The Diastolic Dominance Index: A Bedside Doppler Tool for Aortic Insufficiency in Continuous-Flow Left Ventricular
Sophie R Persaud1, Max Reisman2, Ryo Torii3
1From the Medical Scientist Training Program, Medical College of Wisconsin, Milwaukee, Wisconsin.
Insights
Aortic insufficiency (AI) during HeartMate 3 (HM3) support can be reliably detected using a new Diastolic Dominance Index (DDI). This index, combined with pump power, offers accurate and reproducible AI grading across different HM3 speeds.
Area of Science:
- Cardiovascular Engineering
- Biomedical Engineering
- Medical Device Technology
Background:
- Aortic insufficiency (AI) is a common complication in patients with continuous-flow left ventricular assist devices (LVADs), specifically the HeartMate 3 (HM3).
- Current methods for grading AI during HM3 therapy are inconsistent, necessitating improved diagnostic tools.
- Accurate AI assessment is crucial for optimizing LVAD support and patient outcomes.
Purpose of the Study:
- To develop and validate a novel Doppler-based index for detecting and grading AI in HeartMate 3 LVADs.
- To compare the performance of the new Diastolic Dominance Index (DDI) against established Doppler markers.
- To assess the utility of pump power in conjunction with Doppler indices for AI assessment.
Main Methods:
- A closed-loop cardiovascular simulation was used to analyze 2,312 HeartMate 3 profiles at 5,000 and 6,000 RPM.
- Physiologically screened HM3 data were used to evaluate established Doppler markers (S/D ratio, diastolic acceleration, DFF) and the novel DDI.
- The DDI integrates systolic attenuation and diastolic flow characteristics into a single, zero-centered metric.
- The diagnostic performance was assessed using receiver operating characteristic (ROC) curves, with AI defined as a regurgitant fraction ≥30%.
Main Results:
- The novel Diastolic Dominance Index (DDI) demonstrated robust performance in detecting AI, with Area Under the Curve (AUC) values of 0.87 at 5,000 RPM and 0.83 at 6,000 RPM.
- The DDI showed more stable discrimination across different pump speeds compared to the S/D ratio (AUCs 0.93 and 0.86, respectively).
- Combining DDI with pump power significantly improved AI classification accuracy, achieving >90% accuracy at both speeds and an AUC of 0.93 at 6,000 RPM.
Conclusions:
- The Diastolic Dominance Index (DDI) provides a clinically interpretable and speed-robust method for grading aortic insufficiency in HeartMate 3 LVAD patients.
- The integration of DDI with pump power offers a powerful, accurate, and reproducible framework for AI assessment.
- This approach enhances the clinical management of patients supported by continuous-flow LVADs.
Abstract:
Aortic insufficiency (AI) is a progressive complication of continuous-flow left ventricular assist device support and remains inconsistently graded during HeartMate 3 (HM3) therapy. Using a closed-loop cardiovascular simulation, we analyzed 2,312 physiologically screened HM3 profiles at pump speeds of 5,000 and 6,000 rpm to evaluate Doppler-based approaches for AI detection, defined by regurgitant fraction of greater than or equal to 30%. Established Doppler markers, including systolic-to-diastolic ratio (S/D), diastolic acceleration, and diastolic flow fraction (DFF), were compared with a derived Diastolic Dominance Index (DDI), which integrates systolic attenuation and diastolic predominance into a single metric. Systolic-to-diastolic ratio demonstrated strong discrimination (area under the receiver-operating characteristic curve [AUC]: 0.93 at 5,000 RPM; 0.86 at 6,000 RPM). Diastolic Dominance Index showed comparable performance (AUC: 0.87 and 0.83) and exhibited more stable discrimination across pump speeds, while providing a zero-centered index in which positive values indicate AI severity. Pump power alone was nondiagnostic, but in cases near the DDI decision boundary, it improved classification, achieving an AUC of 0.93 at 6,000 RPM. Simple rule-based combinations of DDI and power achieved greater than 90% accuracy at both speeds. By unifying established Doppler features into a single physiologic index and pairing it with an energetic adjudicator, this framework enables speed-robust, reproducible, and clinically interpretable AI grading during HM3 support. https://links.lww.com/ASAIO/B917.
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