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Multicenter Study
. 2018 Jun;93(6):1442-1451.
doi: 10.1016/j.kint.2018.01.009. Epub 2018 Mar 29.

Predicting Timing of Clinical Outcomes in Patients With Chronic Kidney Disease and Severely Decreased Glomerular Filtration Rate

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Free PMC article
Multicenter Study

Predicting Timing of Clinical Outcomes in Patients With Chronic Kidney Disease and Severely Decreased Glomerular Filtration Rate

Morgan E Grams et al. Kidney Int. .
Free PMC article

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Abstract

Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at high risk for kidney failure, cardiovascular disease (CVD) and death. Accurate estimates of risk and timing of these clinical outcomes could guide patient counseling and therapy. Therefore, we developed models using data of 264,296 individuals in 30 countries participating in the international Chronic Kidney Disease Prognosis Consortium with estimated GFR (eGFR)s under 30 ml/min/1.73m2. Median participant eGFR and urine albumin-to-creatinine ratio were 24 ml/min/1.73m2 and 168 mg/g, respectively. Using competing-risk regression, random-effect meta-analysis, and Markov processes with Monte Carlo simulations, we developed two- and four-year models of the probability and timing of kidney failure requiring kidney replacement therapy (KRT), a non-fatal CVD event, and death according to age, sex, race, eGFR, albumin-to-creatinine ratio, systolic blood pressure, smoking status, diabetes mellitus, and history of CVD. Hypothetically applied to a 60-year-old white male with a history of CVD, a systolic blood pressure of 140 mmHg, an eGFR of 25 ml/min/1.73m2 and a urine albumin-to-creatinine ratio of 1000 mg/g, the four-year model predicted a 17% chance of survival after KRT, a 17% chance of survival after a CVD event, a 4% chance of survival after both, and a 28% chance of death (9% as a first event, and 19% after another CVD event or KRT). Risk predictions for KRT showed good overall agreement with the published kidney failure risk equation, and both models were well calibrated with observed risk. Thus, commonly-measured clinical characteristics can predict the timing and occurrence of clinical outcomes in patients with severely decreased GFR.

Keywords: albuminuria; cardiovascular disease; chronic kidney disease.

Conflict of interest statement

Conflict of Interest Disclosures: JJC received consulting fees from Astellas, lecture fees from Abbott, and grant support from AstraZeneca and ViforPharma donated to Karolinska Institutet. HJLH received consulting fees from Abbvie, AstraZeneca, Boehringer Ingelheim, Fresenius, Janssen, and Merck; lecture fees from AstraZeneca and Boehringer Ingelheim; and grant support from AstraZeneca and Boehringer Ingelheim. KH received lecture fees and is under negotiation for grant funding from Sanofi-Genzyme and has royalties from Partner’s Healthcare patent #5,356,775. SDN received grant support from the National Institutes of Health. BS received consulting fees from Merck Sharp & Dohme and grant support from Amgen, Baxter, Lilly, Fresenius, GlaxoSmithKline, Merck Sharp & Dohme, and Otsuka. AY-MW received lecture fees from Sanofi and travel support from Sanofi and Fresenius Medical Care. AK received grant support from AstraZeneca. ASL received grant support from the National Kidney Foundation, National Institutes of Health, AstraZeneca, and Pharmalink, and holds a provisional patent along with JC (JC, Lesley Inker, and ASL) filed August 15, 2014 –“Precise estimation of glomerular filtration rate from multiple biomarkers” PCT/ US2015/044567. The technology is not licensed in whole or in part to any company. Tufts Medical Center, John Hopkins University, and Metabolon Inc. have a collaboration agreement to develop a product to estimate glomerular filtration rate from a panel of markers. MW received consulting fees from Amgen. JC received grant support from the National Institutes of Health and National Kidney Foundation. All the other authors declared no competing interests.

Figures

Figure 1
Figure 1
Diagram of states and transitions included in the 5-state Markov model States are shown in gray ovals and include CKD Stage G4+, CVD, KRT without CVD, KRT & CVD, and death. The state transition probabilities are denoted by P1 through P8, where P is a function of age, x, and time, where x is a vector of covariates. This vector includes baseline sex, race, history of cardiovascular disease, current smoking, systolic blood pressure, diabetes status, albuminuria (P1-P4 and P6), eGFR (baseline for P1-P3, time-updated for P4 and P6), and transplantation status (for P5, P7, and P8). The probabilities of remaining in a state are denoted by P0, and P9-P11.
Figure 2
Figure 2
Adjusted* cumulative incidence of (A) kidney failure requiring kidney replacement therapy, (B) cardiovascular event, and (C) death as first event from Markov model. Color coding of the lines is described in panel D. The black bold line indicates the equal weighted mean. *Adjusted to age 60 years, half male, non-black, half history of CVD, half smoker, systolic blood pressure 140 mmHg, half diabetes, eGFR 25 ml/min/1.73 m2and urine ACR 100 mg/g. Grey shaded cohorts in panel D do not have cardiovascular events and are not included in panels A-C.
Figure 2
Figure 2
Adjusted* cumulative incidence of (A) kidney failure requiring kidney replacement therapy, (B) cardiovascular event, and (C) death as first event from Markov model. Color coding of the lines is described in panel D. The black bold line indicates the equal weighted mean. *Adjusted to age 60 years, half male, non-black, half history of CVD, half smoker, systolic blood pressure 140 mmHg, half diabetes, eGFR 25 ml/min/1.73 m2and urine ACR 100 mg/g. Grey shaded cohorts in panel D do not have cardiovascular events and are not included in panels A-C.
Figure 2
Figure 2
Adjusted* cumulative incidence of (A) kidney failure requiring kidney replacement therapy, (B) cardiovascular event, and (C) death as first event from Markov model. Color coding of the lines is described in panel D. The black bold line indicates the equal weighted mean. *Adjusted to age 60 years, half male, non-black, half history of CVD, half smoker, systolic blood pressure 140 mmHg, half diabetes, eGFR 25 ml/min/1.73 m2and urine ACR 100 mg/g. Grey shaded cohorts in panel D do not have cardiovascular events and are not included in panels A-C.
Figure 2
Figure 2
Adjusted* cumulative incidence of (A) kidney failure requiring kidney replacement therapy, (B) cardiovascular event, and (C) death as first event from Markov model. Color coding of the lines is described in panel D. The black bold line indicates the equal weighted mean. *Adjusted to age 60 years, half male, non-black, half history of CVD, half smoker, systolic blood pressure 140 mmHg, half diabetes, eGFR 25 ml/min/1.73 m2and urine ACR 100 mg/g. Grey shaded cohorts in panel D do not have cardiovascular events and are not included in panels A-C.
Figure 3
Figure 3
The probability and timing of clinical events at 2 and 4 years with increasing level of albuminuria. Top panel shows 2 years and urine ACR 30 mg/g, bottom panel shows 4 years and urine ACR 1000 mg/g. In these models, the scenario was set at age 60 years, male, white, with a history of cardiovascular disease, not a current smoker, systolic blood pressure of 140 mmHg, no diabetes, and an eGFR of 25 ml/min/1.73m2.
Figure 4
Figure 4
Markov model predicted 2-year survival without kidney failure treated with kidney replacement therapy or cardiovascular events for a range of scenarios (varying systolic blood pressure, race, diabetes, history of cardiovascular disease, and smoking status) for a 60-year old man, comparing estimates using overall mean with cohort type-specific means for the baseline hazards and sub-hazards.

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