Steidley, D. Eric

ABSTRACT Background Intravesical Bacillus Calmette–Guerin (BCG) is a live attenuated vaccine for tuberculosis. Intravesical BCG (iBCG) administration is a highly effective immunotherapy for non‐muscle invasive bladder cancer (NMIBC). Although live attenuated vaccines are generally contraindicated in solid organ transplant recipients (SOTR), iBCG has been utilized for NMIBC in SOTR. We describe the clinical manifestations, management, and outcomes of disseminated Mycobacterium bovis ‐BCG infection (Mb‐BCGi) following iBCG therapy in SOTR for NMIBC. Methods All adult patients (> 18 years of age) diagnosed with NMIBC who received iBCG across three tertiary referral and transplant centers between 2000 and 2025 were identified. The study cohort consisted of SOTR who had received iBCG for NMIBC and subsequently developed disseminated Mb‐BCGi. English language literature review was performed to identify additional cases. Results A total of 4207 patients received iBCG during the study period. Thirty (0.71%) were SOTR, of whom two (6.7%) developed disseminated Mb‐BCGi, including bloodstream infection and prostate abscesses. Literature review identified two additional disseminated Mb‐BCGi cases in SOTR with lungs, bloodstream, and native kidney involvement, and one fatality. All cases required extended antimycobacterial therapy. Median times from SOT to Mb‐BCGi and from iBCG to Mb‐BCGi were 104 and 8 months, respectively. Conclusion SOTR appear to be at a higher risk of disseminated Mb‐BCGi following iBCG. Administration of iBCG in SOTR with NMIBC warrants careful risk‐benefit assessment and subsequent close monitoring. image

Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population; however, their prognostic value in KTx recipients is relatively unknown. Methods Retrospective analysis was conducted on KTx recipients without AF, with at least one pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High-risk scores were associated with true new-onset AF at 30 days (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.

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.

Background: Among cardiac amyloidosis subtypes, transthyretin cardiac amyloidosis (ATTR) has gained increased recognition with the development of disease-specific therapies. Tafamidis, a selective oral stabilizer of the transthyretin tetramer, has emerged as an effective treatment. In the broader heart failure landscape, sodium-glucose cotransporter 2 inhibitors (SGLT2i) have become cornerstone therapies across the ejection fraction spectrum, improving survival and reducing hospitalizations in both HFrEF and HFpEF. However, the clinical benefit of combining Tafamidis with SGLT2i in ATTR remains largely unknown. Hypothesis: Combination therapy with Tafamidis and SGLT2i provides added clinical benefits in patients with ATTR cardiac amyloidosis. Methods: This retrospective cohort study included adults with confirmed ATTR cardiac amyloidosis diagnosed via 99mTc-pyrophosphate scintigraphy and treated with Tafamidis alone or with an SGLT2i across three academic centers from January 2019 to February 2025. Baseline characteristics and outcomes were collected from medical records. The primary endpoint was all-cause mortality. Kaplan-Meier analysis and multivariable Cox proportional hazards modeling were used, adjusting for age, sex, atrial fibrillation, diabetes, hypertension, hyperlipidemia, ejection fraction (EF), and glomerular filtration rate (GFR). Results: Among the 1,211 patients included in the 3-year follow-up analysis,946 received Tafamidis monotherapy and 265 received a combination of Tafamidis and an SGLT2i. The mean age of the cohort was 71.9 years (SD ±9.4), and 87.3% were male. A total of 142 mortality events occurred, with a higher cumulative mortality rate observed in the tafamidis-only group (16.3%) compared to the combination group (9.4%) (log-rank p = 0.042). In the multivariable Cox proportional hazards model, combination therapy with Tafamidis and an SGLT2i was independently associated with a significantly lower risk of mortality (adjusted HR =0.576, 95% CI:0.334–0.992, p =0.04). Additionally, increasing age (HR =1.049, 95% CI:1.024–1.074, p < 0.001) was associated with higher mortality, while EF(HR =0.971, 95% CI:0.958–0.984, p< 0.001) and GFR (HR =0.975, 95% CI:0.965-0.985, p< 0.001) were with lower mortality. Conclusion: Combination therapy with Tafamidis and SGLT2i was associated with improved survival compared to Tafamidis alone in ATTR cardiac amyloidosis. Further prospective trials are needed to validate these findings and guide therapy optimization.

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.

Background: New-onset atrial fibrillation (AF) is common among kidney transplant (KTx) recipients and is associated with reduced patient survival. Predictors of AF after KTx are not well understood, although AF can be associated with traditional and non-traditional risk factors. While artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise in predicting incident AF, its predictive and prognostic implications in the KTx population have not yet been evaluated. Hypothesis: AI-ECG can predict new-onset AF and carries prognostic implications in patients undergoing KTx. Aims: To evaluate the clinical implications of applying AI-ECG to the preoperative ECGs of recipients of a KTx. Methods: Patients without a history of AF who underwent KTx at three referral centers between 2011 and 2021, with at least one preoperative ECG, were included in this retrospective study. Preoperative ECGs were analyzed using a previously developed AI-ECG algorithm to estimate the probabilities of new-onset AF. Based on these probabilities, patients were categorized into two groups: high and low probability of incident AF. The optimal cut-off value for the AI-ECG tool was determined using ROC analysis. The incidence of new-onset AF and mortality at 5 years post KTx were compared between these two groups using univariate and multivariate Cox regression analyses. Results: In total, 6246 patients were included (mean age 52.9 ±14.3 years, 58.7% males). A pre-transplant AI-ECG probability of AF >10% was identified as the most accurate cutoff point to distinguish between patients at low risk and high risk of incident AF (ROC = 0.72). The study found that a preoperative AI-ECG high risk of AF demonstrated not only a strong association with new-onset AF (HR 2.54, 95%CI 2.02-3.19, p<0.001 on multivariable analysis, Figure 1A ) but was also associated with an increased risk of mortality both on univariate (HR 2.73, 95%CI 2.34-3.18, p<0.001) and multivariate (HR 1.57, 95%CI 1.29-1.91, p<0.001 ) analysis at 5-year follow-up ( Figure 1B ). Conclusion: The application of AI-ECG to a single widely available test may help assess the risk of new-onset AF and mortality in patients undergoing KTx. Accurate risk stratification may identify a high-risk subgroup of transplant recipients who can benefit from closer AF monitoring, risk factor modification, and early management.

Athlete’s heart (AH) represents the heart’s remarkable ability to adapt structurally and functionally to prolonged and intensive athletic training. Characterized by increased left ventricular (LV) wall thickness, enlarged cardiac chambers, and augmented cardiac mass, AH typically maintains or enhances systolic and diastolic functions. Despite the positive health implications, these adaptations can obscure the difference between benign physiological changes and early manifestations of cardiac pathologies such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic cardiomyopathy (ACM). This article reviews the imaging characteristics of AH across various modalities, emphasizing echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography as primary tools for evaluating cardiac function and distinguishing physiological adaptations from pathological conditions. The findings highlight the need for precise diagnostic criteria and advanced imaging techniques to ensure accurate differentiation, preventing misdiagnosis and its associated risks, such as sudden cardiac death (SCD). Understanding these adaptations and employing the appropriate imaging methods are crucial for athletes’ effective management and health optimization.

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

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.