Please use this identifier to cite or link to this item: https://doi.org/10.1111/petr.13105
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dc.titlePrelisting predictions of early postoperative survival in infant heart transplantation using classification and regression tree analysis
dc.contributor.authorChen, Ching Kit
dc.contributor.authorManlhiot, Cedric
dc.contributor.authorMital, Seema
dc.contributor.authorSchwartz, Steven M
dc.contributor.authorVan Arsdell, Glen S
dc.contributor.authorCaldarone, Christopher
dc.contributor.authorMcCrindle, Brian W
dc.contributor.authorDipchand, Anne I
dc.date.accessioned2021-05-25T02:07:35Z
dc.date.available2021-05-25T02:07:35Z
dc.date.issued2018-03-01
dc.identifier.citationChen, Ching Kit, Manlhiot, Cedric, Mital, Seema, Schwartz, Steven M, Van Arsdell, Glen S, Caldarone, Christopher, McCrindle, Brian W, Dipchand, Anne I (2018-03-01). Prelisting predictions of early postoperative survival in infant heart transplantation using classification and regression tree analysis. PEDIATRIC TRANSPLANTATION 22 (2). ScholarBank@NUS Repository. https://doi.org/10.1111/petr.13105
dc.identifier.issn13973142
dc.identifier.issn13993046
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/191503
dc.description.abstractInfants listed for heart transplantation experience high waitlist and early post-transplant mortality, and thus, optimal allocation of scarce donor organs is required. Unfortunately, the creation and validation of multivariable regression models to identify risk factors and generate individual-level predictions are challenging. We sought to explore the use of data mining methods to generate a prediction model. CART analysis was used to create a model which, at the time of listing, would predict which infants listed for heart transplantation would survive at least 3 months post-transplantation. A total of 48 infants were included; 13 died while waiting, and six died within 3 months of heart transplant. CART analysis identified RRT, blood urea nitrogen, and hematocrit as terminal nodes with alanine transaminase as an intermediate node predicting death. No patients listed on RRT (n = 10) survived and only three of 12 (25%) patients listed on ECLS survived >3 months post-transplant. CART analysis overall accuracy was 83%, with sensitivity of 95% and specificity 76%. This study shows that CART analysis can be used to generate accurate prediction models in small patient populations. Model validation will be necessary before incorporation into decision-making algorithms used to determine transplant candidacy.
dc.language.isoen
dc.publisherWILEY
dc.sourceElements
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectPediatrics
dc.subjectTransplantation
dc.subjectdata mining
dc.subjectinfant heart transplant
dc.subjectmortality risk
dc.subjectpredictive modeling
dc.subjectEXTRACORPOREAL MEMBRANE-OXYGENATION
dc.subjectCARDIAC TRANSPLANTATION
dc.subjectUNITED-STATES
dc.subjectRISK-FACTORS
dc.subjectCHILDREN
dc.subjectDISEASE
dc.subjectOUTCOMES
dc.subjectREGISTRY
dc.subjectDEATH
dc.subjectRECIPIENTS
dc.typeArticle
dc.date.updated2021-05-23T01:45:38Z
dc.contributor.departmentDEPT OF PAEDIATRICS
dc.description.doi10.1111/petr.13105
dc.description.sourcetitlePEDIATRIC TRANSPLANTATION
dc.description.volume22
dc.description.issue2
dc.published.statePublished
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