342
Views
4
CrossRef citations to date
0
Altmetric
Original Research

A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database

ORCID Icon, ORCID Icon & ORCID Icon
Pages 475-486 | Published online: 04 Jun 2021

References

  • United States Renal Data System. USRDS 2020 annual data report. Available from: https://adr.usrds.org/2020. Accessed March 23, 2021.
  • Gaitonde DY, Cook DL, Rivera IM. Chronic kidney disease: detection and evaluation. Am Fam Physician. 2017;96(12):776–783.
  • Honeycutt AA, Segel JE, Zhuo X, Hoerger TJ, Imai K, Williams D. Medical costs of CKD in the medicare population. J Am Soc Nephrol. 2013;24(9):1478–1483. doi:10.1681/ASN.2012040392
  • Golestaneh L, Alvarez PJ, Reaven NL, et al. All-cause costs increase exponentially with increased chronic kidney disease stage. Am J Manag Care. 2017;23(10 Suppl):S163–S172.
  • Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296–1305. doi:10.1056/NEJMoa041031
  • Yang C, Wang H, Zhao X, et al. CKD in China: evolving spectrum and public health implications. Am J Kidney Dis. 2020;76(2):258–264. doi:10.1053/j.ajkd.2019.05.032
  • Lv JC, Zhang LX. Prevalence and disease burden of chronic kidney disease. Adv Exp Med Biol. 2019;1165:3–15.
  • Sharma A, Alvarez PJ, Woods SD, Fogli J, Dai D. Healthcare resource utilization and costs associated with hyperkalemia in a large managed care population. J Pharm Health Serv Res. 2021;12(1):35–41. doi:10.1093/jphsr/rmaa004
  • 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):1–150.
  • Brenner BM, Cooper ME, de Zeeuw D, et al. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med. 2001;345(12):861–869. doi:10.1056/NEJMoa011161
  • Leon SJ, Tangri N. The use of renin-angiotensin system inhibitors in patients with chronic kidney disease. Can J Cardiol. 2019;35(9):1220–1227. doi:10.1016/j.cjca.2019.06.029
  • Giatras I, Lau J, Levey AS. Effect of angiotensin converting enzyme inhibitors on the progression of nondiabetic renal disease: a meta-analysis of randomized trials. Ann Intern Med. 1997;127(5):337–345. doi:10.7326/0003-4819-127-5-199709010-00001
  • Einhorn LM, Zhan M, Hsu VD, et al. The frequency of hyperkalemia and its significance in chronic kidney disease. Arch Intern Med. 2009;169(12):1156–1162. doi:10.1001/archinternmed.2009.132
  • Kovesdy CP, Matsushita K, Sang Y, et al. Serum potassium and adverse outcomes across the range of kidney function: a CKD prognosis consortium meta-analysis. Eur Heart J. 2018;39(17):1535–1542. doi:10.1093/eurheartj/ehy100
  • Epstein M. Hyperkalemia constitutes a constraint for implementing renin-angiotensin-aldosterone inhibition: the widening gap between mandated treatment guidelines and the real-world clinical arena. Kidney Int Suppl. 2016;6(1):20–28. doi:10.1016/j.kisu.2016.01.004
  • Epstein M, Alvarez PJ, Reaven NL, et al. Evaluation of clinical outcomes and costs based on prescribed dose level of renin-angiotensin-aldosterone system inhibitors. Am J Manag Care. 2016;22(11 Suppl):S311–S324.
  • Bianchi S, Regolisti G. Pivotal clinical trials, meta-analyses and current guidelines in the treatment of hyperkalemia. Nephrol Dial Transplant. 2019;34(Supplement_3):iii51–iii61. doi:10.1093/ndt/gfz213
  • Pereira BJ. Optimization of pre-ESRD care: the key to improved dialysis outcomes. Kidney Int. 2000;57(1):351–365. doi:10.1046/j.1523-1755.2000.00840.x
  • Norouzi J, Yadollahpour A, Mirbagheri SA, Mazdeh MM, Hosseini SA. Predicting renal failure progression in chronic kidney disease using integrated intelligent fuzzy expert system. Comput Math Methods Med. 2016;2016:6080814. doi:10.1155/2016/6080814
  • Johnson ES, Thorp ML, Platt RW, Smith DH. Predicting the risk of dialysis and transplant among patients with CKD: a retrospective cohort study. Am J Kidney Dis. 2008;52(4):653–660. doi:10.1053/j.ajkd.2008.04.026
  • Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305(15):1553–1559. doi:10.1001/jama.2011.451
  • Tangri N, Inker LA, Hiebert B, et al. A dynamic predictive model for progression of CKD. Am J Kidney Dis. 2017;69(4):514–520. doi:10.1053/j.ajkd.2016.07.030
  • Schroeder EB, Yang X, Thorp ML, et al. Predicting 5-year risk of RRT in stage 3 or 4 CKD: development and external validation. Clin J Am Soc Nephrol. 2017;12(1):87–94. doi:10.2215/CJN.01290216
  • Kadatz MJ, Lee ES, Levin A. Predicting progression in CKD: perspectives and precautions. Am J Kidney Dis. 2016;67(5):779–786. doi:10.1053/j.ajkd.2015.11.007
  • Sharma A, Alvarez PJ, Woods SD, Dai D. A model to predict risk of hyperkalemia in patients with chronic kidney disease using a large administrative claims database. Clinicoecon Outcomes Res. 2020;12:657–667. doi:10.2147/CEOR.S267063
  • OPTUM Insight. Symmetry episode treatment groups: measuring health care with meaningful episodes of care (White paper). Available from: https://www.optum.com/content/dam/optum3/optum/en/resources/white-papers/Symmetry_ERG_White_Paper_July181.pdf. Accessed November 13, 2020.
  • Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139. doi:10.1097/01.mlr.0000182534.19832.83
  • Epstein M, Reaven NL, Funk SE, McGaughey KJ, Oestreicher N, Knispel J. Evaluation of the treatment gap between clinical guidelines and the utilization of renin-angiotensin-aldosterone system inhibitors. Am J Manag Care. 2015;21(11 suppl):S212–S220.
  • Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–387. doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
  • Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York: John Wiley & Sons; 2000.
  • Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36. doi:10.1148/radiology.143.1.7063747
  • DeLong ER, DeLong DM, Clark-Pearson DL. Comparing the area under two or more correlated receiving operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845. doi:10.2307/2531595
  • Everett B, Castel LD, McGinnis M, et al. Economic and clinical outcomes resulting from stage 4 chronic kidney disease case management quality improvement initiative. Prof Case Manag. 2017;22(6):291–298. doi:10.1097/NCM.0000000000000253
  • Provenzano M, Rotundo S, Chiodini P, et al. Contribution of predictive and prognostic biomarkers to clinical research on chronic kidney disease. Int J Mol Sci. 2020;21(16):5846. doi:10.3390/ijms21165846
  • Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22. doi:10.1016/j.jclinepi.2019.02.004
  • Weir MR, Bakris GL, Bushinsky DA, et al. Patiromer in patients with kidney disease and hyperkalemia receiving RAAS inhibitors. N Engl J Med. 2015;372(3):211–221. doi:10.1056/NEJMoa1410853
  • Bakris GL, Pitt B, Weir MR, et al. Effect of patiromer on serum potassium level in patients with hyperkalemia and diabetic kidney disease: the AMETHYST-DN randomized clinical trial. JAMA. 2015;314(2):151–161. doi:10.1001/jama.2015.7446
  • Weir MR, Bushinsky DA, Benton WW, et al. Effect of patiromer on hyperkalemia recurrence in older chronic kidney disease patients taking RAAS inhibitors. Am J Med. 2018;131(5):555–564.e3. doi:10.1016/j.amjmed.2017.11.011
  • Spinowitz BS, Fishbane S, Pergola PE, et al. Sodium zirconium cyclosilicate among individuals with hyperkalemia: a 12-month Phase 3 study. Clin J Am Soc Nephrol. 2019;14(6):798–809. doi:10.2215/CJN.12651018
  • Kidney Disease Outcomes Quality Initiative (K/DOQI). K/DOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease. Am J Kidney Dis. 2004;43(5 suppl 1):S1–S290.
  • Kidney Disease: Improving Global Outcomes (KDIGO) Blood Pressure Work Group. KDIGO clinical practice guideline for the management of blood pressure in chronic kidney disease. Kidney Int Suppl. 2012;2(5):337–414.