Deidra C Crews1, Julia J Scialla2, L Ebony Boulware3, Sankar D Navaneethan4, Joseph V Nally4, Xiaobo Liu5, Susana Arrigain5, Jesse D Schold5, Patti L Ephraim6, Stacey E Jolly7, Stephen M Sozio8, Wieneke M Michels9, Dana C Miskulin10, Navdeep Tangri11, Tariq Shafi8, Albert W Wu12, Karen Bandeen-Roche13. 1. Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD. Electronic address: dcrews1@jhmi.edu. 2. Division of Nephrology and Hypertension, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL. 3. Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD; Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC. 4. Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH; Cleveland Clinic, Lerner College of Medicine of Case Western Reserve University, Cleveland, OH. 5. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH. 6. Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 7. Department of General Internal Medicine, Medicine Institute, Cleveland Clinic, Cleveland, OH. 8. Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD. 9. Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD; Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; Division of Nephrology, Department of Medicine, Academic Medical Center, Amsterdam, the Netherlands. 10. Division of Nephrology, Tufts University School of Medicine, Boston, MA. 11. Division of Nephrology, Department of Medicine, Seven Oaks General Hospital, University of Manitoba, Winnipeg, Manitoba, Canada. 12. Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Health, Policy, and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD. 13. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Abstract
BACKGROUND: Previous observational studies examining outcomes associated with the timing of dialysis therapy initiation in the United States have often been limited by lead time and survivor bias. STUDY DESIGN: Retrospective cohort study comparing the effectiveness of early versus later (conventional) dialysis therapy initiation in advanced chronic kidney disease (CKD). The analysis used inverse probability weighting to account for an individual's contribution to different exposure groups over time in a pooled logistic regression model. Patients contributed risk to both exposure categories (early and later initiation) until there was a clear treatment strategy (ie, dialysis therapy was initiated early or estimated glomerular filtration rate [eGFR] decreased to <10mL/min/1.73m(2)). SETTING & PARTICIPANTS: Patients with CKD who had at least one face-to-face outpatient encounter with a Cleveland Clinic health care provider as of January 1, 2005, and at least 3 eGFRs in the range of 20-30mL/min/1.73m(2) measured at least 180 days apart. PREDICTORS: Timing of dialysis therapy initiation as determined using model-based interpolation of eGFR trajectories over time. Timing was defined as early (interpolated eGFR at dialysis therapy initiation≥10mL/min/1.73m(2)) or later (eGFR < 10mL/min/1.73m(2)) and was time-varying. OUTCOMES: Death from any cause occurring from the time that eGFR was equal to 20mL/min/1.73m(2) through September 15, 2009. RESULTS: The study population consisted of 652 patients meeting inclusion criteria. Most (71.3%) of the study population did not initiate dialysis therapy during follow-up. Patients who did not initiate dialysis therapy (n=465) were older, more likely to be white, and had more favorable laboratory profiles than those who started dialysis therapy. Overall, 146 initiated dialysis early and 80 had eGFRs decrease to <10mL/min/1.73m(2). Many participants (n=426) were censored prior to attaining a clear treatment strategy and were considered undeclared. There was no statistically significant survival difference for the early compared with later initiation strategy (OR, 0.85; 95% CI, 0.65-1.11). LIMITATIONS: Interpolated eGFR, moderate sample size, and likely unmeasured confounders. CONCLUSIONS: In patients with advanced CKD, timing of dialysis therapy initiation was not associated with mortality when accounting for lead time bias and survivor bias.
RCT Entities:
BACKGROUND: Previous observational studies examining outcomes associated with the timing of dialysis therapy initiation in the United States have often been limited by lead time and survivor bias. STUDY DESIGN: Retrospective cohort study comparing the effectiveness of early versus later (conventional) dialysis therapy initiation in advanced chronic kidney disease (CKD). The analysis used inverse probability weighting to account for an individual's contribution to different exposure groups over time in a pooled logistic regression model. Patients contributed risk to both exposure categories (early and later initiation) until there was a clear treatment strategy (ie, dialysis therapy was initiated early or estimated glomerular filtration rate [eGFR] decreased to <10mL/min/1.73m(2)). SETTING & PARTICIPANTS: Patients with CKD who had at least one face-to-face outpatient encounter with a Cleveland Clinic health care provider as of January 1, 2005, and at least 3 eGFRs in the range of 20-30mL/min/1.73m(2) measured at least 180 days apart. PREDICTORS: Timing of dialysis therapy initiation as determined using model-based interpolation of eGFR trajectories over time. Timing was defined as early (interpolated eGFR at dialysis therapy initiation≥10mL/min/1.73m(2)) or later (eGFR < 10mL/min/1.73m(2)) and was time-varying. OUTCOMES: Death from any cause occurring from the time that eGFR was equal to 20mL/min/1.73m(2) through September 15, 2009. RESULTS: The study population consisted of 652 patients meeting inclusion criteria. Most (71.3%) of the study population did not initiate dialysis therapy during follow-up. Patients who did not initiate dialysis therapy (n=465) were older, more likely to be white, and had more favorable laboratory profiles than those who started dialysis therapy. Overall, 146 initiated dialysis early and 80 had eGFRs decrease to <10mL/min/1.73m(2). Many participants (n=426) were censored prior to attaining a clear treatment strategy and were considered undeclared. There was no statistically significant survival difference for the early compared with later initiation strategy (OR, 0.85; 95% CI, 0.65-1.11). LIMITATIONS: Interpolated eGFR, moderate sample size, and likely unmeasured confounders. CONCLUSIONS: In patients with advanced CKD, timing of dialysis therapy initiation was not associated with mortality when accounting for lead time bias and survivor bias.
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