1051-P: Inside CKD: Modeling the Future Global Burden of Chronic Kidney Disease in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Introduction: Type 2 diabetes (T2D) is a leading cause of chronic kidney disease (CKD). Future trajectories of CKD prevalence, progression, and outcomes in patients with T2D are key considerations for policy planning and healthcare implementation. Using country-specific patient-level microsimulation, Inside CKD aims to model the global clinical and economic burden of CKD for 2020-2025.Methods: We used the Inside CKD microsimulation to model the clinical burden of CKD in patients with T2D. We constructed virtual populations using country-specific data, including demographics and prevalence of concomitant CKD (by stage) and T2D, from multiple published sources.Results: Preliminary data from three countries demonstrate that in 2020-2025 the number of patients with T2D and CKD is expected to rise by 6.5% in the UK, 13.2% in the US, and 16.6% in Canada (Figure). The largest increase is expected in the 35-64-years age group in the UK (21.8%) and in the ≥ 65-years age group in the US and Canada (16.7% and 21.2%, respectively).Conclusion: This microsimulation models a linear increase in the number of patients with both T2D and CKD over 5 years, including consistent increases in the number of patients with T2D and CKD stages 3b-5. Reliable epidemiologic models can help inform future country-specific healthcare policy and the design of interventions focused on early diagnosis and slowing CKD progression.View largeDownload slideView largeDownload slide DisclosureJ. Sanchez: Employee; Self; AstraZeneca, Stock/Shareholder; Self; AstraZeneca. J. Halimi: None. E. Kanda: Speaker’s Bureau; Self; AstraZeneca K. K. G. Li: None. F. Mennini: None. J. Navarro-gonzalez: None. S. T. Nolan: Employee; Self; AstraZeneca, Employee; Spouse/Partner; Biomarin, Stock/Shareholder; Self; AstraZeneca, Stock/Shareholder; Spouse/Partner; Biomarin. A. Power: Advisory Panel; Self; AstraZeneca, Bayer U. S., Napp Pharmaceuticals, Vifor Pharma Management Ltd., Consultant; Self; AstraZeneca, Speaker’s Bureau; Self; Alexion Pharmaceuticals, Inc., AstraZeneca, Napp Pharmaceuticals, Vifor Pharma Management Ltd. L. Retat: Employee; Self; HealthLumen. N. Tangri: Consultant; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., ClinPredict Inc, Eli Lilly and Company, Mesentech, Otsuka America Pharmaceutical, Inc., PulseData, Roche Pharma, Tricida, Inc., Research Support; Self; Janssen Pharmaceuticals, Inc. L. Webber: Employee; Self; HealthLumen. A. Sultan: Employee; Self; AstraZeneca, Stock/Shareholder; Self; AstraZeneca. J. Wish: Advisory Panel; Self; Akebia Therapeutics, Inc., AstraZeneca, Rockwell Medical, Vifor Pharma Management Ltd., Speaker’s Bureau; Self; Akebia Therapeutics, Inc., AstraZeneca. M. Xu: Employee; Self; HealthLumen. J. Ärnlöv: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Other Relationship; Self; Novartis AG. M. C. Batista: None. C. Cabrera: Employee; Self; AstraZeneca, Stock/Shareholder; Self; AstraZeneca. J. Card-gowers: Employee; Self; HealthLumen. S. Chadban: Advisory Panel; Self; Astellas Pharma Inc., AstraZeneca, Novartis Pharmaceuticals Corporation. G. M. Chertow: Advisory Panel; Self; Ardelyx, Baxter, Cricket Health, DURECT Corporation, Gilead Sciences, Inc., Reata Pharmaceuticals, Inc., Other Relationship; Self; Akebia Therapeutics, Inc., AstraZeneca, Vertex Pharmaceuticals Incorporated. L. Denicola: Consultant; Self; AstraZeneca, Mundipharma International, Novo Nordisk.FundingAstraZeneca
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".