Dadwal, Surbhi

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.

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, accounting for approximately 10% of all cancer cases. Despite the proven effectiveness of conventional screening modalities such as colonoscopy and fecal immunochemical testing (FIT), their invasive nature, high cost, and limited patient compliance hinder widespread adoption. Recent advancements in artificial intelligence (AI) and bowel sound-based signal processing have enabled non-invasive approaches for gastrointestinal diagnostics. Among these, bowel sound analysis—historically considered subjective—has reemerged as a promising biomarker using digital auscultation and machine learning. Objective: This review explores the potential of AI-powered bowel sound analytics for early detection, screening, and characterization of colorectal cancer. It aims to assess current methodologies, summarize reported performance metrics, and highlight translational opportunities and challenges in clinical implementation. Methods: A narrative review was conducted across PubMed, Scopus, Embase, and Cochrane databases using the terms colorectal cancer, bowel sounds, phonoenterography, artificial intelligence, and non-invasive diagnosis. Eligible studies involving human bowel sound-based recordings, AI-based sound analysis, or machine learning applications in gastrointestinal pathology were reviewed for study design, signal acquisition methods, AI model architecture, and diagnostic accuracy. Results: Across studies using convolutional neural networks (CNNs), gradient boosting, and transformer-based models, reported diagnostic accuracies ranged from 88% to 96%. Area under the curve (AUC) values were ≥0.83, with F1 scores between 0.71 and 0.85 for bowel sound classification. In CRC-specific frameworks such as BowelRCNN, AI models successfully differentiate abnormal bowel sound intervals and spectral patterns associated with tumor-related motility disturbances and partial obstruction. Distinct bowel sound-based signatures—such as prolonged sound-to-sound intervals and high-pitched “tinkling” proximal to lesions—demonstrate the physiological basis for CRC detection through bowel sound-based biomarkers. Conclusions: AI-driven bowel sound analysis represents an emerging, exploratory research direction rather than a validated colorectal cancer screening modality. While early studies demonstrate physiological plausibility and technical feasibility, no large-scale, CRC-specific validation studies currently establish sensitivity, specificity, PPV, or NPV for cancer detection. Accordingly, bowel sound analytics should be viewed as hypothesis-generating and potentially complementary to established screening tools, rather than a near-term alternative to validated modalities such as FIT, multitarget stool DNA testing, or colonoscopy.

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.

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: 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.

Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice.