Goswami, Rohan

Cardiac allograft vasculopathy (CAV) remains a leading cause of graft failure after heart transplantation, yet an early, non-invasive risk stratification tool is not available. Artificial intelligence electrocardiography (AI-ECG) can estimate biological heart age, and it has been associated with cardiovascular risk. Its deviation with chronological age, called delta age, and its utility in the prediction of CAV has never been explored. We conducted a retrospective single-center study for heart transplant recipients bridged with Impella 5.5 (Abiomed, Danvers, MA, USA) at Mayo Clinic Florida (2020–2023). ΔAge was calculated at discharge and 1-year post-transplant. The primary outcome was confirmed CAV at 1 year. Univariate and multivariable logistic regression models were used to assess the independent association between the ΔAge and the development of CAV, with discrimination evaluated by AUC and internally validated via bootstrap resampling (2000 iterations). Fifty patients were reviewed. CAV developed in 13 (26%) patients in 3 years. CAV+ patients had a significantly higher ΔAge at discharge compared to CAV- patients (median −1.0 vs. −11.0 years, p = 0.0023) and at 1 year (median +7.0 vs. −4.0 years, p = 0.0080). On univariate analysis, ΔAge at discharge was a significant predictor of the development of CAV (OR 1.084 per year, AUC 0.788). On multivariable adjustment, ΔAge at discharge remained an independent predictor (adjusted OR 1.138, 95% CI 1.023–1.265, p = 0.018, and model AUC 0.859). ΔAge at discharge is a novel and potential predictor of the development of CAV within 3 years. These findings support the integration of AI-ECG into post-transplant surveillance as a scalable, low-cost tool.

Post-transplant lymphoproliferative disorder (PTLD) involving the central nervous system (CNS) is a rare but serious life-threatening complication seen in recipients of solid organ transplant. Primary CNS encompasses 5–15% of all types of PTLD diagnoses, and heart transplant recipients represent 3–5% of those reported cases. Diagnosis is often delayed due to the highly variable presentation, with some cases remaining undiagnosed for years. Multidisciplinary collaboration is crucial for early diagnosis and management. A 53-year-old woman patient presented with altered mental status. MRI revealed nodular ventriculitis and bilateral periventricular hyperdense infiltrates. CSF studies demonstrated lymphocytic pleocytosis, elevated protein, and EBV-PCR-positive results. A stereotactic brain needle biopsy confirmed the presence of EBV-positive diffuse large B-cell lymphoma, consistent with primary CNS PTLD, 14 months after her heart transplant. Despite appropriate management, the patient experienced progressive neurological decline and ultimately suffered a fatal intracerebral hemorrhage. We demonstrate the importance of the early diagnosis and variable presentation of post-heart-transplant PTLD. The importance of surveillance regardless of EBV status and close monitoring of disease progression due to potential life-threatening complications, such as fatal hemorrhages. Therefore, primary CNS-PTLD remains a challenging disease and is being increasingly recognized with improved transplant recipient survival and prolonged exposure to chronic immunosuppression.

Three-dimensional (3D) anatomic modeling derived from high-resolution medical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), has been increasingly adopted in preclinical testing and device development. This white paper describes a cardiac-specific workflow that integrates 3D printing and silicone molding for support device development and procedural simulation. Patient-derived computed tomography angiography data were segmented using FDA-cleared medical modeling software to isolate the left ventricular anatomy and were further processed in computer-aided design (CAD) to ensure accurate physiological wall thickness and structural fidelity. Material jetting 3D printing was performed on a Stratasys J750 using material distributions designed to mimic the mechanical properties of myocardium, thereby approximating myocardial compliance. In parallel, stereolithography apparatus molds were designed from the left ventricle CAD model to cast transparent, pliable left ventricular models in Sorta-Clear™ 18 silicone. The 3D-printed models preserved intricate morphological detail and were suitable for mechanical manipulation and device deployment studies, whereas silicone models offered tunable mechanical properties, transparency for visualization, and durability for repeated use. Together, these complementary modalities provided rapid manufacturing capability and application-relevant physical representation. Case-specific parameters, strengths, and limitations of both models in enhancing patient care and device testing are highlighted, with relevance to heart failure applications. Current knowledge gaps, workflow and integration challenges, and future opportunities are identified, positioning this work as a reference framework for continued innovation in anatomic modeling. Within the collaborative framework of Mayo Clinic’s Anatomic Modeling Unit and Simulation Center, this integrated modeling workflow demonstrates the value of multidisciplinary collaboration between engineers and clinicians. Clinically, these patient-specific left ventricular models may enable pre-procedural device sizing and positioning and may support simulation of mechanical circulatory support (MCS) deployment while identifying possible anatomic constraints prior to intervention. This workflow has direct applicability in advanced heart failure patients undergoing MCS support, such as the Impella axillary MCS device or the durable LVAD, with potential to reduce procedural uncertainty while reducing complications and improving peri-procedural outcomes. Additionally, these models also serve as high-accuracy educational tools, enabling trainees and multidisciplinary care teams to visualize and possibly rehearse procedural steps while gaining hands-on experience in a risk-free environment.

Backround: Atrial fibrillation (AF) is a common complication following veno-venous extracorporeal membrane oxygenation (VV-ECMO), leading to further complications from anticoagulation or stroke. The Artificial Intelligence AF Prediction Dashboard developed at Mayo Clinic generates two predictive outputs: (1) a continuous probability score and (2) a binary threshold-based signal to predict the likelihood of AF at an individual level after 12-lead electrocardiogram (ECG) is obtained. Using both continuous risk (%) and binary (yes/no) likelihood, we present a novel method to understand AF risk after VV-ECMO cannulation based on pre-ECMO 12-lead ECG. Methods: We reviewed a total of 147 patients on ECMO between 2018 and 2024, from which 85 VV-ECMO patients with complete data were included. AI Dashboard outputs were quantified as binary (1/0) or percent likelihood for AF. R was utilized for statistical analysis. Results: 85 patients met our inclusion criteria from which AF after cannulation occurred in 31 (36%). 17 patients had a history of AF prior to VV-ECMO initiation. The mean age of our cohort was 49 years with a mean BMI of 29.3. Baseline AI Dashboard continuous assessment identified 39 patients (46%) compared to the binary assessment identifying 31 patients (36%). Logistic regression in the binary assessment demonstrated an odds ratio of 1.08 (95% CI 1.04 – 1.12) with a p value of <0.001. The binary AF Prediction yielded a 58% sensitivity, 76% specificity, 58% PPV, and 76% NPV, with an accuracy of 67%. The continuous AF Probability score achieved 72% balanced accuracy with sensitivity 74%, specificity 70%, PPV 59% and NPV 83%, indicating fair overall performance and suggesting good utility for ruling out AF in low-risk patients. Although both outputs provided moderate predictive accuracy, the continuous score reported before ECMO cannulation demonstrated stronger overall performance with fewer false negatives and higher rule-out value. Conclusions: We demonstrate the novel use of an AI predictive model in the VV-ECMO population for the prediction of AF risk with both continuous and binary risk cutoffs from baseline ECG. This data highlights the need for further development in ECMO-specific AI models to help mitigate risk after the onset of atrial arrhythmias. As AI-driven tools become increasingly integrated into care, their performance must be continuously validated and recalibrated for specific populations to ensure their safe and effective use.

Background: Impella 5.5 is a temporary MCS device FDA-approved for use up to 14 days. Limited data suggest it may be safe beyond this. We aim assess outcomes based on the duration of Impella 5.5 support in heart failure cardiogenic shock (HFCS) patients at our institution. Methods: We retrospectively reviewed all HFCS patients supported with Impella 5.5 at our institution between 2020-2024. Patients supported for >14 days were compared with those supported for ≤14 days. Categorical variables were analyzed with chi-square tests, continuous variables with t-tests or Mann-Whitney U. Logistic regression identified factors linked to clinical outcomes. Results: 154 patients were included: 38 (25%) with support ≤14 days, 116 (75%) with >14 days. Median support duration was 10 (6–12) vs 31 (22–45) days, respectively. Median support duration was 10 (6-12) days for the ≤14 cohort and 31 days (22-45) days for the >14 cohort. Baseline characteristics, comorbidities, LVEF 18% (15-23) and hemodynamics show no statistical differences. Patients supported for >14 days were more likely to undergo heart transplant compared to those supported ≤14 days (91% vs 74%, p=0.009). In contrast, myocardial recovery was more likely in the ≤14 day cohort (8% vs 1%, p=0.018). One patient suffered stroke post-transplant in the ≤14 day group and two had brachial plexus injuries in the >14 days group. Mortality during the impella-supported period was slightly higher in the ≤14 day group (8% vs 5%, p=0.535) but comparable overall, 13% in both groups, p=0.971. Support >14 days was associated with transplant outcome (OR 3.41, 95% CI 1.32–8.84, p=0.012), and remained significant after adjusting for age, gender, GFR, and EF (OR 4.05, 95% CI 1.31–12.54, p=0.015). Support duration <14 days was associated with recovery or LVAD outcomes (OR 5.01, 95% CI 1.49–16.89, p=0.009), and remained significant after adjustment (OR 5.43, 95% CI 1.11–26.45, p=0.036). No association was observed between duration of support and LVAD/mortality outcomes or with EcPella use. Conclusion: Shorter Impella support duration was independently associated with early myocardial recovery or LVAD candidacy, likely reflecting rapid clinical decision-making. Prolonged Impella support was associated with increased likelihood of transitioning to heart transplantation, likely reflecting clinical stabilization over time. Our multidisciplinary, single-center data suggests Impella 5.5 can be used beyond FDA-approved 14 days in HFCS patients.

Introduction/Background: Atrial fibrillation (AF), a common postoperative arrhythmia, is associated with increased morbidity and mortality across various surgical populations. In patients undergoing orthotopic liver transplantation (LT), the presence of AF has been linked to poorer clinical outcomes. However, data on the prognostic significance of new-onset AF during early post-transplant readmissions in orthotopic LT recipients remain limited. Hypothesis: We postulated that incident AF occurring during 90-day post-LT readmissions is independently associated with increased in-hospital mortality, prolonged length of stay, and higher healthcare costs. Methods: We conducted a retrospective cohort study using data from the National Readmission Database (2016–2019). Adult LT recipients (aged >18 years) readmitted within 90 days were identified via ICD-10 coding. Patients who developed new-onset atrial fibrillation (AF) during readmission were compared to those without AF. Primary outcomes included in-hospital mortality, length of stay (LOS), and total hospitalization costs. Multivariable logistic and linear regression models, adjusted for demographic and clinical confounders, were used. Results: Of 31,557 adult LT recipients (mean age 55.5 years, 35% female), 8,449 (27.6%) (mean age 55.3 years, 36.7% women) were readmitted within 90 days. Among those readmitted, 222 patients (2.6%) developed new-onset atrial fibrillation (AF), and 84 (1.0%) died during the readmission period. New-onset AF was associated with a 7.6-fold increased risk of in-hospital mortality (95% CI: 3.0–19.0; P < 0.05). Additionally, AF onset was linked to a mean increase in LOS of 6.2 days (95% CI: 6.2–7.1; P = 0.001) and an average increase in hospitalization costs of $75,396 (95% CI: $11,932–$138,860; P = 0.02). All associations achieved statistical significance. Conclusion(s): New-onset AF during early post-transplant readmissions is associated with significantly worse clinical outcomes and increased healthcare utilization. These findings underscore the need for proactive rhythm monitoring and risk-stratified arrhythmia management during the liver transplant recovery period.

Background: Heart transplantation is the definitive treatment of end-stage heart failure and is a complex process involving coordination in procurement, preservation, and transport of a donor organ to a recipient. There are different heart transport methods, including the traditional static methods of Cooler and Ryobi and advanced dynamic methods, including SherpaPak and the Organ Care System (OCS). The aim of the study is to compare the post-transplant outcome of these in patients bridged with Impella 5.5. Method: A retrospective study was conducted between 2020 and 2025 after IRB approval. Patient demographics and clinical data was extracted from the medical record. Cox proportional hazards regression model was applied to identify independent predictors of mortality across 30-day, 90-day, and 1-year intervals. Results: 123 patients were included in the analysis. Age, EF and Race were similar between all groups. The transport method was a significant predictor of 1-year mortality. Use of OCS (HR=0.022, p value <0.05) and SherpaPak (HR=0.066, p value <0.05) was associated with a significant decrease in mortality compared to traditional cooler use. Across 30-day, 90-day, and 1-year intervals, OCS showed the strongest survival benefit, while SherpaPak also demonstrated reduced mortality, but less significant compared to OCS. Conclusion: Transport method is a strong predictor of 1-year post-transplant mortality, with OCS showing the most pronounced effect in reducing the risk of 1-year mortality as well as mortality across 30-day and 90-day intervals. These findings support broader adoption of advanced transport systems in clinical practice to improve post-transplant outcomes.

Background: Renal dysfunction is a known prognostic factor in advanced heart failure and among patients receiving temporary mechanical circulatory support (tMCS). However, the predictive value of post-Impella creatinine in determining healthcare resource utilization in patients bridged to transplant with Impella 5.5 remains unclear. This study aimed to evaluate the association between post-Impella creatinine and key hospitalization metrics in this population. Methods: We performed a retrospective cohort study of patients who received Impella 5.5 as a bridge-to-transplant between January 2020 and January 2025. Serum creatinine was collected 72 hours after Impella placement. The primary outcome was device-to-discharge time. Secondary outcomes included transplant-to-discharge time, post-transplant ICU length of stay (LOS), and total LOS (admission-discharge). Univariate and multivariable linear regressions were used to examine the association between post-Impella creatinine and each outcome, adjusting for age, gender, BMI, and LVEF. Results: 133 patients were supported with the Impella 5.5 as BTT and were included (median age 58 years (51–65), 82.7% were male, and 71.4% presented with non-ischemic cardiomyopathy. The median BMI was 27.7 (24.3–32.3), LVEF 17% (15–22), and post-Impella creatinine 1.32 mg/dL (0.96–1.71). The median device-to-discharge time was 45 days (29-64), transplant-to-discharge time was 14 days (12–19), ICU LOS post-transplant was 6 days (4–8), and total LOS was 58 days (42–76). Higher post-Impella creatinine was significantly associated with longer device-to-discharge time (β = 6.67, p = 0.004), longer transplant-to-discharge duration (β = 3.42 p = 0.003), longer ICU LOS (β = 2.19, p = 0.007), and longer total LOS (β = 7.24, p = 0.002) on univariate analysis. Post-impella creatinine remained significantly associated with device-to-discharge time in the multivariate model after adjusting for confounders (β = 6.33, p = 0.006). Importantly, this suggests that patients with lower post-Impella creatinine levels may experience shorter hospitalizations and faster recovery trajectories following transplant. Conclusions: Post-Impella creatinine is a significant predictor of prolonged hospitalization in patients supported with Impella 5.5 as a bridge to transplant. These findings emphasize the prognostic importance of early renal function assessment and may guide risk stratification and resource planning in advanced heart failure care.

Problem Statement: The prognostic value of troponin elevation in septic ICU patients without known history of cardiac disease remains unclear, particularly after accounting for overall illness severity. Background: Minor troponin rises are frequent during sepsis, yet clinicians still lack a clear cut-point that signals danger once cardiac disease is ruled out. We asked whether a modest increase in troponin-T predicts in-hospital death beyond global illness severity. Methods: We studied 1 221 adult first ICU stays labelled “sepsis” in the eICU Collaborative Research Database and removed any case with a cardiac admission diagnosis. Implausible or missing laboratory values were imputed by multiple imputation. Hospital mortality was modelled with logistic regression that included troponin (a continuous and a binary model with threshold > 0.04 ng/mL) together with age, sex, lactate, albumin, BUN, creatinine, intubation status, vasopressor use, dialysis requirement and APACHE-IV predicted mortality. A natural-spline model explored non-linear behavior. Findings were checked in a 1:1 propensity-matched cohort. Results: Overall mortality was 11.7%. Troponin exceeded 0.04 ng/mL in 38% of patients. After adjustment for APACHE-IV probability and other covariates, a troponin level > 0.04 ng/mL was associated with almost twice the odds of death (OR ≈ 1.9), whereas the continuous model suggested only a modest trend (OR ≈ 1.1 per ng/mL). The spline curve rose steeply between 0 and 0.10 ng/mL and flattened thereafter, pinpointing an inflection near 0.05 ng/mL. Matching on all measured confounders confirmed that high-troponin patients had about three-fold greater mortality (OR ≈ 2.8, 1.55-5.05; p<0.001), highlighting that even modest troponin elevation is an independent risk marker for death in septic ICU patients. A bedside heat-map combining troponin, albumin and need for intubation illustrated a simple gradient: risk climbs from 2 % in low-troponin, well-albuminised, non-intubated patients to nearly 7 % when troponin exceeds 0.10 ng/mL in intubated, hypo-albuminemic patients. Conclusions: Even modest troponin elevations (> 0.04 ng/mL) independently predict mortality in sepsis after rigorous adjustment and matching on APACHE-IV physiology, lactate, and organ-support variables. This biologic threshold supports incorporating troponin into bedside triage and future septic cardiomyopathy trials.

Background: Mechanical support strategies used for patients in shock prior to heart transplantation (OHT) correlates with post-transplant outcomes. OHT timing in patients supported by Impella 5.5 remains an evolving decision point, influenced by patient stability and donor logistics. With the emergence of organ transport technologies, we sought to determine whether the choice of system was associated with variations in pre-transplant support duration. Method: A retrospective review between Jan 2020 and May 2025 was completed. Patients were categorized by donor transport technology: traditional cooler, Organ Care System (OCS), or SherpaPak. The primary outcome was duration of Impella support (days) prior to transplant. Group-wise comparisons were performed using Kruskal-Wallis testing. Result: A total of 129 patients were assessed across three transport methods: traditional cooler:12, OCS: 35, SherpaPak: 84. Median age at transplant was 58 (51-65) years with EF of 17% (15-22). Cold ischemic time differed significantly across transport technologies (Kruskal-Wallis H = 77.4, p < 0.001) and was longest in OCS cases (417 min), compared to SherpaPak (236 min) and traditional cooler (234 min). The median duration of Impella support prior to OHT was 27 days for organs transported via traditional cooler, 25 days for those using OCS, and 28.5 days for SherpaPak with near-significant difference by transport method (H=5.77, p=0.056). There was no significant difference of rejection within the first year after OHT. Conclusion: Donor transport technology choice may serve as a surrogate for broader transplant logistics and selection strategies as evidenced by significant differences in ischemic time and near-significant trends in Impella support duration.