Evidence map›Paper›PMID 42516922›Full record

ArticleJHLT open2026

Machine learning analysis identifies key predictors of baseline lung allograft dysfunction and increased mortality in lung transplant patients.

Keerthana Sankar, Laura DiChiacchio, Yuri Matusov, Pedro Catarino, Dominick Megna, Lorenzo Zaffiri, Jeremy Falk, Peter Chen, Reinaldo Rampolla

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JHLT open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Keerthana SankarDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
Laura DiChiacchioDepartment of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
Yuri MatusovDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
Pedro CatarinoDepartment of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
Dominick MegnaDepartment of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
Lorenzo ZaffiriDivision of Pulmonary, Allergy and Critical Care Medicine, Emory University School of Medicine, Atlanta, GA.
Jeremy FalkDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
Peter ChenDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
Reinaldo RampollaDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
NCATS NIH HHS UL1 TR001881
6 · The paper itself

Abstract

Background: Baseline lung allograft dysfunction (BLAD) is a phenotype of early graft dysfunction associated with impaired survival after lung transplantation. Although prior work links BLAD with several pre- and post-transplant factors, a robust set of donor and recipient predictors remains unclear. Research Question: What are important donor and recipient predictors of BLAD? Methods: We retrospectively reviewed 124 adults who underwent bilateral lung transplantation at a single center. Donor and recipient variables were evaluated using machine learning (gradient boosting and classification tree models), with high-importance predictors incorporated into logistic regression. Survival was assessed using Kaplan-Meier and Cox proportional hazards models. Results: BLAD occurred in 58 of 124 patients (46.7%). Our analysis identified 7 key factors associated with BLAD: Increased cold ischemia time ( Conclusions: BLAD is common after bilateral lung transplantation. Moderate to severe BLAD is associated with worse survival. Machine learning identified biological variables associated with BLAD and modifiable peri-operative variables, highlighting opportunities for pre-transplant and intra-operative optimization.

Indexed as

Baseline lung allograft dysfunctionCardiothoracic surgeryLung transplantMachine learningPeri-operative predictors

Identifiers

PMID42516922
PMCPMC13404067

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.