Hospital- and Ventilator-associated Pneumonia early after Lung Transplantation: a prospective Study on Incidence, Pathogen Origin and Outcome.

BACKGROUND: Hospital- (HAP) and ventilator-associated pneumonia (VAP) are important complications early (<30 days) after lung transplantation (LT). However, current incidence, associated factors and outcomes are not well reported. METHODS: LT recipients transplanted at our institution (07/2019-01/2020 and 10/2021-11/2022) were prospectively included. We assessed incidence and presentation of pneumonia and evaluated the impact of associated factors using regression models. In addition, we evaluated molecular relatedness of respiratory pathogens collected peri-transplant and at pneumonia occurrence using pulsed-field-gel-electrophoresis (PFGE). RESULTS: In the first 30 days post-LT, 25/270 (9.3%) recipients were diagnosed with pneumonia (68% [17/25] VAP; 32% [8/25] HAP). Median time to pneumonia was 11 days (IQR 7-13). 49% (132/270) of donor and 16% (44/270) of recipient respiratory peri-transplant cultures were positive. However, pathogens associated with pneumonia were not genetically related to either donor or recipient cultures at transplant, as determined by PFGE.Diagnosed pulmonary hypertension (HR 4.42, 95% CI 1.62-12.08) and immunosuppression use (HR 2.87, 95% CI 1.30-6.56) were pre-transplant factors associated with pneumonia.Pneumonia occurrence was associated with longer hospital stay (HR 5.44, 95% CI 2.22-13.37) and VAP with longer ICU stay (HR 4.31, 95% CI: 1.73-10.75) within the first 30 days post-transplant; 30- and 90-day mortality were similar. CONCLUSIONS: Prospectively assessed early pneumonia incidence occurred in around 10% of LT. Populations at increased risk for pneumonia occurrence include LT with pre-transplant pulmonary hypertension and pre-transplant immunosuppression. Pneumonia was associated with increased healthcare use, highlighting the need for further improvements by preferentially targeting higher-risk patients.

The emerging role of generative artificial intelligence in transplant medicine.

Generative artificial intelligence (AI), a subset of machine learning that creates new content based on training data, has witnessed tremendous advances in recent years. Practical applications have been identified in health care in general, and there is significant opportunity in transplant medicine for generative AI to simplify tasks in research, medical education, and clinical practice. In addition, patients stand to benefit from patient education that is more readily provided by generative AI applications. This review aims to catalyze the development and adoption of generative AI in transplantation by introducing basic AI and generative AI concepts to the transplant clinician and summarizing its current and potential applications within the field. We provide an overview of applications to the clinician, researcher, educator, and patient. We also highlight the challenges involved in bringing these applications to the bedside and need for ongoing refinement of generative AI applications to sustainably augment the transplantation field.

Longitudinal outcomes of COVID-19 in solid organ transplant recipients from 2020 to 2023.

Data regarding coronavirus disease 2019 (COVID-19) outcomes in solid organ transplant recipients (SOTr) across severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) waves, including the impact of different measures, are lacking. This cohort study, conducted from March 2020 to May 2023 in Toronto, Canada, aimed to analyze COVID-19 outcomes in 1975 SOTr across various SARS-CoV-2 waves and assess the impact of preventive and treatment measures. The primary outcome was severe COVID-19, defined as requiring supplemental oxygen, with secondary outcomes including hospitalization, length of stay, intensive care unit (ICU) admission, and 30-day and 1-year all-cause mortality. SARS-CoV-2 waves were categorized as Wildtype/Alpha/Delta (318 cases, 16.1%), Omicron BA.1 (268, 26.2%), Omicron BA.2 (268, 13.6%), Omicron BA.5 (561, 28.4%), Omicron BQ.1.1 (188, 9.5%), and Omicron XBB.1.5 (123, 6.2%). Severe COVID-19 rate was highest during the Wildtype/Alpha/Delta wave (44.6%), and lower in Omicron waves (5.7%-16.1%). Lung transplantation was associated with severe COVID-19 (OR: 4.62, 95% CI: 2.71-7.89), along with rituximab treatment (OR: 4.24, 95% CI: 1.04-17.3), long-term corticosteroid use (OR: 3.11, 95% CI: 1.46-6.62), older age (OR: 1.51, 95% CI: 1.30-1.76), chronic lung disease (OR: 2.11, 95% CI: 1.36-3.30), chronic kidney disease (OR: 2.18, 95% CI: 1.17-4.07), and diabetes (OR: 1.97, 95% CI: 1.37-2.83). Early treatment and ≥3 vaccine doses were associated with reduced severity (OR: 0.29, 95% CI: 0.19-0.46, and 0.35, 95% CI: 0.21-0.60, respectively). Tixagevimab/cilgavimab and bivalent boosters did not show a significant impact. The study concludes that COVID-19 severity decreased across different variants in SOTr. Lung transplantation was associated with worse outcomes and may benefit more from preventive and early therapeutic interventions.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.