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

Andrew T. Sage, Laura L. Donahoe, Alaa A. Shamandy, S. Hossein Mousavi, Bonnie T. Chao, Xuanzi Zhou, Jerome Valero, Sharaniyaa Balachandran, Aadil Ali, Tereza Martinu, George Tomlinson, Lorenzo Sorbo, Jonathan C. Yeung, Mingyao Liu, Marcelo Cypel, Bo Wang () and Shaf Keshavjee ()
Additional contact information
Andrew T. Sage: University Health Network
Laura L. Donahoe: University Health Network
Alaa A. Shamandy: University of Toronto
S. Hossein Mousavi: University Health Network
Bonnie T. Chao: University Health Network
Xuanzi Zhou: University Health Network
Jerome Valero: University Health Network
Sharaniyaa Balachandran: University Health Network
Aadil Ali: University Health Network
Tereza Martinu: University Health Network
George Tomlinson: University Health Network
Lorenzo Sorbo: University Health Network
Jonathan C. Yeung: University Health Network
Mingyao Liu: University Health Network
Marcelo Cypel: University Health Network
Bo Wang: University of Toronto
Shaf Keshavjee: University Health Network

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract 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.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-40468-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40468-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-40468-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().