Table 1.
Characteristics of Japanese HD patients by allopurinol use
| Patient characteristics (mean ± SD or %) | All patients (N = 6,252) | With allopurinol use (N = 1,561) | No allopurinol use (N = 4,691) | p value* |
|---|---|---|---|---|
| Uric acid (mg/dL) | 7.7 ± 1.5 | 7.0 ± 1.5 | 8.0 ± 1.4 | <0.001 |
| Uric acid >9 mg/dL | 17 % | 8 % | 20 % | <0.001 |
| Demographics | ||||
| Age (years) | 60.8 ± 12.8 | 59.7 ± 12.2 | 61.2 ± 13.0 | <0.001 |
| Male (%) | 61 % | 68 % | 59 % | <0.001 |
| Duration on dialysis (months) | 77.1 ± 81.0 | 79.5 ± 79.3 | 76.3 ± 81.5 | 0.09 |
| Dry weight (kg) | 52.8 ± 10.6 | 54.9 ± 11.1 | 52.1 ± 10.3 | <0.001 |
| Laboratory data | ||||
| Serum albumin (g/dL) | 3.77 ± 0.44 | 3.79 ± 0.41 | 3.77 ± 0.45 | <0.001 |
| Serum creatinine (mg/dL) | 10.7 ± 3.0 | 11.3 ± 3.2 | 10.5 ± 3.0 | <0.001 |
| nPCR | 1.03 ± 0.23 | 1.08 ± 0.24 | 1.02 ± 0.23 | <0.001 |
| Cachectic (%) | 5 % | 3 % | 5 % | 0.001 |
| WBC count (1,000 per mm3) | 6.0 ± 2.0 | 6.1 ± 2.0 | 6.0 ± 2.0 | 0.04 |
| Hemoglobin (g/dL) | 9.9 ± 1.4 | 10.0 ± 1.4 | 9.9 ± 1.4 | 0.002 |
| Serum calciumalb (mg/dL) | 9.3 ± 0.9 | 9.4 ± 0.9 | 9.2 ± 1.0 | <0.001 |
| Serum phosphorus (mg/dL) | 5.6 ± 1.6 | 5.8 ± 1.6 | 5.6 ± 1.6 | <0.001 |
| Single pool (Kt/V) | 1.31 ± 0.30 | 1.30 ± 0.29 | 1.31 ± 0.30 | 0.7 |
| Comorbid conditions | ||||
| Coronary artery disease | 28 % | 26 % | 28 % | 0.01 |
| Other cardiovascular disease | 28 % | 28 % | 28 % | 0.6 |
| Cerebrovascular disease | 14 % | 14 % | 14 % | 0.2 |
| Congestive heart failure | 18 % | 16 % | 18 % | 0.04 |
| Diabetes mellitus | 31 % | 25 % | 33 % | <0.001 |
| GI bleeding | 4 % | 4 % | 4 % | 0.5 |
| Hypertension | 67 % | 67 % | 67 % | 0.2 |
| Lung disease | 2 % | 2 % | 2 % | 0.5 |
| Neurological disease | 7 % | 4 % | 7 % | <0.001 |
| Psychiatric disorder | 3 % | 3 % | 3 % | 0.2 |
| Peripheral vascular disease | 13 % | 11 % | 14 % | <0.001 |
| Recurrent cellulitis | 3 % | 2 % | 3 % | 0.01 |
* p value represents association of allopurinol with each patient characteristic, adjusted for DOPPS phase and accounting for facility clustering effects. Linear mixed models for continuous variables and generalized estimating equations (GEE) models with logit link function for binary variables were used