A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol

Abstract Background Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear ne...

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Autores principales:	Mariella Gregorich, Andreas Heinzel, Michael Kammer, Heike Meiselbach, Carsten Böger, Kai-Uwe Eckardt, Gert Mayer, Georg Heinze, Rainer Oberbauer
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Prediction modeling Type 2 diabetes mellitus Chronic kidney disease Mixed model Prognosis Risk calculator Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/9d1d7f432ef74a48bda8db162b2f654f
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spelling	oai:doaj.org-article:9d1d7f432ef74a48bda8db162b2f654f2021-11-21T12:25:32ZA prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol10.1186/s41512-021-00107-52397-7523https://doaj.org/article/9d1d7f432ef74a48bda8db162b2f654f2021-11-01T00:00:00Zhttps://doi.org/10.1186/s41512-021-00107-5https://doaj.org/toc/2397-7523Abstract Background Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear need to identify patients at high risk for fast progression and the implementation of preventative strategies. Existing prediction models of renal function decline, however, aim to assess the risk by artificially grouped patients prior to model building into risk strata defined by the categorization of the least-squares slope through the longitudinally fluctuating eGFR values, resulting in a loss of predictive precision and accuracy. Methods This study protocol describes the development and validation of a prediction model for the longitudinal progression of renal function decline in Caucasian patients with type 2 diabetes mellitus (DM2). For development and internal-external validation, two prospective multicenter observational studies will be used (PROVALID and GCKD). The estimated glomerular filtration rate (eGFR) obtained at baseline and at all planned follow-up visits will be the longitudinal outcome. Demographics, clinical information and laboratory measurements available at a baseline visit will be used as predictors in addition to random country-specific intercepts to account for the clustered data. A multivariable mixed-effects model including the main effects of the clinical variables and their interactions with time will be fitted. In application, this model can be used to obtain personalized predictions of an eGFR trajectory conditional on baseline eGFR values. The final model will then undergo external validation using a third prospective cohort (DIACORE). The final prediction model will be made publicly available through the implementation of an R shiny web application. Discussion Our proposed state-of-the-art methodology will be developed using multiple multicentre study cohorts of people with DM2 in various CKD stages at baseline, who have received modern therapeutic treatment strategies of diabetic kidney disease in contrast to previous models. Hence, we anticipate that the multivariable prediction model will aid as an additional informative tool to determine the patient-specific progression of renal function and provide a useful guide to early on identify individuals with DM2 at high risk for rapid progression.Mariella GregorichAndreas HeinzelMichael KammerHeike MeiselbachCarsten BögerKai-Uwe EckardtGert MayerGeorg HeinzeRainer OberbauerBMCarticlePrediction modelingType 2 diabetes mellitusChronic kidney diseaseMixed modelPrognosisRisk calculatorMedicine (General)R5-920ENDiagnostic and Prognostic Research, Vol 5, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Prediction modeling Type 2 diabetes mellitus Chronic kidney disease Mixed model Prognosis Risk calculator Medicine (General) R5-920
spellingShingle	Prediction modeling Type 2 diabetes mellitus Chronic kidney disease Mixed model Prognosis Risk calculator Medicine (General) R5-920 Mariella Gregorich Andreas Heinzel Michael Kammer Heike Meiselbach Carsten Böger Kai-Uwe Eckardt Gert Mayer Georg Heinze Rainer Oberbauer A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
description	Abstract Background Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear need to identify patients at high risk for fast progression and the implementation of preventative strategies. Existing prediction models of renal function decline, however, aim to assess the risk by artificially grouped patients prior to model building into risk strata defined by the categorization of the least-squares slope through the longitudinally fluctuating eGFR values, resulting in a loss of predictive precision and accuracy. Methods This study protocol describes the development and validation of a prediction model for the longitudinal progression of renal function decline in Caucasian patients with type 2 diabetes mellitus (DM2). For development and internal-external validation, two prospective multicenter observational studies will be used (PROVALID and GCKD). The estimated glomerular filtration rate (eGFR) obtained at baseline and at all planned follow-up visits will be the longitudinal outcome. Demographics, clinical information and laboratory measurements available at a baseline visit will be used as predictors in addition to random country-specific intercepts to account for the clustered data. A multivariable mixed-effects model including the main effects of the clinical variables and their interactions with time will be fitted. In application, this model can be used to obtain personalized predictions of an eGFR trajectory conditional on baseline eGFR values. The final model will then undergo external validation using a third prospective cohort (DIACORE). The final prediction model will be made publicly available through the implementation of an R shiny web application. Discussion Our proposed state-of-the-art methodology will be developed using multiple multicentre study cohorts of people with DM2 in various CKD stages at baseline, who have received modern therapeutic treatment strategies of diabetic kidney disease in contrast to previous models. Hence, we anticipate that the multivariable prediction model will aid as an additional informative tool to determine the patient-specific progression of renal function and provide a useful guide to early on identify individuals with DM2 at high risk for rapid progression.
format	article
author	Mariella Gregorich Andreas Heinzel Michael Kammer Heike Meiselbach Carsten Böger Kai-Uwe Eckardt Gert Mayer Georg Heinze Rainer Oberbauer
author_facet	Mariella Gregorich Andreas Heinzel Michael Kammer Heike Meiselbach Carsten Böger Kai-Uwe Eckardt Gert Mayer Georg Heinze Rainer Oberbauer
author_sort	Mariella Gregorich
title	A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
title_short	A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
title_full	A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
title_fullStr	A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
title_full_unstemmed	A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
title_sort	prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol
publisher	BMC
publishDate	2021
url	https://doaj.org/article/9d1d7f432ef74a48bda8db162b2f654f
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A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol

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