Cardiac magnetic resonance imaging in heart transplant recipients with biopsy-negative graft dysfunction.

AIMS: Graft dysfunction (GD) after heart transplantation (HTx) can develop without evidence of cell- or antibody-mediated rejection. Cardiac magnetic resonance imaging (CMR) has an evolving role in detecting rejection; however, its role in biopsy-negative GD has not been described. This study examines CMR findings, evaluates outcomes based on CMR results, and seeks to identify the possibility of rejection missed through endomyocardial biopsy by using CMR in HTx recipients with biopsy-negative GD. METHODS AND RESULTS: HTx recipients with GD [defined as a decrease in left ventricular ejection fraction (LVEF) by >5% and LVEF < 50%] in the absence of rejection by biopsy or allograft vasculopathy and who underwent CMR were included in the study. The primary outcome was a composite of all-cause mortality, re-transplantation, or persistent LVEF < 50%. Overall, 34 HTx recipients developed biopsy-negative GD and underwent CMR. Left ventricular late gadolinium enhancement (LGE) on CMR was observed in 16 patients with two distinct patterns: diffuse epicardial (n = 13) and patchy (n = 3) patterns. Patients with LGE developed GD later after HTx [4 (1.4-6.8) vs. 0.8 (0.3-1.2) years, P < 0.001], were more often symptomatic (88% vs. 56%, P = 0.06), and had greater haemodynamic derangement (pulmonary capillary wedge pressure: 19 ± 7 vs. 13 ± 3 mmHg, P = 0.002) as compared with those without LGE. No significant difference was observed in the primary composite outcome between patients with LGE and those without LGE (50% vs. 38% of patients with events, P = 0.515). During a median follow-up of 3.8 years, mean LVEF improved similarly in the LGE-negative (37-55%) and LGE-positive groups (32-55%) (P = 0.16). CONCLUSIONS: Biopsy-negative GD occurs with and without LGE when assessed by CMR, indicative of possible rejection/inflammation occurring only in a subset of patients. Irrespective of LGE, LVEF improvement occurs in most GD patients, suggesting that other neurohormonal or immunomodulatory mechanisms may also contribute to GD development.

Machine learning helps predict long-term mortality and graft failure in patients undergoing heart transplant.

OBJECTIVE: We aimed to develop a risk prediction model using a machine learning to predict survival and graft failure (GF) 5 years after orthotopic heart transplant (OHT). METHODS: Using the International Society of Heart and Lung Transplant (ISHLT) registry data, we analyzed 15,236 patients who underwent OHT from January 2005 to December 2009. 342 variables were extracted and used to develop a risk prediction model utilizing a gradient-boosted machine (GBM) model to predict the risk of GF and mortality 5 years after hospital discharge. After excluding variables missing at least 50% of the observations and variables with near zero variance, 87 variables were included in the GBM model. Ten fold cross-validation repeated 5 times was used to estimate the model's external performance and optimize the hyperparameters simultaneously. Area under the receiver operator characteristic curve (AUC) for the GBM model was calculated for survival and GF 5 years post-OHT. RESULTS: The median duration of follow-up was 5 years. The mortality and GF 5 years post-OHT were 27.3% (n = 4161) and 28.1% (n = 4276), respectively. The AUC to predict 5-year mortality and GF is 0.717 (95% CI 0.696-0.737) and 0.716 (95% CI 0.696-0.736), respectively. Length of stay, recipient and donor age, recipient and donor body mass index, and ischemic time had the highest relative influence in predicting 5-year mortality and graft failure. CONCLUSION: The GBM model has a good accuracy to predict 5-year mortality and graft failure post-OHT.

Papillary fibroelastoma diagnosed through multimodality cardiac imaging: a rare tumour in an uncommon location with review of literature.

We describe the case of a woman presenting with transient ischaemic attack, who was found to have a papillary fibroelastoma arising from the aortic wall, an extremely rare location. We describe the multimodality imaging techniques used in diagnosing this patient and review the most recent literature on evaluation and management of patients with cardiac papillary fibroelastomas.