#1881 Evaluating the clinical and economic impact of SGLT-2 inhibitors for CKD management in the UK: insights from the IMPACT CKD microsimulation model
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease (CKD) is a significant public health concern, accounting for 3.2% of annual United Kingdom (UK) health care spending, with increasing burdens expected due to rising comorbidities and an aging population. Sodium-glucose co-transporter-2 inhibitors (SGLT-2i) are an effective first-line therapeutic option that can slow disease progression to later more costly stages including kidney replacement therapy (KRT); however, uptake remains low with an estimated 17% of indicated UK patients receiving treatment. Notably, beyond the label indication, a urine albumin creatinine ratio (UACR) test is an additional barrier for access to SGLT-2is in the UK. The Kidney Disease: Improving Global Outcomes (KDIGO) 2024 Clinical Practice Guideline recommends use of SGLT-2is in alignment with the label population for patients with CKD and type 2 diabetes (T2D), and, for those without T2D, criteria include comorbidity history and UACR status. With a lack of alignment in recommendations between KDIGO, UK Kidney Association, and National Institute for Health and Care Excellence, this study aimed to evaluate the clinical and economic implications of both broad and restrictive SGLT-2i eligibility criteria based on UACR for patients with CKD in the UK to provide insights into the potential long-term benefits of broader eligibility for these therapies. Method The IMPACT CKD microsimulation model was used to simulate a population of patients with diagnosed CKD for 25 years across four scenarios. In order of increasingly restrictive SGLT-2i eligibility criteria for patients with CKD, scenarios considered: 1) use by all patients with CKD, 2) use by the KDIGO 2024 Guideline population, 3) full access for T2D, restricted access for non-T2D with UACR <200 mg/g (i.e., non-T2D UACR restriction), and 4) restricted access to those with CKD and UACR <200 mg/g (i.e., all-CKD UACR restriction). Other guideline-directed medical therapies for CKD (except SGLT-2i) were not increased beyond their current background use. Use of SGLT-2i was modelled to have an improvement in eGFR decline, reduction in cardiovascular and acute kidney events, and a one-time improvement in UACR. The model projected CKD and KRT prevalence, incidence of all-cause mortality, and costs associated with CKD, KRT, and total costs including SGLT-2i treatment with a per patient annual cost of £477.30 over the simulated 25 years. Results Results compare the cumulative 25-year burden of CKD in each of the restricted SGLT-2i eligibility criteria scenarios to the full CKD population scenario (Table 1). Increasingly restrictive SGLT-2i criteria were projected to decrease the cumulative number of CKD patients (non-KRT) from −0.4% in the KDIGO scenario, to −1.0% with non-T2D UACR restriction, and −1.3% in the most restrictive scenario (i.e., all-CKD UACR restriction) due to projected increases in cumulative all-cause mortality. The cumulative number of patients on dialysis were projected to increase by +8.8% in the KDIGO scenario, +24.4% with the non-T2D UACR restriction, and +31.4% with the all-CKD UACR restriction. Restrictive SGLT-2i criteria were projected to increase the CKD-related and KRT costs, respectively, by +1.9% and +6.0% in the KDIGO scenario, +1.6% and +16.6% with the non-T2D UACR restriction, and +2.8% and +21.2% with the all-CKD UACR restriction. Total costs (including SGLT-2i treatment costs) were projected to decrease with more restricted use. Similar cumulative net workdays were projected across all scenarios. Conclusion The results of the present analysis projected benefits for patients and healthcare systems with broad SGLT-2i eligibility criteria, aligning with National Health Service priorities to reduce the dialysis burden, manage CKD progression efficiently, and address inequities in access to care for non-diabetic CKD populations. Decisions to implement SGLT-2i criteria should consider the multidimensional impact of treating fewer patients, including increases in clinical and economic burden.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".