Integrated Assessment of Coronary Physiology Based on Coronary Angiography in Heart Transplant Patients.

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.