Dialysis Outcomes and Practice Patterns Study (DOPPS): Its Strengths, Limitations, and Role in Informing Practices and Policies

Arbor Research Collaborative for Health, Ann Arbor, Michigan.
Clinical Journal of the American Society of Nephrology (Impact Factor: 5.25). 10/2012; 7(11). DOI: 10.2215/CJN.04940512
Source: PubMed
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    ABSTRACT: Randomized Controlled Trials almost invariably utilize the hazard ratio calculated with a Cox proportional hazard model as a treatment efficacy measure. Despite the widespread adoption of HRs, these provide a limited understanding of the treatment effect and may even provide a biased estimate when the assumption of proportional hazards in the Cox model is not verified by the trial data. Additional treatment effect measures on the survival probability or the time scale may be used to supplement HRs but a framework for the simultaneous generation of these measures is lacking. By splitting follow-up time at the nodes of a Gauss Lobatto numerical quadrature rule, techniques for Poisson Generalized Additive Models (PGAM) can be adopted for flexible hazard modeling. Straightforward simulation post-estimation transforms PGAM estimates for the log hazard into estimates of the survival function. These in turn were used to calculate relative and absolute risks or even differences in restricted mean survival time between treatment arms. We illustrate our approach with extensive simulations and in two trials: IPASS (in which the proportionality of hazards was violated) and HEMO a long duration study conducted under evolving standards of care on a heterogeneous patient population. PGAM can generate estimates of the survival function and the hazard ratio that are essentially identical to those obtained by Kaplan Meier curve analysis and the Cox model. PGAMs can simultaneously provide multiple measures of treatment efficacy after a single data pass. Furthermore, supported unadjusted (overall treatment effect) but also subgroup and adjusted analyses, while incorporating multiple time scales and accounting for non-proportional hazards in survival data. By augmenting the HR conventionally reported, PGAMs have the potential to support the inferential goals of multiple stakeholders involved in the evaluation and appraisal of clinical trial results under proportional and non-proportional hazards.
    PLoS ONE 04/2015; 10(4):e0123784. DOI:10.1371/journal.pone.0123784 · 3.53 Impact Factor
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    ABSTRACT: Mortality and morbidity are significantly higher among patients with dialysis catheters, which has been associated with chronic activation of the immune system. We hypothesized that bacteria colonizing the catheter lumen trigger an inflammatory response. We aimed to evaluate the inflammatory profile of hemodialysis patients before and after locking catheters with an antimicrobial lock solution. High-sensitivity C-reactive protein (hsCRP), interleukin (IL)-6, IL-10, and tumor necrosis factor-alpha (TNFa) were measured in serum, and levels of mRNA gene expression of IL-6, IL-10 and TNFa were analyzed in peripheral blood mononuclear cells (PBMC). Samples were obtained at baseline and again after 3 months' use of Taurolidine-citrate-heparin lock solution (TCHLS) in 31 hemodialysis patients. The rate of catheter-related bloodstream infections (CRBSI) was 1.08 per 1000 catheter-days in the heparin period and 0.04 in the TCHLS period (P = 0.023). Compared with baseline, serum levels of hs-CRP and IL-6 showed a median percent reduction of 18.1% and 25.2%, respectively (P < 0.01), without significant changes in TNFa or IL-10. Regarding cytokine gene expression in PBMC, the median mRNA expression level of TNFa and IL-6 decreased by 20% (P < 0.05) and 19.7% (P = 0.01), respectively, without changes in IL-10 expression levels. The use of TCHLS to maintain the catheter lumen sterile significantly reduces the incidence of CRBSI and improves the inflammatory profile in hemodialysis patients with tunneled catheters. Further studies are needed to evaluate the potential beneficial effects on clinical outcomes.
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    ABSTRACT: When randomized controlled trials are unavailable, clinicians have to rely on observational studies. However, analyses using observational data to evaluate specific treatments and their associations with outcomes often are biased through confounding by clinical indication for the treatment of interest. Given the rich observational data and limited clinical trial data available in the dialysis population, successfully accounting for this bias can lead to substantial knowledge generation. In recent decades, much has been learned about statistical methods for observational data, including the fact that even extensive adjustments may not always overcome this bias, particularly when unmeasured confounders exist. In this article, examples based on the international DOPPS (Dialysis Outcomes and Practice Patterns Study) are used to demonstrate the value of practice-based instrumental variable analyses. This methodology leverages the marked differences in practice patterns among dialysis facilities and uses the reasonable assumption that patients are assigned to a dialysis facility without consideration of its specific treatment pattern in order to minimize bias in analyses relying on observational data. Examples using the dialysis facility as an instrument that are reviewed in depth in this article include studies of dialysate sodium concentration, systolic blood pressure targets, and treatment time, demonstrate the value of this methodology to produce advanced knowledge. However, practice-based analyses have potential limitations. Specifically, observation of sufficiently large differences in practice patterns is required and these analyses should consider that the treatment of interest may be associated with other facility treatment practices. These examples from the DOPPS hopefully will stimulate advances in methodologies and critical clinical work toward improving patient care by identifying beneficial treatment practices applicable to dialysis, chronic kidney disease, and beyond.
    American Journal of Kidney Diseases 08/2014; 64(6). DOI:10.1053/j.ajkd.2014.05.025 · 5.76 Impact Factor