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The Impact of Fontan Circulatory Failure on Heart Transplant Survival: A 20-Center Retrospective Cohort Study

2025· article· en· W4412110129 on OpenAlexaff
Kurt R. Schumacher, David N. Rosenthal, Adriana Batazzi, Sunkyung Yu, Garrett Reichle, Maria Bano, Shriprasad R. Deshpande, Matthew J. O’Connor, Humera Ahmed, Sharon Chen, Lydia Wright, Steven J. Kindel, Anna Joong, Michelle Ploutz, Brian Feingold, Justin Godown, Chad Mao, Angela Lorts, Kathleen E. Simpson, Aecha Ybarra, Marc E. Richmond, Shahnawaz Amdani, Jennifer Conway, Elizabeth D. Blume, Melissa K. Cousino

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsStollery Children's Hospital
Fundersnot available
KeywordsMedicineHeart transplantationHeart failureRetrospective cohort studyUnivariate analysisInternal medicineHeart diseaseCohortProportional hazards modelCardiologyMultivariate analysis

Abstract

fetched live from OpenAlex

BACKGROUND: Fontan circulatory failure (FCF) is a chronic state in palliated single ventricle heart disease with high morbidity and mortality rates, including heart failure, multisystem end-organ disease, and need for heart transplant. Specific FCF morbidities have not been rigorously defined, limiting study of how FCF morbidities affect pre–heart transplant and post–heart transplant outcomes. We hypothesized that FCF-related morbidities affect survival from heart transplant waitlisting through 1 year after heart transplant. METHODS: This 20-center, retrospective cohort study collected demographic, medical/surgical history, waitlist, and peri- and post–heart transplant data, and a priori defined FCF-specific morbidities, in Fontan patients who were listed for heart transplant from 2008 through 2022. Univariate 2-group statistics compared surviving individuals with those who died anytime from waitlisting to 1 year after heart transplant, died on the waitlist, or underwent transplant and died within 1 year after transplant. Using covariates from both univariate analyses, multivariable logistic regression determined the primary study outcome of independent FCF risk factors for death between waitlist and 1 year after heart transplant. RESULTS: Of 409 waitlisted patients, 24 (5.9%) died on the waitlist. Of the 341 (83.4%) who underwent transplant, 27 (8.5%) did not survive to 1 year. Univariate risk factors for waitlist death included higher aortopulmonary collateral burden, >1 hospitalization in the previous year, younger age, sleep apnea, higher New York Heart Association class, nonenrollment in school or work, and single-parent home. Risk factors for 1-year post–heart transplant mortality included hypoplastic left heart syndrome diagnosis, patent fenestration, anatomic Fontan obstruction, clinical cyanosis (pulse oximetry <90%), polycythemia, portal variceal disease, mental health condition requiring treatment, and higher human leukocyte antigen class II panel reactive antibody. Of the patients not surviving from waitlisting to 1 year after heart transplant, independent risk factors for death included >1 hospitalization in the year before waitlisting (adjusted odds ratio, 2.0 [95% CI, 1.0–4.1]; P =0.05) and clinical cyanosis (adjusted odds ratio, 5.0 [95% CI, 1.8–13.4]; P =0.002). CONCLUSIONS: Patients with Fontan palliation selected for heart transplant have substantial mortality rates from waitlisting through transplant. Among FCF-specific morbidities, cyanosis is associated with worsened survival and necessitates further study. Clinical morbidity of any type requiring repeated hospital admission also should prompt consideration of heart transplant.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.343
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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