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Related Concept Videos

Aortic Regurgitation III: Medical Management01:25

Aortic Regurgitation III: Medical Management

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Aortic regurgitation (AR) is when the aortic valve does not close or seal properly, leading to backward blood circulation from the aorta into the left ventricle during diastole. Common causes of AR include rheumatic heart disease, congenital valve defects, and aortic root dilation. Managing AR requires a multifaceted approach to alleviate symptoms, preserve left ventricular function, and address the underlying cause of the regurgitation. Patients with symptomatic AR or significant left...
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Multivariable Prediction Models for Atrial Fibrillation after Cardiac Surgery: A Systematic Review and Critical

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  • 1Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

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|October 21, 2025
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Summary

Risk models for atrial fibrillation after cardiac surgery (AFACS) show promise but have high bias. Methodological improvements are needed before clinical adoption for targeted prophylaxis.

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Area of Science:

  • Cardiology
  • Medical Informatics

Background:

  • Atrial fibrillation (AF) is a frequent complication following cardiac surgery.
  • Existing risk prediction models for AF after cardiac surgery (AFACS) lack consistent clinical adoption.
  • Accurate AFACS models are crucial for effective preventative strategies.

Purpose of the Study:

  • To systematically review and assess the quality and risk of bias of AFACS prediction models.
  • To identify limitations hindering the clinical implementation of current AFACS models.

Main Methods:

  • Comprehensive systematic review of studies developing or externally validating AFACS prediction models.
  • Quality assessment and risk of bias evaluation using established criteria.
  • Analysis of model performance metrics (e.g., C-statistic) and methodological limitations.

Main Results:

  • Models demonstrated variable performance, with median C-statistics of 0.71 (apparent) and 0.61 (external validation).
  • All reviewed AFACS model analyses were rated at high risk of bias.
  • Common limitations included small sample sizes, data-driven predictor selection, and inadequate internal validation.

Conclusions:

  • Current AFACS prediction models possess significant methodological limitations.
  • No single model is recommended for clinical use due to identified biases.
  • Future AFACS models require enhanced methodology to facilitate clinical adoption and targeted prophylaxis.