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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| Format: | Article |
| Language: | English |
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BMC
2021-11-01
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| Series: | Diagnostic and Prognostic Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41512-021-00107-5 |
| _version_ | 1818824052711620608 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-18T23:49:45Z |
| format | Article |
| id | doaj.art-9d1d7f432ef74a48bda8db162b2f654f |
| institution | Directory Open Access Journal |
| issn | 2397-7523 |
| language | English |
| last_indexed | 2024-12-18T23:49:45Z |
| publishDate | 2021-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic and Prognostic Research |
| spelling | doaj.art-9d1d7f432ef74a48bda8db162b2f654f2022-12-21T20:47:02ZengBMCDiagnostic and Prognostic Research2397-75232021-11-01511910.1186/s41512-021-00107-5A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocolMariella Gregorich0Andreas Heinzel1Michael Kammer2Heike Meiselbach3Carsten Böger4Kai-Uwe Eckardt5Gert Mayer6Georg Heinze7Rainer Oberbauer8Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of ViennaDivision of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of ViennaSection for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of ViennaDepartment of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)Department of Nephrology, University of Regensburg, University Hospital RegensburgDepartment of Nephrology and Medical Intensive Care, Charité Universitätsmedizin BerlinDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University InnsbruckSection for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of ViennaDivision of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of ViennaAbstract 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.https://doi.org/10.1186/s41512-021-00107-5Prediction modelingType 2 diabetes mellitusChronic kidney diseaseMixed modelPrognosisRisk calculator |
| spellingShingle | 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 Diagnostic and Prognostic Research Prediction modeling Type 2 diabetes mellitus Chronic kidney disease Mixed model Prognosis Risk calculator |
| title | 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_short | 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 |
| topic | Prediction modeling Type 2 diabetes mellitus Chronic kidney disease Mixed model Prognosis Risk calculator |
| url | https://doi.org/10.1186/s41512-021-00107-5 |
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