15 March 2022>: Clinical Research
Development of a Predictive Nomogram for Estimating Medication Nonadherence in Hemodialysis Patients
Ying Wang 1BCDE* , Yinhui Yao 1BCF* , Junhui Hu 1BC , Yingxue Lin 1BF , Chunhua Cai 2B , Yanwu Zhao 1AG*DOI: 10.12659/MSM.934482
Med Sci Monit 2022; 28:e934482
Table 1 Demographics and clinical characteristics of 206 patients on hemodialysis.
Demographic characteristics | Total (n=206) | Adherence (n=120) | Nonadherence (n=86) | p-value |
---|---|---|---|---|
Sex, n (%) | 0.437 | |||
Male | 100 (49) | 55 (46) | 45 (52) | |
Female | 106 (51) | 65 (54) | 41 (48) | |
Age, Median (IQR) | 58.50 (48.25, 66.75) | 61.00 (50.75, 68.00) | 53.50 (47.00, 65.00) | 0.007 |
Education level, n (%) | 0.130 | |||
Junior school | 63 (31) | 37 (31) | 26 (30) | |
High school | 100 (49) | 62 (52) | 38 (44) | |
Junior college | 29 (14) | 17 (14) | 12 (14) | |
Undergraduate | 14 (7) | 4 (3) | 10 (12) | |
Job, n (%) | 0.058 | |||
Farmers | 41 (20) | 19 (16) | 22 (26) | |
Employed | 36 (17) | 17 (14) | 19 (22) | |
Retired | 73 (35) | 50 (42) | 23 (27) | |
Others | 56 (27) | 34 (28) | 22 (26) | |
Monthly per capita income (yuan), n (%) | 0.342 | |||
69 (33) | 37 (31) | 32 (37) | ||
3000–5000 | 124 (60) | 77 (64) | 47 (55) | |
5000–8000 | 13 (6) | 6 (5) | 7 (8) | |
Region, n (%) | 0.053 | |||
Rural area | 46 (22) | 33 (28) | 13 (15) | |
Urban area | 160 (78) | 87 (72) | 73 (85) | |
Hospital level, n (%) | ||||
Level-2 | 67 (33) | 18 (15) | 49 (57) | |
Level-3 | 139 (67) | 102 (85) | 37 (43) | |
Pay, n (%) | 0.384 | |||
New rural cooperative medical insurance (NRCMI) | 81 (39) | 42 (35) | 39 (45) | |
Medical insurance for urban workers (MIUW) | 121 (59) | 76 (63) | 45 (52) | |
Self-supporting | 2 (1) | 1 (1) | 1 (1) | |
Others | 2 (1) | 1 (1) | 1 (1) | |
Marital status, n (%) | 1.000 | |||
Unmarried | 12 (6) | 7 (6) | 5 (6) | |
Married | 194 (94) | 113 (94) | 81 (94) | |
Disease duration (years), Median (IQR) | 3.50 (1.50, 6.50) | 4.00 (2.08, 6.50) | 3.00 (1.05, 6.38) | 0.352 |
Smoking, n (%) | 0.724 | |||
No | 147 (71) | 84 (70) | 63 (73) | |
Yes | 59 (29) | 36 (30) | 23 (27) | |
Alcohol consumption, n (%) | 0.967 | |||
No | 159 (77) | 92 (77) | 67 (78) | |
Yes | 47 (23) | 28 (23) | 19 (22) | |
Hypertension, n (%) | 1.000 | |||
No | 16 (8) | 9 (8) | 7 (8) | |
Yes | 190 (92) | 111 (92) | 79 (92) | |
Diabetes, n (%) | 0.298 | |||
No | 149 (72) | 83 (69) | 66 (77) | |
Yes | 57 (28) | 37 (31) | 20 (23) | |
Coronary heart disease, n (%) | 1.000 | |||
No | 171 (83) | 100 (83) | 71 (83) | |
Yes | 35 (17) | 20 (17) | 15 (17) | |
Cerebrovascular disease, n (%) | 0.182 | |||
No | 189 (92) | 107 (89) | 82 (95) | |
Yes | 17 (8) | 13 (11) | 4 (5) | |
Heart failure, n (%) | 1.000 | |||
No | 163 (79) | 95 (79) | 68 (79) | |
Yes | 43 (21) | 25 (21) | 18 (21) | |
Cancer, n (%) | 1.000 | |||
No | 203 (99) | 118 (98) | 85 (99) | |
Yes | 3 (1) | 2 (2) | 1 (1) | |
Insulin, n (%) | 0.765 | |||
No | 162 (79) | 93 (78) | 69 (80) | |
Yes | 44 (21) | 27 (22) | 17 (20) | |
Total oral drugs per day, Median (IQR) | 6.00 (4.00, 7.00) | 5.00 (4.00, 6.00) | 6.00 (4.00, 9.00) | 0.002 |
Traditional Chinese medicine(CTM), n (%) | < 0.001 | |||
No | 173 (84) | 111 (92) | 62 (72) | |
Yes | 33 (16) | 9 (8) | 24 (28) | |
Cause of the renal disease, n (%) | 0.104 | |||
Diabetes | 49 (24) | 30 (25) | 19 (22) | |
Hypertension | 48 (23) | 25 (21) | 23 (27) | |
Chronic nephritis | 25 (12) | 10 (8) | 15 (17) | |
Others | 84 (35) | 55 (46) | 29 (34) | |
Age-adjusted Charlson Comorbidity Index (ACCI), n (%) | 0.056 | |||
2 | 43 (21) | 19 (16%) | 24 (28%) | |
3 | 26 (13) | 15 (12%) | 11 (13%) | |
4 | 39 (19) | 19 (16%) | 20 (23%) | |
5 | 35 (17) | 21 (18%) | 14 (16%) | |
6 | 32 (16) | 24 (20%) | 8 (9%) | |
7 | 19 (9) | 13 (11%) | 6 (7%) | |
8 | 9 (4) | 8 (7%) | 1 (1%) | |
9 | 3 (1) | 1 (1%) | 2 (2%) | |
Anxiety, n (%) | 0.667 | |||
No | 79 (38) | 48 (40) | 31 (36) | |
Yes | 127 (62) | 72 (60) | 55 (64) | |
Depression, n (%) | 0.138 | |||
No | 87 (42) | 45 (38) | 42 (49) | |
Yes | 119 (58) | 75 (62) | 44 (51) |