Jennifer L Holness1,2, Anita Brink3, M Razeen Davids4, James M Warwick5. 1. Nuclear Medicine Division, Department of Medical Imaging and Clinical Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa. jen.holness@uct.ac.za. 2. Division of Nuclear Medicine, Department of Paediatrics and Child Health, University of Cape Town and Red Cross Children's Hospital, Cape Town, South Africa. jen.holness@uct.ac.za. 3. Division of Nuclear Medicine, Department of Paediatrics and Child Health, University of Cape Town and Red Cross Children's Hospital, Cape Town, South Africa. 4. Division of Nephrology, Department of Medicine, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa. 5. Nuclear Medicine Division, Department of Medical Imaging and Clinical Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa.
Abstract
BACKGROUND: Creatinine-based glomerular filtration rate (GFR)-estimating equations frequently do not perform well in populations that differ from the development populations in terms of mean GFR, age, pathology, ethnicity, and diet. After first evaluating the performance of existing equations, the aim of this study was to demonstrate the utility of an in-house modification of the equations to better fit a specific population. METHODS: Estimated GFR using 8 creatinine-based equations was first compared to 2-sample 51Cr-ethylenediaminetetra-acetic acid plasma clearance in non-cancer and cancer groups independently. The groups were then divided into development and validation sets. Using the development set data, the Microsoft® Excel SOLVER add-in was used to modify the parameters of 7 equations to better fit the data. Using the validation set data, the performance of the original and modified equations was compared. RESULTS: Two hundred fifty-six GFR measurements were performed in 160 children. GFR was overestimated in both groups (non-cancer 4.3-22.6 ml/min/1.73 m2, cancer 17.2-46.6 ml/min/1.73 m2). The root mean square error (RMSE) was 19.1-21.8 ml/min/1.73 m2 (non-cancer) and 18.6-20.8 ml/min/1.73 m2 (cancer). The P30 values were 49.1-73.0% (non-cancer) and 19.6-66.0% (cancer). Modifying the parameters of seven equations resulted in significant improvements in the P30 values in the non-cancer (65.0-85.0%) and cancer (79.6-87.8%) groups. CONCLUSIONS: Modifying the parameters of pediatric GFR estimating-equations using a simple Excel-based tool significantly improved their accuracy in both non-cancer and cancer populations. Graphical abstract.
BACKGROUND: Creatinine-based glomerular filtration rate (GFR)-estimating equations frequently do not perform well in populations that differ from the development populations in terms of mean GFR, age, pathology, ethnicity, and diet. After first evaluating the performance of existing equations, the aim of this study was to demonstrate the utility of an in-house modification of the equations to better fit a specific population. METHODS: Estimated GFR using 8 creatinine-based equations was first compared to 2-sample 51Cr-ethylenediaminetetra-acetic acid plasma clearance in non-cancer and cancer groups independently. The groups were then divided into development and validation sets. Using the development set data, the Microsoft® Excel SOLVER add-in was used to modify the parameters of 7 equations to better fit the data. Using the validation set data, the performance of the original and modified equations was compared. RESULTS: Two hundred fifty-six GFR measurements were performed in 160 children. GFR was overestimated in both groups (non-cancer 4.3-22.6 ml/min/1.73 m2, cancer 17.2-46.6 ml/min/1.73 m2). The root mean square error (RMSE) was 19.1-21.8 ml/min/1.73 m2 (non-cancer) and 18.6-20.8 ml/min/1.73 m2 (cancer). The P30 values were 49.1-73.0% (non-cancer) and 19.6-66.0% (cancer). Modifying the parameters of seven equations resulted in significant improvements in the P30 values in the non-cancer (65.0-85.0%) and cancer (79.6-87.8%) groups. CONCLUSIONS: Modifying the parameters of pediatric GFR estimating-equations using a simple Excel-based tool significantly improved their accuracy in both non-cancer and cancer populations. Graphical abstract.
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