Evidence map›Paper›PMID 42763507›Full record

ArticleJHLT open2026

Inactive waitlist phenotypes and failure to survive to lung transplantation.

Sajid Kadir, Sandeep Sasidharan, Abbas Shahmohammadi, Sama Al-Bayati, Guil Rozenbaum, Omid Zad, Andres Pelaez

Abstract read
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

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Sajid KadirPepin Heart Institute, AdventHealth Tampa, Tampa, FL.
Sandeep SasidharanDepartment of Medicine, University of Florida College of Medicine, Jacksonville, FL.
Abbas ShahmohammadiDepartment of Critical Care Medicine, San Antonio Methodist Hospital, San Antonio, TX.
Sama Al-BayatiDepartment of Medicine, University of Miami, Miami, FL.
Guil RozenbaumDepartment of Medicine, University of Miami, Miami, FL.
Omid ZadDepartment of Critical Care, Advent Health Medical Group, Advent Health Tampa, Tampa, FL.
Andres PelaezDepartment of Critical Care Medicine, San Antonio Methodist Hospital, San Antonio, TX.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Inactive waitlist status is common in lung transplantation, but the extent to which distinct inactivity phenotypes identify candidates at risk of failing to survive to transplantation is uncertain. Methods: We analyzed adult U.S. lung transplant candidates listed from January 1, 2005, through March 31, 2026, using OPTN STAR files. Inactivity was characterized by cumulative inactive days, first inactivation reason, and cycling. Logistic models evaluated death or removal for being too sick; sequential severity adjustment, multiple imputation, 90- and 180-day landmark competing-risk analyses, and exclusion of initially inactive candidates assessed robustness. Results: Among 56,268 candidates, 16,301 (29.0%) experienced inactivity. Compared with candidate choice, temporary too-sick inactivation had the strongest association with death or too-sick removal in the original model (OR, 5.72; 95% CI, 4.88-6.71) and after full severity adjustment with multiple imputation (OR, 4.40; 95% CI, 3.74-5.17). At the 90-day landmark, adjusted subdistribution hazards increased across nonzero burden groups (sHRs, 1.25-1.45). At 180 days, burden greater than 10 days remained associated with subsequent risk (sHRs, 1.21-1.45), whereas greater than 0-10 days was not. Excluding 1354 candidates initially listed inactive did not materially change the too-sick estimate (OR, 5.83; 95% CI, 4.94-6.88). Conclusions: Inactive status represents heterogeneous clinical and administrative phenotypes. Temporary too-sick inactivation remained strongly associated with failure to survive to transplantation after extensive severity adjustment, whereas landmark analyses separately demonstrated that sustained early inactive burden was associated with subsequent risk.

Indexed as

competing riskslung transplantationmultistate modelOPTNUNOSwaitlist inactivitywaitlist mortality

Identifiers

PMID42763507
PMCPMC13589055

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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.