Hemoglobin variability does not predict mortality in European hemodialysis patients

Abstract

Patients with CKD exhibit significant within-patient hemoglobin (Hb) level variability, especially with the use of erythropoiesis stimulating agents (ESAs) and iron. Analyses of dialysis cohorts in the United States produced conflicting results regarding the association of Hb variability with patient outcomes. Here, we determined Hb variability in 5037 European hemodialysis (HD) patients treated over 2 years to identify predictors of high variability and to evaluate its association with all-cause and cardiovascular disease (CVD) mortality. We assessed Hb variability with various methods using SD, residual SD, time-in-target (11.0 to 12.5 g/dl), fluctuation across thresholds, and area under the curve (AUC). Hb variability was significantly greater among incident patients than prevalent patients. Compared with previously described cohorts in the United States, residual SD was similar but fluctuations above target were less frequent. Using logistic regression, age, body mass index, CVD history, dialysis vintage, serum albumin, Hb, angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) use, ESA use, dialysis access type, dialysis access change, and hospitalizations were significant predictors of high variability. Multivariable adjusted Cox regression showed that SD, residual SD, time-in-target, and AUC did not predict all-cause or CVD mortality during a median follow-up of 12.4 months (IQR: 7.7 to 17.4). However, patients with consistently low levels of Hb (<11 g/dl) and those who fluctuated between the target range and <11 g/dl had increased risks for death (RR 2.34; 95% CI: 1.24 to 4.41 and RR 1.74; 95% CI: 1.00 to 3.04, respectively). In conclusion, although Hb variability is common in European HD patients, it does not independently predict mortality.

Figure 1.

Calculating AUC with the trapezoidal rule. To simultaneously capture the magnitude of variability and the frequency at which the fluctuations occur as a single, quantitative index, the AUC between measured Hb values and the mean Hb concentration was calculated. The letters A through J represent the points along a patient profile for Hb. We sliced Hb profiles into 90-day intervals in this study because (1) most Hb measures were taken monthly, (2) at least three measurements of Hb were needed to compute an AUC, and (3) 3 months provide ample time for patients to respond to ESA therapy and have a measurable effect on Hb. The summed integrations across the two 90-day intervals produce an overall index of variability in units of g/dl × days. The area above the curve and the area below the curve were calculated separately for each 3-month interval. Integration was used to calculate the areas, approximating with the “trapezoidal rule”: where y_i is the i^th Hb measurement taken on day d_i and m_j is the mean Hb value in quarter j. To integrate the areas, all points that make up the areas needed to be known, therefore it was necessary to linearly interpolate the days where the Hb profile intersected with the mean Hb reference line (points D, G, I) and the Hb value where the Hb profile intersected with the end of the interval (i.e., at day 90 and day 180 [points E and J]).

Figure 2.

AUC is highly correlated with within-person SD (A) and residual SD (B) but not with time spent in target (C) (n = 5037).

Figure 3.

Distribution of AUC is consistent with categories of method of fluctuation across thresholds. CT, consistently within the target range; CL, consistently low; CH, consistently high; LAH, low-amplitude fluctuation with high Hb levels; LAL, low-amplitude fluctuation with low Hb levels; HA, high-amplitude fluctuation. Frequency of patients in each group: CT = 228, CL = 376, CH = 292, LAH = 1145, LAL = 1682, and HA = 1314. Values above and below the whiskers have been excluded from the box plot. The whiskers are defined as the upper and lower adjacent values where the upper adjacent value is the largest data value that is less than or equal to the 75th percentile + 1.5 × IQR and the lower adjacent value is the smallest data value that is greater or equal to the 25th percentile − 1.5 × IQR.

Figure 4.

Kaplan–Meier analysis showing patients with consistently low hemoglobin levels have the highest risk of mortality (n = 5037). CT, consistently within the target range; CL, consistently low; CH, consistently high; LAH, low-amplitude fluctuation with high Hb levels; LAL, low-amplitude fluctuation with low Hb levels; HA, high-amplitude fluctuation.