Ultrasound Estimation of Right Atrial Pressure Predicts Readmission in Patients with Heart Failure

Authors

  • Michael Knapp University of Pittsburgh School of Medicine, Pittsburgh, PA, USA Author
  • Benay Ozbay University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA Author
  • Abdallah Naser Department of Medicine, Allegheny Health Network, Pittsburgh, PA, USA Author
  • Brian Jiang Des Moines University College of Osteopathic Medicine, West Des Moines, IA, USA Author
  • Marc Simon Department of Cardiology, University of California San Francisco, San Francisco, CA, USA Author
  • Lingyi Peng Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA Author
  • Yan Ma Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA Author
  • John J Pacella University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA Author

DOI:

https://doi.org/10.47363/JCRRR/2026(7)221

Keywords:

Acute Decompensated Heart Failure, Point-of-Care Ultrasound, Right Atrial Pressure, Jugular Vein Distensibility, 30-Day Readmission, Residual Congestion

Abstract

Background: Acute decompensated heart failure (ADHF) is a leading cause of hospital readmissions, resulting in high healthcare costs and poor outcomes. Non-invasive risk stratification tools are needed to reduce 30-day readmission rates. This study evaluated whether a point-of-care ultrasound (POCUS)-measured distensibility index (DI) of the right internal jugular vein (RIJV) predicts 30-day readmission risk in ADHF patients.

Methods: A prospective, observational cohort study was conducted in 288 ADHF patients at a tertiary academic center and an affiliated urban community hospital. DI was calculated as the percentage change in RIJV cross-sectional area during the Valsalva maneuver. Patients with DI≥66% were classified as having normal right atrial pressure (RAP), and those with DI<66% as having elevated RAP. The outcome was 30-day readmission due to ADHF, determined from patient charts and verified through follow-up phone calls. Multivariable logistic regression assessed the association between DI and 30-day readmission.

Results: Thirty-day readmission occurred in 52 (18%) patients. Patients discharged with DI<66% had a higher readmission risk (odds ratio (OR): 5.29, 95% confidence interval (CI): 2.77-10.60, p<0.001). After adjustment for age, sex, chronic kidney disease, and HFpEF, DI<66% remained independently associated with readmission (adjusted OR: 15.24; 95% CI, 3.79-61.29; p<0.001). DI<66% at discharge had a NPV of 92%. Stratified analyses demonstrated a consistent direction of association across hospital settings.

Conclusions: POCUS-derived DI is a reliable and non-invasive method for RAP assessment and predicting 30-day readmissions in ADHF patients. This rapid, non-invasive assessment may enhance discharge risk stratification and support physiologic evaluation of residual congestion.

Author Biographies

  • Michael Knapp, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Michael Knapp, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

  • Benay Ozbay, University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA

    Benay Ozbay, University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA

  • Abdallah Naser, Department of Medicine, Allegheny Health Network, Pittsburgh, PA, USA

    Abdallah Naser, Department of Medicine, Allegheny Health Network, Pittsburgh, PA, USA

  • Brian Jiang, Des Moines University College of Osteopathic Medicine, West Des Moines, IA, USA

    Brian Jiang, Des Moines University College of Osteopathic Medicine, West Des Moines, IA, USA

  • Marc Simon, Department of Cardiology, University of California San Francisco, San Francisco, CA, USA

    Marc Simon, Department of Cardiology, University of California San Francisco, San Francisco, CA, USA

  • Lingyi Peng, Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA

    Lingyi Peng, Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA

  • Yan Ma, Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA

    Yan Ma, Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA

  • John J Pacella, University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA

    John J Pacella, University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA

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Published

2026-08-31

Issue

Vol. 7 No. 4 (2026): Volume 7 Issue 4

Section

Articles