Abstract
Background:
Prior studies have shown an association between high blood urea nitrogen (BUN) and an elevated risk of mortality in heart failure patients, but data on the prognostic significance of BUN and other markers of kidney function in acute ischemic stroke (AIS) patients are sparse.
Methods and Results:
A total of 3,355 AIS patients were enrolled from December 2013 to May 2014, across 22 hospitals. Admission BUN was divided into quartiles (Q1, <4.39 mmol/L; Q2, ≥4.39 and <5.40 mmol/L; Q3, ≥5.40 and <6.70 mmol/L and Q4, ≥6.70 mmol/L) and estimated glomerular filtration rate (eGFR), creatinine (Cr) and BUN/Cr were also categorized. Cox proportional hazard and logistic regression models were used to estimate the effect of BUN, eGFR, Cr and BUN/Cr on all-cause in-hospital mortality and poor outcome on discharge (modified Rankin Scale score ≥3) in AIS patients. During hospitalization, 120 patients (3.6%) died from all causes and 1,287 (38.4%) had poor outcome at discharge. BUN was independently associated with all-cause in-hospital mortality (adjusted HR for Q4 vs. Q1, 3.75; 95% CI: 1.53–9.21; P-trend=0.003) but not poor outcome at discharge (P-trend=0.229). No significant association was found, however, between reduced eGFR, increased Cr and BUN/Cr and all-cause in-hospital mortality and poor outcome at discharge (all P-trend ≥0.169).
Conclusions:
Increased BUN at admission is a significant prognostic factor associated with in-hospital mortality in AIS patients, but not with poor discharge outcome.
Chronic kidney disease (CKD) is a worldwide public health problem with an estimated prevalence of 10.8% in the Chinese population.1–3
A growing body of studies have suggested that CKD, as indicated by reduced estimated glomerular filtration rate (eGFR), increased creatinine (Cr) and proteinuria, is not only a notable risk factor for cardiovascular disease4–7
but is also independently associated with long-term poor outcome and mortality in acute ischemic stroke (AIS) patients.8–10
The prognostic role of CKD in in-hospital mortality and short-term outcome in AIS patients, however, has been debated.11,12
Blood urea nitrogen (BUN) concentration has been traditionally considered as a less specific marker of kidney function than other CKD biomarkers. Interestingly, some recent studies have indicated that higher BUN independently increases mortality in heart failure13–15
and acute coronary syndrome (ACS) patients.16
These studies suggest that the prognostic role of BUN maybe stronger than eGFR because it is a reflection of both kidney function and hemodynamic status.13–16
Moreover, higher BUN/Cr was found to be associated with poor outcome in AIS patients.17,18
The relationship between BUN and in-hospital mortality and short-term outcomes in AIS patients, however, is unclear. Moreover, the prognostic value of BUN in comparison with other CKD biomarkers is also unknown.
In the present study, we evaluated and compared the prognostic significance of BUN and other CKD biomarkers (eGFR, Cr and BUN/Cr) in in-hospital mortality and poor discharge outcomes after AIS.
Methods
Subjects
From December 2013 to May 2014, we recruited patients with AIS or transient ischemic attack (TIA) from 22 hospitals in Suzhou, a major city in southeast China. Patients aged ≥18 years with a clinical diagnosis of AIS or TIA were considered eligible. Diagnosis of ischemic stroke was made according to World Health Organization criteria based on patient history, clinical data, and neuroimaging (computed tomography or magnetic resonance imaging). A team of investigators, including neurologists, reviewed the eligibility of study participants (Appendix S1). Additional exclusion criteria were as follows: (1) finally diagnosis of TIA; and (2) time from onset to admission >7 days. A total of 3,450 patients were potentially eligible for this analysis. Of these patients, 95 were further excluded because of lack of BUN and Cr concentration on admission (Figure 1).
Ethics Statement
This study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University, as well as ethics committees at the participating hospitals. Written consent was obtained from all study participants or their immediate family members.
Data Collection and Outcome Assessment
We collected baseline information, including patient demographics, vascular risk factors, stroke severity (National Institutes of Health Stroke Scale, NIHSS; Modified Rankin Scale score, mRS), medication use, imaging data and diagnosis-related information. Vascular risk factors included history of stroke, history of hypertension, history of diabetes mellitus, history of atrial fibrillation (AF), history of coronary artery disease (CAD), current or previous smoking status, and alcohol consumption. Information on these factors was obtained by interviews with patients or their family members (if patients were not able to communicate). Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg, or use of antihypertensive medications. Diabetes mellitus was defined as fasting glucose ≥7.0 mmol/L (126 mg/dL), non-fasting glucose ≥11.1 mmol/L (200 mg/dL) with classic symptoms of hyperglycemia or hyperglycemic crisis, or use of glucose-lowering drugs. AF was defined as a history of AF, confirmed on ≥1 electrocardiograms or the presence of arrhythmia during hospitalization. Blood samples were collected within 24 h of hospital admission. Laboratory variables, including BUN and Cr were assayed at local laboratories. eGFR was based on a single Cr measurement that was performed at baseline, using the 4-variable Modification of Diet in Renal Disease formula: GFR=186×(serum Cr)−1.154×(age)−0.203×(0.742 if the subject is female).19
The primary endpoint was all-cause in-hospital mortality, and secondary outcome was poor outcome (defined as having a mRS score ≥3) at hospital discharge.
Statistical Analysis
Study participants were categorized, based on quartiles of BUN at admission: Q1, <4.39 mmol/L; Q2, ≥4.39 and <5.40 mmol/L; Q3, ≥5.40 and <6.70 mmol/L; and Q4, ≥6.70 mmol/L; on quartiles of Cr at admission: Q1, <56.5 μmol/L; Q2, 56.5–68.0 μmol/L; Q3, 68.0–83.2 μmol/L; and Q4, ≥83.2 μmol/L; on quartiles of BUN/Cr at admission: Q1, <15.4; Q2, 15.4–19.3; Q3, 19.3–24.3; and Q4, ≥24.3; and into 3 groups based on baseline eGFR: group 1, <60 mL/min/1.73 m2; group 2, 60–89 mL/min/1.73 m2; and group 3, ≥90 mL/min/1.73 m2.20
Continuous variables are expressed as mean±SD or median (IQR) and were compared using analysis of variance or Wilcoxon rank-sum test. Categorical variables are expressed as frequency (%) and were compared using the chi-squared test.
The crude cumulative risks of all-cause in-hospital mortality for each group of admission BUN, eGFR, Cr and BUN/Cr were compared on Kaplan-Meier analysis using log-rank test. Associations between BUN, eGFR, Cr and BUN/Cr and the risk of all-cause in-hospital mortality were also estimated using Cox proportional hazard regression models. Crude and multivariable logistic regression models were used for secondary outcome assessment with regard to the risk of poor outcome at hospital discharge. Hazard ratios (HR) and 95% CI, and odds ratios (OR) and 95% CI were calculated for each group with the lowest BUN, Cr and BUN/Cr quartile (Q1) and the highest eGFR group as references. Potential confounders such as age, sex, SBP, time from onset to admission, cigarette smoking status, alcohol consumption, history of hypertension, history of diabetes mellitus, history of CAD, history of AF, history of stroke, antihypertensive therapy, stroke subtype (cardioembolic stroke vs. others), serum uric acid, total protein, and baseline NIHSS score were included in the multivariate models. In addition, subgroup analyses were conducted in confounder adjusted models stratified by age (≥70 years old vs. <70 years old), sex, baseline SBP (≥150 mmHg vs. <150 mmHg), history of stroke, baseline eGFR (≥90 mL/min/1.73 m2
vs. <90 mL/min/1.73 m2), and history of diabetes mellitus. To assess the robustness of the association between kidney function biomarkers and short-term outcome, we also performed sensitivity analyses to assess the relationship between kidney function biomarkers and death within 7 days. All P-values were 2-tailed, with P<0.05 considered statistically significant. All analyses were conducted using SPSS version 17.0.
Results
There were 3,450 AIS patients who had met the diagnostic criteria, of whom, 95 (2.8%) were excluded due to missing data on BUN and Cr. Complete data on conventional risk factors and BUN and Cr at admission were available for 3,355 patients (1,937 men and 1,418 women). The median age was 70 years (IQR, 60–79), and the median NIHSS score was 4.0 (IQR, 2.0–7.0). In comparison with participants with lower BUN, those with higher BUNs were more likely to be older, tend to be non-smokers, had more severe stroke (higher mRS) and other comorbidities including hypertension, diabetes mellitus, and AF. Patients with higher BUN also differed in metabolic profile (higher fasting plasma glucose, serum Cr and serum uric acid but lower eGFR) and lower baseline DBP (Table 1). Baseline participant characteristics according to eGFR are listed in
Table S1. In comparison with participants with higher eGFR, those with lower eGFR were more likely to be older, tend to be female, non-smokers, with no alcohol consumption, had more severe stroke and other comorbidities including hypertension, diabetes mellitus, CAD, stroke and AF.
Table 1.
Baseline AIS Patient Characteristics vs. BUN Quartiles (n=3,355)
| |
BUN (mmol/L) |
P-value |
| Q1 [<4.39] |
Q2 [4.39–5.40] |
Q3 [5.40–6.70] |
Q4 [≥6.70] |
| No. subjects |
832 |
803 |
864 |
856 |
|
| Demographics |
| Age (years) |
64.7±13.0 |
67.7±12.6 |
69.2±12.2 |
72.7±12.3 |
<0.001 |
| Male sex |
449 (54.0) |
485 (60.4) |
512 (59.3) |
491 (57.4) |
0.045 |
| Cigarette smoking status |
173 (20.8) |
161 (20.0) |
194 (22.5) |
142 (16.6) |
0.020 |
| Alcohol consumption |
84 (10.1) |
76 (9.5) |
96 (11.1) |
72 (8.4) |
0.291 |
| Clinical features |
| Time from onset to admission (h) |
24.0 (6.0–48.0) |
24.0 (5.0–48.0) |
24.0 (5.0–48.0) |
24.0 (5.0–72.0) |
0.980 |
| Hospital stay (days) |
10.0 (8.0–14.0) |
10.0 (8.0–14.0) |
10.0 (8.0–14.0) |
11.0 (8.0–15.0) |
0.276 |
| Baseline SBP (mmHg) |
152.2±22.7 |
151.1±21.5 |
153.1±21.8 |
152.9±24.7 |
0.274 |
| Baseline DBP (mmHg) |
86.6±13.1 |
85.5±12.6 |
85.8±13.1 |
84.1±14.0 |
0.001 |
| TG (mmol/L) |
1.2 (0.9–1.8) |
1.3 (0.9–1.7) |
1.2 (0.9–1.6) |
1.3 (0.9–1.7) |
0.694 |
| TC (mmol/L) |
4.5 (3.8–5.2) |
4.6 (3.9–5.2) |
4.6 (3.9–5.3) |
4.6 (3.9–5.3) |
0.781 |
| LDL-C (mmol/L) |
2.7 (2.2–3.3) |
2.6 (2.1–3.2) |
2.7 (2.1–3.3) |
2.7 (2.1–3.3) |
0.377 |
| HDL-C (mmol/L) |
1.2 (1.0–1.4) |
1.2 (1.0–1.4) |
1.2 (1.0–1.4) |
1.2 (1.0–1.4) |
0.378 |
| FPG (mmol/L) |
5.6 (5.0–6.7) |
5.7 (5.0–6.9) |
5.7 (5.0–7.2) |
6.0 (5.1–7.7) |
<0.001 |
| Total protein (g/L) |
66.4±6.8 |
66.2±6.8 |
66.4±6.4 |
65.7±7.3 |
0.096 |
| Cr (μmol/L) |
61.2±15.3 |
67.4±16.6 |
72.3±19.6 |
96.6±60.2 |
<0.001 |
| eGFR (mL/min/1.73 m2) |
114.8±34.3 |
103.6±28.4 |
96.2±29.3 |
78.4±33.8 |
<0.001 |
| Uric acid (μmol/L) |
281.5±82.1 |
304.7±90.2 |
322.0±98.8 |
361.6±118.0 |
<0.001 |
| Baseline mRS score |
2 (1–3) |
2 (1–3) |
2 (2–4) |
3 (2–4) |
<0.001 |
| Baseline NIHSS score |
4 (2–6) |
3 (2–6) |
3 (2–7) |
4 (2–9) |
<0.001 |
| Medical history |
| Hypertension |
613 (73.7) |
635 (79.1) |
670 (77.5) |
707 (82.6) |
<0.001 |
| Diabetes mellitus |
191 (23.0) |
186 (23.2) |
215 (24.9) |
270 (31.5) |
<0.001 |
| Coronary artery disease |
42 (5.0) |
47 (5.9) |
43 (5.0) |
57 (6.7) |
0.391 |
| Atrial fibrillation |
99 (11.9) |
102 (12.7) |
126 (14.6) |
191(22.3) |
<0.001 |
| Stroke |
185 (22.2) |
191 (23.8) |
179 (20.7) |
196 (22.9) |
0.488 |
| Medication history |
| Antihypertensive therapy |
448 (53.8) |
477 (59.4) |
481 (55.7) |
556 (65.0) |
<0.001 |
| Antiplatelet therapy |
51 (6.1) |
55 (6.8) |
72 (8.3) |
72 (8.4) |
0.200 |
| Anticoagulation therapy |
12 (1.4) |
8 (1.0) |
8 (0.9) |
9 (1.1) |
0.754 |
| Antiglycemic therapy |
130 (15.6) |
138 (17.2) |
138 (16.0) |
220 (25.7) |
<0.001 |
| Statin therapy |
22 (2.6) |
24 (3.0) |
28 (3.2) |
30 (3.5) |
0.769 |
| Thrombolysis treatment |
18 (2.2) |
23 (2.9) |
24 (2.8) |
16 (1.9) |
0.480 |
| Stroke syndrome |
|
|
|
|
0.001 |
| TACS |
67 (8.1) |
71 (8.8) |
76 (8.8) |
122 (14.3) |
|
| PACS |
410 (49.3) |
404 (50.3) |
442 (51.2) |
421 (49.2) |
|
| POCS |
201 (24.2) |
196 (24.4) |
194 (22.5) |
195 (22.8) |
|
| LACS |
154 (18.5) |
132 (16.4) |
152 (17.6) |
118 (13.8) |
|
Data given as mean±SD, median (IQR) or n (%). AIS, acute ischemic stroke; BUN, blood urea nitrogen; Cr, creatinine; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LACS, lacunar syndrome; LDL-C, low-density lipoprotein cholesterol; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; PACS, partial anterior circulation syndrome; POCS, posterior circulation syndrome; SBP, systolic blood pressure; TACS, total anterior circulation syndrome; TC, total cholesterol; TG, triglycerides.
During hospitalization, 120 patients (3.6%) died from all causes. The cumulative incidence of all-cause mortality increased across the BUN, Cr, BUN/Cr and decreased across eGFR categories (log-rank P≤0.013 for all;
Figure 2). In the unadjusted Cox proportional hazards model, in-hospital mortality was significantly higher in participants with admission BUN in the highest quartile (≥6.70 mmol/L) compared with those in the lowest quartile (<4.39 mmol/L; (HR, 8.25; 95% CI: 3.97–17.2; P-trend <0.001). After adjusting for age, sex, time from onset to admission, baseline NIHSS score, eGFR and other traditional risk factors, the HR for the highest quartile of BUN at admission was 3.75 (95% CI: 1.53–9.21; P-trend=0.003) as compared with the lowest quartile for mortality (Table 2). In the unadjusted Cox model, reduced eGFR and increased Cr, BUN/Cr were also associated with higher in-hospital mortality (P-trend ≤0.001 for all). The relationship between those biomarkers and in-hospital mortality, however, was not significant after adjusting for potential confounders (P-trend ≥0.250 for all;
Table 2).
Table 2.
Indicators of In-Hospital All-Cause Mortality
| |
n (%) |
Unadjusted |
Model 1 |
Model 2 |
| HR (95% CI) |
P-trend |
HR (95% CI) |
P-trend |
HR (95% CI) |
P-trend |
| BUN (mmol/L) |
|
|
<0.001 |
|
<0.001 |
|
0.003 |
| <4.39 |
8 (1.0) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| 4.39–5.40 |
18 (2.2) |
2.29 (1.00–5.06) |
|
1.97 (0.86–4.54) |
|
2.07 (0.81–5.30) |
|
| 5.40–6.70 |
24 (2.8) |
2.88 (1.29–6.41) |
|
2.26 (1.01–5.04) |
|
1.58 (0.63–3.95) |
|
| ≥6.70 |
70 (8.2) |
8.25 (3.97–17.2) |
|
5.53 (2.63–11.6) |
|
3.75 (1.53–9.21) |
|
| eGFR (mL/min/1.73 m2) |
|
|
<0.001 |
|
0.001 |
|
0.308 |
| <60 |
38 (10.4) |
4.21 (2.71–6.53) |
|
2.30 (1.44–3.67) |
|
1.33 (0.67–2.62) |
|
| 60–89 |
39 (3.7) |
1.63 (1.06–2.52) |
|
1.11 (0.71–1.74) |
|
1.54 (0.92–2.59) |
|
| ≥90 |
43 (2.2) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| Cr (μmol/L) |
|
|
0.001 |
|
0.019 |
|
0.343 |
| <56.5 |
23 (2.7) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| 56.5–68.0 |
22 (2.8) |
1.05 (0.59–1.89) |
|
1.05 (0.58–1.89) |
|
1.64 (0.80–3.38) |
|
| 68.0–83.2 |
23 (2.7) |
1.07 (0.60–1.91) |
|
1.04 (0.57–1.90) |
|
1.69 (0.81–3.52) |
|
| ≥83.2 |
52 (5.9) |
2.20 (1.35–3.59) |
|
1.79 (1.06–3.05) |
|
1.55 (0.73–3.27) |
|
| BUN/Cr |
|
|
0.001 |
|
0.009 |
|
0.250 |
| <15.4 |
18 (2.2) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| 15.4–19.3 |
25 (2.9) |
1.36 (0.74–2.49) |
|
1.21 (0.66–2.23) |
|
0.87 (0.44–1.72) |
|
| 19.3–24.3 |
35 (4.2) |
1.92 (1.09–3.39) |
|
1.67 (0.94–2.97) |
|
1.38 (0.74–2.59) |
|
| ≥24.3 |
42 (5.0) |
2.27 (1.31–3.94) |
|
1.97 (1.11–3.48) |
|
1.26 (0.64–2.46) |
|
Model 1, adjusted for age and sex. Model 2, adjusted for age, sex, SBP, time from onset to admission, cigarette smoking status, alcohol drinking, history of hypertension, history of diabetes mellitus, history of coronary artery disease, history of atrial fibrillation, history of stroke, antihypertensive therapy, and baseline National Institutes of Health Stroke Scale score, stroke subtype (cardioembolic stroke vs. others), serum uric acid and serum total protein. (In the multivariate model for serum BUN, eGFR was additionally adjusted. In the multivariate model for eGFR and Cr, BUN was additionally adjusted.) HR, hazards ratio. Other abbreviations as in Table 1.
There were 1,287 patients with poor outcome (mRS score ≥3) at hospital discharge. In the unadjusted model, the odds of poor outcome at discharge were significantly higher in participants with admission BUN in the highest quartile compared with those in the lowest quartile (OR, 1.54; 95% CI: 1.27–1.87; P-trend <0.001;
Table 3). The relationship, however, was not significant after adjusting for potential confounders (P-trend=0.229). Although associations were found between eGFR, Cr, BUN/Cr and poor discharge outcome in crude analysis, the relationship was not significant after adjusting for potential confounders (P-trend ≥0.169 for all;
Table 3).
Table 3.
Indicators of Poor Outcome at Hospital Discharge (mRS Score ≥3)
| |
n (%) |
Unadjusted |
Model 1 |
Model 2 |
| OR (95% CI) |
P-trend |
OR (95% CI) |
P-trend |
OR (95% CI) |
P-trend |
| BUN (mmol/L) |
|
|
<0.001 |
|
0.008 |
|
0.229 |
| <4.39 |
303 (36.4) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| 4.39–5.40 |
269 (33.5) |
0.88 (0.72–1.09) |
|
0.83 (0.67–1.02) |
|
0.94 (0.71–1.24) |
|
| 5.40–6.70 |
314 (36.3) |
1.00 (0.82–1.22) |
|
0.90 (0.74–1.11) |
|
0.95 (0.71–1.27) |
|
| ≥6.70 |
401 (46.8) |
1.54 (1.27–1.87) |
|
1.29 (1.05–1.58) |
|
1.19 (0.88–1.61) |
|
| eGFR (mL/min/1.73 m2) |
|
|
<0.001 |
|
0.676 |
|
0.169 |
| <60 |
174 (47.5) |
1.60 (1.28–2.01) |
|
1.14 (0.90–1.44) |
|
0.75 (0.50–1.14) |
|
| 60–89 |
415 (39.3) |
1.14 (0.98–1.33) |
|
0.92 (0.78–1.09) |
|
0.90 (0.71–1.14) |
|
| ≥90 |
698 (36.1) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| Cr (μmol/L) |
|
|
0.602 |
|
0.791 |
|
0.453 |
| <56.5 |
322 (38.4) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| 56.5–68.0 |
299 (37.4) |
0.96 (0.78–1.17) |
|
1.00 (0.81–1.24) |
|
1.36 (0.94–1.98) |
|
| 68.0–83.2 |
321 (38.1) |
0.99 (0.81–1.20) |
|
1.04 (0.84–1.29) |
|
1.48 (0.93–2.37) |
|
| ≥83.2 |
345 (39.5) |
1.05 (0.86–1.27) |
|
1.02 (0.82–1.27) |
|
1.36 (0.73–2.54) |
|
| BUN/Cr |
|
|
<0.001 |
|
0.015 |
|
0.184 |
| <15.4 |
301 (36.4) |
1.00 (Ref.) |
|
1.00 (Ref.) |
|
1.00 (Ref.) |
|
| 15.4–19.3 |
305 (35.4) |
0.96 (0.79–1.17) |
|
0.90 (0.73–1.10) |
|
0.93 (0.71–1.22) |
|
| 19.3–24.3 |
292 (35.3) |
0.95 (0.78–1.16) |
|
0.85 (0.69–1.05) |
|
0.86 (0.64–1.15) |
|
| ≥24.3 |
389 (46.4) |
1.51 (1.24–1.84) |
|
1.32 (1.08–1.63) |
|
1.27 (0.95–1.70) |
|
Model 1, adjusted for age and sex. Model 2, adjusted for age, sex, SBP, time from onset to admission, cigarette smoking status, alcohol drinking, history of hypertension, history of diabetes mellitus, history of coronary artery disease, history of atrial fibrillation, history of stroke, antihypertensive therapy, and baseline National Institutes of Health Stroke Scale score, stroke subtype (cardioembolic stroke vs. others), serum uric acid and serum total protein. (In the multivariate model for serum BUN, eGFR was additionally adjusted. In the multivariate model for eGFR and Cr, BUN was additionally adjusted.) Abbreviations as in Table 1.
Significant associations between high BUN and in-hospital mortality were observed in most subgroups (Table 4). No significant interaction, however, between admission BUN and subgroup variables was observed (P-interaction >0.05 for all).
Table 4.
Indicators of In-Hospital Mortality vs. BUN Quartile
| |
BUN quartiles |
P-trend† |
P-interaction |
| Q1 |
Q2 |
Q3 |
Q4 |
| Total participants |
1.00 (Ref.) |
2.07 (0.81–5.30) |
1.58 (0.63–3.95) |
3.75 (1.53–9.21) |
0.003 |
|
| Age (years) |
|
|
|
|
|
0.866 |
| ≥70 (median) |
1.00 (Ref.) |
1.92 (0.57–6.50) |
2.00 (0.65–6.19) |
3.94 (1.28–12.11) |
0.006 |
|
| <70 |
1.00 (Ref.) |
3.66 (0.68–19.73) |
0.31 (0.03–3.70) |
3.19 (0.59–17.25) |
0.506 |
|
| Sex |
|
|
|
|
|
0.620 |
| Female |
1.00 (Ref.) |
3.99 (0.82–19.47) |
3.56 (0.76–16.64) |
5.81 (1.21–27.84) |
0.031 |
|
| Male |
1.00 (Ref.) |
1.12 (0.33–3.81) |
0.37 (0.08–1.65) |
2.20 (0.68–7.05) |
0.125 |
|
| Baseline SBP (mmHg) |
|
|
|
|
|
0.901 |
| ≥150 (median) |
1.00 (Ref.) |
1.76 (0.55–5.70) |
0.98 (0.30–3.20) |
3.98 (1.35–11.77) |
0.004 |
|
| <150 |
1.00 (Ref.) |
3.26 (0.59–18.15) |
2.31 (0.46–11.49) |
3.29 (0.60–17.94) |
0.280 |
|
| History of diabetes mellitus |
|
|
|
|
|
0.883 |
| No |
1.00 (Ref.) |
3.27 (1.02–10.50) |
2.90 (0.92–9.13) |
5.60 (1.79–17.59) |
0.003 |
|
| Yes |
1.00 (Ref.) |
1.13 (0.15–8.37) |
0.24 (0.03–1.92) |
1.56 (0.24–10.24) |
0.758 |
|
| History of stroke |
|
|
|
|
|
0.757 |
| No |
1.00 (Ref.) |
3.07 (1.05–8.97) |
1.52 (0.51–4.55) |
4.67 (1.65–13.27) |
0.006 |
|
| Yes |
1.00 (Ref.) |
0.72 (0.08–6.34) |
1.47 (0.25–8.62) |
1.82 (0.27–12.23) |
0.326 |
|
| eGFR (mL/min/1.73 m2) |
|
|
|
|
|
0.310 |
| ≥90 |
1.00 (Ref.) |
3.34 (1.07–10.41) |
1.06 (0.29–3.95) |
3.83 (1.16–12.66) |
0.106 |
|
| <90 |
1.00 (Ref.) |
1.79 (0.20–15.78) |
2.68 (0.34–21.17) |
6.51 (0.85–49.78) |
0.001 |
|
†Models were adjusted for age, sex, SBP, time from onset to admission, cigarette smoking status, alcohol drinking, history of hypertension, history of diabetes mellitus, history of coronary artery disease, history of atrial fibrillation, history of stroke, antihypertensive therapy, and baseline National Institutes of Health Stroke Scale score, stroke subtype (cardioembolic stroke vs. others), serum uric acid and serum total protein and eGFR, except for the stratified variable. Abbreviations as in Table 1.
A total of 89 patients (2.7%) died from all causes within 7 days. We observed a trend of increased mortality risk with increasing BUN (HR, 2.85; 95% CI: 1.12–7.31) for the highest quartile; P-trend=0.055) for 7 days mortality. The relationship between reduced eGFR and increased Cr, BUN/Cr and mortality within 7 days were not significant on multivariable analysis (P-trend ≥0.273 for all;
Table S2).
Discussion
In a contemporary registry of 3,355 Chinese patients with AIS, we investigated the relationship between biomarkers of CKD and in-hospital mortality and short-term hospital discharge outcome. We found that higher BUN (but not eGFR, Cr and BUN/Cr) at admission was significantly associated with increased risk of in-hospital mortality after adjustment for potential confounders. Lack of an independent relationship between both BUN and other CKD biomarkers and poor functional outcome at hospital discharge, however, was also seen.
Previous studies on the prognostic significance of kidney function biomarkers including eGFR, Cr and BUN/Cr and the short-term outcome and mortality in AIS patients have been conflicting.11,12,17,21,22
The difference in study results may be attributed to variations in patient population, sample size and adjusted confounders. BUN is generally considered as being a less specific marker of kidney function than serum Cr and eGFR. There has been recent evidence, however, for significant associations between higher BUN and poor outcome and mortality in heart failure and ACS patients.13–16
A study of 9,420 ACS patients by Kirtane et al found that those with higher BUN have an approximately 2.2–4.7-fold higher risk of mortality, and that the prognostic significance of BUN is stronger than eGFR.16
Past studies investigating the relationship between BUN and short-term outcome of AIS patients are limited. In the present study, we found higher BUN to be significantly associated with an increased risk of in-hospital mortality. Patients with higher BUN (≥6.70 mmol/l) appeared to have a 3.75-fold increase in the risk of in-hospital mortality after adjustment for potential confounders, including eGFR. This association between higher BUN and increased in-hospital mortality is still significant in different subgroups including patients with and without eGFR reduction and in the sensitivity analyses (BUN and all-caused mortality within 7 days).
The exact mechanisms underlying the relationship between high BUN and in-hospital mortality after AIS remain unclear. Several hypotheses have been proposed. First, a possible reason for the link between higher BUN and in-hospital mortality is that higher BUN at admission might reflect hemodynamic deterioration,13
which is a known predictor of poor outcome and mortality due to stroke in previous studies.23,24
Second, sympathetic nervous activity associated with urea reabsorption might contribute to higher BUN after AIS,25
and lead to increased rates of mortality after stroke.26
Third, a strong association between cerebral microbleeds or white matter lesions and CKD has been reported,27–30
and these are also important risk factors for mortality after AIS.31,32
This study has several strengths, including the large sample size, the multicenter study design, and also the fact that it has identified an association between higher BUN and in-hospital mortality after AIS. We also recognize some limitations of the present study. First, this cohort included patients whose time from onset to admission exceeded 48 h, therefore the BUN and eGFR at admission might not accurately reflect the levels at stroke onset. Also selection bias may be present in because some patients were excluded due to lack of BUN and Cr data. In addition, the follow-up period was relatively short, which prevented an evaluation of the long-term effects of the biomarkers on AIS outcomes. Furthermore, we did not collect and could not adjust the analysis for heart failure, which may be associated with in-hospital mortality. We did not have information on the cause of death, which would improve the understanding of the underlying causes for higher BUN. Finally, we lacked data on proteinuria, which is an important biomarker of CKD and is associated with mortality after AIS.
Conclusions
Higher BUN is significantly associated with an increased risk of all-cause in-hospital mortality in AIS patients, and the prognostic role of BUN is stronger than that of reduced eGFR and increased Cr or BUN/Cr ratio. BUN, a routinely measured kidney function biomarker, may be a simple but valuable marker of mortality in AIS patients. Additional research is needed to validate the present findings and to elucidate the association of BUN with long-term outcome after AIS.
Acknowledgments
We sincerely thank the study participants and their relatives and the clinical staff for their support and contribution to this study. This work was supported in part by grants from the National Natural Science Foundation of China (81471195), Suzhou Clinical Research Center of Neurological Disease (Szzx201503) and Jiangsu Provincial Medical Key Discipline Project, Second Affiliated Hospital of Soochow University Preponderant Clinic Discipline Group Project Funding (XKQ2015002). This was also partly supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Disclosures
The authors declare no conflicts of interest.
Supplementary Files
Supplementary File 1
Appendix S1. Investigators
Table S1.
Baseline AIS patient characteristics vs. eGFR quartile (n=3,355)
Table S2.
Indicators of all-cause mortality in <7 days
Please find supplementary file(s);
http://dx.doi.org/10.1253/circj.CJ-17-0485
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