Kidney Week

Abstract: TH-PO004

Health System Economic Burden in Patients with CKD: Insights from the Klinrisk Model

Session Information

• Augmented Intelligence for Prediction and Image Analysis
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Augmented Intelligence, Digital Health, and Data Science

• 300 Augmented Intelligence, Digital Health, and Data Science

Authors

• Tangri, Navdeep, University of Manitoba, Winnipeg, Manitoba, Canada

Navdeep Tangri, MD, PhD

• Singh, Rakesh, Bayer U.S. LLC, Whippany, New Jersey, United States

Rakesh Singh, MS, PhD

• Betts, Keith A., Analysis Group Inc, Los Angeles, California, United States

Keith A. Betts, PhD

• Du, Yuxian, Bayer U.S. LLC, Whippany, New Jersey, United States

Yuxian Du, PhD

• Gao, Sophie, Analysis Group Inc, Los Angeles, California, United States

Sophie Gao, MS

• Katta, Arvind, Bayer U.S. LLC, Whippany, New Jersey, United States

Arvind Katta, PharmD, MS

• Farag, Youssef MK, Bayer U.S. LLC, Whippany, New Jersey, United States

Youssef MK Farag, MD, PhD, MPH, FASN

• Fatoba, Samuel T., Bayer U.S. LLC, Whippany, New Jersey, United States

Samuel T. Fatoba, PhD, MPH

• Liu, Hongjiao, Analysis Group Inc, Los Angeles, California, United States

Hongjiao Liu, PhD

• Chen, Jingyi, Analysis Group Inc, Los Angeles, California, United States

Jingyi Chen, MS, MSc

• Ferguson, Thomas W., University of Manitoba, Winnipeg, Manitoba, Canada

Thomas W. Ferguson, MS, MSc

• Whitlock, Reid, University of Manitoba, Winnipeg, Manitoba, Canada

Reid Whitlock, MSc

• Leon Mantilla, Silvia Juliana, University of Manitoba, Winnipeg, Manitoba, Canada

Silvia Juliana Leon Mantilla, MD, MS, MSc

• Singh, Ajay K., Harvard Medical School, Boston, Massachusetts, United States

Ajay K. Singh, MD

Background

CKD is associated with substantial economic burden. Early identification of at-risk CKD can facilitate appropriate and timely intervention and reduce CKD-related medical costs. This study assessed the association between CKD progression risk and healthcare burden.

Methods

A retrospective observational study was conducted in 1,050,552 adult patients with CKD from Optum’s electronic health records database (1/1/2007 - 9/30/2022). A previously published and validated machine learning model, Klinrisk (Tangri N, et al Clin Kidney J. 2024 Mar 6;17(4)) was applied to classify patients into 3 groups based on their risk of CKD progression (low, medium, and high). All-cause inpatient (IP) admissions, emergency room (ER) visits, outpatient (OP) visits were evaluated in each risk group during the 1 year after CKD. Average medical costs (2023 USD) were calculated as the average length of stay for IP admissions, average number of ER and OP visits multiplied by the corresponding unit costs as estimated from a prior study.

Results

Patients with higher predicted CKD progression risk had higher healthcare utilization (HRU). High-risk patients averaged 1.49 IP admissions, 0.83 ER visits, and 35.75 OP visits per year compared with 0.31 IP admissions, 0.63 ER visits, and 24.45 OP visits among low-risk patients. The total annual medical costs for low-, medium-, and high-risk patients were $16,018, $22,090, and $50,218, respectively (Figure). IP costs were the major cost driver for high-risk patients.

Conclusion

Patients at high risk of CKD progression as predicted by Klinrisk were associated with high HRU and medical costs and may benefit from early intervention with guideline-directed therapies, to reduce economic burden.

Annualized all-cause medical costs stratified by CKD progression risk