# Screening Strategies for Chronic Kidney Disease in US Populations

> **NIH NIH F32** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $74,886

## Abstract

PROJECT SUMMARY/ABSTRACT
Screening for chronic kidney disease (CKD) can potentially decrease kidney disease-related morbidity and
mortality via early diagnosis and initiation of evidence-based therapies. However, whether patients should be
screened for CKD remains highly controversial, as is the optimal target population in whom screening should
be implemented. Due to insufficient evidence, statements from professional organizations have either no
recommendation for screening for CKD or are discordant on whether they recommend screening in high-risk
populations.
We will address this critical public health question and evaluate the potential benefits of screening for CKD
across different populations by developing an enhanced version of the Cardiovascular Disease (CVD) Policy
Model that includes kidney parameters and outcomes, called the CKD Policy Model. The CVD Policy Model is
a validated state-transition Markov model of CVD events and mortality in US adults. Markov modeling is an
established technique that allows us to synthesize evidence and simulate and quantify the expected benefits of
interventions on downstream outcomes. We have access to pooled longitudinal cohorts, comprising over
65,000 individuals followed for up to 30 years with sequential measurements of estimated glomerular filtration
rate (eGFR) and urine albumin-to-creatinine ratio (UACR). We will adapt and enhance the CVD Policy Model
for nephrology applications by incorporating categories of eGFR and UACR to model CKD stage transitions in
two dimensions. We will determine incident CKD probabilities based on a combination of demographics (age,
sex, race/ethnicity) and risk factors, including history of diabetes, history of hypertension, and family history of
kidney disease.
Having developed and tailored the new CKD Policy Model, we will use a Markov decision analysis to project
the impact of CKD screening. The treatment intervention triggered by CKD identification will include three
treatments: 1) angiotensin converting enzyme inhibitors and angiotensin receptor blockers (ACEi/ARBs); 2)
statins; and 3) blood pressure regimen intensification. The outcomes of the model will be the changing
incidence of CVD events, end-stage renal disease, and mortality.
The overall goal of this proposal is to establish an evidence-based framework for developing potential CKD
screening strategies. Our specific aims are: 1) to determine individualized probabilities of incident CKD and
CKD progression based on patient demographics and risk factors and develop the CKD Policy Model; and 2)
to estimate the expected impact of different selective CKD screening strategies on CVD events, incident
ESRD, and cause-specific and all-cause mortality using Markov modeling. The results of this research will
identify the optimal characteristics to guide patient selection for first CKD screening and the optimal repeat
screening frequency, and ultimately inform the design of a future pragmatic CKD screening trial.

## Key facts

- **NIH application ID:** 10305997
- **Project number:** 7F32DK122627-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Sri Lekha Tummalapalli
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $74,886
- **Award type:** 7
- **Project period:** 2019-12-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10305997

## Citation

> US National Institutes of Health, RePORTER application 10305997, Screening Strategies for Chronic Kidney Disease in US Populations (7F32DK122627-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10305997. Licensed CC0.

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