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Updated: Feb 8, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
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Algorithmic Detection of Adverse Bleeding Events among Percutaneous Liver and Renal Biopsies

Zeyad Ahmed Metwalli1, Ross Edward Taylor2, Jyotsna Pera1

  • 1Department of Interventional Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas.

Journal of Vascular and Interventional Radiology : JVIR
|February 6, 2026
PubMed

Insights

A new algorithm accurately detects bleeding adverse events (AEs) after liver and renal biopsies, outperforming traditional quality tracking systems. This improves patient safety monitoring for these common procedures.

Area of Science:

  • Medical Informatics
  • Interventional Radiology
  • Patient Safety

Background:

  • Adverse events (AEs) following percutaneous biopsies require accurate detection for patient safety.
  • Provider-reported quality tracking (QT) systems may not capture all bleeding AEs.
  • Electronic medical record (EMR) data offers potential for improved AE detection.

Purpose of the Study:

  • To evaluate an algorithm for detecting bleeding AEs in percutaneous liver and renal biopsies.
  • To compare the algorithm's performance against a provider-reported QT system.
  • To enhance the accuracy of AE identification and classification.

Main Methods:

  • Retrospective review of 4821 liver and 1320 renal biopsies over 3 years.
  • Utilized an algorithm analyzing EMR keywords and events for bleeding AEs.
  • Compared algorithm-flagged AEs with QT system data and manual chart reviews.

Main Results:

  • The algorithm identified bleeding AEs with high sensitivity (100% liver, 99% renal) and specificity (99.5% liver, 98.9% renal).
  • The QT system showed lower sensitivity (63.2% liver, 29.8% renal) but high specificity.
  • The algorithm significantly outperformed the QT system in AE detection (p<0.001).

Conclusions:

  • A procedure-specific algorithm enhances bleeding AE detection in percutaneous biopsies.
  • This automated approach improves upon traditional provider-reported systems.
  • The findings support the use of algorithms for improved patient safety monitoring.
Abstract

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