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Updated: May 22, 2026

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Implantation of the Syncardia Total Artificial Heart
Published on: July 18, 2014
Performance of Risk Scores in Predicting Right Ventricular Failure After LVAD Implantation
Ibrahim Mortada1, Mohammed Mhanna2, Shiva Raju Gollapally Krishna1
1Department of Cardiovascular Medicine, University of Texas Medical Branch, Galveston, TX, USA.
Angiology
|May 21, 2026
Summary
Predicting right ventricular failure (RVF) after continuous-flow left ventricular assist device (CF-LVAD) implantation is crucial. The EUROMACS score showed the best predictive value among existing tools, though overall prediction remains modest.
Area of Science:
- Cardiology
- Medical Devices
- Heart Failure Management
Background:
- Continuous-flow left ventricular assist devices (CF-LVADs) are vital for advanced heart failure.
- Right ventricular failure (RVF) complicates up to 40% of CF-LVAD cases, increasing morbidity and mortality.
- Accurate prediction of RVF is essential for improved patient outcomes.
Purpose of the Study:
- To evaluate and compare the predictive performance of established risk scores for RVF post-CF-LVAD implantation.
- To identify the most accurate existing score for predicting RVF in CF-LVAD recipients.
Main Methods:
- Retrospective study of 205 CF-LVAD recipients from March 2009 to May 2024.
- Comparison of RVF risk scores: Michigan, Penn/Fitzpatrick, EUROMACS, and CRITT using ROC analysis.
- Internal validation and derivation of Youden-optimized cutoffs for sensitivity and specificity.
Main Results:
- 39.5% of patients developed post-LVAD RVF.
- The EUROMACS score demonstrated the highest predictive value (C-statistic 0.670), followed by CRITT (0.653).
- Discrimination of existing RVF scores was modest, indicating a need for refinement.
Conclusions:
- The EUROMACS score performed best among evaluated risk scores for predicting RVF post-CF-LVAD.
- Existing models show modest predictive ability, highlighting the need for enhanced models using contemporary data.
- Rigorous external validation is recommended for refined RVF prediction tools.
