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Integrated Assessment of Coronary Physiology Based on Coronary Angiography in Heart Transplant Patients
Simone Fezzi1,2, Gabriele Pesarini1, Ludovica Guerrieri1
1Division of Cardiology, Department of Medicine, University of Verona, Verona, Italy.
Insights
Computational analysis of coronary flow (μFR) and microvascular resistance (AMR) from angiography improves risk assessment in heart transplant recipients. These techniques enhance early detection of cardiac allograft vasculopathy, aiding in better patient management.
Area of Science:
- Cardiology
- Transplant Medicine
- Medical Imaging Analysis
Background:
- Early detection of cardiac allograft vasculopathy (CAV) post-heart transplant (HTx) using invasive coronary angiography is difficult.
- CAV poses a significant challenge to long-term graft survival.
Purpose of the Study:
- To evaluate if computational techniques assessing epicardial lesions (μFR) and microvascular function (AMR) improve risk stratification in HTx patients.
- To determine the utility of quantitative flow ratio (μFR) and angiography microvascular resistance (AMR) in predicting clinical events.
Main Methods:
- Retrospective analysis of 86 HTx patients with <50% coronary stenosis.
- Calculation of quantitative flow ratio (μFR) based on Murray's law for epicardial assessment.
- Measurement of angiography microvascular resistance (AMR) for microvascular assessment.
- Correlation of μFR and AMR with target vessel failure (TVF) and heart failure (HF) hospitalizations over 43 months.
Main Results:
- A significant association was found between lower μFR values and TVF.
- A μFR threshold of ≤0.93 predicted TVF with 76.0% accuracy.
- Microvascular dysfunction (AMR ≥ 2.5) was significantly associated with increased HF hospitalizations (HR: 7.36).
Conclusions:
- Angiography-derived computational analysis of epicardial and microvascular physiology can enhance risk stratification in heart transplant patients.
- These non-invasive computational methods offer a promising approach for early detection and management of CAV.
Background:
Early detection of cardiac allograft vasculopathy after heart transplant (HTx) with invasive coronary angiography is challenging.
Aims:
The study aimed to determine if computational techniques able to assess epicardial lesions, by means of Murray's law-based quantitative flow ratio (μFR), and microvascular physiology, by means of angiography microvascular resistance (AMR), enhance risk stratification in HTx patients with nonsignificant coronary artery disease.
Methods:
The cohort consisted of 86 consecutive HTx patients (200 epicardial vessels) with stenosis < 50% at baseline. μFR ≤ 0.80 indicated coronary ischemia, while AMR ≥ 2.5 suggested microvascular dysfunction. Clinical events were assessed over a median follow-up of 43 months, focusing on the relationship between μFR and target vessel failure (TVF), and between AMR and heart failure (HF) hospitalizations.
Results:
At baseline, mean μFR was 0.94 ± 0.08, with eight vessels (4.0%) disclosing flow-limiting lesions, while AMR was 2.4 ± 0.7, with 80 vessels/40 patients (40.0%/46.5%) having microvascular dysfunction. TVF-related segments were associated with lower mean μFR values (0.89 ± 0.14 vs. 0.95 ± 0.05; p < 0.007) compared TVF-free segments. At the receiver operating characteristic curve a μFR ≤ 0.93 demonstrated an area under the curve of 0.632 (95% CI: 0.562-0.699) in predicting TVF occurrence, showing an accuracy of 76.0%, a sensitivity of 46.9%, a specificity of 81.6%, a negative predictive value of 89.0%, and a positive predictive value of 32.6%. μFR ≤ 0.93 showed a significant interaction with TVF occurrence at 43 months (32.6% vs. 11.0%; HR: 2.96; 95% CI: 1.26-6.96; p = 0.013). Microvascular dysfunction showed a significant interaction with HF hospitalizations occurence (AMR: 2.8 ± 4.4 vs. 2.4 ± 4.5; p = 0.001; CMD: 27.5% vs. 4.3%; HR: 7.36; 95% CI: 2.45-22.07; p = 0.002).
Conclusions:
Angiography-derived epicardial and microvascular physiology computation may improve risk stratification of heart transplanted patients.
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