Morgan E Grams1, Wei Yang2, Casey M Rebholz3, Xue Wang2, Anna C Porter4, Lesley A Inker5, Edward Horwitz6, James H Sondheimer7, L Lee Hamm8, Jiang He9, Matthew R Weir10, Bernard G Jaar11, Tariq Shafi11, Lawrence J Appel12, Chi-Yuan Hsu13. 1. Division of Nephrology, Johns Hopkins University, Baltimore, MD; Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. Electronic address: mgrams2@jhmi.edu. 2. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 3. Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 4. Section of Nephrology, Department of Medicine, University of Illinois at Chicago, Chicago, IL. 5. Division of Nephrology, Tufts Medical Center, Boston, MA. 6. Division of Nephrology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH. 7. Division of Nephrology and Hypertension, Department of Internal Medicine, Wayne State University School of Medicine, Detroit, MI. 8. Department of Medicine, Tulane University School of Medicine, New Orleans, LA. 9. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. 10. Division of Nephrology, University of Maryland School of Medicine, Baltimore, MD. 11. Division of Nephrology, Johns Hopkins University, Baltimore, MD; Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD. 12. Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 13. Division of Nephrology, University of California, San Francisco, San Francisco, CA; Division of Research, Kaiser Permanente Northern California, Oakland, CA.
Abstract
BACKGROUND: People with advanced chronic kidney disease are at risk for the development of end-stage renal disease (ESRD), but also many other adverse outcomes, including cardiovascular disease (CVD) events and death. Determination of risk factors that explain the variability in prognosis and timing of these adverse outcomes can aid patient counseling and medical decision making. STUDY DESIGN: Prospective research cohort. SETTING & PARTICIPANTS: 1,798 participants with estimated glomerular filtration rates (eGFRs)<30mL/min/1.73m2 in the CRIC Study were followed up for a median of 5.5 years. PREDICTORS: Age, race, sex, eGFR, proteinuria, diabetes mellitus, body mass index, ejection fraction, systolic blood pressure, history of CVD, and smoking history. OUTCOMES: ESRD, CVD (congestive heart failure, stroke, myocardial infarction, and peripheral artery disease), and death. RESULTS: Baseline age of the cohort was 60 years, 46% were women, and 46% were African American. Although 52.3% of participants progressed to ESRD during follow-up, the path by which this occurred was variable. For example, predicted 1-year probabilities for a hypothetical 60-year-old white woman with eGFR of 30mL/min/1.73m2, urine protein excretion of 1.8g/d, and no diabetes or CVD (risk characteristics similar to the average participant) were 3.3%, 4.1%, and 0.3%, for first developing CVD, ESRD, and death, respectively. For a 40-year-old African American man with similar characteristics but higher systolic blood pressure, the corresponding 1-year probabilities were 2.4%, 13.2%, and 0.1%. For all participants, the development of ESRD or CVD increased the risk for subsequent mortality, with no differences by patient race or body mass index. LIMITATIONS: The CRIC population was specifically recruited for kidney disease, and the vast majority had seen a nephrologist. CONCLUSIONS: The prognosis and timing of adverse outcomes in chronic kidney disease vary by patient characteristics. These results may help guide the development of personalized approaches for managing patients with advanced CKD.
BACKGROUND:People with advanced chronic kidney disease are at risk for the development of end-stage renal disease (ESRD), but also many other adverse outcomes, including cardiovascular disease (CVD) events and death. Determination of risk factors that explain the variability in prognosis and timing of these adverse outcomes can aid patient counseling and medical decision making. STUDY DESIGN: Prospective research cohort. SETTING & PARTICIPANTS: 1,798 participants with estimated glomerular filtration rates (eGFRs)<30mL/min/1.73m2 in the CRIC Study were followed up for a median of 5.5 years. PREDICTORS: Age, race, sex, eGFR, proteinuria, diabetes mellitus, body mass index, ejection fraction, systolic blood pressure, history of CVD, and smoking history. OUTCOMES: ESRD, CVD (congestive heart failure, stroke, myocardial infarction, and peripheral artery disease), and death. RESULTS: Baseline age of the cohort was 60 years, 46% were women, and 46% were African American. Although 52.3% of participants progressed to ESRD during follow-up, the path by which this occurred was variable. For example, predicted 1-year probabilities for a hypothetical 60-year-old white woman with eGFR of 30mL/min/1.73m2, urine protein excretion of 1.8g/d, and no diabetes or CVD (risk characteristics similar to the average participant) were 3.3%, 4.1%, and 0.3%, for first developing CVD, ESRD, and death, respectively. For a 40-year-old African American man with similar characteristics but higher systolic blood pressure, the corresponding 1-year probabilities were 2.4%, 13.2%, and 0.1%. For all participants, the development of ESRD or CVD increased the risk for subsequent mortality, with no differences by patient race or body mass index. LIMITATIONS: The CRIC population was specifically recruited for kidney disease, and the vast majority had seen a nephrologist. CONCLUSIONS: The prognosis and timing of adverse outcomes in chronic kidney disease vary by patient characteristics. These results may help guide the development of personalized approaches for managing patients with advanced CKD.
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