MO543ASSESSMENT OF RISK FACTORS FOR CARDIOVASCULAR EVENTS AND MORTALITY IN DIALYSIS PATIENTS IN THE AURORA STUDY, A RETROSPECTIVE ANALYSIS
Authors: Fellström Bengt , Eriksson Niclas , Morga Antonia , Wilpshaar Wim , Young James , Jiletcovici Alina , Smerud Knut , Cockburn Elinor , Alexandre Ana Filipa 
Published: May 2021

Background and Aims

Patients with chronic kidney disease (CKD) are at higher risk of cardiovascular disease (CVD), which can also lead to end-stage renal disease (ESRD).1 As anaemia is an independent risk factor for CVD,2 treating anaemia might reduce CV events in this patient population. This study aimed to evaluate the impact of anaemia and other risk factors on long-term CV risk in haemodialysis (HD) patients with CKD. A secondary aim was to establish a CV risk equation for this patient population.

Method

This retrospective study used data from the Phase 3 AURORA study (NCT04042350) of 2776 ESRD patients aged 50–80 years receiving regular HD/haemofiltration, for a mean 3.2 year follow up.3 The primary endpoint of our analysis was time to first major adverse cardiovascular event (CV MACE; non-fatal stroke, non-fatal myocardial infarction, and CV mortality; n=804). Secondary endpoints included time to non-fatal stroke (ischaemic or haemorrhagic; n=98), coronary revascularisation therapy (n=300) and all-cause mortality (n=1296), and development of a CV risk equation. Ferritin and transferrin baseline values were determined for this analysis using the original frozen AURORA study patient samples (&gt;10 years old). Statistical analyses were performed using univariate and multiple Cox regression models. For each outcome a full model was estimated and then simplified by approximation with fewer factors. This was done using linear regression against the linear predictor of the full Cox regression model. In a stepwise manner, the least contributing variable was removed until the subset of variables approximated the full model to 95%. Model performance was measured using the C-statistic, and internally validated using bootstrap.

Results

Incidence rates for CV MACE, non-fatal stroke, coronary revascularisation, and all-cause mortality were 9.36, 1.11, 3.57, and 13.73 per 100 patient-years, respectively. Certain established risk factors among HD patients, such as age, gender, previous history of CVD, diabetes mellitus, smoking, blood pressure, high phosphate and C-reactive protein levels, and low albumin levels, were also findings for this study, although non-fatal stroke was underpowered to show significance (Table). Elevated haemoglobin levels (≥127 g/L) demonstrated a protective effect on the risk of all-cause mortality (hazard ratio [HR] 0.916, p=0.010 for 127 g/L [upper quartile] versus 107 g/L [lower quartile]), but were also associated with an increased risk of coronary revascularisations (HR 1.164, p=0.011 for 127 g/L versus 107 g/L). Haemoglobin levels ≤107 g/L were associated with an approximately 9% increased annual risk of mortality (HR 1/0.916=1.092). Elevated ferritin and transferrin levels were significant and independent risk factors for CV MACE (HR [95% CI] 1.130 [1.025, 1.246] and 1.202 [0.987, 1.464], respectively), and all-cause mortality (HR [95% CI] 1.088 [1.008, 1.174] and 1.402 [1.198,1.641], respectively), but further analyses of iron metabolism markers, such as hepcidin, are needed to draw meaningful conclusions. The age of the samples may have also impacted the results. Risk prediction models were developed, and the predictive ability was 0.66–0.68 (C-statistic).

Conclusion

This analysis confirmed that this cohort is representative of a HD population. Moreover, elevated haemoglobin levels were associated with increased survival, concomitantly with coronary revascularisation. Elevated ferritin and transferrin levels were identified as potential risk factors for CV MACE and all-cause mortality, but further studies are required to better understand their value in estimating CV risk. Risk predication models were developed and performed well but require validation against an independent patient cohort.