MO486<i>INSIDE CKD</i>: MODELLING THE IMPACT OF IMPROVED SCREENING FOR CHRONIC KIDNEY DISEASE IN THE AMERICAS AND ASIA-PACIFIC REGION
Bibliographic record
Abstract
Abstract Background and Aims With an estimated global prevalence of 10%, chronic kidney disease (CKD) and its associated complications place a substantial strain on healthcare systems worldwide, which is compounded by the burden of undiagnosed CKD. Early CKD diagnosis followed by guideline-recommended interventions can improve patient outcomes and reduce associated healthcare-related costs, particularly by delaying or preventing the development of complications and progression to kidney failure. Urinary albumin-to-creatinine ratio (UACR) can be used to screen for CKD, but adherence to screening recommendations is suboptimal in routine care. Inside CKD aims to model the global clinical and economic burden of CKD using country-specific, patient-level microsimulation models. We used the Inside CKD microsimulation to model the potential clinical and economic impacts of routine measurement of UACR followed by appropriate intervention in patients aged 45 years and over in the US and Canada. Method The Inside CKD microsimulation model was used to model the clinical and economic impacts associated with measurement of UACR with subsequent appropriate intervention during routine primary care visits versus current practice in individuals aged 45 years and over. The model covers the period 2020–2025. In preliminary analyses, virtual populations representing the general populations of the US and Canada were constructed using published country-specific data, including demographics, prevalence of CKD and comorbidities (type 2 diabetes, uncontrolled hypertension and heart failure), incidence of complications (heart failure, myocardial infarction, stroke and acute kidney injury) and costs associated with CKD. The model also included parameters relating to the proportion of patients who visit a primary care physician at least once a year, the proportion of patients who agreed to UACR measurements, and the diagnostic sensitivity and specificity of UACR measurements. The modelling is being expanded to additional countries in the Americas and the Asia-Pacific region. Results Preliminary results from the US and Canada show that over the 2020–2025 period routine measurement of UACR during primary care visits followed by appropriate intervention could prevent progression to CKD stages 3b–5 in approximately 1.3M patients in the US and 160 000 in Canada, compared with current clinical practice, with linear increases in the cumulative numbers of prevented cases (Figure). Associated savings in healthcare costs in 2025 are projected to be approximately US$16B in the US and C$2.5B in Canada, corresponding to a reduction in cost for that year of 4.4% and 7.4%, respectively, compared with current clinical practice. Conclusion Preliminary results from the Inside CKD microsimulation model in the US and Canada show that routine measurement of UACR with subsequent intervention in primary care would prevent progression to CKD stages 3b–5 in a substantial number of patients compared with current screening practices, and could therefore decrease associated healthcare costs considerably. This analysis is being extended to further countries in the Americas and the Asia-Pacific region.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".