Faculty Bibliography

The benefits of bilateral lung transplantation (BLT) versus single lung transplantation (SLT) are still debated. One impediment to clinical recommendations is that BLT vs. SLT advantages may vary based on underlying disease. Since both options are clinically tenable in patients with emphysema, we conducted a comprehensive assessment of lung allograft survival in this population. Using U.S. registry data, we studied time to all-cause allograft failure in 8,092 patients 12 years or older transplanted from 2006 to 2022, adjusting for recipient, donor, and transplant factors by inverse propensity weighting. Median allograft survival was 6.6 years in BLT compared to 5.3 years in SLT, a 25% risk-adjusted survival advantage of _0.81.3_1.8 years. Risk-adjusted bilateral survival advantages varied between 0.9 and 2.4 years across eleven subgroups. Median allograft survival in BLT was 1.2 years greater than right SLT and 2.0 years greater than left SLT. During the 16-year study period, allograft survival steadily improved for BLT but not for SLT. Although the 25% BLT survival advantage pre-dated the pandemic, COVID-19 may have contributed to an apparent SLT survival decline. Recognizing the possible influence of residual confounding due to selection biases, these findings may aid offer decision-making when both donor lungs are available.

Primary graft dysfunction (PGD) is a major source of morbidity and mortality following lung transplantation, presenting as acute lung injury within 72 hours post-transplantation. Despite advances in surgical techniques and perioperative care, the complex interplay of donor, recipient, and perioperative factors contributes to its development, underscoring the multifactorial nature of PGD. Clinical management of recipients with PGD relies on supportive care strategies, including lung-protective ventilation, inhaled nitric oxide, and extracorporeal membrane oxygenation (ECMO). Severe cases of PGD may result in significant short- and long-term adverse outcomes, including early mortality. Even for patients who recover from PGD, there is also an associated increased risk of chronic lung allograft dysfunction, further compounding its clinical impact. This review provides a brief review of current knowledge regarding PGD, detailing risk factors, diagnostic criteria, and management approaches while identifying critical gaps in understanding its pathophysiology. Ongoing research is essential to develop innovative therapeutic strategies and improve outcomes for lung transplant recipients.

CLINICAL CONDITION/UNASSIGNED:The authors present the case of a young man who presented with cardiogenic shock requiring venoarterial extracorporeal membrane oxygenation and microaxial flow pump complicated by acute spinal cord infarction (SCI) leading to bilateral lower extremity paraplegia. KEY QUESTIONS/UNASSIGNED:The key questions included the following: 1) What is the incidence and pathophysiology for SCI with mechanical circulatory support (MCS)?; 2) Which configurations of MCS carry a greater risk of SCI? How do we approach MCS escalation, recognizing that with each device we carry additive risk of complications?; 3) What data guide anticoagulation strategies for MCS?; and 4) What strategies can we implement to support patients who have suffered SCI from MCS? OUTCOME/RESULTS:Our patient was transitioned to a right ventricular assist device with Impella 5.5 as a bridge to therapy, and underwent cardiac transplantation 4 weeks after presentation with ongoing inpatient rehabilitation. TAKE-HOME MESSAGES/CONCLUSIONS:Contemporary MCS carries a small but significant risk of SCI which is often irreversible. More data are required to guide anticoagulation strategies for MCS and mitigate risk.

RATIONALE/BACKGROUND:Lower airway enrichment with oral commensals has been previously associated with grade 3 severe primary graft dysfunction (PGD) after lung transplantation (LT). We aimed to determine whether this dysbiotic signature is present across all PGD severity grades, including milder forms, and whether it is associated with a distinct host inflammatory endotype. METHODS:Lower airway samples from 96 LT recipients with varying degrees of PGD were used to evaluate the lung allograft microbiota via 16S rRNA gene sequencing. Bronchoalveolar lavage (BAL) cytokine concentrations and cell differential percentages were compared across PGD grades. In a subset of samples, we evaluated the lower airway host transcriptome using RNA sequencing methods. RESULTS:Differential analyses demonstrated lower airway enrichment with supraglottic-predominant taxa (SPT) in both moderate and severe PGD. Dirichlet Multinomial Mixtures (DMM) modeling identified two distinct microbial clusters. A greater percentage of subjects with moderate-severe PGD were identified within the dysbiotic cluster (C-SPT) than within the no PGD group (48 and 29%, respectively) though this difference did not reach statistical significance (p=0.06). PGD severity associated with increased BAL neutrophil concentration (p=0.03) and correlated with BAL concentrations of MCP-1/CCL2, IP-10/CXCL10, IL-10, and TNF-α (p<0.05). Furthermore, microbial signatures of dysbiosis correlated with neutrophils, MCP-1/CCL-2, IL-10, and TNF-α (p<0.05). C-SPT exhibited differential expression of TNF, SERPINE1 (PAI-1), MPO, and MMP1 genes and upregulation of MAPK pathways, suggesting that dysbiosis regulates host signaling to promote neutrophilic inflammation. CONCLUSIONS:Lower airway dysbiosis within the lung allograft is associated with a neutrophilic inflammatory endotype, an immune profile commonly recognized as the hallmark for PGD pathogenesis. This data highlights a putative role for lower airway microbial dysbiosis in the pathogenesis of this syndrome.

BACKGROUND:Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the GRACE and TIMI risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This article aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS. METHODS:October 2024, as well as Google Scholar and manual screening of reference lists from included studies and the grey literature for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. Best-performing ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals in estimating risk of all-cause mortality. RESULTS:Twelve studies were included (250,510 patients). The summary C-statistic of best-performing ML models across all endpoints was 0.88 (95% CI, 0.86-0.91), compared to traditional methods 0.82 (95% CI, 0.80-0.85). The difference in C-statistic between ML models and traditional methods was 0.06 (p<0.0007). Five studies undertook external validation. PROBAST tool demonstrated high risk of bias for all studies. Common sources of bias included reporting bias and selection bias. Best-performing ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. CONCLUSION/CONCLUSIONS:Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the need for greater model validation.

OBJECTIVE/UNASSIGNED:Anticoagulation monitoring in patients supported on extracorporeal membrane oxygenation is challenging given the risks of both bleeding and thrombotic complications. Based on our early clinical experience, we revised our heparin protocol by reducing our target anti-factor Xa assay from 0.3 to 0.7 U/mL to 0.15 to 0.5 U/mL, while instituting a partial thromboplastin time cutoff of 70 seconds. We evaluated the impact of this change on bleeding/thrombotic complications. METHODS/UNASSIGNED:A single-center retrospective study of adult patients on extracorporeal membrane oxygenation support was conducted from January 2015 to August 2022. Patients were stratified into groups based on protocol revision: Pre-Revision (2015-2018) or Post-Revision (2019-2022). Our primary end point was the incidence of bleeding/thrombotic complications. Time in therapeutic range was calculated to determine protocol adherence. Poisson regression was performed to correlate time in therapeutic range with the likelihood of complication. RESULTS/UNASSIGNED:008). CONCLUSIONS/UNASSIGNED:A modified heparin monitoring protocol defined by a lower therapeutic anti-factor Xa assay target and a set partial thromboplastin time cutoff correlated with decreases in bleeding/thrombotic complications in patients on extracorporeal membrane oxygenation.

BACKGROUND:Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy. METHODS:PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. The primary outcome was comparative discrimination measured by C-statistics with 95% CIs between ML models and traditional methods in estimating the risk of all-cause mortality at 30 days and 1 year. RESULTS:Nine studies were included (29 608 patients). The summary C-statistic of the top performing ML models was 0.79 (95% CI 0.71 to 0.86), compared with traditional methods 0.68 (95% CI 0.61 to 0.76). The difference in C-statistic between all ML models and traditional methods was 0.11 (p<0.00001). Of the nine studies, two studies provided externally validated models and three studies reported calibration. Prediction Model Risk of Bias Assessment Tool tool demonstrated high risk of bias for all studies. CONCLUSION/CONCLUSIONS:ML models outperformed traditional risk scores in the discrimination of all-cause mortality following TAVI. While integration of ML algorithms into electronic healthcare systems may improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.

Pulmonary artery sarcoma is a rare intravascular tumor that mimics pulmonary embolus. Early recognition and referral to surgery is important because nonsurgically treated tumors have a poor prognosis. Here, we describe a case of pulmonary artery sarcoma diagnosed with multimodal imaging, which also aided in surgical planning.