More than 200,000 people in the US suffer from end-stage renal disease (ESRD). The annual mortality for patients treated by dialysis is 20-25 percent; total government and private expenditures for ESRD are 12 billion dollars per year. Despite completion of a number of randomized controlled trials, comprehensive treatment guidelines for diverse causes of renal disease have not been developed. We propose that the management and outcomes of chronic renal disease might be improved by clarification of the effect of interventions in randomized controlled trials by using pooled analysis of primary data, also known as individual patient data meta-analysis. We selected the use of angiotensin-converting enzyme (ACE) inhibitors in non-diabetic renal disease as the most appropriate subject for an individual patient data meta-analysis in chronic renal disease. We have formed the ACE Inhibition in Progressive Renal Disease Study Group, including investigators of eleven randomized trial, with data on 1760 patients. We conducted a meta-analysis of secondary data which showed a reduced risk of ESRD, without an adverse effect on mortality, in the ACE- inhibitor group. Using meta-regression analyses, we were not able to explain the variation among clinical trials in efficacy and safety of ACE inhibitors, nor to determine whether the benefits and risks of ACE inhibition were related to patient or treatment characteristics. The methodological objectives of the proposed project are to apply techniques for using longitudinal outcomes with adjustment for baseline (fixed) and follow-up (time-dependent) covariates in regression analysis of pooled individual patient data, and to develop protocols to streamline and simplify combining and analyzing individual patient data from randomized controlled trials. Clinical objectives are to explain the apparently diverse results of these studies, and to develop recommendations for ACE-inhibitor treatment in this patient population.
The specific aims are to assemble, prepare and maintain the individual patient database, and to perform pooled analyses using survival data and longitudinal outcomes. We believe the results will lead to improved outcomes for patients with chronic renal disease and more widespread application of individual patient data meta-analysis to translate findings from research into practice.
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