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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...

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Detalles Bibliográficos
Autores principales: Sage, Andrew T., Donahoe, Laura L., Shamandy, Alaa A., Mousavi, S. Hossein, Chao, Bonnie T., Zhou, Xuanzi, Valero, Jerome, Balachandran, Sharaniyaa, Ali, Aadil, Martinu, Tereza, Tomlinson, George, Del Sorbo, Lorenzo, Yeung, Jonathan C., Liu, Mingyao, Cypel, Marcelo, Wang, Bo, Keshavjee, Shaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412608/
https://www.ncbi.nlm.nih.gov/pubmed/37558674
http://dx.doi.org/10.1038/s41467-023-40468-7
Descripción
Sumario: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.