ArticleThe Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation2018
Clinical phenomapping and outcomes after heart transplantation.
Article in The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- Recipient Pericardial Apolipoprotein Levels Might Be an Indicator of Worse Outcomes after Orthotopic Heart Transplantation.International journal of molecular sciences · 2024Article
- Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review.Cardiovascular diagnosis and therapy · 2023Review
- DCAF12 and HSPA1A May Serve as Potential Diagnostic Biomarkers for Myasthenia Gravis.BioMed research international · 2022Article
- CRL4-DCAF12 Ubiquitin Ligase Controls MOV10 RNA Helicase during Spermatogenesis and T Cell Activation.International journal of molecular sciences · 2021Article
- Recent advances in precision medicine for individualized immunosuppression.Current opinion in organ transplantation · 2020Article
- Hierarchical Clustering Analysis for Predicting 1-Year Mortality After Starting Hemodialysis.Kidney international reports · 2020Article
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25 authors.
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Abstract
backgroundSurvival after heart transplantation (HTx) is limited by complications related to alloreactivity, immune suppression, and adverse effects of pharmacologic therapies. We hypothesize that time-dependent phenomapping of clinical and molecular data sets is a valuable approach to clinical assessments and guiding medical management to improve outcomes.
methodsWe analyzed clinical, therapeutic, biomarker, and outcome data from 94 adult HTx patients and 1,557 clinical encounters performed between January 2010 and April 2013. Multivariate analyses were used to evaluate the association between immunosuppression therapy, biomarkers, and the combined clinical end point of death, allograft loss, retransplantation, and rejection. Data were analyzed by K-means clustering (K = 2) to identify patterns of similar combined immunosuppression management, and percentile slopes were computed to examine the changes in dosages over time. Findings were correlated with clinical parameters, human leucocyte antigen antibody titers, and peripheral blood mononuclear cell gene expression of the AlloMap (CareDx, Inc., Brisbane, CA) test genes. An intragraft, heart tissue gene coexpression network analysis was performed.
resultsUnsupervised cluster analysis of immunosuppressive therapies identified 2 groups, 1 characterized by a steeper immunosuppression minimization, associated with a higher likelihood for the combined end point, and the other by a less pronounced change. A time-dependent phenomap suggested that patients in the group with higher event rates had increased human leukocyte antigen class I and II antibody titers, higher expression of the FLT3 AlloMap gene, and lower expression of the MARCH8 and WDR40A AlloMap genes. Intramyocardial biomarker-related coexpression network analysis of the FLT3 gene showed an immune system-related network underlying this biomarker.
conclusionsTime-dependent precision phenotyping is a mechanistically insightful, data-driven approach to characterize patterns of clinical care and identify ways to improve clinical management and outcomes.
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