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Use of Imputation and Decision Modeling to Improve Diagnosis and Management of Patients at Risk for New-Onset Diabetes After Transplantation

期刊

ANNALS OF TRANSPLANTATION
卷 26, 期 -, 页码 -

出版社

INT SCIENTIFIC INFORMATION, INC
DOI: 10.12659/AOT.928624

关键词

Data Interpretation, Statistical; Diabetes Mellitus; Organ Transplantation

资金

  1. National Science Foundation (NSF) [CMMI-1562645]

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The study found that the incidence of NODAT increased when HbA1c and FBG data were collected perfectly. Perfect screening was cost-saving for kidney transplant patients and cost-effective for liver and heart transplant patients.
Background: New-onset diabetes after transplantation (NODAT) is a complication of solid organ transplantation. We sought to determine the extent to which NODAT goes undiagnosed over the course of 1 year following transplantation, analyze missed or later-diagnosed cases of NODAT due to poor hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) collection, and to estimate the impact that improved NODAT screening metrics may have on longterm outcomes. Material/Methods: This was a retrospective study utilizing 3 datasets from a single center on kidney, liver, and heart transplantation patients. Retrospective analysis was supplemented with an imputation procedure to account for missing data and project outcomes under perfect information. In addition, the data were used to inform a simulation model used to estimate life expectancy and cost-effectiveness of a hypothetical intervention. Results: Estimates of NODAT incidence increased from 27% to 31% in kidney transplantation patients, from 31% to 40% in liver transplantation patients, and from 45% to 67% in heart transplantation patients, when HbA1c and FBG were assumed to be collected perfectly at all points. Perfect screening for kidney transplantation patients was cost-saving, while perfect screening for liver and heart transplantation patients was cost-effective at a willingness-to-pay threshold of 5100 000 per life-year. Conclusions: Improved collection of HbA1c and FBG is a cost-effective method for detecting many additional cases of NODAT within the first year alone. Additional research into both improved glucometric monitoring as well as effective strategies for mitigating NODAT risk will become increasingly important to improve health in this population.

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