Kidney Function Trajectories and Health Care Costs: Identifying High-Need, High-Cost Patients

Chi D Chu^{1

2

3

4}, Delphine S Tuot^{1

2

4}, Sri Lekha Tummalapalli^{3

5

6}

Affiliations

¹ Department of Medicine, University of California, San Francisco, California.
² Department of Medicine, Priscilla Chan and Mark Zuckerberg San Francisco General Hospital, San Francisco, California.
³ Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, California and San Francisco VA Health Care System, San Francisco, California.
⁴ Division of Nephrology, Zuckerberg San Francisco General Hospital, San Francisco, California.
⁵ Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, and Division of Nephrology & Hypertension, Department of Medicine, Weill Cornell Medicine, New York, New York.
⁶ The Rogosin Institute, New York, New York.

Editorial

Chi D Chu et al. Kidney Med. 2023.

. 2023 May 11;5(6):100664.

doi: 10.1016/j.xkme.2023.100664. eCollection 2023 Jun.

Chi D Chu^{1

2

3

4}, Delphine S Tuot^{1

2

4}, Sri Lekha Tummalapalli^{3

5

6}

¹ Department of Medicine, University of California, San Francisco, California.
² Department of Medicine, Priscilla Chan and Mark Zuckerberg San Francisco General Hospital, San Francisco, California.
³ Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, California and San Francisco VA Health Care System, San Francisco, California.
⁴ Division of Nephrology, Zuckerberg San Francisco General Hospital, San Francisco, California.
⁵ Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, and Division of Nephrology & Hypertension, Department of Medicine, Weill Cornell Medicine, New York, New York.
⁶ The Rogosin Institute, New York, New York.

No abstract available

References

1. Zheng Z., Waikar S.S., Schmidt I.M., et al. Subtyping CKD patients by consensus clustering: the chronic renal insufficiency cohort (CRIC) study. J Am Soc Nephrol. 2021;32(3):639–653. - PMC - PubMed
1. United States Renal Data System . National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2022. 2022 USRDS Annual Data Report: Epidemiology of kidney disease in the United States.https://adr.usrds.org/2022
1. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1–150.
1. Tangri N., Stevens L.A., Griffith J., et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305(15):1553–1559. - PubMed
1. Grams M.E., Sang Y., Ballew S.H., et al. Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate. Kidney Int. 2018;93(6):1442–1451. - PMC - PubMed