A radiographic score for human donor lungs on ex vivo lung perfusion predicts transplant outcomes.

BACKGROUND: Ex vivo lung perfusion (EVLP) is an advanced platform for isolated lung assessment and treatment. Radiographs acquired during EVLP provide a unique opportunity to assess lung injury. The purpose of our study was to define and evaluate EVLP radiographic findings and their association with lung transplant outcomes. METHODS: We retrospectively evaluated 113 EVLP cases from 2020-21. Radiographs were scored by a thoracic radiologist blinded to outcome. Six lung regions were scored for 5 radiographic features (consolidation, infiltrates, atelectasis, nodules, and interstitial lines) on a scale of 0 to 3 to derive a score. Spearman's correlation was used to correlate radiographic scores to biomarkers of lung injury. Machine learning models were developed using radiographic features and EVLP functional data. Predictive performance was assessed using the area under the curve. RESULTS: Consolidation and infiltrates were the most frequent findings at 1 hour EVLP (radiographic lung score 2.6 (3.3) and 4.6 (4.3)). Consolidation (r = -0.536 and -0.608, p < 0.0001) and infiltrates (r = -0.492 and -0.616, p < 0.0001) were inversely correlated with oxygenation (∆pO2) at 1 hour and 3 hours of EVLP. First-hour consolidation and infiltrate lung scores predicted transplant suitability with an area under the curve of 87% and 88%, respectively. Prediction of transplant outcomes using a machine learning model yielded an area under the curve of 80% in the validation set. CONCLUSIONS: EVLP radiographs provide valuable insight into donor lungs being assessed for transplantation. Consolidation and infiltrates were the most common abnormalities observed in EVLP lungs, and radiographic lung scores predicted the suitability of donor lungs for transplant.

A machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization.

Ex vivo lung perfusion (EVLP) is a data-intensive platform used for the assessment of isolated lungs outside the body for transplantation; however, the integration of artificial intelligence to rapidly interpret the large constellation of clinical data generated during ex vivo assessment remains an unmet need. We developed a machine-learning model, termed InsighTx, to predict post-transplant outcomes using n = 725 EVLP cases. InsighTx model AUROC (area under the receiver operating characteristic curve) was 79 ± 3%, 75 ± 4%, and 85 ± 3% in training and independent test datasets, respectively. Excellent performance was observed in predicting unsuitable lungs for transplantation (AUROC: 90 ± 4%) and transplants with good outcomes (AUROC: 80 ± 4%). In a retrospective and blinded implementation study by EVLP specialists at our institution, InsighTx increased the likelihood of transplanting suitable donor lungs [odds ratio=13; 95% CI:4-45] and decreased the likelihood of transplanting unsuitable donor lungs [odds ratio=0.4; 95%CI:0.16-0.98]. Herein, we provide strong rationale for the adoption of machine-learning algorithms to optimize EVLP assessments and show that InsighTx could potentially lead to a safe increase in transplantation rates.

Identification of regional variation in gene expression and inflammatory proteins in donor lung tissue and ex vivo lung perfusate.

OBJECTIVE: Diagnosing lung injury is a challenge in lung transplantation. It has been unclear if a single biopsy specimen is truly representative of the entire organ. Our objective was to investigate lung inflammatory biomarkers using human lung tissue biopsies and ex vivo lung perfusion perfusate. METHODS: Eight human donor lungs declined for transplantation were air inflated, flash frozen, and partitioned from apex to base. Biopsies were then sampled throughout the lung. Perfusate was sampled from 4 lung lobes in 8 additional donor lungs subjected to ex vivo lung perfusion. The levels of interleukin-6, interleukin-8, interleukin-10, and interleukin-1ß were measured using quantitative reverse transcription polymerase chain reaction from lung biopsies and enzyme-linked immunosorbent assay from ex vivo lung perfusion perfusate. RESULTS: The median intra-biopsy equal-variance P value was .50 for messenger RNA biomarkers in tissue biopsies. The median intra-biopsy coefficient of variance was 18%. In donors with no apparent focal injuries, the biopsies in each donor showed no difference in various lung slices, with a coefficient of variance of 20%. The exception was biopsies from the lingula and injured focal areas that demonstrated larger differences. Cytokines in ex vivo lung perfusion perfusate showed minimal variation among different lobes (coefficient of variance = 4.9%). CONCLUSIONS: Cytokine gene expression in lung biopsies was consistent, and the biopsy analysis reflects the whole lung, except when specimens were collected from the lingula or an area of focal injury. Ex vivo lung perfusion perfusate also provides a representative measurement of lung inflammation from the draining lobe. These results will reassure clinicians that a lung biopsy or an ex vivo lung perfusion perfusate sample can be used to inform donor lung selection.

A biomarker assay to risk-stratify patients with symptoms of respiratory tract infection.

BACKGROUND: Patients who present to an emergency department (ED) with respiratory symptoms are often conservatively triaged in favour of hospitalisation. We sought to determine if an inflammatory biomarker panel that identifies the host response better predicts hospitalisation in order to improve the precision of clinical decision making in the ED. METHODS: From April 2020 to March 2021, plasma samples of 641 patients with symptoms of respiratory illness were collected from EDs in an international multicentre study: Canada (n=310), Italy (n=131) and Brazil (n=200). Patients were followed prospectively for 28 days. Subgroup analysis was conducted on confirmed coronavirus disease 2019 (COVID-19) patients (n=245). An inflammatory profile was determined using a rapid, 50-min, biomarker panel (RALI-Dx (Rapid Acute Lung Injury Diagnostic)), which measures interleukin (IL)-6, IL-8, IL-10, soluble tumour necrosis factor receptor 1 (sTNFR1) and soluble triggering receptor expressed on myeloid cells 1 (sTREM1). RESULTS: RALI-Dx biomarkers were significantly elevated in patients who required hospitalisation across all three sites. A machine learning algorithm that was applied to predict hospitalisation using RALI-Dx biomarkers had a mean±sd area under the receiver operating characteristic curve of 76±6% (Canada), 84±4% (Italy) and 86±3% (Brazil). Model performance was 82±3% for COVID-19 patients and 87±7% for patients with a confirmed pneumonia diagnosis. CONCLUSIONS: The rapid diagnostic biomarker panel accurately identified the need for inpatient care in patients presenting with respiratory symptoms, including COVID-19. The RALI-Dx test is broadly and easily applicable across many jurisdictions, and represents an important diagnostic adjunct to advance ED decision-making protocols.

Engineered mesenchymal stromal cell therapy during human lung ex vivo lung perfusion is compromised by acidic lung microenvironment.

Ex vivo lung perfusion (EVLP) is an excellent platform to apply novel therapeutics, such as gene and cell therapies, before lung transplantation. We investigated the concept of human donor lung engineering during EVLP by combining gene and cell therapies. Premodified cryopreserved mesenchymal stromal cells with augmented anti-inflammatory interleukin-10 production (MSCIL-10) were administered during EVLP to human lungs that had various degrees of underlying lung injury. Cryopreserved MSCIL-10 had excellent viability, and they immediately and efficiently elevated perfusate and lung tissue IL-10 levels during EVLP. However, MSCIL-10 function was compromised by the poor metabolic conditions present in the most damaged lungs. Similarly, exposing cultured MSCIL-10 to poor metabolic, and especially acidic, conditions decreased their IL-10 production. In conclusion, we found that "off-the-shelf" MSCIL-10 therapy of human lungs during EVLP is safe and feasible, and results in rapid IL-10 elevation, and that the acidic target-tissue microenvironment may compromise the efficacy of cell-based therapies.