| Literature DB >> 30400889 |
Jo-Yen Chao1,2, Hsu-Chih Chien2, Te-Hui Kuo1,3, Yu-Tzu Chang1,4, Chung-Yi Li3, Ming-Cheng Wang1,2, Yea-Huei Kao Yang5.
Abstract
BACKGROUND: Patients with end stage renal disease have a high all-cause and cardiovascular mortality. Secondary hyperparathyroidism and vitamin D deficiency are considered part of the mechanism for the excess mortality observed. We aimed to evaluate the relationship between vitamin D use and all-cause mortality.Entities:
Keywords: Activated vitamin D; End-stage renal disease (ESRD); Hemodialysis; Mortality; Prescribing pattern
Mesh:
Substances:
Year: 2018 PMID: 30400889 PMCID: PMC6219061 DOI: 10.1186/s12882-018-1111-2
Source DB: PubMed Journal: BMC Nephrol ISSN: 1471-2369 Impact factor: 2.388
Baseline characteristics of activated vitamin D users versus non-users according to status by landmark time, before and after propensity score (PS) matching
| Entire cohort | After PS match | |||||
|---|---|---|---|---|---|---|
| Vitamin D users | Non-users |
| Vitamin D users | Non-users |
| |
| N (%) | 8151 (15.5) | 44,606 (84.5) | 7232 (25.0) | 21,696 (75.0) | ||
| Age, year | 58.9 (14.1) | 62.5 (13.3) | 0.26 | 60.7 (13.5) | 60.8 (13.7) | < 0.01 |
| < 53 | 2847 (34.9) | 10,949 (24.6) | 0.25 | 1933 (26.7) | 5967 (27.5) | 0.02 |
| ≥ 53 and < 64 | 2128 (26.1) | 11,653 (26.1) | 1932 (26.7) | 5728 (26.4) | ||
| ≥ 64 and < 73 | 1749 (21.5) | 11,325 (25.4) | 1776 (24.6) | 5123 (23.6) | ||
| ≥ 73 | 1427 (17.5) | 10,679 (23.9) | 1591 (22.0) | 4878 (22.5) | ||
| Gender (male) | 3680 (45.2) | 22,619 (50.7) | 0.11 | 3540 (48.9) | 10,647 (49.7) | < 0.01 |
| Comorbidities | ||||||
| DM | 3327 (40.8) | 26,616 (59.7) | 0.38 | 3325 (46.0) | 10,032 (46.2) | < 0.01 |
| CHF | 2200 (27.0) | 15,195 (34.1) | 0.15 | 2136 (29.5) | 6438 (29.7) | < 0.01 |
| MI | 1932 (23.7) | 13,868 (31.1) | 0.17 | 1896 (26.2) | 5574 (25.7) | 0.01 |
| PVD | 259 (3.2) | 1509 (3.4) | 0.01 | 242 (3.4) | 687 (3.2) | 0.01 |
| CVD | 774 (9.5) | 7095 (15.9) | 0.19 | 774 (10.7) | 2388 (11.0) | 0.01 |
| COPD | 14 (0.2) | 128 (0.3) | 0.02 | 14 (0.2) | 40 (0.2) | < 0.01 |
| CTD | 176 (2.2) | 1021 (2.3) | < 0.01 | 163 (2.3) | 498 (2.3) | < 0.01 |
| PUD | 1344 (16.5) | 8023 (18.0) | 0.04 | 1252 (17.3) | 3605 (16.6) | 0.02 |
| Neoplasia | 10 (0.1) | 56 (0.1) | < 0.01 | 9 (0.1) | 30 (0.1) | < 0.01 |
| Chronic liver diseases | 1001 (12.3) | 5353 (12.0) | < 0.01 | 917 (12.7) | 2643 (12.2) | 0.02 |
| Vascular access type | 0.15 | 0.06 | ||||
| AVF | 6372 (78.2) | 34,240 (76.7) | 5811 (80.4) | 17,145 (79.2) | ||
| AVG | 617 (7.6) | 4308 (9.7) | 585 (8.1) | 1833 (8.5) | ||
| Permanent catheter | 116 (1.4) | 1097 (2.5) | 110 (1.5) | 443 (2.0) | ||
| Double lumen catheter | 539 (6.6) | 3219 (7.2) | 389 (5.4) | 1392 (6.4) | ||
| Unknown | 507 (6.2) | 1742 (3.9) | 337 (4.7) | 883 (4.1) | ||
| Medications | ||||||
| Antiplateletsb | 3929 (48.2) | 24,796 (55.6) | 0.15 | 3687 (50.9) | 10,900 (50.2) | 0.01 |
| Aspirin / Clopidogrel | 2324 (28.5) | 15,600 (35.0) | 0.14 | 2189 (30.3) | 6567 (30.3) | < 0.01 |
| Cilostazol | 154 (1.9) | 1146 (2.6) | 0.05 | 147 (2.0) | 440 (2.0) | < 0.01 |
| Warfarin | 143 (1.8) | 988 (2.2) | 0.03 | 140 (1.9) | 387 (1.8) | 0.01 |
| Statins | 1373 (16.8) | 9535 (21.4) | 0.12 | 1303 (18.0) | 3820 (17.6) | 0.01 |
| Insulin | 1615 (19.8) | 12,898 (28.9) | 0.21 | 1604 (22.2) | 4825 (22.2) | < 0.01 |
| OAD | 1812 (22.2) | 16,003 (35.9) | 0.30 | 1809 (25.0) | 5579 (25.7) | 0.02 |
| Metformin | 179 (2.2) | 1857 (4.2) | 0.11 | 179 (2.5) | 530 (2.4) | < 0.01 |
| Sulfonylurea | 917 (11.3) | 8041 (18.0) | 0.19 | 917 (12.7) | 2893 (13.3) | 0.02 |
| α-glucosidase inhibitors | 148 (1.8) | 1478 (3.3) | 0.09 | 148 (2.1) | 458 (2.1) | < 0.01 |
| TZD | 81 (1.0) | 684 (1.5) | 0.05 | 80 (1.1) | 253 (1.2) | < 0.01 |
| DPP-4 inhibitors | 2 (0.02) | 5 (0.01) | 0.01 | 0 (0.00) | 1 (0.00) | < 0.01 |
| Meglitinides | 485 (6.0) | 3938 (8.8) | 0.11 | 485 (6.7) | 1444 (6.7) | < 0.01 |
| ACEI / ARB | 3972 (48.7) | 23,726 (53.2) | 0.09 | 3569 (49.4) | 10,730 (49.5) | < 0.01 |
| Beta-blockers | 4173 (51.2) | 24,243 (54.4) | 0.06 | 3764 (52.1) | 11,205 (51.7) | 0.01 |
| Diuretics | 5737 (70.4) | 34,377 (77.1) | 0.15 | 5229 (72.3) | 15,793 (72.8) | 0.01 |
| ESA | 1887 (23.2) | 10,133 (22.7) | 0.01 | 1691 (23.4) | 5011 (23.1) | 0.01 |
Note:
(1) The landmark time is the 360th day of initiation of hemodialysis
(2) Values for categorical variables are given as numbers (percent); for continuous variables, as means (standard deviation)
Abbreviations: DM diabetes mellitus, CHF congestive heart failure, MI myocardial infarction, PVD peripheral vascular disease, CVD cerebrovascular disease, COPD chronic obstructive pulmonary disease, CTD connective tissue disease including rheumatoid arthritis, systemic lupus erythematosus, etc, PUD peptic ulcer disease; Chronic liver diseases: chronic viral hepatitis, cirrhosis and its complications, AVF arteriovenous fistula, AVG arteriovenous graft, PS propensity score, OAD oral antidiabetic drugs, TZD thiazolidinediones, DPP-4 inhibitors dipeptidyl peptidase 4 inhibitors, ACEI / ARB angiotensin converting enzyme inhibitors/angiotensin II receptor blockers, ESA erythropoiesis-stimulating agents
aStandardized difference (d): statistically significantly different between two comparison groups if d > 0.10
Antiplatelets included aspirin, clopidogrel, cilostazol, dipyridamole and ticlopidine
Fig. 1Flow diagram shows inclusion of hemodialysis patients for analysis. Numbers of incident hemodialysis patients included for analysis, linked to the outpatient and admission claims from the catastrophic illness certificate (CIC) for end-stage renal disease (ESRD) database within the National Health Insurance Research Database (NHIRD)
Fig. 2Kaplan-Meier Survival curve of activated vitamin D users versus non-users according to status by landmark time. Vitamin D users had a significantly lower risk of death, compared with non-users. Note: The landmark time is the 360th day of initiation of hemodialysis
Multivariate Cox proportional hazards models examining activated vitamin D treatment as compared with no treatment by landmark time
| Model | HR (95% CI) |
|---|---|
| Unadjusted | 0.69 (0.66–0.72) |
| Adjusted | |
| Age and sex | 0.79 (0.76–0.82) |
| Age, sex, and comorbidities | 0.90 (0.86–0.94) |
| Age, sex, vascular access type, and comorbidities | 0.90 (0.87–0.94) |
| Age, sex, comorbidities, and medications | 0.90 (0.87–0.94) |
| Age, sex, vascular access type, comorbidities and medications | 0.91 (0.87–0.95) |
| Propensity score (PS) method | |
| PS trimming (1–99%) | 0.71 (0.68–0.74) |
| PS trimming + IPTW | 0.94 (0.92–0.96) |
| PS matching | 0.94 (0.90–0.98) |
Note: The landmark time is the 360th day of initiation of hemodialysis
Propensity score (PS): PS was calculated with logistic regression using covariates of age, sex, vascular access type, baseline comorbidities, and medications. The PS matched methods we employed compared vitamin D users versus non-users without further adjustment of baseline covariates
Abbreviation: HR hazard ratio, CI confidence intervals, PS propensity score, IPTW inverse probability treatment weighting
Fig. 3Result of group-based trajectory analysis. Trajectory of vitamin D dosage grouping from initiation of hemodialysis in the first 360 days. Trajectory model using 2 groups. Every 0.25 μg of calcitriol or alfacalcidol was defined as one dosage unit. The predicted dosage unit in each group is plotted with dotted lines. The observed proportion of individuals in each group are plotted in solid lines. After exclusion of the patients with upper 99th percentile dosage (n = 196) and application of trajectory analysis, the majority (dark black line) of vitamin D users (n = 6849) received a median of 110 (IQR 45–220) dosage units, while the remaining 326 patients (grey line) received higher cumulative dosages, median 805 (IQR 635–1080) dosage units, in the first 360 days
Crude mortality rate and multivariate adjusted hazard ratio for mortality according to the different dosage categories of oral activated vitamin D based on trajectory analysis
| N (%) | Follow-up (person-years) | Death (%) | Crude mortality rate (per 100 person-year) | Adjusted HRb | |
|---|---|---|---|---|---|
| Non-users | 45,386 (86.0) | 145,396.7 | 18,853 (41.5) | 12.97 | Reference |
| Conventional dose vitamin D | 6849 (13.0) | 24,398.0 | 2112 (30.8) | 8.66 | 0.88 (0.84–0.92) |
| High dose vitamin D usersa | 522 (1.0) | 2312.6 | 136 (26.1) | 5.88 | 0.66 (0.55–0.78) |
| Overall | 52,757 (100) | 172,107.3 | 21,101 (40.0) | 12.26 |
Abbreviations: HR hazard ratio, CI confidence intervals
aThe high dose vitamin D users consisted of the upper 99th percentile of dosage prescriptions (n = 196) that were previously excluded from trajectory analysis plus the minority of higher dose vitamin D users (n = 326) in the trajectory analysis
bThe Cox model was adjusted by covariates including age, sex, vascular access type, baseline comorbidities, and medications
Multivariate Cox proportional hazard models examining activated vitamin D treatment as compared with no treatment by landmark time in hospital-setting hemodialysis patients
| Model | HR (95% CI) |
|---|---|
| Unadjusted | 0.69 (0.66–0.73) |
| Adjusted | |
| Urbanization and hospital level | 0.72 (0.68–0.76) |
| Age and sex | 0.78 (0.74–0.82) |
| Age, sex, urbanization, and hospital level | 0.80 (0.76–0.84) |
| Age, sex, vascular access type, and comorbidities | 0.89 (0.85–0.94) |
| Age, sex, comorbidities, and baseline medications | 0.90 (0.85–0.95) |
| Age, sex, urbanization, hospital level, vascular access, comorbidities, and baseline medications | 0.91 (0.87–0.96) |
| Propensity score (PS) method | |
| PS trimming (1–99%) | 0.70 (0.67–0.74) |
| PS trimming + IPTW | 0.95 (0.92–0.97) |
| PS matching (1: 3) | 0.95 (0.89–1.00) |
Propensity score (PS): PS was calculated with logistic regression using covariates of age, sex, vascular access type, baseline comorbidities, medications, and levels of hospital and urbanization. The PS matched methods was employed compared vitamin D users versus non-users without further adjustment of baseline covariates
Abbreviations: HR hazard ratio, CI confidence intervals, PS propensity score, IPTW inverse probability treatment weighting