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Prelisting predictions of early postoperative survival in infant heart transplantation using classification and regression tree analysis.

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اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • المصدر:
      Publisher: Munksgaard Country of Publication: Denmark NLM ID: 9802574 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1399-3046 (Electronic) Linking ISSN: 13973142 NLM ISO Abbreviation: Pediatr Transplant Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Copenhagen ; Malden, MA : Munksgaard, c1997-
    • الموضوع:
      Data Mining* ; Decision Support Techniques* ; Patient Selection*; Clinical Decision-Making/*methods ; Health Care Rationing/*methods ; Heart Transplantation/*mortality; Female ; Follow-Up Studies ; Humans ; Infant ; Infant, Newborn ; Male ; Regression Analysis ; Retrospective Studies ; Risk Assessment ; Risk Factors ; Sensitivity and Specificity ; Waiting Lists
    • نبذة مختصرة :
      Infants 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.
      (© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)
    • Comments:
      Comment in: Pediatr Transplant. 2018 May;22(3):e13157. (PMID: 29569787)
    • Contributed Indexing:
      Keywords: data mining; infant heart transplant; mortality risk; predictive modeling
    • الموضوع:
      Date Created: 20171223 Date Completed: 20180905 Latest Revision: 20180905
    • الموضوع:
      20231215
    • الرقم المعرف:
      10.1111/petr.13105
    • الرقم المعرف:
      29271030