**3.2 Patient-level factors**

Various studies have reported that young age is a risk factor for haemoglobin variability (De Nicola et al., 2007; Eckardt et al., 2010). Boudville and colleagues reported that the odds of haemoglobin variability decreased by 11% with each 10-yr increment in age (Boudville et al., 2009). A few studies have reported that women were more likely to have fluctuations in the haemoglobin concentration (Ebben et al., 2006; Gilbertson et. al., 2008). In a study of 119 non-dialysis CKD patients, male gender was directly associated with increased time-intarget haemoglobin (De Nicola et al., 2007).

Eckardt and colleagues studied the magnitude and frequency of haemoglobin variability as a quantitative index by integrating the area under the curve (AUC) between measured haemoglobin values and the mean haemoglobin concentration (Eckardt et al., 2010). High degree of haemoglobin variability was defined as AUC >50th percentile. The mean body mass index (BMI) was lowest in the highest quartile of AUC. On multivariate logistic regression, BMI 25 to 30 kg/m2 and >30 kg/m2 were independently associated with decreased odds of haemoglobin variability compared to the reference category of BMI 18 to 25 kg/m2. Similarly, Lau and colleagues also observed less positive deflection of haemoglobin in heavier patients (Lau et al., 2010).

There is an excess burden of comorbid conditions in CKD, leading to erythropoietin hyporesponsiveness. In an observational study involving 152,846 ESKD patients on haemodialysis, Ebben and colleagues reported that having 2 or more comorbid conditions, 6 or more days of hospitalisation, and occurrence of infectious hospitalisations were independently associated with haemoglobin variability (Ebben et al., 2006).

Eckardt and colleagues found that incident dialysis vintage, change in haemodialysis vascular access, use of catheter for haemodialysis, haemoglobin lower than 11 g/dL, use of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, and hospitalisation were positively associated with an increased risk of haemoglobin variability (Eckardt et al., 2010). Also, patients treated with an ESA were twice likely to experience hemoglobin variability than those not treated with an ESA. As expected, higher serum albumin concentration was negatively associated with haemoglobin variability. Interestingly, history of cardiovascular disease was negatively associated with haemoglobin variability. Furthermore, the investigators did not find any association of C-reactive protein and leukocyte count with haemoglobin variability. The reasons for these findings are not entirely clear, but this study included a highly selected cohort in whom complete data on monthly haemoglobin values for 6 mo and medications were available, raising a possibility of selection bias. Similarly, Lau and colleagues also found a positive association between

anaemia management practices and facility-level haemoglobin standard deviations (Pisoni et. al., 2011). This study identified factors that decreased haemoglobin variability which include reviewing ESA dose at least twice a week and checking haemoglobin levels on a weekly basis. There was also less haemoglobin variability in facilities with a greater percentage of patients prescribed an ESA likely related to better anaemia management with the introduction of an ESA and fewer patients outside the target haemoglobin concentration. The factors that were more likely associated with haemoglobin variability were: facilities with a wider target haemoglobin range, higher upper target haemoglobin, and ESA

Various studies have reported that young age is a risk factor for haemoglobin variability (De Nicola et al., 2007; Eckardt et al., 2010). Boudville and colleagues reported that the odds of haemoglobin variability decreased by 11% with each 10-yr increment in age (Boudville et al., 2009). A few studies have reported that women were more likely to have fluctuations in the haemoglobin concentration (Ebben et al., 2006; Gilbertson et. al., 2008). In a study of 119 non-dialysis CKD patients, male gender was directly associated with increased time-in-

Eckardt and colleagues studied the magnitude and frequency of haemoglobin variability as a quantitative index by integrating the area under the curve (AUC) between measured haemoglobin values and the mean haemoglobin concentration (Eckardt et al., 2010). High degree of haemoglobin variability was defined as AUC >50th percentile. The mean body mass index (BMI) was lowest in the highest quartile of AUC. On multivariate logistic regression, BMI 25 to 30 kg/m2 and >30 kg/m2 were independently associated with decreased odds of haemoglobin variability compared to the reference category of BMI 18 to 25 kg/m2. Similarly, Lau and colleagues also observed less positive deflection of

There is an excess burden of comorbid conditions in CKD, leading to erythropoietin hyporesponsiveness. In an observational study involving 152,846 ESKD patients on haemodialysis, Ebben and colleagues reported that having 2 or more comorbid conditions, 6 or more days of hospitalisation, and occurrence of infectious hospitalisations were

Eckardt and colleagues found that incident dialysis vintage, change in haemodialysis vascular access, use of catheter for haemodialysis, haemoglobin lower than 11 g/dL, use of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, and hospitalisation were positively associated with an increased risk of haemoglobin variability (Eckardt et al., 2010). Also, patients treated with an ESA were twice likely to experience hemoglobin variability than those not treated with an ESA. As expected, higher serum albumin concentration was negatively associated with haemoglobin variability. Interestingly, history of cardiovascular disease was negatively associated with haemoglobin variability. Furthermore, the investigators did not find any association of C-reactive protein and leukocyte count with haemoglobin variability. The reasons for these findings are not entirely clear, but this study included a highly selected cohort in whom complete data on monthly haemoglobin values for 6 mo and medications were available, raising a possibility of selection bias. Similarly, Lau and colleagues also found a positive association between

independently associated with haemoglobin variability (Ebben et al., 2006).

administration by subcutaneous route (compared to intravenous route).

**3.2 Patient-level factors** 

target haemoglobin (De Nicola et al., 2007).

haemoglobin in heavier patients (Lau et al., 2010).

catheter use and haemoglobin variability; and a negative association with high baseline haemoglobin (Lau et al., 2010).

Weinhandl and colleagues studied the risk factors for haemoglobin variability in Medicare haemodialysis patients (Weinhandl et. al., 2011). The study cohort included 3 groups of haemodialysis patients: historical prevalent (prevalent on July 1, 1996; n=78,602), contemporary prevalent (prevalent on July 1, 2006; n= 133,246), and incident (January 1, 2005 - June 30, 2006; n=24,999). In both the prevalent groups, the presence of all comorbid conditions, except hepatic disease, was associated with greater haemoglobin variability. These conditions included atherosclerotic heart disease, congestive heart failure, arrhythmia and other cardiac diseases, cerebrovascular disease, peripheral vascular disease, cancer, chronic obstructive pulmonary disease, diabetes and gastrointestinal bleeding. In the incident group, the presence of cerebrovascular disease, peripheral vascular disease, chronic obstructive pulmonary disease, diabetes and gastrointestinal bleeding were associated with haemoglobin variability. In all 3 groups, cumulative hospital days and number of months with haemoglobin <10 g/dL were positively associated with haemoglobin variability. Similar findings have been reported by Gilbertson and colleagues (Gilbertson et al., 2009).
