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Cost-effectiveness of screening for chronic kidney disease using a cumulative eGFR-based statistic
Reyhaneh Zafarnejad1, Qiushi Chen2, Paul M Griffin2
1Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Routine screening for chronic kidney disease (CKD) using CUSUMGFR is cost-effective. Universal annual screening starting at age 30 offers the best value, easily integrating into clinical practice for early detection and prevention.
Area of Science:
- Nephrology
- Health Economics
- Preventive Medicine
Background:
- Chronic kidney disease (CKD) screening lacks established guidelines, hindering timely interventions.
- Early detection of CKD is crucial for slowing disease progression and preventing complications.
Purpose of the Study:
- To evaluate the cost-effectiveness of CKD screening using a novel cumulative sum statistic of estimated glomerular filtration rate (CUSUMGFR).
- To compare various CUSUMGFR-based screening strategies against the status quo.
Main Methods:
- A microsimulation model was developed to simulate CKD progression, complications, and treatment adherence in a US cohort (age ≥ 30).
- Evaluated universal screening (annual/biennial, starting at 30/60) and targeted screening (hypertension, diabetes) using CUSUMGFR.
- Assessed disability-adjusted life years (DALYs) and direct health costs, calculating incremental cost-effectiveness ratios (ICERs).
Main Results:
- All CUSUMGFR-based screening policies demonstrated cost-effectiveness within a willingness-to-pay (WTP) range of $50,000-$100,000.
- Universal annual screening from age 30 emerged as the most cost-effective non-dominated policy at a WTP of ~$25,000.
- Incorporating SGLT2 inhibitors was cost-saving; CKD progression rate, adherence, and testing costs were key sensitivity parameters.
Conclusions:
- CUSUMGFR-based CKD screening policies are highly cost-effective for early detection of end-stage kidney disease risk.
- The simplicity of CUSUMGFR, requiring only a basic blood test, facilitates its integration into clinical workflows for monitoring and prevention.
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