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.

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).