Elevated B-Type Natriuretic Peptide as a Predictor of Unfavorable Outcomes in Post-Acute Stroke Patients

Abstract

Background: B-type natriuretic peptide (BNP) is a key biomarker for heart failure (HF) and widely used for risk stratification. Elevated BNP levels in acute stroke are linked to poor outcomes, but its prognostic value in the post-acute phase remains unclear.

Methods and Results: This retrospective study included 876 patients admitted to a rehabilitation hospital after acute cerebral infarction or hemorrhage between February 2019 and December 2022. Patients were classified into 4 groups based on BNP or N-terminal prohormone of BNP. The primary outcome was all-cause unfavorable events, including in-hospital death or transfer due to worsening condition. The severely elevated BNP group had a significantly higher risk of all-cause unfavorable events (hazard ratio: 2.34; 95% confidence interval: 1.26–4.32) than the normal group. No significant difference was observed in the mildly or moderately elevated BNP groups. BNP showed superior predictive value over HF diagnosis in terms of area under the receiver operating characteristic curve (0.712 vs. 0.691), net reclassification improvement (0.304, P=0.002), and integrated discrimination improvement (0.025, P=0.015). Higher BNP was associated with lower body mass index, reduced estimated glomerular filtration rate, longer time from stroke onset, atrial fibrillation, and cardioembolic stroke.

Conclusions: BNP levels in the post-acute stroke phase were significantly associated with unfavorable outcomes and may serve as a useful prognostic marker.

Central Figure

Stroke is the third leading cause of death worldwide, and its incidence continues to rise.¹ Heart failure (HF) is a major risk factor for stroke as well as increased deaths and delayed recovery.^2–4 Detecting the presence of HF is crucial, but misdiagnosis of HF has been reported to occur at a rate of approximately 16–68%.⁵ Moreover, HF symptoms may be masked and overlooked due to multiple comorbidities associated with aging.

B-type natriuretic peptide (BNP) is recognized as a crucial biomarker for the severity of HF and prognositication.^6–8 Its usefulness has also been demonstrated in the detection of patients with structural HF in a primary care setting.⁹ Recent statements on BNP and N-terminal prohormone of BNP (NT-proBNP) have recommended their standardized use for both evaluation and prognostication in patients with suspected HF,¹⁰ and this has also been investigated in previous studies.¹¹

In acute stroke patients, BNP levels may temporarily rise regardless of the presence of heart disease,^12,13 which suggests that acute-phase BNP may not accurately reflect HF conditions. BNP levels decrease around 21 days after stroke,¹² and it may be more appropriate during this period to use BNP levels for assessing HF severity and guiding cardiovascular (CV) treatment, while also supporting rehabilitation planning. Although several studies have reported an association between elevated BNP levels and unfavorable outcomes in the acute phase of stroke,^14–16 the relationship between BNP levels and unfavorable outcomes in the post-acute phase remains unclear. Furthermore, the factors that contribute to BNP levels in the post-acute phase also remain unknown.

Clarifying the relationship between BNP levels at admission in the post-acute phase and unfavorable outcomes, their predictive value compared to HF diagnosis, and the factors influencing BNP may help improve post-acute stroke rehabilitation. Therefore, in this study, we aimed to (1) investigate the utility of BNP levels as a predictor of unfavorable outcomes in post-acute stroke patients, (2) evaluate the incremental predictive value of BNP compared to HF diagnosis, and (3) identify factors associated with BNP levels.

Methods

Study Population

This retrospective observational study included 1,347 patients admitted to Tsurumaki Onsen Hospital between February 2019 and December 2022 after treatment for cerebral infarction or intracerebral hemorrhage at acute-care hospitals. The exclusion criteria were as follows: (1) readmitted after transfer to acute-care hospital (n=103), (2) missing BNP/NT-proBNP data (n=365), (3) missing body mass index (BMI) data (n=1), and (4) discharged on admission day (n=2). Ultimately, 876 patients were included in the analysis (Figure 1).

Figure 1.

Flow chart of the study population. We collected data from 1,347 individuals between February 2019 and December 2022 with available baseline data. A total of 876 participants were divided into 4 groups according to BNP or NT-proBNP levels. BMI, body mass index; BNP, B-type natriuretic peptide; NT-proBNP, N-terminal prohormone of BNP.

This study received approval from the Ethics Committee of Kitasato University (Approval No. 2023-013) and was conducted in accordance with the Declaration of Helsinki and applicable ethical guidelines. An opt-out procedure informed participants of their right to decline participation.

Measurements

Clinical and demographic data at admission were collected from electronic medical records, including age, sex, BMI, the number of days from stroke onset, acute-phase treatment (tissue-type plasminogen activator [t-PA] or mechanical thrombectomy), type of stroke (cerebral infarction or intracerebral hemorrhage), subtype of cerebral infarction (atheroma, cardioembolic or lacunar), comorbidities (HF, atrial fibrillation [AF], diabetes mellitus, hypertension, chronic kidney disease [CKD], anemia, cancer, chronic obstructive pulmonary disease [COPD], and previous stroke), pre-stroke disability (activities of daily living [ADL] and instrumental ADL [IADL]), geriatric nutritional risk index (GNRI), Brunnstrom recovery stage (BRS), blood sample data (BNP, NT-proBNP, albumin, estimated glomerular filtration rate [eGFR], sodium, potassium, C-reactive protein [CRP], white blood cell count [WBC], and hemoglobin), and functional independence measure (FIM). Blood tests, including BNP or NT-proBNP measurement, were performed upon admission to Tsurumaki Onsen Hospital. We collected data on medication use, and in patients with HF the prescription of angiotensin receptor-neprilysin inhibitors (ARNI).

Outcomes

The primary endpoint was defined as all-cause unfavorable outcomes, including in-hospital death and transfer to acute-care hospital due to worsening of the disease. The secondary endpoint was unfavorable outcomes due to major adverse CV events (MACE),^17,18 including worsening of HF, recurrence of stroke, myocardial infarction, and other suspected CV diseases. Time to the endpoint was the number of days from admission to adverse event, with a maximum follow-up period of 180 days. The censoring date was the discharge day without the occurrence of an adverse event.

Statistical Analysis

Continuous variables are summarized using medians and interquartile ranges (IQR); categorical variables are described as frequencies and percentages. The patients were classified into 4 groups based on BNP and NT-proBNP cutoff values:⁸ (1) normal: BNP <35 pg/mL or NT-proBNP <125 pg/mL, (2) mildly elevated: BNP ≥35 and <100 pg/mL or NT-proBNP ≥125 and <300 pg/mL, (3) moderately elevated: BNP ≥100 and <200 pg/mL or NT-proBNP ≥300 and <900 pg/mL, and (4) severely elevated: BNP ≥200 pg/mL or NT-proBNP ≥900 pg/mL.

Baseline characteristics across BNP groups were compared using the Jonckheere-Terpstra trend test for continuous variables and the Cochran-Armitage trend test for categorical variables, in order to assess ordinal trends.

To assess potential selection bias, we compared the key baseline characteristics of patients included in the analysis group (n=876) with those excluded due to missing BNP data (n=365), given the substantial number of such cases. Baseline characteristics between the 2 independent groups were compared using the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables, as appropriate.

Kaplan-Meier analysis and the log-rank test assessed the time to the endpoint across the 4 groups. Multivariable Cox regression analysis calculated the adjusted hazard ratio (HR) and 95% confidence interval (CI) for the relationship between each group and unfavorable outcomes using the normal BNP group as the reference. The adjustment variables were age, sex, BMI, AF, cancer, cardioembolic stroke, eGFR, CRP, and the number of days from stroke onset, which are potential factors associated with death and disease progression in patients with stroke. We also performed the primary analysis after excluding patients with eGFR <30 mL/min/1.73 m² because the association between severe renal dysfunction and elevated BNP is well known.^8,19

To compare the prognostic utility of BNP levels with that of a prior diagnosis of HF, we constructed a reference model including established prognostic factors (age, sex, BMI, AF, cancer, cardioembolic stroke, eGFR, CRP, and the number of days from stroke onset). We then evaluated changes in predictive performance when either BNP or HF diagnosis was added to this reference model. Predictive performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) for multivariable logistic regression models. The AUC of the reference model alone was compared with that of the model incorporating either BNP or HF diagnosis. In addition, we evaluated the incremental prognostic value of BNP or HF diagnosis by calculating the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) when each variable was added to the reference model. The outcome was defined as all-cause unfavorable outcomes, treated as a binary variable.

We performed multiple regression analysis to investigate factors independently associated with BNP levels. The dependent variable was BNP level; when BNP data were missing, NT-proBNP was converted to log BNP²⁰ and exponentiated. The conversion formula for log BNP was:

Log BNP = (Log NT-proBNP + 0.009 × BMI + 0.007 × eGFR − 1.21) / 1.03.

The independent variables included baseline factors and other factors potentially influencing BNP levels in patients with CV disease or stroke:^10,13 age, sex, BMI, the number of days from stroke onset, AF, COPD, eGFR, subtype of cerebral infarction, and BRS for the upper extremities (BRS-UE) and lower extremities (BRS-LE).

Statistical analyses were conducted using JMP® Pro version 18.0 (SAS Institute Inc., Cary, NC, USA) and R Studio statistical software (version 4.3.3; R Foundation for Statistical Computing, Vienna, Austria). P<0.05 was considered statistically significant.

Results

Clinical Characteristics

Table 1 shows the baseline characteristics of the patients across BNP categories. Using trend tests, we assessed whether there were statistically significant trends with increasing BNP levels. The median age was 77 years (IQR: 69–83); 53.8% of the patients were male. Cerebral infarction accounted for 69.7%, and intracerebral hemorrhage accounted for 30.3%. Atherothrombotic stroke accounted for 12.0%, cardioembolic stroke 21.9%, lacunar infarction 4.5%, and other/unspecified types 31.3%. Median BNP and NT-proBNP levels were 40.6 pg/mL and 158.5 pg/mL, respectively. BNP was measured a median of 22 days (IQR: 16–30) after stroke onset. The patient distribution was as follows: normal BNP group (n=391, 44.6%), mildly elevated group (n=180, 20.6%), moderately elevated group (n=140, 16.0%), and severely elevated group (n=165, 18.8%). The prescription rates of HF-related medications and anticoagulants showed a significant increasing trend with higher BNP levels. To assess potential selection bias, we compared the analyzed cohort with the 365 patients excluded due to missing BNP data (Supplementary Table 1). The excluded group was younger, more often female, and showed a lower CV risk profile, but outcome rates did not differ significantly between groups.

Table 1.

Patients’ Baseline Characteristics

	Overall (n=876)	Normal BNP (n=391)	Mildly elevated BNP (n=180)	Moderately elevated BNP (n=140)	Severely elevated BNP (n=165)	P for trend
Age, years	77 [69–83]	71 [62–78]	80 [72–85]	80 [74–85]	83 [78–87]	<0.001
Men, n (%)	471 (53.8)	228 (58.3)	93 (51.7)	76 (54.3)	74 (44.8)	0.007
BMI, kg/m2	21.9 [19.4–24.0]	22.6 [20.2–24.4]	22.0 [19.3–24.0]	21.4 [18.7–23.9]	20.2 [18.0–22.6]	<0.001
No. of days from stroke onset, days	22 [16–30]	20 [15–27]	22 [16–29]	25 [17–38]	24 [17–33]	<0.001
Length of stay, days	88.0 [43.3–141.0]	84 [38–141]	94 [50–141]	93 [51–148]	85 [50–137]	0.305
Comorbidity
HF, n (%)	106 (12.1)	5 (1.3)	10 (5.6)	20 (14.3)	71 (43.0)	<0.001
AF, n (%)	232 (26.5)	23 (5.9)	33 (18.3)	64 (45.7)	112 (67.9)	<0.001
Diabetes mellitus, n (%)	224 (25.6)	94 (24.0)	46 (25.6)	44 (31.4)	40 (24.2)	0.514
Hypertension, n (%)	514 (58.7)	241 (61.6)	99 (55.0)	79 (56.4)	95 (57.6)	0.279
CKD, n (%)	353 (40.3)	118 (30.2)	70 (38.9)	65 (46.4)	100 (60.6)	<0.001
Anemia, n (%)	286 (32.6)	74 (18.9)	59 (32.8)	66 (47.1)	87 (52.7)	<0.001
Cancer, n (%)	100 (11.4)	31 (7.9)	24 (13.3)	22 (15.7)	23 (13.9)	0.011
COPD, n (%)	10 (1.1)	4 (1.0)	2 (1.1)	0 (0)	4 (2.4)	0.397
Previous stroke, n (%)	161 (18.4)	55 (14.1)	39 (21.7)	39 (27.9)	28 (17.0)	0.056
Pre-stroke disability of ADL, n (%)	73 (8.3)	9 (2.3)	19 (10.6)	19 (13.6)	26 (15.8)	<0.001
Pre-stroke disability of IADL, n (%)	211 (24.1)	50 (12.8)	51 (28.3)	40 (28.6)	70 (42.4)	<0.001
Blood findings
Alb, g/dL	3.7 [3.4–4.1]	4.0 [3.7–4.3]	3.7 [3.4–4.0]	3.6 [3.1–3.9]	3.3 [2.9–3.6]	<0.001
eGFR, mL/min/1.73 m2	63.9 [52.3–76.4]	68.2 [57.4–79.5]	63.1 [55.0–76.7]	61.6 [47.4–77.0]	54.2 [41.8–69.9]	<0.001
Sodium, mEq/L	139 [136–141]	139 [137–141]	139 [137–140]	138 [135–141]	139 [136–141]	0.237
Potassium, mEq/L	4.0 [3.8–4.3]	4.0 [3.8–4.3]	4.0 [3.8–4.3]	4.0 [3.7–4.3]	4.0 [3.8–4.4]	0.717
CRP, mg/dL	0.18 [0.06–0.92]	0.12 [0.05–0.42]	0.20 [0.06–0.91]	0.29 [0.07–1.20]	0.64 [0.15–1.50]	<0.001
WBC, /μL	6,400 [5,200–7,800]	6,300 [5,200–7,700]	6,500 [5,200–8,175]	6,300 [5,050–7,800]	6,700 [5,400–8,300]	0.154
Hemoglobin, g/dL	13.2 [12.0–14.3]	13.7 [12.8–14.9]	13.2 [12.0–14.1]	12.7 [11.5–13.8]	12.3 [10.8–13.5]	<0.001
BNP, pg/mL	40.6 [14.9–108.2]	13.5 [7.4–21.8]	49.1 [41.0–65.2]	127.1 [87.6–147.4]	290.9 [194.5–459.7]	<0.001
NT-proBNP, pg/mL	158.5 [57.3–602.8]	50.0 [35.0–82.0]	177.0 [150.5–219.0]	512.5 [373.0–638.8]	2,084.0 [1,381.0–3,698.5]	<0.001
Medication use
ACE inhibitors/ARB, n (%)	319 (36.4)	168 (43.0)	55 (30.6)	44 (31.4)	52 (31.5)	0.003
β-blockers, n (%)	180 (20.6)	27 (6.9)	28 (15.6)	47 (33.6)	78 (47.2)	<0.001
Diuretics, n (%)	100 (11.4)	13 (3.3)	19 (10.6)	20 (14.3)	48 (29.0)	<0.001
Warfarin, n (%)	33 (3.8)	2 (0.5)	3 (1.7)	6 (4.3)	22 (13.3)	<0.001
NOAC/DOAC, n (%)	243 (27.7)	43 (11.0)	40 (22.2)	67 (47.9)	93 (56.3)	<0.001
ARNI (HF patients only), n (%)	3 (2.8)	0 (0.0)	0 (0.0)	0 (0.0)	3 (1.8)	0.283
Nutritional status
GNRI	97.1 [88.5–104.9]	102.0 [95.5–109.3]	96.7 [89.7–102.9]	92.6 [85.9–99.7]	87.9 [80.9–93.9]	<0.001
Type of stroke
Cerebral infarction, n (%)	611 (69.7)	223 (57.0)	126 (70.0)	114 (81.4)	147 (89.7)	<0.001
Subtypes of cerebral infarction
Atheroma, n (%)	105 (12.0)	52 (13.3)	28 (15.6)	13 (9.3)	12 (7.3)	0.029
Cardioembolic, n (%)	192 (21.9)	19 (4.9)	28 (15.6)	54 (38.6)	91 (55.2)	<0.001
Lacunar, n (%)	39 (4.5)	21 (5.4)	11 (6.1)	7 (5.0)	0 (0.0)	0.014
Acute-phase treatment
t-PA, n (%)	70 (8.0)	26 (6.6)	10 (5.6)	16 (11.4)	18 (10.9)	0.035
Mechanical thrombectomy, n (%)	86 (9.8)	15 (3.8)	14 (7.7)	23 (16.4)	34 (20.6)	<0.001
BRS-UE
≥IV, n (%)	573 (65.4)	260 (66.5)	125 (69.4)	87 (62.1)	101 (61.2)	0.166
BRS-LE
≥IV, n (%)	592 (67.6)	275 (70.3)	130 (72.2)	91 (65.0)	96 (58.2)	0.005
ADL
Admission FIM motor	42 [20–64]	51 [27–70]	40 [22–61]	36 [17–56]	31 [14–52]	<0.001
Admission FIM cognition	23 [14–31]	28 [18–33]	24 [15–31]	18 [11–27]	16 [9–25]	<0.001
Admission FIM total	67 [38–93]	79 [49–102]	64 [40–92]	56 [27–80]	44 [24–75]	<0.001

ACE, angiotensin-converting enzyme; ADL, activities of daily living; AF, atrial fibrillation; Alb, albumin; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; BMI, body mass index; BNP, B-type natriuretic peptide; BRS-LE, Brunnstrom Recovery Stage of lower extremity; BRS-UE, Brunnstrom Recovery Stage of upper extremity; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; DOAC, direct oral anticoagulant; eGFR, estimated glomerular filtration rate; FIM, Functional Independence Measure; GNRI, Geriatric Nutritional Risk Index; HF, heart failure; IADL, instrumental activities of daily living; NOAC, non-vitamin K antagonist oral anticoagulant; NT-proBNP, N-terminal prohormone of BNP; t-PA, tissue-type plasminogen activator; WBC, white blood cells.

Association of BNP Levels With Unfavorable Outcomes

During the median follow-up of 88.0 days (IQR: 43.3–141.0), 107 all-cause unfavorable events (12.2%) occurred (Supplementary Table 2). A total of 12 patients (11.2%) died in hospital, and 95 patients (88.8%) were transferred to acute-care hospitals. Among the in-hospital deaths, 3 (2.8%) were classified as CV death: HF worsening (n=1), pulmonary embolism (n=1), and sudden cardiac death of intrinsic origin (n=1). Among the 95 transferred to acute-care hospitals, 36 cases (33.7%) were due to CV causes, including recurrent stroke and TIA (n=19), HF worsening (n=8), arrhythmia (n=4), valve replacement surgery (n=2), infective endocarditis (n=1), and other CV causes (n=2).

Figure 2 shows the Kaplan-Meier analysis and log-rank test. The severely elevated BNP group was associated with a higher rate of all-cause adverse events (log-rank, P<0.001). Table 2 shows that in the multivariable Cox regression analysis, the severely elevated BNP group had a significantly higher HR for all-cause adverse events compared to the normal BNP group (HR: 2.34; 95% CI: 1.26–4.32; P=0.007). In contrast, there was no difference between the normal BNP group and the mildly elevated BNP group (HR: 1.01; 95% CI: 0.55–1.88; P=0.967) or the moderately elevated BNP group (HR: 0.95; 95% CI: 0.48–1.89; P=0.883).

Figure 2.

Kaplan-Meier curves for all-cause unfavorable outcomes according to cutoff values for B-type natriuretic peptide (BNP).

Table 2.

Multivariable Cox Regression Analysis for All-Cause Unfavorable Outcomes by BNP Group

	No. of events/ patients (%)	HR	95% CI	P value
Normal BNP	28/391 (7.2)	1.00	Ref.
Mildly elevated BNP	18/180 (10.0)	1.01	0.55–1.88	0.967
Moderately elevated BNP	15/140 (10.7)	0.95	0.48–1.89	0.883
Severely elevated BNP	46/165 (27.9)	2.34	1.26–4.32	0.007

Adjusted by age, sex, BMI, AF, cancer, cardioembolic stroke, eGFR, CRP, the number of days from stroke onset. CI, confidence interval; HR, hazard ratio. Other abbreviations as in Table 1.

Figure 3 presents the Kaplan-Meier analysis and log-rank test for CV-related adverse events; its results were consistent with the all-cause unfavorable outcomes. The severely elevated BNP group had a higher incidence rate (log-rank, P<0.001). In the multivariable Cox regression (Table 3), the severely elevated BNP group had a significantly higher HR than the normal BNP group (HR: 3.61; 95% CI: 1.28–10.19; P=0.015). In contrast, no significant difference was found between the normal BNP group and the mildly elevated (HR: 1.22; 95% CI: 0.45–3.35; P=0.693) or the moderately elevated BNP group (HR: 0.66; 95% CI: 0.17–2.61; P=0.555).

Figure 3.

Kaplan-Meier curves for unfavorable outcomes due to cardiovascular causes according to cutoff values for B-type natriuretic peptide (BNP). CV, cardiovascular.

Table 3.

Multivariable Cox Regression Analysis for Unfavorable Outcomes Due to Cardiovascular Causes by BNP Group

	No. of events/ patients (%)	HR	95% CI	P value
Normal BNP	10/391 (2.6)	1.00	Ref.
Mildly elevated BNP	7/180 (3.9)	1.22	0.45–3.35	0.693
Moderately elevated BNP	3/140 (2.1)	0.66	0.17–2.61	0.555
Severely elevated BNP	19/165 (11.5)	3.61	1.28–10.19	0.015

Adjusted by age, sex, BMI, AF, cancer, cardioembolic stroke, eGFR, CRP, the number of days from stroke onset. Abbreviations as in Tables 1,2.

Association of BNP Levels With Unfavorable Outcomes in Patients Without Severe Renal Dysfunction

Supplementary Table 3 shows that, in patients without severe renal dysfunction, the results of a multivariable Cox regression analysis were consistent with the primary analysis, indicating that only the severely elevated BNP group had a significantly higher HR for all-cause adverse events compared to the normal BNP group (HR: 2.40; 95% CI: 1.28–4.50; P=0.006).

Incremental Prognostic Utility of BNP Compared With HF Diagnosis

To compare the prognostic utility of BNP with that of a prior HF diagnosis, we evaluated the AUCs of the logistic regression models, as shown in Table 4. The reference model included age, sex, BMI, AF, cancer, cardioembolic stroke, eGFR, CRP, and the number of days from stroke onset, with an AUC of 0.686 (95% CI: 0.632–0.739). The addition of HF diagnosis increased the AUC to 0.691 (95% CI: 0.637–0.745), whereas the addition of BNP increased the AUC to 0.712 (95% CI: 0.657–0.766), indicating a stronger incremental value.

Table 4.

Analysis of the Predictive Value of BNP and HF Diagnosis for All-Cause Unfavorable Outcomes

	AUC	95% CI	P value	NRI	95% CI	P value	IDI	95% CI	P value
Reference model	0.686	0.632 to 0.739	Ref.			Ref.			Ref.
+ HF diagnosis	0.691	0.637 to 0.745	0.383	0.045	−0.143 to 0.233	0.638	0.004	−0.002 to 0.010	0.189
+ BNP	0.712	0.657 to 0.766	0.007	0.304	0.109 to 0.499	0.002	0.025	0.005 to 0.044	0.015

Reference model served as the multivariable logistic regression model and included the following variables: age, sex, BMI, AF, cancer, cardioembolic stroke, eGFR, CRP, and the number of days from stroke onset. AUC, area under the curve; IDI, integrated discrimination improvement; NRI, net reclassification improvement. Other abbreviations as in Table 1.

To further assess the incremental prognostic value, we calculated the NRI and IDI. Table 4 shows that adding BNP to the reference model resulted in a significant NRI of 0.304 (95% CI: 0.109–0.499; P=0.002) and an IDI of 0.025 (95% CI: 0.005–0.044; P=0.015), indicating a statistically significant improvement in risk classification. Conversely, the addition of HF diagnosis led to a non-significant NRI of 0.045 (95% CI: −0.143–0.233; P=0.638) and an IDI of 0.004 (95% CI: −0.002–0.010; P=0.189).

Factors Associated With BNP Levels in Patients With Post-Acute Stroke

Table 5 shows that higher BNP levels were associated with lower BMI (β=−0.074, P=0.025), more days from stroke onset (β=0.110, P=0.001), lower eGFR (β=−0.072, P=0.041), and the presence of AF (β=0.210, P<0.001) or cardioembolic stroke (β=0.125, P=0.003).

Table 5.

Multiple Regression Analysis of Factors Associated With BNP Levels in Patients With Post-Acute Stroke

Variables	β	P value
Age	0.069	0.052
Male	−0.013	0.692
BMI	−0.074	0.025
No. of days from stroke onset	0.110	0.001
AF	0.210	<0.001
COPD	−0.002	0.927
Cardioembolic	0.125	0.003
Atheroma	0.034	0.300
Lacunar	−0.024	0.458
BRS-UE (≥IV)	0.014	0.794
BRS-LE (≥IV)	−0.077	0.161
eGFR	−0.072	0.041

Abbreviations as in Table 1.

Discussion

In this study, the severely elevated BNP group had a higher risk of all-cause and CV-related unfavorable outcomes, including in-hospital death and transfer to acute-care hospitals. BNP showed a greater ability than a prior diagnosis of HF to predict all-cause unfavorable outcomes in post-acute stroke patients, demonstrating higher AUCs. Furthermore, BNP significantly improved risk classification (NRI and IDI), whereas HF diagnosis did not provide a significant improvement. Additionally, BMI, the number of days from stroke onset, AF, cardioembolic stroke, and eGFR were independently associated with BNP levels in post-acute stroke patients.

This study demonstrated that elevated BNP levels are associated with an increased risk of unfavorable outcomes in post-acute stroke. BNP reflects the severity of HF and is a known prognostic marker in the general population and patients with heart disease.^21–23 However, its significance in stroke remains unclear. The acute-phase BNP level temporarily increases,¹² making it insufficient for assessing cardiac load. In support of this, when we compared the prognostic value of BNP and a prior diagnosis of HF using reclassification indices, only BNP significantly improved risk prediction, as evidenced by a significant NRI and IDI. These findings highlight that BNP levels may be useful for evaluating HF status and predicting prognosis when stratifying risk among post-acute stroke patients. Notably, patients were classified using BNP cutoff values established in a recent statement and their prognoses were assessed. These criteria may also be applicable to post-acute stroke patients.

Elevated BNP is likely linked to unfavorable outcomes due to underlying cardiac dysfunction and increased CV burden. BNP is secreted in response to ventricular wall stress and pressure overload,¹⁰ making it a key marker of HF and left ventricular dysfunction. AF also contributes to BNP elevation,¹⁰ and is a well-known risk factor for various adverse outcomes, including death.^24,25 In this study, BNP elevation was associated with AF, especially in the severely elevated BNP group. The findings suggest BNP reflects chronic CV risk, including HF and AF, rather than being a transient acute-phase change. The superior prognostic performance of BNP compared to HF diagnosis may be attributed to its ability to reflect the present severity of CV risk more accurately.

In addition, although the prescription rates of antihypertensive and anticoagulant medications increased significantly with higher BNP levels, the severely elevated BNP group still demonstrated unfavorable outcomes, including higher numbers of in-hospital deaths and transfers to an acute-care hospital due to clinical deterioration. This suggests that the use of these medications may reflect underlying disease severity rather than directly improving outcomes in this population. In this study, the prescription rate of ARNI was extremely low, likely reflecting their early adoption phase in Japan, and thus their impact on short-term outcomes could not be evaluated.

Furthermore, the positive association between BNP levels and the number of days from stroke onset contrasted with findings from previous studies,^12,26 as shown in the multiple regression analysis. This may indicate that patients with higher BNP levels are in a more severe condition and require longer to stabilize after stroke. The severely elevated BNP group had the highest prevalence of comorbidities (HF, renal dysfunction, and anemia), the highest CRP levels, and the lowest GNRI and FIM scores. Such poor health status may both delay stabilization and contribute to BNP elevation.

Study Strengths

First, even after adjusting for key prognostic factors in stroke patients, including renal function, paralysis, and comorbidities, BNP remained significantly associated with unfavorable outcomes. This finding suggests that BNP is not merely a marker of comorbidities but may serve as an independent prognostic predictor. In addition, BNP demonstrated superior prognostic value compared to a prior diagnosis of HF, as indicated by significant improvements in AUC, NRI, and IDI values. These results support the role of BNP as an incremental predictor for risk stratification in the post-acute stroke setting. Second, this study is the largest study to date examining BNP and prognosis in post-acute stroke patients. Previous research has focused on acute BNP, so the clinical significance of BNP measurement in the post-acute phase has not been thoroughly examined. Our analysis, based on a large sample size, demonstrated that BNP levels measured beyond the acute phase remained useful for prognostic prediction. Clinically, this may help identify patients who require close monitoring and individualized modification of rehabilitation intensity. Third, we classified patients using BNP cutoff values established in recent statements by professional societies and clearly demonstrated the prognostic risks associated with each severity level, which suggests that BNP cutoff values established for HF assessment criteria may also be applicable to post-acute stroke patients. Overall, our findings emphasize the clinical significance of measuring BNP in the post-acute stroke patient, and suggest it may serve as a useful marker for rehabilitation planning and CV risk management.

Study Limitations

First, although this study included a larger sample than previous studies,^14,27 its retrospective observational design and single-center setting limit its external validity. Second, this study included only patients with available BNP data, which raises the possibility of selection bias. The excluded patients were generally younger, more often female, and had a lower CV risk profile. However, because outcome rates did not differ between groups, the influence of this bias on our main conclusions is likely limited. Third, BNP levels were measured only once upon admission to hospital, and therefore, temporal changes in BNP levels could not be assessed.

Conclusions

In post-acute stroke patients, higher BNP levels were associated with an increased risk of unfavorable outcomes, including in-hospital death and transfer to acute-care hospitals. BNP showed better predictive performance than a prior diagnosis of HF, supporting its clinical utility as a prognostic indicator in the post-acute phase. Additionally, we found that BNP levels in the post-acute phase increased as the number of days from stroke onset increased. These findings highlight the importance of measuring BNP in the post-acute phase of stroke for prognostic assessment and rehabilitation planning.

Acknowledgments

We express our gratitude to the staff at Tsurumaki Onsen Hospital for their daily observations, treatment, rehabilitation, and care of the patients.

During the preparation of this manuscript, the authors used ChatGPT (OpenAI) to assist with writing and language editing. The authors carefully reviewed the AI-generated content to ensure that it accurately reflected their intended meaning and they assume full responsibility for the integrity and accuracy of the final manuscript.

Sources of Funding

This research received no external funding.

Disclosures

K.K. is an Associate Editor of Circulation Reports. He received funding unrelated to the submitted work from Eiken Chemical Co. Ltd. and SoftBank Corporation. M.Y. has no conflicts of interest concerning this work, he owns company stock (<5% of the total) and earns a salary as a director at his place of employment. The remaining authors have no conflicts of interest to disclose. This study was not supported by any sponsor or funder, nor did it receive any financial support from public, commercial, or nonprofit funding organizations.

IRB Information

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Kitasato University (Approval No. 2023-013).

Data Availability

Research data are not publicly available on legal or ethical grounds. Further inquiry can be directed to the corresponding author.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-25-0132

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