Hardaway, Brian

Background Medication nonadherence has been associated with poor outcomes in transplant recipients yet identifying these behaviors pre-transplant remains challenging. The simplified medication adherence questionnaire (SMAQ) is a brief, reliable instrument to assess medication adherence. The aim of this study was to perform SMAQ testing on thoracic organ candidates and examine correlations between these scores and individual patient factors predicting medication nonadherence. Methods Consenting waitlisted lung, heart, and heart-lung transplant candidates from six thoracic transplant centers were mailed a survey that included demographic information and the Simplified Medication Adherence Questionnaire (SMAQ). Univariate logistic regression analysis was performed to assess association between pre-transplant medication adherence and demographic characteristics, psychosocial factors, and mortality. Results Among 298 participants, 60.7% were medication nonadherent. Nonadherence was associated with some college education (OR: 1.73; CI: 1.06, 2.81), lower positive affect (OR: 0.95; CI: 0.92, 0.99), and higher uncertainty (OR: 1.02; CI: 1.01, 1.04). Higher positivity ratio predicted adherence. Conclusion Identifiable patient-specific characteristics associated with medication nonadherence can be screened for during thoracic transplant evaluation. Interventions aimed at improving these factors during the pre-transplant period may improve clinical post-transplant outcomes, however, larger studies are needed to validate these findings.

Background. Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods. RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results. A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions. Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.

Mitral regurgitation (MR) in lung transplant (LTx) recipients is a rare but complex cardiopulmonary phenomenon. The overlap between symptoms of MR and post-LTx complications, such as primary graft dysfunction, complicates its diagnosis and management. This systematic review aims to characterize the incidence, pathophysiology, presentation, and management of MR in LTx patients. We conducted a literature search in PubMed, Web of Science, Scopus, and Embase. The search yielded 133 studies, of which 11 were included. Data regarding MR development, clinical presentation, echocardiographic findings, and management strategies were extracted and analyzed. Perioperative MR, occurring during or immediately post-LTx, was frequently attributed to the systolic anterior motion of the mitral valve (MV) occurring in the setting of hypovolemia and/or inotrope usage. Later postoperative MR, on the other hand, was associated with reverse ventricular remodeling and mitral annular dilation, although some variability in the literature was present. Preexisting MR or ventricular dysfunction, elongated MV leaflets, and bilateral LTx procedure appeared to be risk factors for MR development. Management was dependent on the timing of MR and its pathophysiology, where systolic anterior motion–related MR was treated with medical therapy, whereas postoperative MR often required MV interventions. In conclusion, the diagnosis of MR in LTx patients is complicated by its symptomatic overlap with other post-LTx complications. Echocardiographic monitoring in patients with preexisting MR, regardless of severity, is essential for early identification and management. Further research is needed to better understand the underlying mechanisms and refine management strategies for MR in this population.

AbstractBackgroundThe prognosis in patients with advanced cardiac amyloidosis (CA) remains poor.ObjectivesWe sought to describe survival post heart transplantation (HT) in amyloid compared with non‐amyloid recipients, highlight waitlist times within the new allocation system across three Organ Procurement and Transplantation Network (OPTN) regions, and describe multiorgan transplantation (MOT) in hereditary amyloidosis.MethodsThis is a retrospective review of end‐stage CA patients who underwent HT at Mayo Clinic from January 2007 to December 2020. Wait time was compared in the new versus old OPTN allocation era starting December 18, 2018 by Wilcoxon rank sum test. All‐cause mortality for those with and without CA was compared using Kaplan–Meier estimates with log rank analysis, censoring December 16, 2022.ResultsFifty‐five patients with CA underwent HT between 2007 and 2020, 8 light chain amyloidosis (AL) (14.5%), 28 hereditary transthyretin (ATTRv) (50.9%), 17 wildtype transthyretin (ATTRwt) (30.9%), and 2 hereditary apolipoprotein A1 (AApoA1) amyloidosis patients (3.6%). No significant difference in overall survival post‐transplant was seen in amyloid compared with non‐amyloid (p = 0.816). Median time to HT was shorter in the new system, 45 days (IQR 24, 78) versus 174 days (IQR 76.5, 483.5), p = 0.006. There was a decline in MOT in hereditary amyloidosis over time with the concomitant rise in disease‐targeted therapies.ConclusionsHT survival in CA patients was similar to non‐amyloid patients. The new allocation system benefits this cohort with shorter wait times. There is less MOT in hereditary amyloidosis with increased utilization of disease‐targeted therapy.

Cardiac allograft vasculopathy (CAV) is a distinct form of coronary artery disease that represents a major cause of death beyond the first year after heart transplantation. The pathophysiology of CAV is still not completely elucidated; it involves progressive circumferential wall thickening of both the epicardial and intramyocardial coronary arteries. Coronary angiography is still considered the gold-standard test for the diagnosis of CAV, and intravascular ultrasound (IVUS) can detect early intimal thickening with improved sensitivity. However, these tests are invasive and are unable to visualize and evaluate coronary microcirculation. Increasing evidence for non-invasive surveillance techniques assessing both epicardial and microvascular components of CAV may help improve early detection. These include computed tomography coronary angiography (CTCA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and vasodilator stress myocardial contrast echocardiography perfusion imaging. This review summarizes the current state of diagnostic modalities and their utility and prognostic value for CAV and also evaluates emerging tools that may improve the early detection of this complex disease.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

AbstractBackgroundMany advanced heart failure patients have both a left ventricular assist device (LVAD) and an implantable cardioverter‐defibrillator (ICD). This study examines incidence, clinical impact, and management of LVAD‐related EMI.MethodsWe performed a three‐center retrospective analysis of transvenous ICD implanted patients with LVAD implanted between January 1, 2005 and December 31, 2020. The primary outcome was EMI after LVAD implantation, categorized as LVAD‐related noise or telemetry interference.ResultsThe rate of LVAD‐related EMI among the 737 patients (mean age 58.6 ± 12.8 years) studied was 5.0%. Telemetry interference (1.5%) compromised ICD interrogation in all patients. This was resolved successfully with use of a metal shield, encased wand, radiofrequency tower, different ICD programmer or by increasing distance between ICD programmer and LVAD (n = 6). ICD replacement was required to reestablish successful communication in three patients. LVAD‐related noise (3.5%) led to oversensing (n = 4), inappropriate mode switches (n = 4), noise reversion (n = 3), inhibition of pacing (n = 2), inappropriate detection as atrial fibrillation (AF) (n = 2) and inappropriate detection as ventricular tachycardia (VT) and/or ventricular fibrillation (VF) (n = 2). This noise interference persisted (n = 3), resolved spontaneously (n = 16), resolved with programming change (n = 6) or required lead revision (n = 1).ConclusionsEMI from LVAD impacts ICD function, although, the incidence rate is low. Physicians implanting both, LVAD in patients with ICD (more common) or ICD in patients with LVAD, should be aware of possible interferences. Telemetry failure not resolved by metal shielding was overcome by ICD generator replacement to a different manufacturer. In most cases, LVAD‐related noise resolves spontaneously.