Evidence map›Paper›PMID 39867199›Full record

ArticleReviews in cardiovascular medicine2025

Risk Estimation of Severe Primary Graft Dysfunction in Heart Transplant Recipients Using a Smartphone.

Souhila Ait-Tigrine, Roger Hullin, Elsa Hoti, Matthias Kirsch, Piergiorgio Tozzi

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Article in Reviews in cardiovascular medicine, 2025. 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

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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

5 authors.

Souhila Ait-TigrineInternal Medicine, Lausanne University Hospital CHUV Lausanne, 1011 Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-9042-2105
Roger HullinCardiology, Lausanne University Hospital CHUV Lausanne, 1011 Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-5738-1903
Elsa HotiLausanne University School of Medicine, 1005 Lausanne, Switzerland.
Matthias KirschCardiac Surgery, Lausanne University Hospital CHUV Lausanne, 1011 Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-7220-4964
Piergiorgio TozziCardiac Surgery, Lausanne University Hospital CHUV Lausanne, 1011 Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-7648-2531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Currently, there are no standardized guidelines for graft allocation in heart transplants (HTxs), particularly when considering organs from marginal donors and donors after cardiocirculatory arrest. This complexity highlights the need for an effective risk analysis tool for primary graft dysfunction (PGD), a severe complication in HTx. Existing score systems for predicting PGD lack superior predictive capability and are often too complex for routine clinical use. This study sought to develop a user-friendly score integrating variables from these systems to enhance the efficacy of the organ allocation process. Methods: Severe PGD was defined as the need for mechanical circulatory support and/or death from an unknown etiology within the first 24 hours following HTx. We used a meta-analytical approach to create a derivation cohort to identify risk factors. We then applied a logistic regression analysis to generate an equation predicting severe PGD risk. We used our previous experience in HTx to create a validation cohort. Subsequently, we implemented the formula in a smartphone application. Results: The meta-analysis comprising six studies revealed a 10.5% ( 95% confidence interval (CI): 5.3-12.4) incidence rate of severe PGD and related 30-day mortality of 38.6%. Eleven risk factors were identified: female donors, female donor to male recipient, undersized donor, donor age, recipient on ventricular assist device support, recipient on amiodarone treatment, recipient with diabetes and renal dysfunction, re-sternotomy, graft ischemic time, and bypass time. An equation to predict the risk, including the 11 parameters (GREF-11), was created using logistic regression models and validated based on our experience involving 116 patients. In our series, 29 recipients (25%) required extracorporeal membrane oxygenation support within 24 hours post-HTx. The overall 30-day mortality was 4.3%, 3.4%, and 6.8% in the non-PGD and severe PGD groups, respectively. The area under the receiver operating characteristic (AU-ROC) curve of the model in the validation cohort was 0.804. Conclusions: The GREF-11 application should offer HTx teams several benefits, including standardized risk assessment and bedside clinical decision support, thereby helping minimize the risk of severe PGD post-HTx.

Indexed as

ECMOheart transplantheart transplantationprimary graft dysfunctionprimary graft failurerisk prediction scoretemporary circulatory support

Identifiers

PMID39867199
PMCPMC11759961

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