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Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation

OBJECTIVE: New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies. RESEARCH DESIGN AND METHODS: We conducted a single-center retrospective cohort study using pretran...

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Autores principales: Chakkera, Harini A., Weil, E. Jennifer, Swanson, Christine M., Dueck, Amylou C., Heilman, Raymond L., Reddy, Kunam S., Hamawi, Khaled, Khamash, Hasan, Moss, Adyr A., Mulligan, David C., Katariya, Nitin, Knowler, William C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Diabetes Association 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3177751/
https://www.ncbi.nlm.nih.gov/pubmed/21949218
http://dx.doi.org/10.2337/dc11-0752
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author Chakkera, Harini A.
Weil, E. Jennifer
Swanson, Christine M.
Dueck, Amylou C.
Heilman, Raymond L.
Reddy, Kunam S.
Hamawi, Khaled
Khamash, Hasan
Moss, Adyr A.
Mulligan, David C.
Katariya, Nitin
Knowler, William C.
author_facet Chakkera, Harini A.
Weil, E. Jennifer
Swanson, Christine M.
Dueck, Amylou C.
Heilman, Raymond L.
Reddy, Kunam S.
Hamawi, Khaled
Khamash, Hasan
Moss, Adyr A.
Mulligan, David C.
Katariya, Nitin
Knowler, William C.
author_sort Chakkera, Harini A.
collection PubMed
description OBJECTIVE: New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies. RESEARCH DESIGN AND METHODS: We conducted a single-center retrospective cohort study using pretransplant clinical and laboratory measurements to construct a risk score for NODAT. NODAT was defined by hemoglobin A(1c) (HbA(1c)) ≥6.5%, fasting serum glucose ≥126 mg/dL, or prescribed therapy for diabetes within 1 year posttransplant. Three multivariate logistic regression models were constructed: 1) standard model, with both continuous and discrete variables; 2) dichotomous model, with continuous variables dichotomized at clinically relevant cut points; and 3) summary score defined as the sum of the points accrued using the terms from the dichotomous model. RESULTS: A total of 316 subjects had seven pretransplant variables with P < 0.10 in univariate logistic regression analyses (age, planned corticosteroid therapy posttransplant, prescription for gout medicine, BMI, fasting glucose and triglycerides, and family history of type 2 diabetes) that were selected for multivariate models. Areas under receiver operating curves for all three models were similar (0.72, 0.71, and 0.70). A simple risk score calculated as the sum of points from the seven variables performed as well as the other two models in identifying risk of NODAT. CONCLUSIONS: A risk score computed from seven simple pretransplant variables can identify risk of NODAT.
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spelling pubmed-31777512012-10-01 Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation Chakkera, Harini A. Weil, E. Jennifer Swanson, Christine M. Dueck, Amylou C. Heilman, Raymond L. Reddy, Kunam S. Hamawi, Khaled Khamash, Hasan Moss, Adyr A. Mulligan, David C. Katariya, Nitin Knowler, William C. Diabetes Care Original Research OBJECTIVE: New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies. RESEARCH DESIGN AND METHODS: We conducted a single-center retrospective cohort study using pretransplant clinical and laboratory measurements to construct a risk score for NODAT. NODAT was defined by hemoglobin A(1c) (HbA(1c)) ≥6.5%, fasting serum glucose ≥126 mg/dL, or prescribed therapy for diabetes within 1 year posttransplant. Three multivariate logistic regression models were constructed: 1) standard model, with both continuous and discrete variables; 2) dichotomous model, with continuous variables dichotomized at clinically relevant cut points; and 3) summary score defined as the sum of the points accrued using the terms from the dichotomous model. RESULTS: A total of 316 subjects had seven pretransplant variables with P < 0.10 in univariate logistic regression analyses (age, planned corticosteroid therapy posttransplant, prescription for gout medicine, BMI, fasting glucose and triglycerides, and family history of type 2 diabetes) that were selected for multivariate models. Areas under receiver operating curves for all three models were similar (0.72, 0.71, and 0.70). A simple risk score calculated as the sum of points from the seven variables performed as well as the other two models in identifying risk of NODAT. CONCLUSIONS: A risk score computed from seven simple pretransplant variables can identify risk of NODAT. American Diabetes Association 2011-10 2011-09-15 /pmc/articles/PMC3177751/ /pubmed/21949218 http://dx.doi.org/10.2337/dc11-0752 Text en © 2011 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
spellingShingle Original Research
Chakkera, Harini A.
Weil, E. Jennifer
Swanson, Christine M.
Dueck, Amylou C.
Heilman, Raymond L.
Reddy, Kunam S.
Hamawi, Khaled
Khamash, Hasan
Moss, Adyr A.
Mulligan, David C.
Katariya, Nitin
Knowler, William C.
Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation
title Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation
title_full Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation
title_fullStr Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation
title_full_unstemmed Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation
title_short Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation
title_sort pretransplant risk score for new-onset diabetes after kidney transplantation
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3177751/
https://www.ncbi.nlm.nih.gov/pubmed/21949218
http://dx.doi.org/10.2337/dc11-0752
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