Narula, Tathagat

Background: The role of venous Doppler ultrasound in assessing venous congestion in the setting of post-lung transplantation surgery is unknown. We performed a feasibility study on the use of venous Doppler ultrasound in the immediate post-operative period after lung transplantation in a retrospective, single-center observational evaluation. Patients had at least two venous congestion Doppler ultrasound exams involving interrogation of IVC diameter, hepatic vein, portal vein and renal vein Doppler assessments. Results: A total of 9 patients were included in our study with a median age of 60 years, 66.7% being male, and a median BMI of 20.9. All patients had VExUS grade ≥1 on their first venous Doppler exam. All nine patients had a decrease in portal vein pulsatility from initial to final exams. Correlation between portal venous pulsatility and net fluid balance was not statistically significant R= 0.33 (P= 0.39). Other parameters, including VExUS grade, IVC diameter, HV and RV Doppler changes from initial to final ultrasound exams showed variable changes in the post-operative period. Conclusion: Venous congestion Doppler ultrasound assessments appear feasible in immediate postoperative lung transplant patients. A multitude of factors beyond fluid status likely affect waveforms.

ABSTRACT Background Treating Mycobacterium avium complex (MAC) infection pre‐lung transplant (LT) can be challenging to treat, given prolonged therapy and side effects. The impact of pre‐transplant MAC treatment on recurrence and outcomes post‐LT remains unclear, especially in patients with interstitial lung disease (ILD) who have a median survival of less than 2 years. Methods We performed a retrospective cohort study of adult LT patients at the Mayo Clinic who had positive MAC sputum cultures pre‐LT. The cohort was stratified and compared based on the type of treatment (complete, partial, or no treatment) for MAC pulmonary infection. The primary outcome was MAC recurrence post‐LT, and secondary outcomes were mortality, FEV‐1, and FVC at 1‐year post‐LT. Results Among the cohort of 47 patients, 9 (19.1%) were partially treated and 11 (23.4%) were completely treated for MAC infection pre‐LT. Post‐transplant MAC pulmonary infection recurrence occurred in eight (17%) of the total cohort, with four (14.8%) in the non‐treated, four (44.4%) in the partially‐treated, and none (0%) in the completely treated patients ( p = 0.026). There was no difference in mortality in patients who had MAC recurrence post‐transplantation (13% vs. 9.7%, p > 0.9) and those who were completely or partially treated, compared to non‐treated (0% vs. 13% vs. 13%, p > 0.9). The FEV1 and FVC at 1‐year post‐LT did not differ by the pre‐transplant treatment or post‐transplant MAC recurrence status. Conclusion The role of treatment for MAC infection pre‐transplant is questionable, especially in patients with diseases like ILD who may not have enough wait time to complete therapy.

Abstract Introduction: The data on outcomes comparing lung transplantation with ABO-identical versus ABO-compatible blood group is limited. Composite Allocation Score(CAS) was introduced on March 9th, 2023, and has considered additional points for the difficult-to-match blood group. We aimed to study the trend and outcomes of patients who underwent lung transplants with ABO identical versus compatible blood groups. Methods: We queried the United Network of Organ Sharing (UNOS) database of those who underwent lung transplantation between 10/1/1987 to 3/31/2024 and compared the outcomes between those who had ABO-identical vs ABO-compatible blood groups. Combined organ transplants and recipients age <18 were excluded. Results: A total of 50,406 patients were included in our study, out of which 4,111 (8.15%) were ABO-compatible and 47,347 (91.85%) were ABO-identical. Among the recipient demographics, the ABO-compatible group compared to the ABO-identical group showed a lesser lung allocation score (40 ± 16 vs 42 ± 16), but higher A-blood group (59% vs 38%), obstructive lung disease (38% vs 33%), CAS (49 ± 13 vs 47 ± 10), mechanical ventilator support pre-transplant (6.2% vs 5.2%), need for ex-vivo lung perfusion (9.6% vs 5.8%) and listing for single lung transplant (39% vs 34%), all p<0.05. The donors for the ABO-compatible group had a closer distance to the transplant center (205 ± 285 vs 226 ± 265 nautical miles), lesser white race (61% vs 64%), and gender mismatch (30% vs 32%) but higher male gender (64% vs 61%), O-blood group (81% vs 49%) and ethnicity mismatch (45% vs 41%), all p<0.05. The univariate analysis showed a higher 30-day (7.2% vs 5.8%), 1-year (22% vs 19%), and 5-year (54% vs 50%) mortality among patients with ABO-compatible compared to ABO-identical recipients, all p<0.001. However, the adjusted odds ratio of 30-day (0.76, 95% CI 0.36-1.47, p=0.4), 1-year (1.07, 95% CI 0.69-1.66, p=0.7) and 5-year (0.69, 95% CI 0.46-1.05, p=0.081) mortality were not statistically significant with ABO-identical when compared to ABO-compatible group on adjusting to the covariates in the multivariate analysis. The trend of ABO-compatible lung transplantation has been increasing with the introduction of CAS (figure 1). Conclusion: Patients who underwent lung transplantation with the ABO-compatible blood group donor organs had similar outcomes compared to the ABO-identical blood group. The findings of our study reinforce the importance of increasing the use of ABO-compatible organs to enhance the lung transplantation rate, aligning with the observed trend following the introduction of CAS.

ABSTRACTBronchial dehiscence is a known complication following lung transplantation. In cases involving extensive airway necrosis and air leaks, bronchoscopic placement of self‐expanding metallic stents provides an effective and safe management strategy. These stents facilitate granulation and tissue reepithelialization. We present a case of bronchial anastomotic dehiscence size mismatch after a lung transplant. This complication was successfully managed using three stents placed via a telescoping technique with flexible bronchoscopy.

Objective: We tested the feasibility and effectiveness of a percutaneous atrial transseptal extracorporeal membrane oxygenation (ECMO) cannulation strategy in a right ventricular failure (RVF) model. Methods: We performed 4 nonsurvival porcine experiments. Percutaneous transseptal access was achieved using a steerable introducer. For guidance, we used fluoroscopy, transesophageal echocardiogram (TEE), and intracardiac echocardiography (ICE). A ProtekDuo rapid deployment cannula (LivaNova, London, UK) was advanced across the septum into the left atrium by 2 to 3 cm. Pulmonary hypertension (PH) was induced by partially clamping the pulmonary artery. ECMO flow was cycled from high (2 to 3 L/min) to low (0.2 to 0.3 L/min) over 2 to 3 hours. Results: Transseptal access using TEE and fluoroscopy was successful in 1 animal and unsuccessful in 1 animal. ICE provided optimal visualization for the remaining 2 animals. Mean arterial pressure (MAP) was associated immediately and consistently with high versus low ECMO flow rate (mean difference: 29 ± 3.1 mm Hg, P = 0.004) but was not restored to baseline values. RV pressure values were dynamic. Given time to equilibrate, mean RV pressure was restored to a baseline level. Conclusions: Percutaneous right atrium to left atrium transseptal cannulation relieved PH-RVF. MAP was restored to a viable level, and mean RV pressure was restored to a baseline level. Transseptal ECMO shows promise as a cannulation strategy to bridge patients with PH-RVF to lung transplant.

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.

Abstract Background Current approaches utilizing non-invasive methods to screen for cardiac allograft rejection (gene expression profiling and cell free DNA) have yet to be broadly integrated into heart transplant management and have shown limited discrimination (AUCs of 0.68 and 0.77, respectively). Changes in the electrocardiogram (ECG) have been reported at the time of severe cardiac rejection, including low voltages and conduction abnormalities. It remains unknown if subtle ECG changes correlating with cardiac allograft rejection can be detected earlier using machine learning methods. Purpose We sought to develop an artificial intelligence (AI) model to detect cardiac allograft rejection based on the 12 lead ECG. Methods We identified all patients who underwent a heart transplant at 3 hospital sites within a single health system from Jan 1998 through Apr 2021 and extracted digital 12-lead ECG data as well as endomyocardial biopsy pathology results from the electronic medical record. We partitioned our data into a training (80%), validation (10%), and test set (10%) based on a group stratification sampling. Each patient was present in only one set and each set had a positivity rate of 2.6% with 6,074/758/758 ECGs belonging to 1,146/140/141 unique patients in each set respectively. Cardiac allograft rejection was defined as moderate or severe acute cellular rejection based on International Society for Heart and Lung Transplantation (ISHLT) guidelines. A convolutional neural network, using the 12-lead ECG data as input, was trained with hyperparameter optimization for regularization, learning rate adjustments, and class weights. Model performance metrics were based on the test data and estimated using the final model architecture. Results 1,587 heart transplant recipients who had at least one endomyocardial biopsy were evaluated for inclusion. We limited our sample to ECGs performed within 30 days of the biopsy date (7,590 ECGs, representing 1,425 unique patients). Our study population had a median age of 55.8 years and 28.7% were female. The median number of ECG-biopsy pairs per patient was 5. The majority of endomyocardial biopsy results were classified as none or mild rejection (97.1%), and 2.9% had moderate/severe rejection. The ECG-based AI model detected cardiac allograft rejection with an area under the receiver operative curve (AUC) of 0.84 in the test set. The sensitivity, specificity, positive and negative predictive values were 95%, 52.6%. 5.2% and 99.7% respectively. Conclusions An AI-ECG model appears to outperform novel non-invasive laboratory tests (gene expression profiling and cell free DNA) for detecting cardiac allograft rejection and does not require a blood draw or the additional complexities surrounding sample processing. This model relies on a readily available and relatively inexpensive test, the ECG. In addition, AI predictions can be made available within a few minutes following ECG acquisition. Funding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic

Abstract Background We have previously shown bone marrow-derived mesenchymal stem cells (MSCs) may shift immune responses toward anti-inflammatory pathways and stabilize the course of obstructive chronic lung allograft syndrome (o-CLAD) after lung transplantation. In this study, we measured the response of lower dose infusions. Methods We infused low-dose MSCs intravenously in 13 patients who had developed moderate-to-severe o-CLAD. Three had previously received an infusion of MSCs from a different donor and were re-dosed at 1 × 106 MSC/kg, while 5 received a first dose at 1 × 106 MSC/kg and five received an even lower dose at 0.5 × 106 MSC/kg. We recorded pulmonary function tests before and after infusion, and patients were followed clinically for 12 months. Results Infusions were well tolerated, and no significant adverse events were recorded in the first 30 days. There was significant decline (mean ± SD) in forced vital capacity (FVC) (3.49 ± 1.03 vs 3.18 ± 0.94 L, P = .03) and forced expiratory volume in 1 second (FEV1) (2.28 ± 0.86 vs 1.77 ± 0.49 L, P = .04) over the year preceding infusion. FVC (3.18 ± 0.94 vs 3.46 ± 0.99 L, P = .53) and FEV1 was not significantly changed (1.77 ± 0.49 vs 1.88 ± 0.75, P = .72) when comparing values immediately prior to infusion to those obtained 1 year after infusion, indicating a possible stabilizing effect on lung function decline due to o-CLAD. Conclusion Intravenous infusions of bone marrow-derived MSCs are well tolerated in lung transplant recipients with moderate-to-severe CLAD. Low-dose MSCs appear to slow progression of CLAD in some patients.