Abstract
Background
Mortality is extremely high immediately after transition to dialysis, but the association of blood pressure (BP) before dialysis initiation with mortality after dialysis initiation remains unknown.
Study Design
Observational study.
Setting & Participants
17,729 US veterans transitioning to dialysis October 2007–September 2011, with a median follow-up of 2.0 years.
Predictor
Systolic BP (SBP) and diastolic BP (DBP) averaged over the last one-year pre-dialysis transition period as six (<120 to ≥160 mmHg in 10–mm Hg increments) and five (<60 to ≥90 mmHg in 10–mm Hg increments) categories, respectively, and as continuous measures.
Outcomes & Measurements
Post-dialysis all-cause mortality, assessed over different follow-up periods (i.e., <3, 3–<6, 6–<12, and ≥12 months after dialysis initiation) using Cox regressions adjusted for demographics, comorbidities, medications, cardiovascular medication adherence, body mass index, estimated glomerular filtration rate, and type of vascular access.
Results
The mean pre-dialysis SBPs and DBPs were 141.2±16.1 (SD) and 73.7±10.6 mm Hg, respectively. There was a reverse J-shaped association of SBP with all-cause mortality, with significantly higher mortality seen in SBP <140 mmHg. The mortality risks associated with lower SBP were greatest in the first 3 months after dialysis initiation, with multivariable-adjusted HRs of 2.40 (95% CI, 1.96–2.93), 1.99 (95% CI, 1.66–2.40), 1.35 (95% CI, 1.13–1.62), 0.98 (95% CI, 0.78–1.22), and 0.76 (95% CI, 0.57–1.00) for SBP <120, 120–<130, 130–<140, 150–<160, and ≥160 (versus 140–<150) mmHg, respectively. No consistent association was observed between pre-dialysis DBP and post-dialysis mortality.
Limitations
Results cannot infer causality and may not be generalizable to women or the general US population.
Conclusions
Lower pre-dialysis SBP is associated with higher all-cause mortality in the immediate post-dialysis period. Pre-dialysis DBP showed no consistent association with post-dialysis mortality. Further studies are needed to clarify ideal pre-dialysis SBP levels among incident dialysis patients as a potential means to improve the excessively high early dialysis mortality.
INDEX WORDS: blood pressure (BP), mortality, chronic kidney disease (CKD), dialysis, transition, dialysis initiation, end-stage renal disease (ESRD), incident ESRD, systolic BP (SBP), diastolic BP (DBP), transition of care, reverse epidemiology, risk factor paradox
Despite numerous advances in our understanding of chronic kidney disease (CKD) progression, the incidence of end-stage renal disease (ESRD) remains exceedingly high.1,2 Furthermore, patients with advanced non–dialysis-dependent CKD (NDD-CKD) transitioning to ESRD have the highest mortality within the first few months after the transition to dialysis and have an exceptionally high health and economic burden.1 It is, therefore, of paramount importance to focus on this vulnerable population and identify modifiable risk factors and interventions during the time period preceding transition to dialysis that could ameliorate their adverse clinical outcomes.
Hypertension is a well-known risk factor for cardiovascular disease and death in the general population;3,4 whereas, paradoxically, low blood pressure (BP) has been associated with higher mortality in dialysis patients, a phenomenon referred to as “reverse epidemiology” or “risk factor paradox”.5–9 Among patients with NDD-CKD and hypertension, some observational studies have indicated a J-shaped association between systolic BP (SBP) and mortality,10–14 suggesting the unique contribution of SBP to the risk of mortality in patients with different levels of kidney function. Current clinical guidelines recommend a target BP of <140/90 or <130/80 mmHg for patients with CKD, depending on their age, severity of albuminuria, and comorbidities,15,16 but there is a paucity of evidence on the association between BP and mortality in patients with advanced NDD-CKD, particularly among those in the transition period from advanced NDD-CKD to maintenance dialysis. To address this knowledge gap, we aimed to investigate the associations of SBP and diastolic BP (DBP) in the predialysis transition period with post-dialysis all-cause mortality, using a large nationally representative cohort of US veterans transitioning to dialysis.
METHODS
Study Population
We analyzed longitudinal data from the Transition of Care in CKD (TC-CKD) study, a retrospective cohort study examining US veterans transitioning to chronic kidney failure requiring renal replacement therapy (RRT) from October 1, 2007, through September 30, 2011.17–19 A total of 52,172 US veterans were identified from the US Renal Data System (USRDS)1 as a source population. The algorithm for the cohort definition is shown in Figure S1 (provided as online supplementary material). In the present study, we used only outpatient BP measurements available from US Department of Veterans Affairs (VA) medical centers, given the potential fluctuation of BP among hospitalized patients. Therefore, patients without any outpatient BP measurements at a VA medical center were excluded (n = 19,533). We also excluded those who did not have at least three outpatient BP measurements within one year prior to dialysis initiation (i.e., one-year “pre-dialysis period”; n = 14,645), those who initiated RRT with pre-emptive kidney transplantations (n = 163), or those who were missing follow-up data (n = 102), resulting in a study population of 17,729 patients.
Exposure Variable
The primary exposures of interest were SBP and DBP averaged over the one-year pre-dialysis period. The SBP and DBP were measured by trained staff using a standardized method. We divided the averaged SBP and DBP values into six and five categories, respectively (SBP, <120 to ≥160 mm Hg in 10–mm Hg increments; DBP, <60 to ≥90 mm Hg in 10–mm Hg increments). The SBP and DBP categories of 140–<150 and 70–<80 mmHg, respectively, were used as references in all analyses under the assumption that mortality risks are the lowest in these groups. The SBP and DBP were also treated as continuous variables to examine nonlinear associations by using fractional polynomials.
Covariates
Data from the USRDS Patient and Medical Evidence files were used to determine patients’ baseline demographic characteristics and type of vascular access at the time of dialysis initiation. Information about comorbidities at the time of dialysis initiation was extracted from the VA Inpatient and Outpatient Medical SAS Datasets,20 using the International Classification of Diseases, Ninth Revision, Clinical Modification, diagnostic and procedure codes and Current Procedural Terminology codes, as well as from VA/Centers for Medicare & Medicaid Services (CMS) data. We calculated the Charlson Comorbidity Index score using the Deyo modification for administrative datasets, without including kidney disease.21 Cardiovascular disease was defined as the presence of diagnostic codes for coronary artery disease, angina, myocardial infarction, or cerebrovascular disease. Medication data were collected from both CMS Data (Medicare Part D) and VA pharmacy dispensation records.22 Patients who received at least one dispensation of medications within the one-year pre-dialysis period were recorded as having been treated with these medications. Cardiovascular medication adherence was defined as the proportion of days covered by a drug during the one-year pre-dialysis period, capped at 100%.19 Laboratory data were obtained from VA research databases as previously described,23,24 and their baseline values were defined as the average of each covariate during the one-year pre-dialysis period preceding dialysis initiation. Using serum creatinine and demographic data, estimated glomerular filtration rate (eGFR) was calculated with the CKD-EPI (CKD Epidemiology Collaboration) creatinine equation.25 The last eGFR before dialysis initiation was obtained from USRDS Form 2728. The eGFR slope was calculated from an ordinary least squares regression model using all available outpatient eGFR measurements within the one-year pre-dialysis period and stratified into four a priori categories for the analyses as follows: <−10, −10–<−5, −5–<0, and ≥0 mL/min/1.73m2.17
Outcome Assessment
The primary outcome of interest was all-cause mortality after dialysis initiation. All-cause mortality data, censoring events, and associated dates were obtained from VA and USRDS data sources.1 The start of the follow-up period was the date of dialysis initiation, and patients were followed up until death or other censoring events including kidney transplantation, loss of follow-up, or December 27, 2012.17–19
Statistical Analysis
Baseline patient characteristics were summarized according to SBP categories, and presented as number (percentage) for categorical variables and the mean ± standard deviation for continuous variables with a normal distribution or median (interquartile range [IQR]) for those with a skewed distribution. Differences across categories were assessed using analysis of variance and chi-squared tests for continuous and categorical variables, respectively. The association of SBP and DBP with all-cause mortality was estimated using Cox proportional hazards models. The proportionality assumption was tested by plotting log [-log (survival rate)] against log (survival time) and by scaled Schoenfeld residuals. There was a violation in the proportionality assumption for the primary exposure variables; hence, hazard ratios (HRs) were presented for four discrete time periods (i.e., <3, 3–<6, 6–<12, and ≥12 months after dialysis initiation) to accommodate the violation. Given the exceptionally high mortality during the immediate post-transition period, we selected the first three months as the primary time window for our main analyses. Models were incrementally adjusted for the following potential confounders based on theoretical considerations and their availability in this study: model 1, unadjusted; model 2, adjusted for age, sex, race/ethnicity, and marital status; model 3 additionally accounted for comorbidities (cardiovascular disease, congestive heart failure [CHF], peripheral vascular disease, lung disease, diabetes mellitus, liver disease, and Charlson comorbidity index), and body mass index (BMI) (and SBP for the associations with pre-dialysis DBP) averaged over the one-year pre-dialysis period, eGFR slope, and last eGFR before dialysis initiation; and model 4 additionally included medications (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, β-blockers, calcium channel blockers, vasodilators, diuretics, statins, and erythropoiesis-stimulating agents), cardiovascular medication adherence, and type of vascular access (arteriovenous fistula, arteriovenous graft, or catheter). We used fractional polynomial regression models to examine nonlinear associations of SBP and DBP with mortality,26 in which the SBP and DBP were treated as continuous variables.
We conducted several sensitivity analyses to evaluate the robustness of our main findings. The associations of SBP and DBP with outcomes were examined in subgroups of patients stratified by age, race, BMI, eGFR, and presence/absence of select comorbidities, using three SBP and DBP categories (SBP, <130, 130–<150, and ≥150 mmHg; DBP, <70, 70–<80, and ≥80 mmHg). Potential interactions were formally tested by including relevant interaction terms. We also investigated whether accounting for serum albumin and blood hemoglobin levels further attenuates the associations of SBP and DBP with mortality in the group of 14,573 patients with available albumin and hemoglobin measurements as an additional model (model 5). In addition, we repeated our main analyses by including patients with one or two outpatient BP measurements.
Compared to patients in the main cohort (n = 17,729), those who were excluded from the source cohort (n = 34,443) were older (71.6 versus 67.8 years) and were less likely to be male (92.5% versus 98.1%) and African-American (20.5% versus 31.4%). Of the variables included in multivariable models, data points were missing for race (0.2%), vascular access type (7.5%), BMI (<0.01%), eGFR slope (10.5%), last eGFR (2.0%), serum albumin (3.3%), and blood hemoglobin (2.3%). Of the 17,729 patients in our study population, 15,719 (88.7%) had complete data available for the main adjusted multivariable model (model 4). Due to the relatively low proportion of missingness, missing data was not imputed. The reported P values are two-sided and reported as significant at <0.05 for all analyses. All analyses were conducted using STATA/MP Version 14 (STATA Corporation, College Station, TX). The study was approved by the Institutional Review Boards of the Memphis (protocol number 555872) and Long Beach VA Medical Centers (protocol number MIRB1282), with exemption from informed consent.
RESULTS
Baseline Characteristics
Patients’ baseline characteristics in the overall cohort and stratified by SBP categories are presented in Table 1. The overall mean age at baseline was 67.8±11.2 (standard deviation) years, 98.1% were male, 31.4% were African-American, and 73.0% were diabetic. The median last eGFR level before dialysis initiation was 11.0 (IQR, 8.1–14.7) mL/min/1.73m2. During the one-year pre-dialysis period, there were a median of 10 (IQR, 5–16) outpatient BP measurements per patient, and the mean SBPs and DBPs were 141.2±16.1 and 73.7±10.6 mmHg, respectively. Compared to patients with lower SBP, those with higher SBP were younger; were more likely to be African-American; and had a higher prevalence of diabetes and a lower prevalence of other comorbidities. They were also more likely to be prescribed antihypertensive medications; were less likely to adhere to cardiovascular medications; and had lower serum albumin, blood hemoglobin, and eGFR levels.
Table 1.
Baseline patient characteristics overall and according to pre-dialysis SBP
| Total | SBP (mmHg) | ||||||
|---|---|---|---|---|---|---|---|
| <120 | 120 – <130 | 130 – <140 | 140 – <150 | 150 – <160 | ≥160 | ||
| (N = 17,729) | (n = 1,497) | (n = 2,560) | (n = 4,380) | (n = 4,376) | (n = 2,856) | (n = 2,060) | |
| Systolic BP (mmHg) | 141.2±16.1 | 112.2±6.4 | 125.4±2.8 | 135.2±2.9 | 144.7±2.9 | 154.4±2.8 | 169.3±9.0 |
| Diastolic BP (mmHg) | 73.7±10.6 | 63.6±7.0 | 68.1±8.1 | 71.7±8.7 | 74.7±8.8 | 78.3±9.7 | 84.3±11.5 |
| Age (y) | 67.8±11.2 | 70.8±10.7 | 69.5±11.3 | 68.5±11.3 | 67.4±11.2 | 65.9±11.0 | 65.3±10.9 |
| Male sex | 17,388 (98.1) | 1,475 (98.5) | 2,511 (98.1) | 4,309 (98.4) | 4,297 (98.2) | 2,800 (98.0) | 1,996 (96.9) |
| African-American race | 5,559 (31.4) | 279 (18.6) | 590 (23.0) | 1,183 (27.0) | 1,426 (32.6) | 1,119 (39.2) | 962 (46.7) |
| Married marital status | 9,204 (51.9) | 836 (55.8) | 1,347 (52.6) | 2,426 (55.4) | 2,233 (51.0) | 1,434 (50.2) | 928 (45.0) |
| BMI (kg/m2) | 29.9±6.6 | 29.3±6.8 | 29.7±6.6 | 30.1±6.5 | 30.1±6.5 | 30.1±6.7 | 29.8±6.5 |
| Diabetes mellitus | 12,941 (73.0) | 966 (64.5) | 1,726 (67.4) | 3,073 (70.2) | 3,292 (75.2) | 2,239 (78.4) | 1,645 (79.9) |
| Cardiovascular diseasea | 8,315 (46.9) | 817 (54.6) | 1,281 (50.0) | 2,073 (47.3) | 1,992 (45.5) | 1,266 (44.3) | 886 (43.0) |
| Congestive heart failure | 9,930 (56.0) | 1,037 (69.3) | 1,456 (56.9) | 2,425 (55.4) | 2,376 (54.3) | 1,549 (54.2) | 1,087 (52.8) |
| Peripheral vascular disease | 7,354 (41.5) | 659 (44.0) | 1,148 (44.8) | 1,889 (43.1) | 1,825 (41.7) | 1,123 (39.3) | 710 (34.5) |
| Chronic pulmonary disease | 8,136 (45.9) | 902 (60.3) | 1,317 (51.4) | 2,072 (47.3) | 1,908 (43.6) | 1,178 (41.2) | 759 (36.8) |
| Liver disease | 2,393 (13.5) | 280 (18.7) | 368 (14.4) | 558 (12.7) | 571 (13.0) | 389 (13.6) | 227 (11.0) |
| Malignancies | 4,401 (24.8) | 435 (29.1) | 745 (29.1) | 1,176 (26.8) | 1,050 (24.0) | 623 (21.8) | 372 (18.1) |
| Charlson Comorbidity Index | 5 (3, 7) | 5 (3, 7) | 5.0 (3, 7) | 5 (3, 7) | 5 (3, 7) | 5.0 (3, 6) | 5 (3, 6) |
| Catheter vascular access type | 12,345 (69.6) | 1,210 (80.8) | 1,835 (71.7) | 2,886 (65.9) | 2,867 (65.5) | 1,971 (69.0) | 1,576 (76.5) |
| Medications | |||||||
| ACE inhibitors/ARBs | 11,238 (63.4) | 887 (59.3) | 1,524 (59.5) | 2,721 (62.1) | 2,753 (62.9) | 1,929 (67.5) | 1,424 (69.1) |
| β-blockers | 14,036 (79.2) | 1,117 (74.6) | 1,906 (74.5) | 3,401 (77.6) | 3,520 (80.4) | 2,373 (83.1) | 1,719 (83.4) |
| Calcium channel blockers | 1,3047 (73.6) | 492 (32.9) | 1,472 (57.5) | 3,263 (74.5) | 3,567 (81.5) | 2,482 (86.9) | 1,771 (86.0) |
| Diuretics | 14,854 (83.8) | 1,207 (80.6) | 1,993 (77.9) | 3,600 (82.2) | 3,719 (85.0) | 2,534 (88.7) | 1,801 (87.4) |
| Vasodilators | 1,075 (6.1) | 11 (0.7) | 48 (1.9) | 156 (3.6) | 273 (6.2) | 285 (10.0) | 302 (14.7) |
| Statins | 12,772 (72.0) | 1,012 (67.6) | 1,823 (71.2) | 3,273 (74.7) | 3,210 (73.4) | 2,039 (71.4) | 1,415 (68.7) |
| Vitamin D analogs | 6,391 (36.0) | 373 (24.9) | 864 (33.8) | 1,711 (39.1) | 1,700 (38.8) | 1,075 (37.6) | 668 (32.4) |
| ESAs | 6,762 (38.1) | 398 (26.6) | 816 (31.9) | 1,687 (38.5) | 1,847 (42.2) | 1,256 (44.0) | 758 (36.8) |
| CV medication adherence >80% | 13,793 (77.8) | 1,214 (81.1) | 2,092 (81.7) | 3,527 (80.5) | 3,399 (77.7) | 2,116 (74.1) | 1,445 (70.1) |
| Laboratory parametersb | |||||||
| Serum albumin (g/dL) | 3.4±0.6 | 3.4±0.6 | 3.4±0.6 | 3.4±0.6 | 3.4±0.6 | 3.3±0.6 | 3.2±0.6 |
| Blood hemoglobin (g/dL) | 10.9±1.4 | 11.1±1.6 | 11.1±1.5 | 11.0±1.4 | 10.8±1.3 | 10.7±1.3 | 10.5±1.4 |
| Mean eGFR (mL/min/1.73m2) | 15.7 (11.9, 22.2) | 22.3 (15.4, 33.2) | 17.8 (13.1, 27.7) | 15.6 (12.0, 22.4) | 14.9 (11.5, 19.8) | 14.8 (11.2, 19.6) | 14.4 (10.8, 19.3) |
| First eGFR predialysis*, mL/min/1.73 m2 | 19.8 (14.4, 29.6) | 27.3 (18.2, 45.0) | 21.6 (15.2, 36.1) | 19.1 (14.3, 28.7) | 19.0 (14.0, 26.7) | 19.1 (14.0, 27.4) | 19.0 (13.8, 27.6) |
| Last eGFR (mL/min/1.73m2) | 11.0 (8.1, 14.7) | 13.3 (9.3, 19.0) | 12.0 (8.5, 16.3) | 11.2 (8.3, 14.9) | 10.7 (8.0, 14.1) | 10.1 (7.6, 13.5) | 10.0 (7.3, 13.4) |
| eGFR slope | |||||||
| <−10 mL/min/1.73 m2 per y | 6,604 (41.6) | 557 (43.3) | 845 (37.6) | 1,379 (35.1) | 1,621 (40.6) | 1,241 (47.7) | 961 (53.0) |
| −10 to <−5 mL/min/1.73 m2 per y | 3,836 (24.2) | 200 (15.6) | 455 (20.2) | 1,018 (25.9) | 1,089 (27.3) | 640 (24.6) | 434 (23.9) |
| −5 to <0 mL/min/1.73 m2 per y | 3,313 (20.9) | 224 (17.4) | 517 (23.0) | 959 (24.4) | 859 (21.5) | 489 (18.8) | 265 (14.6) |
| ≥0 mL/min/1.73 m2 per y | 2,116 (13.3) | 305 (23.7) | 432 (19.2) | 574 (14.6) | 423 (10.6) | 229 (8.8) | 153 (8.4) |
Note: Values for categorical variables are given as percentages; values for continuous variables, as mean ± standard deviation or median [interquartile range]. All P-values for comparing differences across categories were statistically significant.
CV disease include coronary artery disease, angina, myocardial infarction, or cerebrovascular disease.
All laboratory results except for last eGFR were averaged over the one-year pre-dialysis period.
First eGFR during 1-y predialysis period
Abbreviations: ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; BMI, body mass index; CV, cardiovascular; eGFR, estimated glomerular filtration rate; ESA, erythropoiesis-stimulating agent; SBP, systolic blood pressure
Association of Predialysis SBP with Postdialysis All-Cause Mortality
During a median follow-up of 2.0 (IQR, 1.1–3.1) years following dialysis initiation (total time at risk, 37,969 patient-years), a total of 9,064 all-cause deaths occurred (crude incidence rate, 238.7 [95% CI, 233.9–243.7] per 1000 patient-years). Among 9,064 all-cause deaths, 1,515 deaths occurred in the first three months after dialysis initiation, followed by 1,097, 1,529, and 4,923 deaths in 3–<6, 6–<12, and ≥12 months, respectively. Table 2 shows the unadjusted- and multivariable-adjusted HRs in the first three months after dialysis initiation associated with pre-dialysis SBP categories. In the crude model, SBP categories were inversely associated with all-cause mortality, with significantly higher death risk seen in SBP categories of <140 (versus 140–<150) mmHg (Table 2). After adjustment for potential confounders, the association between SBP and mortality was attenuated but remained statistically significant (adjusted HRs of SBP <120, 120–<130, and 130–<140 [versus 140–<150] mm Hg are 2.40 [95% CI, 1.96–2.93], 1.99 [95% CI, 1.66–2.40], and 1.35 [95% CI, 1.13–1.62], respectively, in model 4). The SBP categories ≥150 mmHg were associated with a lower risk of mortality without reaching statistical significance (adjusted HRs of SBP 150–<160 and ≥160 [versus 140–<150] mmHg are 0.98 [95% CI, 0.78–1.22] and 0.76 [95% CI, 0.57–1.00], respectively, in model 4; Table 2). Similar associations were observed in different follow-up periods after the first three months of dialysis initiation, with the highest death risk seen in patients with SBP <120 mmHg (adjusted HRs of SBP <120 [versus 140–<150] mmHg of 1.71 [95% CI, 1.34–2.18], 2.16 [95% CI, 1.75–2.65], and 1.24 [95% CI, 1.08–1.42] for 3–<6, 6–<12, and ≥12 months, respectively, in model 4; Table S1).
Table 2.
Adjusted hazard ratios for all-cause mortality in the first 3 months after dialysis initiation by categories of pre-dialysis SBP
| SBP <120 (n=1,497) | SBP 120 – <130 (n=2,560) | SBP 130 – <140 (n=4,380) | SBP 140 – <150 (n=4,376) | SBP 150 – <160 (n=2,856) | SBP ≥160 (n=2,060) | |
|---|---|---|---|---|---|---|
| Events | 318 (21.2) | 350 (13.7) | 364 (8.3) | 245 (5.8) | 149 (5.2) | 89 (4.3) |
| Hazard Ratio | ||||||
| Model 1 | 4.17 (3.53–4.93) | 2.56 (2.17–3.01) | 1.51 (1.28–1.77) | 1.00 (reference) | 0.93 (0.76–1.14) | 0.77 (0.60–0.98) |
| Model 2 | 3.58 (3.02–4.23) | 2.32 (1.97–2.73) | 1.44 (1.22–1.69) | 1.00 (reference) | 1.00 (0.81–1.23) | 0.86 (0.67–1.09) |
| Model 3 | 2.85 (2.37–3.43) | 2.19 (1.83–2.61) | 1.37 (1.15–1.63) | 1.00 (reference) | 0.97 (0.78–1.21) | 0.80 (0.61–1.05) |
| Model 4 | 2.40 (1.96–2.93) | 1.99 (1.66–2.40) | 1.35 (1.13–1.62) | 1.00 (reference) | 0.98 (0.78–1.22) | 0.76 (0.57–1.00) |
Note: Data are presented as number (percentage) or hazard ratio (95% confidence interval). Model 1 is unadjusted; model 2 is adjusted for age, sex, race/ethnicity, and marital status; model 3 is adjusted for the variables in model 2 plus comorbidities (cardiovascular disease, congestive heart failure, peripheral vascular disease, lung disease, diabetes mellitus, liver disease, and Charlson comorbidity index), body mass index averaged over the one-year pre-dialysis period, eGFR slope, and last eGFR before dialysis initiation; and model 4 is adjusted for the variables in model 3 plus medications (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, β-blockers, calcium channel blockers, vasodilators, diuretics, statins, and erythropoiesis-stimulating agents), cardiovascular medication adherence, and type of vascular access (arteriovenous fistula, arteriovenous graft, or catheter).
Abbreviations: eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure (in mm Hg)
Figure 1 shows the multivariable-adjusted association of SBP with mortality in the first three months after dialysis initiation using fractional polynomials. There was a reverse J-shaped association, with significantly higher death risk seen in patients with lower SBP (Figure 1). When comparing the adjusted HRs across different follow-up periods, the mortality risk associated with lower SBP was highest in the first three months after dialysis initiation (Figure 1 and Fig S2). In subgroup analyses, compared with SBP 130–<150 mmHg, lower SBP (<130 mmHg) was associated with higher mortality overall and across all subgroups; higher SBP (≥150 mmHg) was associated with lower mortality in the first three months after dialysis initiation (Figure 2). A similar pattern of association between SBP and mortality was observed in the 3–<6 and 6–<12 month periods after dialysis initiation (Fig S3). In contrast, among patients who survived the first 12 months after dialysis initiation, both lower and higher pre-dialysis SBPs were associated with higher mortality overall and in most examined subgroups. Statistically significant interactions were present for BMI, CHF, and eGFR, with greater contributions of lower SBP to mortality among patients with a BMI of ≥30 kg/m2, those with CHF, and those with a last eGFR of ≥10 mL/min/1.73 m2 (Fig S3). Results were consistent after further adjustment for serum albumin and blood hemoglobin levels and even after including patients with one or two outpatient SBP measurements (Table S2 and Fig S4).
Figure 1. Association of pre-dialysis SBP with all-cause mortality in the first 3 months after dialysis initiation.
Solid and dashed lines represent hazard ratio and 95% CI, respectively.
A hazard reference ratio of 1 (solid horizontal line) and a histogram of observed SBP values are overlaid.
The x-axis shows SBP levels, trimmed at 0.5% and 99.5%.
Model is adjusted for age, sex, race/ethnicity, marital status, comorbidities (cardiovascular disease, congestive heart failure, peripheral vascular disease, lung disease, diabetes mellitus, liver disease, and Charlson comorbidity index), BMI averaged over the one-year pre-dialysis period, eGFR slope, last eGFR before dialysis initiation, medications (ACEIs/ARBs, β-blockers, calcium channel blockers, vasodilators, diuretics, statins, and ESAs), cardiovascular medication adherence, and type of vascular access (arteriovenous fistula, arteriovenous graft, or catheter).
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; eGFR, estimated glomerular filtration rate; ESA, erythropoietin stimulating agent; SBP, systolic blood pressure
Figure 2. Adjusted hazard ratios (95% CIs) of all-cause mortality in the first 3 months after dialysis initiation associated with pre-dialysis SBP categories in selected subgroups.
Model is adjusted for age, sex, race/ethnicity, marital status, comorbidities (cardiovascular disease, congestive heart failure, peripheral vascular disease, lung disease, diabetes mellitus, liver disease, and Charlson comorbidity index), BMI averaged over the one-year pre-dialysis period, eGFR slope, last eGFR before dialysis initiation, medications (ACEIs/ARBs, β-blockers, calcium channel blockers, vasodilators, diuretics, statins, and ESAs), cardiovascular medication adherence, and type of vascular access (arteriovenous fistula, arteriovenous graft, or catheter).
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; CHF, congestive heart failure; CVD, cardiovascular disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; ESA, erythropoietin stimulating agent; SBP, systolic blood pressure
Association of Predialysis DBP With Postdialysis All-Cause Mortality
Table 3 shows the unadjusted- and multivariable-adjusted HRs for all-cause mortality in the first three months after dialysis initiation associated with pre-dialysis DBP categories. In the crude model, DBP categories were inversely associated with mortality, with the highest risk seen in DBP category of <60 mmHg (Table 3). The association of DBP categories with mortality was substantially attenuated after multivariable adjustment, and no longer statistically significant (adjusted HRs of DBP <60, 60–<70, 80–<90, and ≥90 [versus 70–<80] mmHg are 0.96 [95% CI, 0.78–1.17], 1.03 [95% CI, 0.89–1.19], 0.90 [95% CI, 0.73–1.12], and 1.05 [95% CI, 0.71–1.53], respectively, in model 4; Table 3). As shown in Figure 3, the multivariable-adjusted association between DBP and mortality was slightly J-shaped, without statistical significance. There was no consistent association of DBP with mortality in different follow-up periods (Table S3 and Fig S5).
Table 3.
Adjusted hazard ratios for all-cause mortality in the first 3 months after dialysis initiation by categories of pre-dialysis DBP
| DBP <60 (n=1,517) | DBP 60 – <70 (n=5,269) | DBP 70 – <80 (n=6,152) | DBP 80 – <90 (n=3,574) | DBP ≥90 (n=1,217) | |
|---|---|---|---|---|---|
| Events | 250 (16.5) | 608 (11.5) | 442 (7.2) | 171 (4.8) | 44 (3.6) |
| Hazard Ratio | |||||
| Model 1 | 2.43 (2.08–2.84) | 1.64 (1.45–1.86) | 1.00 (reference) | 0.66 (0.55–0.78) | 0.49 (0.36–0.67) |
| Model 2 | 1.72 (1.46–2.02) | 1.32 (1.16–1.49) | 1.00 (reference) | 0.85 (0.71–1.01) | 0.82 (0.59–1.12) |
| Model 3 | 0.90 (0.74–1.09) | 0.96 (0.84–1.11) | 1.00 (reference) | 1.01 (0.83–1.24) | 1.25 (0.86–1.81) |
| Model 4 | 0.96 (0.78–1.17) | 1.03 (0.89–1.19) | 1.00 (reference) | 0.90 (0.73–1.12) | 1.05 (0.71–1.53) |
Note: Data are presented as number (percentage) or hazard ratio (95% confidence interval). Model 1 is unadjusted; model 2 is adjusted for age, sex, race/ethnicity, and marital status; model 3 is adjusted for the variables in model 2 plus comorbidities (cardiovascular disease, congestive heart failure, peripheral vascular disease, lung disease, diabetes mellitus, liver disease, and Charlson comorbidity index), Systolic blood pressure and body mass index averaged over the one-year pre-dialysis period, eGFR slope, and last eGFR before dialysis initiation; and model 4 is adjusted for the variables in model 3 plus medications (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, β-blockers, calcium channel blockers, vasodilators, diuretics, statins, and erythropoiesis-stimulating agents), cardiovascular medication adherence, and type of vascular access (arteriovenous fistula, arteriovenous graft, or catheter).
Abbreviations: DBP, diastolic blood pressure (in mm Hg); eGFR, estimated glomerular filtration rate;
Figure 3. Association of pre-dialysis DBP with all-cause mortality in the first 3 months after dialysis initiation.
Solid and dashed lines represent hazard ratio and 95% CI, respectively.
A hazard reference ratio of 1 (solid horizontal line) and a histogram of observed DBP values are overlaid.
The x-axis shows DBP levels, trimmed at 0.5% and 99.5%.
Model is adjusted for age, sex, race/ethnicity, marital status, comorbidities (cardiovascular disease, congestive heart failure, peripheral vascular disease, lung disease, diabetes mellitus, liver disease, and Charlson comorbidity index), SBP and BMI averaged over the one-year pre-dialysis period, eGFR slope, last eGFR before dialysis initiation, medications (ACEIs/ARBs, β-blockers, calcium channel blockers, vasodilators, diuretics, statins, and ESAs), cardiovascular medication adherence, and type of vascular access (arteriovenous fistula, arteriovenous graft, or catheter).
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; ESA, erythropoietin stimulating agent; SBP, systolic blood pressure
In subgroup analyses, the pattern of association between DBP and mortality was qualitatively similar in selected subgroups across different follow-up periods (Fig S6). The associations were similar after further adjustment for serum albumin and blood hemoglobin levels and after including patients with one or two outpatient DBP measurements (Table S4 and Fig S7).
DISCUSSION
In this large national cohort of US veterans transitioning to dialysis, we found a reverse J-shaped association of SBP over the one-year pre-dialysis period with all-cause mortality following dialysis initiation, independent of demographics, comorbidities, BMI, eGFR, medications, cardiovascular medication adherence, and type of vascular access. We also found that patients with pre-dialysis SBP <140 mmHg had significantly higher death risks after dialysis initiation. These associations were most robust for mortality within the immediate 3 months following dialysis transition. In contrast, pre-dialysis DBP showed no consistent association with post-dialysis mortality.
Several epidemiologic studies have repeatedly demonstrated that lower SBP is associated with higher mortality risk in dialysis patients,5–9 which has also been termed “reverse epidemiology”27 or “risk factor paradox”28 to contrast it with the well-established association of higher SBP with greater mortality risk in the general population.3,4 Previous studies have also described a J-shaped association of SBP with all-cause mortality in patients with NDD-CKD,10–13 suggesting that the pattern seen in dialysis patients may also be present in NDD-CKD, and probably becomes manifest once patients reach late stages of CKD.29 Regarding the SBP-mortality association in later stages of CKD, a few observational studies have offered seemingly conflicting evidence, some suggesting a J-shaped association,11,12 while one other showing no association between SBP and mortality.30 More recently, using extended follow-up data from the MDRD (Modification of Diet in Renal Disease) Study and the African American Study of Kidney Disease and Hypertension trial, Ku et al.31,32 have shown an association of strict BP control (mean arterial pressure of ≤92 [versus 102–107] mmHg) with lower mortality risk in patients with moderate to advanced NDD-CKD. These studies, however, enrolled relatively small numbers of individuals with advanced NDD-CKD, including patients who did—as well as those who did not—reach ESRD. In this study, we therefore extended these observations to a large and unique cohort of patients all of whom transitioned to dialysis, and for the first time investigated the associations of SBP and DBP in the immediate pre-dialysis transition period with mortality after dialysis initiation.
Several potential explanations have been suggested for the underlying mechanisms of the observed reverse J-shaped association with all-cause mortality. Over the course of CKD progression, patients with CKD develop a wide variety of comorbid conditions, such as ischemic heart disease and CHF.33,34 Patients with advanced NDD-CKD have an exceptionally high burden of such conditions, and low SBP may be a marker of more severe underlying comorbid conditions, which could therefore confound the association between BP and mortality. However, low SBP could also be a direct mediator of the effects of severe comorbidities on clinical outcomes by causing compromised blood flow to vital organs, which could explain the higher mortality observed in our study. Furthermore, cardiovascular drug overdose (regardless of whether it was for cardio- or reno-protection) and/or ultrafiltration procedures during the hemodialysis treatment (particularly for those with low pre-dialysis SBP)35 could possibly play contributory roles in the associations seen in our study.
Our findings support the hypothesis that the effect of higher SBP, a conventional cardiovascular risk factor affecting relatively long-term outcomes, on future mortality might be outweighed by the short-term effects of lower SBP accompanied by the aforementioned pathophysiological processes intrinsic to this population—hence, the observation that the reverse J-shaped association was more evident for short-term mortality immediately after dialysis transition. It is also important to note that we observed notable interactions in a few subgroups of patients who survived the first 12 months after dialysis initiation. Among this particular population, the contribution of higher SBP (>150 mmHg) to higher all-cause mortality was more evident in non-CHF patients. The inverse association of SBP with all-cause mortality seen in CHF patients during this long-term period is generally consistent with what has been described in the general CHF population.36 It is thus possible that low SBP in some patients with advanced NDD-CKD is preceded by cardiovascular (i.e., CHF) and diabetic (i.e., autonomic neuropathy) consequences, and its role in the outcomes of these patients may require special consideration, particularly if they survive the first 12 months after dialysis initiation.
Given the considerable uncertainty about the optimal approach to BP management in advanced NDD-CKD patients, our study may have several clinical implications. First, physicians should be aware of the marked immediate post-dialysis death risk associated with low pre-dialysis SBP in patients with advanced NDD-CKD transitioning to dialysis. Notwithstanding considerations about causality (or lack thereof), a low pre-dialysis SBP could thus be used as a predictor of higher early post-transition mortality, and could therefore be useful when counseling patients or when planning dialysis preparations (e.g. vascular access placement). If we allow for the possibility that the SBP-mortality relationship may be causal, our findings suggest caution when implementing BP-lowering strategies based on current hypertension treatment guidelines which recommend a target BP of <140/90 or <130/80 mmHg without specifying a desirable lower limit for the target BP.15,16 The optimal SBP levels and the effect of antihypertensive medications to lower elevated SBP towards such levels towards improving clinical outcomes in this unique population deserves future prospective studies, including controlled clinical trials.
This study needs to be interpreted with acknowledgment of several limitations. First, because this was an observational study, only associations, not cause-effect relationships, can be established. Most important, we cannot conclude that the mortality risk associated with various SBPs in our study is equal to the risk imparted by the same SBPs when they occur as a result of antihypertensive interventions in clinical practice. Therefore, it must be emphasized that our current findings should not divert clinicians’ efforts from lowering elevated SBP to prevent cardiovascular complications and kidney disease progression in NDD-CKD patients. Second, most of our patients were male US veterans; hence, the results may not apply to women or patients from other geographical areas. Also of note, all patients in this cohort survived to the point of initiating dialysis and were anchored on that time point. Third, the effect of longitudinal changes in BP and other confounders such as cardiovascular medications over the post-dialysis follow-up period were not accounted for; thus, it is possible that such time-dependent factors might affect the observed risk estimates. However, given the nature of this study, which examined the impact of pre-dialysis SBP and DBP on post-dialysis outcomes, the obtained results with the use of fixed baseline covariates would still be of value, with important clinical implications for CKD patients in the transition period. Finally, as with all observational studies, we were not able to eliminate the possibility of unmeasured confounders such as proteinuria.
In conclusion, in this large national cohort of US veterans transitioning to dialysis, we found a reverse J-shaped association of pre-dialysis SBP with all-cause mortality following dialysis initiation, with significantly higher death risk seen in SBP of <140 mmHg, but found no consistent association with pre-dialysis DBP. Our findings suggest that pre-transition low SBP could be a useful predictor of early post-transition mortality, and also that physicians should be cautious when substantially lowering SBP below the currently established targets in this unique patient population. Future clinical trials are needed to clarify the ideal pre-dialysis SBP and to determine whether active interventions targeting pre-dialysis SBP could be applied to improve the high early mortality seen among incident dialysis patients.
Supplementary Material
Acknowledgments
Support: This study is supported by grant 5U01DK102163 from the National Institutes of Health to Drs Kalantar-Zadeh and Kovesdy, and by resources from the VA. The data reported here have been supplied in part by the USRDS. Support for VA/CMS data is provided by the VA, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, and VA Information Resource Center (project numbers SDR 02-237 and 98-004). Funders of this study had no role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.
Footnotes
Drs Kovesdy and Kalantar-Zadeh are employees of the VA. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as official policy or interpretation of the VA or the US government. The results of this paper have not been published previously in whole or part.
Financial Disclosure: The authors declare that they have no other relevant financial interests.
Contributions: Research idea: KS, CPK; study design: KS, MZM, PKP, FT, JLL, VAR, MS, CMR, ES, JJS, KY, KK-Z, CPK; data acquisition: KS, MZM, PKP, JLL, VAR, MS, ES, CPK; data analysis/interpretation: KS, PKP, KK-Z, CPK; supervision or mentorship: KY, KK-Z, CPK. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. CPK and KS take responsibility that this study has been reported honestly, accurately, and transparently; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
Peer Review: Evaluated by two external peer reviewers, a Statistical Editor, a Co-Editor, and Editor-in-Chief Levey.
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