391-P: Normalizing Glomerular Filtration Rate (GFR) Using Extracellular Volume (ECV) Instead of Body Surface Area (BSA) : Results from an Analysis of the Preventing Early Renal Loss (PERL) Trial
Bibliographic record
Abstract
Standardization of GFR by BSA can underestimate kidney function in overweight individuals. Standardization of GFR by ECV, rather than BSA, may be a way to avoid measurement bias. Our aim was to compare the distribution and agreement of GFR measures in people with type 1 diabetes at risk of kidney disease, and to determine associations with clinical hemodynamic measures. In this exploratory analysis of the PERL clinical trial of allopurinol, the distribution and agreement of 4 measurements for GFR were compared in 542 participants with eligible data. GFR was measured using gold-standard iohexol plasma clearance procedures, and ECV was calculated using the Jødal-Bröchner-Mortensen method, which estimates ECV using results from clearance studies. GFR was standardized using ECV corrected for 14 L of fluid volume rather than 1.73 m2 of BSA. Agreement between measurements was assessed by Bland-Altman and Spearman correlation analyses. The trial primary outcome was re-analyzed using ECV-adjusted GFR. Mean age and BMI were 51±years and 29.5±6.0 kg/m2. The distribution of normalized GFR measures overlapped and agreement was strong; GFR/ECV overestimated GFR/BSA by 2.9 numerical units (mean 70.5 ml/min/14 L vs. 67.6 ml/min/1.73 m2) . Heart rate was associated with unstandardized GFR (r=-0.10, p=0.023) . Associations between diastolic blood pressure and all measures of GFR were observed (r ranged from 0.17-0.18; p-values<0.001) . Use of GFR/ECV did not change the main trial results. These data show that ECV calculation and GFR standardization by ECV are feasible, providing similar measure of GFR as compared to BSA adjustments, although PERL trial outcomes were not affected by the method used to normalize GFR. Future studies are needed to determine if ECV can be used as an outcome measure for renal interventions, including those that are affected by natriuresis and measures of circulating volume. Disclosure L.Lovblom: None. R.Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. S.Polsky: Advisory Panel; Medtronic, Other Relationship; diaTribe, Research Support; Dexcom, Inc., Eli Lilly and Company, Leona M. and Harry B. Helmsley Charitable Trust, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Sanofi-Aventis U.S. A.B.Karger: Consultant; Roche Diagnostics, Research Support; Kyowa Kirin Co., Ltd., Siemens. A.Galecki: None. B.A.Perkins: Advisory Panel; Abbott Diabetes, Insulet Corporation, Sanofi, Board Member; Novo Nordisk, Other Relationship; Abbott Diabetes, Insulet Corporation, Medtronic, Novo Nordisk, Research Support; BMO Bank of Montreal, Novo Nordisk. D.Cherney: Other Relationship; AbbVie Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Research & Development, LLC, Lilly, Maze, BMS, CSL-Behring, Merck, Otsuka, Novartis and Novo-Nordisk , Mitsubishi Tanabe Pharma Corporation, Sanofi, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca and Novo-Nordisk. S.O.Lanctot: Employee; Medtronic. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. P.Bjornstad: Advisory Panel; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Horizon Therapeutics plc, LG Chem, Lilly, Novo Nordisk, Consultant; AstraZeneca, Bristol-Myers Squibb Company. M.Mauer: None. L.Caramori: Advisory Panel; Bayer AG, Consultant; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk, Research Support; Bayer AG, Novartis AG. S.Rosas: Advisory Panel; AstraZeneca, Teladoc Health, Other Relationship; Bayer AG, Research Support; AstraZeneca, Bayer AG. P.Rossing: Consultant; Astellas Pharma Inc., AstraZeneca, Bayer AG, Gilead Sciences, Inc., Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Speaker's Bureau; Eli Lilly and Company. K.R.Tuttle: Advisory Panel; Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., Consultant; AstraZeneca, Eli Lilly and Company, Research Support; Bayer AG, Goldfinch Bio, Inc., Novo Nordisk, Travere. Funding Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; PERL ClinicalTrials.gov number, NCT02017171
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".