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. 2014 Dec 8;2014(12):CD010590.
doi: 10.1002/14651858.CD010590.pub2.

Erythropoiesis-stimulating agents for anaemia in adults with chronic kidney disease: a network meta-analysis

Suetonia C Palmer  1 , Valeria Saglimbene, Dimitris Mavridis, Georgia Salanti, Jonathan C Craig, Marcello Tonelli, Natasha Wiebe, Giovanni F M Strippoli
Affiliations

Erythropoiesis-stimulating agents for anaemia in adults with chronic kidney disease: a network meta-analysis

Suetonia C Palmer et al. Cochrane Database Syst Rev. .

Update in

Abstract

Background: Several erythropoiesis-stimulating agents (ESAs) are available for treating anaemia in people with chronic kidney disease (CKD). Their relative efficacy (preventing blood transfusions and reducing fatigue and breathlessness) and safety (mortality and cardiovascular events) are unclear due to the limited power of head-to-head studies.

Objectives: To compare the efficacy and safety of ESAs (epoetin alfa, epoetin beta, darbepoetin alfa, or methoxy polyethylene glycol-epoetin beta, and biosimilar ESAs, against each other, placebo, or no treatment) to treat anaemia in adults with CKD.

Search methods: We searched the Cochrane Renal Group's Specialised Register to 11 February 2014 through contact with the Trials' Search Co-ordinator using search terms relevant to this review.

Selection criteria: Randomised controlled trials (RCTs) that included a comparison of an ESA (epoetin alfa, epoetin beta, darbepoetin alfa, methoxy polyethylene glycol-epoetin beta, or biosimilar ESA) with another ESA, placebo or no treatment in adults with CKD and that reported prespecified patient-relevant outcomes were considered for inclusion.

Data collection and analysis: Two independent authors screened the search results and extracted data. Data synthesis was performed by random-effects pairwise meta-analysis and network meta-analysis. We assessed for heterogeneity and inconsistency within meta-analyses using standard techniques and planned subgroup and meta-regression to explore for sources of heterogeneity or inconsistency. We assessed our confidence in treatment estimates for the primary outcomes within network meta-analysis (preventing blood transfusions and all-cause mortality) according to adapted GRADE methodology as very low, low, moderate, or high.

Main results: We identified 56 eligible studies involving 15,596 adults with CKD. Risks of bias in the included studies was generally high or unclear for more than half of studies in all of the risk of bias domains we assessed; no study was low risk for allocation concealment, blinding of outcome assessment and attrition from follow-up. In network analyses, there was moderate to low confidence that epoetin alfa (OR 0.18, 95% CI 0.05 to 0.59), epoetin beta (OR 0.09, 95% CI 0.02 to 0.38), darbepoetin alfa (OR 0.17, 95% CI 0.05 to 0.57), and methoxy polyethylene glycol-epoetin beta (OR 0.15, 95% CI 0.03 to 0.70) prevented blood transfusions compared to placebo. In very low quality evidence, biosimilar ESA therapy was possibly no better than placebo for preventing blood transfusions (OR 0.27, 95% CI 0.05 to 1.47) with considerable imprecision in estimated effects. We could not discern whether all ESAs were similar or different in their effects on preventing blood transfusions and our confidence in the comparative effectiveness of different ESAs was generally very low. Similarly, the comparative effects of ESAs compared with another ESA, placebo or no treatment on all-cause mortality were imprecise.All proprietary ESAs increased the odds of hypertension compared to placebo (epoetin alfa OR 2.31, 95% CI 1.27 to 4.23; epoetin beta OR 2.57, 95% CI 1.23 to 5.39; darbepoetin alfa OR 1.83, 95% CI 1.05 to 3.21; methoxy polyethylene glycol-epoetin beta OR 1.96, 95% CI 0.98 to 3.92), while the effect of biosimilar ESAs on developing hypertension was less certain (OR 1.18, 95% CI 0.47 to 2.99). Our confidence in the comparative effects of ESAs on hypertension was low due to considerable imprecision in treatment estimates. The comparative effects of all ESAs on cardiovascular mortality, myocardial infarction (MI), stroke, and vascular access thrombosis were uncertain and network analyses for major cardiovascular events, end-stage kidney disease (ESKD), fatigue and breathlessness were not possible. Effects of ESAs on fatigue were described heterogeneously in the available studies in ways that were not useable for analyses.

Authors' conclusions: In the CKD setting, there is currently insufficient evidence to suggest the superiority of any ESA formulation based on available safety and efficacy data. Directly comparative data for the effectiveness of different ESA formulations based on patient-centred outcomes (such as quality of life, fatigue, and functional status) are sparse and poorly reported and current research studies are unable to inform care. All proprietary ESAs (epoetin alfa, epoetin beta, darbepoetin alfa, and methoxy polyethylene glycol-epoetin beta) prevent blood transfusions but information for biosimilar ESAs is less conclusive. Comparative treatment effects of different ESA formulations on other patient-important outcomes such as survival, MI, stroke, breathlessness and fatigue are very uncertain.For consumers, clinicians and funders, considerations such as drug cost and availability and preferences for dosing frequency might be considered as the basis for individualising anaemia care due to lack of data for comparative differences in clinical benefits and harms.

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Conflict of interest statement

  1. Suetonia C Palmer: none known

  2. Valeria Saglimbene: none known

  3. Dimitris Mavridis: none known

  4. Georgia Salanti: none known

  5. Jonathan C Craig: none known

  6. Marcello Tonelli: Dr Tonelli has received an investigator‐initiated grant and honoraria from Amgen Australia for an academic lecture series ‐‐ neither were related to ESA or anaemia. All honoraria were donated to charity

  7. Natasha Wiebe: none known

  8. Giovanni FM Strippoli: none known

Figures

Figure 1
Figure 1
Overview of anaemia in chronic disease
Figure 2
Figure 2
Study flow diagram.
Figure 3
Figure 3
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies. The coloured bars correspond to each risk of bias adjudication summarised across all studies. The numbers shown in the bars indicate the raw number of studies which were adjudicated the corresponding risks of bias (green = low risk; yellow = unclear risk; red = high risk). The size of each coloured box within the bars indicates the proportion of studies with the adjudicated risk. For example, there were 7 studies (13%) which were adjudicated as low risk of bias from reported sequence generation methods. The description of the risk domains considered is given in the vertical axis. *Other threats to validity include one or more of: sponsor involved in study design, analysis, or authorship; imbalance between treatment comparisons and/or premature termination of trial
Figure 4
Figure 4
Risk of bias summary: review authors' judgements about each risk of bias item for each included study. Each study is shown in the vertical axis and the corresponding risk of bias for each domain adjudicated by two authors is shown by coloured circles within the grid. Green (+) = low risk, yellow (?) = unclear risk, red (‐) = high risk. Other threats to validity include one or more of: sponsor involved in study design, analysis, or authorship; imbalance between treatment comparisons and/or premature termination of trial
Figure 5
Figure 5
Networks of the treatment efficacy and safety of ESA drugs in the treatment of anaemia in chronic kidney disease. Values lower than 1 favour the active treatment in the comparison
Figure 6
Figure 6
Forest plots for results from network meta‐analyses comparing ESAs versus placebo
Figure 7
Figure 7
Contributions matrix: percentage contribution of each direct estimate to the network meta‐analysis estimates for all‐cause mortality. Rows correspond to network meta‐analysis odds ratios (separated for mixed and indirect evidence) and the columns correspond to direct meta‐analysis odds ratios. The size of the shaded boxes are proportional to the percentage contribution of each direct estimate to the network meta‐analysis and to the entire network (lowest row). The last row shows the number of included direct comparisons. The names of the treatment comparisons are shown in the first column. For example, information for the network estimate of epoetin alfa versus darbepoetin alfa is derived from both direct and indirect evidence (generating a mixed estimate. Of this mixed network estimate, trials directly comparing epoetin alfa versus darbepoetin alfa contribute 44.8 % of the information to the network estimate of effect and trials directly comparing epoetin alfa versus placebo contribute 4.6% of the network estimated effect, etc. We used the 'netweight' command in STATA to generate the plot. The contribution matrix shows how much each direct comparison in the network contributes to each network (mixed or indirect) estimate
Figure 8
Figure 8
Contributions matrix: percentage contribution of each direct estimate to the network meta‐analysis estimates for preventing blood transfusions. Rows correspond to network meta‐analysis odds ratios (separated for mixed and indirect evidence) and the columns correspond to direct meta‐analysis odds ratios. The size of the shaded boxes are proportional to the percentage contribution of each direct estimate to the network meta‐analysis and to the entire network (lowest row). The last row shows the number of included direct comparisons. The names of the treatment comparisons are shown in the first column. For example, information for the network estimate of epoetin alfa versus darbepoetin alfa is derived from both direct and indirect evidence (generating a mixed estimate. Of this mixed network estimate, trials directly comparing epoetin alfa versus darbepoetin alfa contribute 78.7% of the information to the network estimate of effect and trials directly comparing epoetin alfa versus placebo contribute 7.2% of the network estimated effect, etc. We used the 'netweight' command in STATA to generate the plot. The contribution matrix shows how much each direct comparison in the network contributes to each network (mixed or indirect) estimate
Figure 9
Figure 9
Study limitations distribution for each network estimate for pairwise comparisons of erythropoiesis‐stimulating agents on the primary safety outcome (all‐cause mortality). Calculations are based on the contributions of direct evidence to the network estimates and the overall risks of bias from all bias domains (sequence generation, allocation concealment, blinding of participants and investigators, blinding of outcome assessment, attrition from follow‐up, selective outcome reporting and other sources of bias) within studies contributing to the direct evidence. The colours represent risk (green, low; yellow unclear; red, high). The direct comparisons are described in the vertical axis
Figure 10
Figure 10
Study limitations distribution for each network estimate for pairwise comparisons of erythropoiesis‐stimulating agents on the primary efficacy outcome (preventing blood transfusions). Calculations are based on the contributions of direct evidence to the network estimates and the overall risks of bias from all bias domains (sequence generation, allocation concealment, blinding of participants and investigators, blinding of outcome assessment, attrition from follow‐up, selective outcome reporting and other sources of bias) within studies contributing to the direct evidence. The colours represent risk (green, low; yellow unclear; red, high). The direct comparisons are described in the vertical axis
Analysis 1.1
Analysis 1.1
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 1 Blood transfusion.
Analysis 1.2
Analysis 1.2
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 2 Fatigue.
Analysis 1.3
Analysis 1.3
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 3 Breathlessness.
Analysis 1.4
Analysis 1.4
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 4 All‐cause mortality.
Analysis 1.5
Analysis 1.5
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 5 Cardiovascular mortality.
Analysis 1.6
Analysis 1.6
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 6 Myocardial infarction.
Analysis 1.7
Analysis 1.7
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 7 Stroke.
Analysis 1.8
Analysis 1.8
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 8 Hypertension.
Analysis 1.9
Analysis 1.9
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 9 Vascular access thrombosis.
Analysis 1.10
Analysis 1.10
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 10 End‐stage kidney disease.
Analysis 1.11
Analysis 1.11
Comparison 1 ESA versus ESA or placebo/no treatment, Outcome 11 Major cardiovascular events.
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    1. Palazzuoli A, Quatrini I, Calabro A, Antonelli G, Caputo M, Campagna MS, et al. Anemia correction by erythropoietin reduces BNP levels, hospitalization rate, and NYHA class in patients with cardio‐renal anemia syndrome. Clinical & Experimental Medicine 2011;11(1):43‐8. [MEDLINE: ] - PubMed

References to ongoing studies

    1. Besarab A, Canaud B, Francisco AL, Kerr P, Locatelli F, Lok CE, et al. Randomized comparison of IV C.E.R.A. (Continuous Erythropoietin Receptor Activator) and Darbepoetin Alfa (DA) at extended administration intervals for the maintenance of Hb levels in patients with CKD on dialysis: rationale and design [abstract no: PUB377]. Journal of the American Society of Nephrology 2006;17(Abstracts):896A. [CENTRAL: CN‐00740564]
    1. NCT00442702. A randomized, open label study to compare the effect of monthly subcutaneous mircera with that of darbepoetin alfa, given according to local label, on the management of anemia in patients with chronic kidney disease not on dialysis. www.clinicaltrials.gov/show/NCT00442702 (accessed 8 October 2014).
    1. NCT00559273. An open‐label, randomized, multicenter, parallel‐group study to demonstrate correction of anemia using once every 4 weeks subcutaneous injections of mircera in patients with chronic kidney disease who are not on dialysis. www.clinicaltrials.gov/ct2/show/NCT00559273 (accessed 8 October 2014).
    1. NCT00717821. A randomized, controlled, open label, French multicenter parallel group study to compare the hemoglobin maintenance with once monthly administration of mircera versus epoetin beta or darbepoetin alfa in patients with chronic kidney disease on hemodialysis. www.clinicaltrials.gov/ct2/show/NCT00717821 (accessed 8 October 2014).
    1. NCT00773513. A randomized, open label study to assess all‐cause mortality and cardiovascular morbidity in patients with chronic kidney disease on dialysis and those not on renal replacement therapy under treatment with mircera or reference ESAs. www.clinicaltrials.gov/ct2/show/NCT00773513 (accessed 8 October 2014).

Additional references

    1. Seidenfeld J, Piper M, Bohlius J, Weingart O, Trelle S, Engert A, et al. Comparative effectiveness of epoetin and darbepoetin for managing anemia in patients undergoing cancer treatment. Comparative effectiveness review No. 3. (Prepared by Blue Cross and Blue Shield Association Technology Evaluation Center Evidence‐based Practice Center under Contract No. 290‐02‐0026). Rockville, MD: Agency for Healthcare Research and Quality; May 2006. www.effectivehealthcare.ahrq.gov/reports/final.cfm (accessed 8 October 2014).
    1. Grant MD, Piper M, Bohlius J, Tonia T, Robert N, Vats V, et al. Epoetin and darbepoetin for managing anemia in patients undergoing cancer treatment: comparative effectiveness update. Comparative Effectiveness Review No. 113. (Prepared by the Blue Cross and Blue Shield Association Technology Evaluation Center Evidence‐based Practice Center under Contract No. 290‐2007‐10058‐I). AHRQ Publication No. 13‐EHC077‐EF. Rockville, MD: Agency for Healthcare Research and Quality. 2013. http://effectivehealthcare.ahrq.gov/ehc/products/170/1481/cancer‐anemia‐... (accessed 8 October 2014). - PubMed
    1. Bohlius J, Schmidlin K, Brillant C, Schwarzer G, Trelle S, Seidenfeld J, et al. Erythropoietin or Darbepoetin for patients with cancer ‐ meta‐analysis based on individual patient data. Cochrane Database of Systematic Reviews 2009, Issue 3. [DOI: 10.1002/14651858.CD007303.pub2] - DOI - PMC - PubMed
    1. Boulware LE, Tangri N, Ephraim PL, Scialla JJ, Sozio SM, Crews DC, et al. Comparative effectiveness studies to improve clinical outcomes in end stage renal disease: the DEcIDE patient outcomes in end stage renal disease study. BMC Nephrology 2012;13:167. [MEDLINE: ] - PMC - PubMed
    1. Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta‐analysis of randomized controlled trials. Journal of Clinical Epidemiology 1997;50(6):683‐91. [MEDLINE: ] - PubMed

References to other published versions of this review

    1. Palmer SC, Salanti G, Craig JC, Mavridis D, Strippoli GF. Erythropoiesis stimulating agents for anaemia in adults with chronic kidney disease: a network meta‐analysis. Cochrane Database of Systematic Reviews 2013, Issue 7. [DOI: 10.1002/14651858.CD010590] - DOI - PMC - PubMed

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