the sensitivity, specificity, and predictive values of this new screening test.

Main Methods:

  • A cohort of 50 patients undergoing cardiac surgery were tested pre-bypass by stimulating the mid-right atrium with increasing electrical current.
  • Atrial fibrillation (AF) inducibility was recorded, and patients were monitored postoperatively for AF development.

Main Results:

  • The screening test demonstrated high sensitivity (0.94) and negative predictive value (0.93) for postoperative AF.
  • While specificity (0.41) and positive predictive value (0.47) were lower, inducible AF correlated with older age and longer ICU stays.
  • 17 out of 18 patients who developed AF had inducible AF, while 13 out of 32 AF-free patients had negative tests.

Conclusions:

  • The intraoperative electrophysiologic screening test is a promising tool for identifying patients likely to develop postoperative AF.
  • This targeted approach may enable more efficient prophylactic therapy, benefiting at-risk patients and optimizing resource allocation.

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