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Abstract

BACKGROUND

Systolic blood pressure (BP) is a key factor in the outcomes of patients with acute ischemic stroke (AIS) receiving endovascular thrombectomy (EVT). However, the factors that mediate the association between BP and clinical outcome are unclear.

METHODS

Consecutive patients with AIS in the anterior circulation underwent continuous BP monitoring for 24 hours. The 3-month modified Rankin scale (mRS) score was defined as the clinical functional outcome. The systolic BPI indices (BPIs) were successive variation, standard deviation, variability independent of mean BP (VIM), and 24-hour mean BP. Regression analysis was used to assess the correlation between different BPIs and functional outcomes, whereas mediation analysis was employed to assess the potential mediating effects of baseline risk factors through BP on functional outcomes.

RESULTS

A total of 140 of 292 patients (47.9%) achieved functional independence, and 87 (29.8%) experienced hemorrhagic transformation (HT). A history of stroke or hypertension and NIHSS score at onset were associated with SD and VIM (P < 0.05). BP variation (BPV) was still strongly associated with functional outcomes after adjustment for different risk factors. Mediation analysis revealed that stroke affected functional outcomes by affecting BPV, while the hypertension history affected functional prognosis by impacting the 24-hour mean BP and BPV. In addition, higher National Institute of Health stroke scale (NIHSS) scores were associated with increased BPV, whereas increased BPV was correlated with a greater proportion of unfavorable outcomes.

CONCLUSIONS

To our knowledge, this study is the first to explore the mediating effects of different BPIs on the relationships between risk factors and functional outcomes and may provide new insights and potential mechanisms for improving AIS prognosis.

Graphical Abstract
Graphical Abstract
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acute ischemic stroke, blood pressure, endovascular thrombectomy, hypertension, mediation analysis

Endovascular thrombectomy (EVT) has become one of the standard treatments for patients with large vessel occlusion stroke (LVOS).1,2 Blood pressure (BP) is an important factor affecting the prognosis of stroke patients. Multiple observational studies have reported an association between elevated average BP and unfavorable outcomes after EVT. However, the findings of the recent ENCHANTED2-MT and OPTIMAL-BP studies suggest that intensive BP reduction may lead to adverse effects on patient outcomes.3,4

The effect of BP on acute ischemic stroke (AIS) outcomes is long-range and multifaceted, and previous research has confirmed the correlation between preoperative, intraoperative, and postoperative BP levels and outcomes.5–7 Simultaneously, there is a perspective suggesting that increased BP after AIS is an acute stress response, a response to stroke severity, rather than a direct factor for an adverse prognosis.4 Different vascular risk factors may ultimately affect the outcome of AIS through different pathways. For instance, transient ischemic attack (TIA) is associated with a decreased infarct volume,8 and some studies suggest that TIA within 4 days may improve patient prognosis through the mechanism of ischemic preconditioning and a decreased systemic immune-inflammation index.9 In addition, patients with a history of coronary atherosclerotic heart disease (CHD) had worse functional outcomes after AIS.10,11 The potential reasons include the following: (i) Brain-heart syndrome11; (ii) CHD coexisting with atherosclerotic AIS12; and (iii) Acute coronary syndrome (ACS)-related AIS/TIA.13 However, how vascular risk factors might influence the clinical outcome of AIS by affecting BP remains unclear. To our knowledge, there is currently no mediation model investigating the role of different BP indices (BPIs) in the clinical outcome of patients following AIS EVT treatment for various risk factors, and revealing these underlying mechanisms is crucial for identifying potential targets for therapeutic intervention.

To further explore the complex mechanisms underlying the associations between risk factors for AIS and functional outcome, we conducted a mediation analysis to assess and quantify the models. This study aimed to clarify whether BP mediates the correlation between risk factors and clinical outcomes. Our findings may provide novel insights and potential mechanisms for improving the prognosis of AIS patients.

METHODS

This was a retrospective, multicenter study conducted at 3 hospitals in China (ChiCTR2300077202). The study flowchart is shown in Figure 1. Patients who were diagnosed with anterior circulation AIS via CT or MRI within 24 hours of onset and who received EVT treatment were included. The exclusion criterion included pre-stroke mRS scores exceeding 2. Patients were consecutively enrolled from 3 hospitals in China: Beijing Hospital (215 patients from January 2017 to March 2023), Peking University Shenzhen Hospital (46 patients from January 2020 to May 2023), and Hainan General Hospital (68 patients from January 2021 to January 2023). The study was approved by the local institutional review board (2023BJYYEC-364-01).

Flow chart of the study population.
Figure 1.

Flow chart of the study population.

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BP parameters

BP was measured hourly in the ward after EVT and recorded for at least the first 24 hours. In this study, BP was measured at all three centers using a combination of manual and automatic methods. Hourly BP readings within the first 24 hours after EVT were recorded via devices (Philips G30, oscillometric technique). Blood pressure variability (BPV) was assessed by successive variation (SV), standard deviation (SD), and variability independent of mean BP (VIM). SV represents changes or alterations in BP measurements taken continuously over a 24-hour period, giving insight into immediate fluctuations in BP levels. VIM is a more sophisticated modification of SD that results through nonlinear regression to eliminate its correlation with average BP. Systolic BP has been reported to be more significantly correlated with clinical outcomes than diastolic BP,14 so this study focused on systolic blood pressure (SBP).

Data collection and outcome variables

All baseline clinical data were obtained from the hospital medical records system. Demographics (age and sex), medical history (hypertension, diabetes, dyslipidemia, atrial fibrillation), pre-hospital NIHSS score at onset, and drug use (anticoagulant or antiplatelet medication) were also collected. Baseline risk factors were defined as a history of stroke, present atrial fibrillation, history of hypertension, diabetes, dyslipidemia, or CHD in the electronic medical records.15–20 The recanalization status was evaluated via a modified cerebral infarction embolysis (mTICI) grading system. Successful recanalization was defined as an mTICI score of 2b or 3. All patients underwent routine Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) within 24 hours post-EVT to assess intracranial hemorrhage.14 The patient’s 90-day functional outcome was determined using the modified Rankin scale (mRS), where scores ranging from 0 to 2 indicated a favorable functional outcome, whereas scores of 3 or higher were categorized as an unfavorable functional outcome. The mRS scores were assessed at baseline and during follow-up by experienced neurologists, mainly during outpatient and emergency consultations.

Statistical analysis

We first performed descriptive statistics for baseline characteristics and outcomes. BP levels were recorded every hour, and patients with missing BP data exceeding 4 time points within the first 24 hours were excluded. When calculating the BPV, the average of the 24-hour BP values was used for imputation. Categorical variables are expressed as numbers and percentages, and chi-square tests were used to compare significant differences between risk factors and clinical outcomes. Continuous variables with a normal distribution are expressed as the mean ± SD. A t-test was used to compare continuous variables. Binary logistic regression was performed to evaluate the associations between BP parameters and risk factors and between 3-month functional outcome and hemorrhagic transformation (HT). A Linear regression model was used to assess the association of X (covariate) with Y (NIHSS or BPIs). The t-value is the calculated statistic used for hypothesis testing of regression coefficients. B (coefficient) represents the estimated effect of each independent variable on the dependent variable in the regression model. The standardized coefficient (β) quantifies the strength and direction of the linear relationship between each independent variable and the dependent variable, expressed in terms of SD units. The standard error (SE) is a critical indicator of the precision of the estimated coefficients; a smaller SE implies that the estimates are more accurate and less susceptible to the variability inherent in sampling. The models that were adjusted for included age, sex, BMI, hypertension, diabetes, atrial fibrillation, dyslipidemia, history of stroke, coronary atherosclerotic heart disease (CHD), anticoagulant or antiplatelet therapy, admission NIHSS score, the use of intravenous tissue plasminogen activator (IV-tPA) therapy, and the mTICI rating. For 24-hour mean SBP, odds ratios (ORs) and 95% confidence intervals (CIs) per 10 mm Hg were estimated.

To further explore the mediating role of BP parameters between risk factors and clinical outcomes, we established a mediating model. One independent variable (baseline risk factor), one dependent variable (3-month functional outcome), and one mediating variable (BP parameters, HTs) were used for mediating analysis. We performed a mediation analysis considering each BP index as a mediating factor using the “mediation” package in R. To best examine the mediating effects, we derived the total effects, average causal mediation effects (ACMEs), and average direct effects (ADEs) with 95% CIs using nonparametric bootstrapping with 1,000 simulations. Age, sex, BMI, hypertension, diabetes mellitus, atrial fibrillation, dyslipidemia, stroke history, coronary atherosclerotic heart disease, anticoagulant or antiplatelet therapy, admission NIHSS score, and mTICI grade were included in the mediation model as covariables. If the direct effect and the indirect effect are in the same direction, the proportion is the indirect effect divided by the total effect; otherwise, it is the indirect effect divided by the direct effect.21 All the statistical analyses were performed using SPSS v25 (IBM Corporation, NY). All tests used an α-level of 0.05 for significance.

RESULTS

Demographic and baseline characteristics

A total of 329 AIS patients aged older than 18 years were treated with EVT. Fourteen patients were excluded—14 because of failed follow-up and 23 because of incomplete BP records. Ultimately, 292 patients were included in the analysis (Figure 1). The median age of the patients was 70.5 years, and 169 patients (57.9%) were men. The median baseline NIHSS score was 14 (9–19). After EVT, 235 (80.5%) patients were successfully recanalized, with 24-hour SBP values of 128 mm Hg, respectively. Missing values for BP (n = 19) were imputed using 24-hour average BP. The mean systolic BP was 138 mm Hg at 1 hour and 131 mm Hg at 24 hours. Finally, 87 (29.8%) patients had HT, while 140 (47.9%) patients had a favorable outcome. In total, 49 patients (16.8%) died during the follow-up period. The results revealed that age (median: 68.50 vs. 72.00) and NIHSS score (median: 11.5 vs. 16) were lower in the favorable outcome group, which also had lower proportions of patients with a history of atrial fibrillation (42 [30.0%] vs. 74 [48.7%]) and CHD (25 [17.9%] vs. 58 [38.2%]), HT (22 [25.3%] vs. 65 [74.7%]), and a higher ratio of mTICI grade (130 [92.9%] vs. 105[69.1%]). The baseline characteristics of the patients are shown in Table 1.

Table 1.
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Baseline characteristics stratified by functional outcome

CovariatesTotal
(n = 292)		Favorable
(n = 140)		Unfavorable
(n = 152)		P value
Age, median (IQR)70.5 (61–81)68.50 (58–78.5)72 (63–81)0.022
Gender (%)0.075
 Male169 (57.9)89 (52.7)80 (47.3)
 Female123 (42.1)51 (41.5)72 (58.5)
BMI, mean (SD)24.16 (4.76)24.18 (4.62)24.13 (4.83)0.934
Hypertension (%)0.097
 No88 (30.1)49 (55.7)39 (44.3)
 Yes204 (69.9)91 (44.6)113 (55.4)
Atrial fibrillation (%)0.001
 No176 (60.3)98 (55.7)78 (44.3)
 Yes116 (39.7)42 (36.2)74 (63.8)
Stroke (%)0.895
 No216 (74.0)103 (47.7)113 (52.3)
 Yes76 (26.0)37 (48.7)39 (51.3)
CHD (%)<0.001
 No209 (71.6)115 (55.0)94 (45.0)
 Yes83 (28.4)25 (30.1)58 (69.9)
Diabetes (%)0.338
 No195 (66.8)97 (49.7)98 (50.3)
 Yes97 (33.2)43 (44.3)54 (55.7)
Dyslipidemia (%)0.793
 No213 (72.9)101 (47.4)112 (52.6)
 Yes79 (27.1)39 (49.4)40 (50.6)
Anticoagulation (%)1.000
 No271 (92.8)130 (48.0)141 (52.0)
 Yes21 (7.2)10 (47.6)11 (52.4)
Antiplatelet (%)0.690
 No215 (73.6)105 (48.8)110 (51.2)
 Yes77 (26.4)35 (45.5)42 (54.5)
IV-tPA (%)0.213
 No198 (67.8)100 (50.5)98 (49.5)
 Yes94 (32.2)40 (42.6)54 (57.4)
Occlude area, n (%)0.753
 ICA111 (38.0)49 (44.1)62 (52.9)
 MCA/ACA(M1/A1)160 (54.8)78 (48.8)82 (51.2)
 Distal MCA/ACA58 (19.9)28 (48.3)30 (51.7)
HT (%)<0.001
 No205 (70.2)118 (57.6)87 (42.4)
 Yes87 (29.8)22 (25.3)65 (74.7)
mTICI (%)<0.001
 0–2a57 (19.5)10 (17.5)47 (82.5)
 2b–3235 (80.5)130 (55.3)105 (44.7)
NIHSS, median (IQR)14 (9–19)11.5 (7–16)16 (12–21.5)<0.001
BPI (SD)
 SD13.25 (4.04)11.95 (3.34)14.46 (4.26)<0.001
 SV13.72 (4.31)12.19 (3.19)15.13 (4.72)<0.001
 VIM13.31 (3.98)12.09 (3.29)14.43 (4.24)<0.001
 24-hour SBP129 (13)126 (13)131 (14)0.002
CovariatesTotal
(n = 292)		Favorable
(n = 140)		Unfavorable
(n = 152)		P value
Age, median (IQR)70.5 (61–81)68.50 (58–78.5)72 (63–81)0.022
Gender (%)0.075
 Male169 (57.9)89 (52.7)80 (47.3)
 Female123 (42.1)51 (41.5)72 (58.5)
BMI, mean (SD)24.16 (4.76)24.18 (4.62)24.13 (4.83)0.934
Hypertension (%)0.097
 No88 (30.1)49 (55.7)39 (44.3)
 Yes204 (69.9)91 (44.6)113 (55.4)
Atrial fibrillation (%)0.001
 No176 (60.3)98 (55.7)78 (44.3)
 Yes116 (39.7)42 (36.2)74 (63.8)
Stroke (%)0.895
 No216 (74.0)103 (47.7)113 (52.3)
 Yes76 (26.0)37 (48.7)39 (51.3)
CHD (%)<0.001
 No209 (71.6)115 (55.0)94 (45.0)
 Yes83 (28.4)25 (30.1)58 (69.9)
Diabetes (%)0.338
 No195 (66.8)97 (49.7)98 (50.3)
 Yes97 (33.2)43 (44.3)54 (55.7)
Dyslipidemia (%)0.793
 No213 (72.9)101 (47.4)112 (52.6)
 Yes79 (27.1)39 (49.4)40 (50.6)
Anticoagulation (%)1.000
 No271 (92.8)130 (48.0)141 (52.0)
 Yes21 (7.2)10 (47.6)11 (52.4)
Antiplatelet (%)0.690
 No215 (73.6)105 (48.8)110 (51.2)
 Yes77 (26.4)35 (45.5)42 (54.5)
IV-tPA (%)0.213
 No198 (67.8)100 (50.5)98 (49.5)
 Yes94 (32.2)40 (42.6)54 (57.4)
Occlude area, n (%)0.753
 ICA111 (38.0)49 (44.1)62 (52.9)
 MCA/ACA(M1/A1)160 (54.8)78 (48.8)82 (51.2)
 Distal MCA/ACA58 (19.9)28 (48.3)30 (51.7)
HT (%)<0.001
 No205 (70.2)118 (57.6)87 (42.4)
 Yes87 (29.8)22 (25.3)65 (74.7)
mTICI (%)<0.001
 0–2a57 (19.5)10 (17.5)47 (82.5)
 2b–3235 (80.5)130 (55.3)105 (44.7)
NIHSS, median (IQR)14 (9–19)11.5 (7–16)16 (12–21.5)<0.001
BPI (SD)
 SD13.25 (4.04)11.95 (3.34)14.46 (4.26)<0.001
 SV13.72 (4.31)12.19 (3.19)15.13 (4.72)<0.001
 VIM13.31 (3.98)12.09 (3.29)14.43 (4.24)<0.001
 24-hour SBP129 (13)126 (13)131 (14)0.002

Abbreviations: BPI, blood pressure index; CHD, coronary atherosclerotic heart disease; HT, hemorrhagic transformation; IQR, interquartile range; IV-tPA, intravenous tissue plasminogen activator; mTICI, modified Thrombolysis in Cerebral Infarction Score; SBP, systolic blood pressure; SD, standard deviation; SV, successive variation; VIM, variability independent of mean blood pressure.

Table 1.
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Baseline characteristics stratified by functional outcome

CovariatesTotal
(n = 292)		Favorable
(n = 140)		Unfavorable
(n = 152)		P value
Age, median (IQR)70.5 (61–81)68.50 (58–78.5)72 (63–81)0.022
Gender (%)0.075
 Male169 (57.9)89 (52.7)80 (47.3)
 Female123 (42.1)51 (41.5)72 (58.5)
BMI, mean (SD)24.16 (4.76)24.18 (4.62)24.13 (4.83)0.934
Hypertension (%)0.097
 No88 (30.1)49 (55.7)39 (44.3)
 Yes204 (69.9)91 (44.6)113 (55.4)
Atrial fibrillation (%)0.001
 No176 (60.3)98 (55.7)78 (44.3)
 Yes116 (39.7)42 (36.2)74 (63.8)
Stroke (%)0.895
 No216 (74.0)103 (47.7)113 (52.3)
 Yes76 (26.0)37 (48.7)39 (51.3)
CHD (%)<0.001
 No209 (71.6)115 (55.0)94 (45.0)
 Yes83 (28.4)25 (30.1)58 (69.9)
Diabetes (%)0.338
 No195 (66.8)97 (49.7)98 (50.3)
 Yes97 (33.2)43 (44.3)54 (55.7)
Dyslipidemia (%)0.793
 No213 (72.9)101 (47.4)112 (52.6)
 Yes79 (27.1)39 (49.4)40 (50.6)
Anticoagulation (%)1.000
 No271 (92.8)130 (48.0)141 (52.0)
 Yes21 (7.2)10 (47.6)11 (52.4)
Antiplatelet (%)0.690
 No215 (73.6)105 (48.8)110 (51.2)
 Yes77 (26.4)35 (45.5)42 (54.5)
IV-tPA (%)0.213
 No198 (67.8)100 (50.5)98 (49.5)
 Yes94 (32.2)40 (42.6)54 (57.4)
Occlude area, n (%)0.753
 ICA111 (38.0)49 (44.1)62 (52.9)
 MCA/ACA(M1/A1)160 (54.8)78 (48.8)82 (51.2)
 Distal MCA/ACA58 (19.9)28 (48.3)30 (51.7)
HT (%)<0.001
 No205 (70.2)118 (57.6)87 (42.4)
 Yes87 (29.8)22 (25.3)65 (74.7)
mTICI (%)<0.001
 0–2a57 (19.5)10 (17.5)47 (82.5)
 2b–3235 (80.5)130 (55.3)105 (44.7)
NIHSS, median (IQR)14 (9–19)11.5 (7–16)16 (12–21.5)<0.001
BPI (SD)
 SD13.25 (4.04)11.95 (3.34)14.46 (4.26)<0.001
 SV13.72 (4.31)12.19 (3.19)15.13 (4.72)<0.001
 VIM13.31 (3.98)12.09 (3.29)14.43 (4.24)<0.001
 24-hour SBP129 (13)126 (13)131 (14)0.002
CovariatesTotal
(n = 292)		Favorable
(n = 140)		Unfavorable
(n = 152)		P value
Age, median (IQR)70.5 (61–81)68.50 (58–78.5)72 (63–81)0.022
Gender (%)0.075
 Male169 (57.9)89 (52.7)80 (47.3)
 Female123 (42.1)51 (41.5)72 (58.5)
BMI, mean (SD)24.16 (4.76)24.18 (4.62)24.13 (4.83)0.934
Hypertension (%)0.097
 No88 (30.1)49 (55.7)39 (44.3)
 Yes204 (69.9)91 (44.6)113 (55.4)
Atrial fibrillation (%)0.001
 No176 (60.3)98 (55.7)78 (44.3)
 Yes116 (39.7)42 (36.2)74 (63.8)
Stroke (%)0.895
 No216 (74.0)103 (47.7)113 (52.3)
 Yes76 (26.0)37 (48.7)39 (51.3)
CHD (%)<0.001
 No209 (71.6)115 (55.0)94 (45.0)
 Yes83 (28.4)25 (30.1)58 (69.9)
Diabetes (%)0.338
 No195 (66.8)97 (49.7)98 (50.3)
 Yes97 (33.2)43 (44.3)54 (55.7)
Dyslipidemia (%)0.793
 No213 (72.9)101 (47.4)112 (52.6)
 Yes79 (27.1)39 (49.4)40 (50.6)
Anticoagulation (%)1.000
 No271 (92.8)130 (48.0)141 (52.0)
 Yes21 (7.2)10 (47.6)11 (52.4)
Antiplatelet (%)0.690
 No215 (73.6)105 (48.8)110 (51.2)
 Yes77 (26.4)35 (45.5)42 (54.5)
IV-tPA (%)0.213
 No198 (67.8)100 (50.5)98 (49.5)
 Yes94 (32.2)40 (42.6)54 (57.4)
Occlude area, n (%)0.753
 ICA111 (38.0)49 (44.1)62 (52.9)
 MCA/ACA(M1/A1)160 (54.8)78 (48.8)82 (51.2)
 Distal MCA/ACA58 (19.9)28 (48.3)30 (51.7)
HT (%)<0.001
 No205 (70.2)118 (57.6)87 (42.4)
 Yes87 (29.8)22 (25.3)65 (74.7)
mTICI (%)<0.001
 0–2a57 (19.5)10 (17.5)47 (82.5)
 2b–3235 (80.5)130 (55.3)105 (44.7)
NIHSS, median (IQR)14 (9–19)11.5 (7–16)16 (12–21.5)<0.001
BPI (SD)
 SD13.25 (4.04)11.95 (3.34)14.46 (4.26)<0.001
 SV13.72 (4.31)12.19 (3.19)15.13 (4.72)<0.001
 VIM13.31 (3.98)12.09 (3.29)14.43 (4.24)<0.001
 24-hour SBP129 (13)126 (13)131 (14)0.002

Abbreviations: BPI, blood pressure index; CHD, coronary atherosclerotic heart disease; HT, hemorrhagic transformation; IQR, interquartile range; IV-tPA, intravenous tissue plasminogen activator; mTICI, modified Thrombolysis in Cerebral Infarction Score; SBP, systolic blood pressure; SD, standard deviation; SV, successive variation; VIM, variability independent of mean blood pressure.

Factors related to the BPI or NIHSS score

To identify baseline risk factors associated with BPI and NIHSS scores, we developed a linear regression model (Table 2). There were significant positive correlations between atrial fibrillation (β = 0.236, P = 0.004) and CHD (β = 0.194, P < 0.001) and the NIHSS score. A history of hypertension was associated with greater SV (β = 0.203, P = 0.001), SD (β = 0.184, P = 0.003), VIM (β = 0.153, P = 0.013), and 24-hour mean SBP (β = 0.261, P < 0.001). In addition, a history of stroke was significantly associated with VIM (β = 0.132, P = 0.037) and SD (β = 0.139, P = 0.027) but was not consistent with other BPIs. Other relevant medical history data concerning BP-related indices did not significantly differ. The NIHSS score was significantly positively correlated with SV (β = 0.132, P = 0.030), SD (β = 0.148, P = 0.014), and VIM (β = 0.166, P = 0.006), while the association between the NIHSS score and 24-hour SBP was not significant (β = −0.082, P = 0.152).

Table 2.
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Association between baseline risk factors and NIHSS score or BPI

BSEβtP
X → NIHSSa
 Stroke−0.3051.083−0.018−0.2820.778
 AF3.0031.0430.1942.8800.004
 Hypertension−0.6951.006−0.042−0.6910.490
 Diabetes−0.1840.979−0.011−0.1880.851
 Dyslipidemia−1.0711.049−0.063−1.0210.308
 CHD3.9571.0850.2363.647<0.001
X → BPIa
 SV
  Stroke0.7260.6200.0741.1710.243
  Atrial Fibrillation0.1360.6060.0150.2250.822
  Hypertension1.8990.5760.2033.2950.001
  Diabetes−0.2110.56−0.023−0.3770.706
  Dyslipidemia−0.6460.602−0.067−1.0730.284
  CHD0.3080.6360.0320.4840.629
 SD
  Stroke1.2750.5750.1392.2170.027
  Atrial Fibrillation−0.5090.562−0.062−0.9050.366
  Hypertension1.6200.5350.1843.0300.003
  Diabetes−0.2080.520−0.024−0.4000.690
  Dyslipidemia−0.6200.558−0.068−1.1100.268
  CHD0.4390.5900.0490.7450.457
 VIM
  Stroke1.1970.570.1322.1010.037
  Atrial Fibrillation−0.5120.557−0.063−0.9200.359
  Hypertension1.3260.5290.1532.5040.013
  Diabetes−0.2800.515−0.033−0.5440.587
  Dyslipidemia−0.5370.553−0.060−0.9710.332
  CHD0.4320.5840.0490.7400.460
 24-hour SBPc
  Stroke0.0780.1810.0260.4330.666
  Atrial Fibrillation0.0600.1770.0220.3380.735
  Hypertension0.7560.1680.2614.493<0.001
  Diabetes0.2550.1640.0901.5580.120
  Dyslipidemia−0.1520.176−0.051−0.8640.388
  CHD0.0330.1860.0110.1760.861
NIHSS → BPIb
 NIHSS (+ SV)0.0750.0340.1322.1800.030
 NIHSS (+ SD)0.0790.0320.1482.4840.014
 NIHSS (+ VIM)0.0870.0310.1662.7580.006
 NIHSS (+ 24-hour SBP)−0.0140.010−0.082−1.4360.152
BSEβtP
X → NIHSSa
 Stroke−0.3051.083−0.018−0.2820.778
 AF3.0031.0430.1942.8800.004
 Hypertension−0.6951.006−0.042−0.6910.490
 Diabetes−0.1840.979−0.011−0.1880.851
 Dyslipidemia−1.0711.049−0.063−1.0210.308
 CHD3.9571.0850.2363.647<0.001
X → BPIa
 SV
  Stroke0.7260.6200.0741.1710.243
  Atrial Fibrillation0.1360.6060.0150.2250.822
  Hypertension1.8990.5760.2033.2950.001
  Diabetes−0.2110.56−0.023−0.3770.706
  Dyslipidemia−0.6460.602−0.067−1.0730.284
  CHD0.3080.6360.0320.4840.629
 SD
  Stroke1.2750.5750.1392.2170.027
  Atrial Fibrillation−0.5090.562−0.062−0.9050.366
  Hypertension1.6200.5350.1843.0300.003
  Diabetes−0.2080.520−0.024−0.4000.690
  Dyslipidemia−0.6200.558−0.068−1.1100.268
  CHD0.4390.5900.0490.7450.457
 VIM
  Stroke1.1970.570.1322.1010.037
  Atrial Fibrillation−0.5120.557−0.063−0.9200.359
  Hypertension1.3260.5290.1532.5040.013
  Diabetes−0.2800.515−0.033−0.5440.587
  Dyslipidemia−0.5370.553−0.060−0.9710.332
  CHD0.4320.5840.0490.7400.460
 24-hour SBPc
  Stroke0.0780.1810.0260.4330.666
  Atrial Fibrillation0.0600.1770.0220.3380.735
  Hypertension0.7560.1680.2614.493<0.001
  Diabetes0.2550.1640.0901.5580.120
  Dyslipidemia−0.1520.176−0.051−0.8640.388
  CHD0.0330.1860.0110.1760.861
NIHSS → BPIb
 NIHSS (+ SV)0.0750.0340.1322.1800.030
 NIHSS (+ SD)0.0790.0320.1482.4840.014
 NIHSS (+ VIM)0.0870.0310.1662.7580.006
 NIHSS (+ 24-hour SBP)−0.0140.010−0.082−1.4360.152

Abbreviations: B, coefficient; β, standardized coefficient; BPI, blood pressure indices; CHD, coronary atherosclerotic heart disease; mTICI, modified Thrombolysis in Cerebral Infarction Score; SBP, systolic blood pressure; SD, standard deviation; SE, standard error; SV, successive variation; VIM, variability independent of mean blood pressure.

aA Linear regression model was used to assess the association of baseline risk factors with NIHSS scores or BPIs. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension history, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

bA Linear regression model was used to assess the association of NIHSS score with BPIs. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension history, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

c24-hour SBP was included in the model as the BP recording divided by 10.

Table 2.
Open in new tab

Association between baseline risk factors and NIHSS score or BPI

BSEβtP
X → NIHSSa
 Stroke−0.3051.083−0.018−0.2820.778
 AF3.0031.0430.1942.8800.004
 Hypertension−0.6951.006−0.042−0.6910.490
 Diabetes−0.1840.979−0.011−0.1880.851
 Dyslipidemia−1.0711.049−0.063−1.0210.308
 CHD3.9571.0850.2363.647<0.001
X → BPIa
 SV
  Stroke0.7260.6200.0741.1710.243
  Atrial Fibrillation0.1360.6060.0150.2250.822
  Hypertension1.8990.5760.2033.2950.001
  Diabetes−0.2110.56−0.023−0.3770.706
  Dyslipidemia−0.6460.602−0.067−1.0730.284
  CHD0.3080.6360.0320.4840.629
 SD
  Stroke1.2750.5750.1392.2170.027
  Atrial Fibrillation−0.5090.562−0.062−0.9050.366
  Hypertension1.6200.5350.1843.0300.003
  Diabetes−0.2080.520−0.024−0.4000.690
  Dyslipidemia−0.6200.558−0.068−1.1100.268
  CHD0.4390.5900.0490.7450.457
 VIM
  Stroke1.1970.570.1322.1010.037
  Atrial Fibrillation−0.5120.557−0.063−0.9200.359
  Hypertension1.3260.5290.1532.5040.013
  Diabetes−0.2800.515−0.033−0.5440.587
  Dyslipidemia−0.5370.553−0.060−0.9710.332
  CHD0.4320.5840.0490.7400.460
 24-hour SBPc
  Stroke0.0780.1810.0260.4330.666
  Atrial Fibrillation0.0600.1770.0220.3380.735
  Hypertension0.7560.1680.2614.493<0.001
  Diabetes0.2550.1640.0901.5580.120
  Dyslipidemia−0.1520.176−0.051−0.8640.388
  CHD0.0330.1860.0110.1760.861
NIHSS → BPIb
 NIHSS (+ SV)0.0750.0340.1322.1800.030
 NIHSS (+ SD)0.0790.0320.1482.4840.014
 NIHSS (+ VIM)0.0870.0310.1662.7580.006
 NIHSS (+ 24-hour SBP)−0.0140.010−0.082−1.4360.152
BSEβtP
X → NIHSSa
 Stroke−0.3051.083−0.018−0.2820.778
 AF3.0031.0430.1942.8800.004
 Hypertension−0.6951.006−0.042−0.6910.490
 Diabetes−0.1840.979−0.011−0.1880.851
 Dyslipidemia−1.0711.049−0.063−1.0210.308
 CHD3.9571.0850.2363.647<0.001
X → BPIa
 SV
  Stroke0.7260.6200.0741.1710.243
  Atrial Fibrillation0.1360.6060.0150.2250.822
  Hypertension1.8990.5760.2033.2950.001
  Diabetes−0.2110.56−0.023−0.3770.706
  Dyslipidemia−0.6460.602−0.067−1.0730.284
  CHD0.3080.6360.0320.4840.629
 SD
  Stroke1.2750.5750.1392.2170.027
  Atrial Fibrillation−0.5090.562−0.062−0.9050.366
  Hypertension1.6200.5350.1843.0300.003
  Diabetes−0.2080.520−0.024−0.4000.690
  Dyslipidemia−0.6200.558−0.068−1.1100.268
  CHD0.4390.5900.0490.7450.457
 VIM
  Stroke1.1970.570.1322.1010.037
  Atrial Fibrillation−0.5120.557−0.063−0.9200.359
  Hypertension1.3260.5290.1532.5040.013
  Diabetes−0.2800.515−0.033−0.5440.587
  Dyslipidemia−0.5370.553−0.060−0.9710.332
  CHD0.4320.5840.0490.7400.460
 24-hour SBPc
  Stroke0.0780.1810.0260.4330.666
  Atrial Fibrillation0.0600.1770.0220.3380.735
  Hypertension0.7560.1680.2614.493<0.001
  Diabetes0.2550.1640.0901.5580.120
  Dyslipidemia−0.1520.176−0.051−0.8640.388
  CHD0.0330.1860.0110.1760.861
NIHSS → BPIb
 NIHSS (+ SV)0.0750.0340.1322.1800.030
 NIHSS (+ SD)0.0790.0320.1482.4840.014
 NIHSS (+ VIM)0.0870.0310.1662.7580.006
 NIHSS (+ 24-hour SBP)−0.0140.010−0.082−1.4360.152

Abbreviations: B, coefficient; β, standardized coefficient; BPI, blood pressure indices; CHD, coronary atherosclerotic heart disease; mTICI, modified Thrombolysis in Cerebral Infarction Score; SBP, systolic blood pressure; SD, standard deviation; SE, standard error; SV, successive variation; VIM, variability independent of mean blood pressure.

aA Linear regression model was used to assess the association of baseline risk factors with NIHSS scores or BPIs. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension history, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

bA Linear regression model was used to assess the association of NIHSS score with BPIs. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension history, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

c24-hour SBP was included in the model as the BP recording divided by 10.

Relationships between risk factors and clinical outcome

To establish the regression model and identify factors associated with clinical outcomes, binary logistic regression analysis was used (Table 3). All BPIs remained statistically significant with mRS score after adjusting baseline risk factors (with all P values < 0.05). The results showed that CHD (OR = 2.068, 95% CI: 1.038, 4.120, P = 0.039) was significantly associated with functional outcome after adjusting for baseline risk factors and other covariates; moreover, no associations were shown for other baseline risk factors, but diabetes was significantly associated with functional outcome. After further adjusting for HT, (OR = 2.880, 95% CI: 1.516, 5.469, P = 0.001) a significant difference was associated with a poor prognosis. Only CHD and diabetes had significant direct effects when different BPIs were adjusted for separately in the model. In addition, the NIHSS score was consistently the most relevant factor for outcome in the models adjusted for HT or BPI.

Table 3.
Open in new tab

Binary logistic regression analysis with the mRS score as the functional outcome

BOR95% CIP
X, NIHSS → mRSa
 NIHSS0.0961.1011.056, 1.148<0.001
 Stroke−0.0490.9520.484, 1.8760.888
 Atrial Fibrillation0.4671.5950.831, 3.0590.160
 Hypertension0.4041.4980.799, 2.8090.207
 Diabetes0.5921.8080.976, 3.3490.060
 Dyslipidemia−0.4190.6580.340, 1.2720.213
 CHD0.7262.0681.038, 4.1200.039
X, HT → mRSa
 HT1.0582.881.516, 5.4690.001
 NIHSS0.0821.0861.040, 1.134<0.001
 Stroke−0.0700.9320.465, 1.8700.843
 Atrial Fibrillation0.4401.5530.794 3.0400.199
 Hypertension0.3591.4320.755, 2.7180.272
 Diabetes0.5981.8180.970, 3.4060.062
 Dyslipidemia−0.3980.6780.344, 1.3370.262
 CHD0.7442.1051.041, 4.2550.038
X, BPI → mRSa
 SV0.1921.2121.122, 1.309<0.001
 SD0.1761.1921.102, 1.291<0.001
 VIM0.1671.1821.092, 1.279<0.001
 24-hour SBP0.2771.3191.046, 1.6640.019
 Stroke (+ SV)−0.1780.8370.400, 1.7500.636
 Stroke (+ SD)−0.2850.7520.362, 1.5630.445
 Stroke (+ VIM)−0.2570.7730.375, 1.5960.486
 Stroke (+ 24-hour SBPc)−0.0630.9390.470, 1.8750.859
 Atrial Fibrillation (+ SV)0.4761.6090.803, 3.2230.180
 Atrial Fibrillation (+ SD)0.5801.7860.903, 3.5320.096
 Atrial Fibrillation (+ VIM)0.5721.7710.899, 3.4900.099
 Atrial Fibrillation (+ 24-hour SBP)0.4541.5750.814, 3.0490.177
 Hypertension (+ SV)0.0651.0670.542, 2.0980.851
 Hypertension (+ SD)0.1211.1280.579, 0.5790.723
 Hypertension (+ VIM)0.1831.7710.621, 2.3190.587
 Hypertension (+ 24-hour SBP)0.2161.2410.643, 2.3940.520
 Diabetes (+ SV)0.6751.9641.024, 3.7660.042
 Diabetes (+ SD)0.6321.8810.991, 3.5720.053
 Diabetes (+ VIM)0.6471.9091.007, 3.6210.048
 Diabetes (+ 24-hour SBP)0.4961.6420.882, 3.0560.118
 Dyslipidemia (+ SV)−0.3260.7220.358, 1.4560.363
 Dyslipidemia (+ SD)−0.3860.6800.343, 1.3470.268
 Dyslipidemia (+ VIM)−0.4100.6640.336, 1.3130.239
 Dyslipidemia (+ 24-hour SBP)−0.3640.6950.357, 3.0560.284
 CHD (+ SV)0.7742.1691.053, 4.4650.036
 CHD (+ SD)0.7082.0311.001, 1.0010.050
 CHD (+ VIM)0.7032.0210.998, 4.0910.051
 CHD (+ 24-hour SBP)0.7512.1181.054, 4.2580.035
NIHSS → BPI → mRSb
 NIHSS (+ SV)0.0961.1001.052, 1.150<0.001
 NIHSS (+ SD)0.0941.0981.051, 1.148<0.001
 NIHSS (+ VIM)0.0921.0971.050, 1.146<0.001
 NIHSS (+ 24-hour SBP)0.1021.1071.061, 1.155<0.001
BOR95% CIP
X, NIHSS → mRSa
 NIHSS0.0961.1011.056, 1.148<0.001
 Stroke−0.0490.9520.484, 1.8760.888
 Atrial Fibrillation0.4671.5950.831, 3.0590.160
 Hypertension0.4041.4980.799, 2.8090.207
 Diabetes0.5921.8080.976, 3.3490.060
 Dyslipidemia−0.4190.6580.340, 1.2720.213
 CHD0.7262.0681.038, 4.1200.039
X, HT → mRSa
 HT1.0582.881.516, 5.4690.001
 NIHSS0.0821.0861.040, 1.134<0.001
 Stroke−0.0700.9320.465, 1.8700.843
 Atrial Fibrillation0.4401.5530.794 3.0400.199
 Hypertension0.3591.4320.755, 2.7180.272
 Diabetes0.5981.8180.970, 3.4060.062
 Dyslipidemia−0.3980.6780.344, 1.3370.262
 CHD0.7442.1051.041, 4.2550.038
X, BPI → mRSa
 SV0.1921.2121.122, 1.309<0.001
 SD0.1761.1921.102, 1.291<0.001
 VIM0.1671.1821.092, 1.279<0.001
 24-hour SBP0.2771.3191.046, 1.6640.019
 Stroke (+ SV)−0.1780.8370.400, 1.7500.636
 Stroke (+ SD)−0.2850.7520.362, 1.5630.445
 Stroke (+ VIM)−0.2570.7730.375, 1.5960.486
 Stroke (+ 24-hour SBPc)−0.0630.9390.470, 1.8750.859
 Atrial Fibrillation (+ SV)0.4761.6090.803, 3.2230.180
 Atrial Fibrillation (+ SD)0.5801.7860.903, 3.5320.096
 Atrial Fibrillation (+ VIM)0.5721.7710.899, 3.4900.099
 Atrial Fibrillation (+ 24-hour SBP)0.4541.5750.814, 3.0490.177
 Hypertension (+ SV)0.0651.0670.542, 2.0980.851
 Hypertension (+ SD)0.1211.1280.579, 0.5790.723
 Hypertension (+ VIM)0.1831.7710.621, 2.3190.587
 Hypertension (+ 24-hour SBP)0.2161.2410.643, 2.3940.520
 Diabetes (+ SV)0.6751.9641.024, 3.7660.042
 Diabetes (+ SD)0.6321.8810.991, 3.5720.053
 Diabetes (+ VIM)0.6471.9091.007, 3.6210.048
 Diabetes (+ 24-hour SBP)0.4961.6420.882, 3.0560.118
 Dyslipidemia (+ SV)−0.3260.7220.358, 1.4560.363
 Dyslipidemia (+ SD)−0.3860.6800.343, 1.3470.268
 Dyslipidemia (+ VIM)−0.4100.6640.336, 1.3130.239
 Dyslipidemia (+ 24-hour SBP)−0.3640.6950.357, 3.0560.284
 CHD (+ SV)0.7742.1691.053, 4.4650.036
 CHD (+ SD)0.7082.0311.001, 1.0010.050
 CHD (+ VIM)0.7032.0210.998, 4.0910.051
 CHD (+ 24-hour SBP)0.7512.1181.054, 4.2580.035
NIHSS → BPI → mRSb
 NIHSS (+ SV)0.0961.1001.052, 1.150<0.001
 NIHSS (+ SD)0.0941.0981.051, 1.148<0.001
 NIHSS (+ VIM)0.0921.0971.050, 1.146<0.001
 NIHSS (+ 24-hour SBP)0.1021.1071.061, 1.155<0.001

Abbreviations: B, coefficient; β, standardized coefficient; BPI, blood pressure index; CHD, coronary atherosclerotic heart disease; CI, confidence interval; HT, hemorrhagic transformation; mRS, modified Rankin scale; mTICI, modified Thrombolysis in Cerebral Infarction Score; OR, odds ratio; SBP, systolic blood pressure; SD, standard deviation; SE, standard error; SV, successive variation; VIM, variability independent of mean blood pressure.

aBinary logistic regression was performed to evaluate the associations between baseline risk factors and between 3-month functional outcomes, with NIHSS score, HT, or BPIs as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

bBinary logistic regression was performed to evaluate the associations between NIHSS score and between 3-month functional outcome, with BPIs as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

c24-hour SBP was included in the model as the BP recording divided by 10.

Table 3.
Open in new tab

Binary logistic regression analysis with the mRS score as the functional outcome

BOR95% CIP
X, NIHSS → mRSa
 NIHSS0.0961.1011.056, 1.148<0.001
 Stroke−0.0490.9520.484, 1.8760.888
 Atrial Fibrillation0.4671.5950.831, 3.0590.160
 Hypertension0.4041.4980.799, 2.8090.207
 Diabetes0.5921.8080.976, 3.3490.060
 Dyslipidemia−0.4190.6580.340, 1.2720.213
 CHD0.7262.0681.038, 4.1200.039
X, HT → mRSa
 HT1.0582.881.516, 5.4690.001
 NIHSS0.0821.0861.040, 1.134<0.001
 Stroke−0.0700.9320.465, 1.8700.843
 Atrial Fibrillation0.4401.5530.794 3.0400.199
 Hypertension0.3591.4320.755, 2.7180.272
 Diabetes0.5981.8180.970, 3.4060.062
 Dyslipidemia−0.3980.6780.344, 1.3370.262
 CHD0.7442.1051.041, 4.2550.038
X, BPI → mRSa
 SV0.1921.2121.122, 1.309<0.001
 SD0.1761.1921.102, 1.291<0.001
 VIM0.1671.1821.092, 1.279<0.001
 24-hour SBP0.2771.3191.046, 1.6640.019
 Stroke (+ SV)−0.1780.8370.400, 1.7500.636
 Stroke (+ SD)−0.2850.7520.362, 1.5630.445
 Stroke (+ VIM)−0.2570.7730.375, 1.5960.486
 Stroke (+ 24-hour SBPc)−0.0630.9390.470, 1.8750.859
 Atrial Fibrillation (+ SV)0.4761.6090.803, 3.2230.180
 Atrial Fibrillation (+ SD)0.5801.7860.903, 3.5320.096
 Atrial Fibrillation (+ VIM)0.5721.7710.899, 3.4900.099
 Atrial Fibrillation (+ 24-hour SBP)0.4541.5750.814, 3.0490.177
 Hypertension (+ SV)0.0651.0670.542, 2.0980.851
 Hypertension (+ SD)0.1211.1280.579, 0.5790.723
 Hypertension (+ VIM)0.1831.7710.621, 2.3190.587
 Hypertension (+ 24-hour SBP)0.2161.2410.643, 2.3940.520
 Diabetes (+ SV)0.6751.9641.024, 3.7660.042
 Diabetes (+ SD)0.6321.8810.991, 3.5720.053
 Diabetes (+ VIM)0.6471.9091.007, 3.6210.048
 Diabetes (+ 24-hour SBP)0.4961.6420.882, 3.0560.118
 Dyslipidemia (+ SV)−0.3260.7220.358, 1.4560.363
 Dyslipidemia (+ SD)−0.3860.6800.343, 1.3470.268
 Dyslipidemia (+ VIM)−0.4100.6640.336, 1.3130.239
 Dyslipidemia (+ 24-hour SBP)−0.3640.6950.357, 3.0560.284
 CHD (+ SV)0.7742.1691.053, 4.4650.036
 CHD (+ SD)0.7082.0311.001, 1.0010.050
 CHD (+ VIM)0.7032.0210.998, 4.0910.051
 CHD (+ 24-hour SBP)0.7512.1181.054, 4.2580.035
NIHSS → BPI → mRSb
 NIHSS (+ SV)0.0961.1001.052, 1.150<0.001
 NIHSS (+ SD)0.0941.0981.051, 1.148<0.001
 NIHSS (+ VIM)0.0921.0971.050, 1.146<0.001
 NIHSS (+ 24-hour SBP)0.1021.1071.061, 1.155<0.001
BOR95% CIP
X, NIHSS → mRSa
 NIHSS0.0961.1011.056, 1.148<0.001
 Stroke−0.0490.9520.484, 1.8760.888
 Atrial Fibrillation0.4671.5950.831, 3.0590.160
 Hypertension0.4041.4980.799, 2.8090.207
 Diabetes0.5921.8080.976, 3.3490.060
 Dyslipidemia−0.4190.6580.340, 1.2720.213
 CHD0.7262.0681.038, 4.1200.039
X, HT → mRSa
 HT1.0582.881.516, 5.4690.001
 NIHSS0.0821.0861.040, 1.134<0.001
 Stroke−0.0700.9320.465, 1.8700.843
 Atrial Fibrillation0.4401.5530.794 3.0400.199
 Hypertension0.3591.4320.755, 2.7180.272
 Diabetes0.5981.8180.970, 3.4060.062
 Dyslipidemia−0.3980.6780.344, 1.3370.262
 CHD0.7442.1051.041, 4.2550.038
X, BPI → mRSa
 SV0.1921.2121.122, 1.309<0.001
 SD0.1761.1921.102, 1.291<0.001
 VIM0.1671.1821.092, 1.279<0.001
 24-hour SBP0.2771.3191.046, 1.6640.019
 Stroke (+ SV)−0.1780.8370.400, 1.7500.636
 Stroke (+ SD)−0.2850.7520.362, 1.5630.445
 Stroke (+ VIM)−0.2570.7730.375, 1.5960.486
 Stroke (+ 24-hour SBPc)−0.0630.9390.470, 1.8750.859
 Atrial Fibrillation (+ SV)0.4761.6090.803, 3.2230.180
 Atrial Fibrillation (+ SD)0.5801.7860.903, 3.5320.096
 Atrial Fibrillation (+ VIM)0.5721.7710.899, 3.4900.099
 Atrial Fibrillation (+ 24-hour SBP)0.4541.5750.814, 3.0490.177
 Hypertension (+ SV)0.0651.0670.542, 2.0980.851
 Hypertension (+ SD)0.1211.1280.579, 0.5790.723
 Hypertension (+ VIM)0.1831.7710.621, 2.3190.587
 Hypertension (+ 24-hour SBP)0.2161.2410.643, 2.3940.520
 Diabetes (+ SV)0.6751.9641.024, 3.7660.042
 Diabetes (+ SD)0.6321.8810.991, 3.5720.053
 Diabetes (+ VIM)0.6471.9091.007, 3.6210.048
 Diabetes (+ 24-hour SBP)0.4961.6420.882, 3.0560.118
 Dyslipidemia (+ SV)−0.3260.7220.358, 1.4560.363
 Dyslipidemia (+ SD)−0.3860.6800.343, 1.3470.268
 Dyslipidemia (+ VIM)−0.4100.6640.336, 1.3130.239
 Dyslipidemia (+ 24-hour SBP)−0.3640.6950.357, 3.0560.284
 CHD (+ SV)0.7742.1691.053, 4.4650.036
 CHD (+ SD)0.7082.0311.001, 1.0010.050
 CHD (+ VIM)0.7032.0210.998, 4.0910.051
 CHD (+ 24-hour SBP)0.7512.1181.054, 4.2580.035
NIHSS → BPI → mRSb
 NIHSS (+ SV)0.0961.1001.052, 1.150<0.001
 NIHSS (+ SD)0.0941.0981.051, 1.148<0.001
 NIHSS (+ VIM)0.0921.0971.050, 1.146<0.001
 NIHSS (+ 24-hour SBP)0.1021.1071.061, 1.155<0.001

Abbreviations: B, coefficient; β, standardized coefficient; BPI, blood pressure index; CHD, coronary atherosclerotic heart disease; CI, confidence interval; HT, hemorrhagic transformation; mRS, modified Rankin scale; mTICI, modified Thrombolysis in Cerebral Infarction Score; OR, odds ratio; SBP, systolic blood pressure; SD, standard deviation; SE, standard error; SV, successive variation; VIM, variability independent of mean blood pressure.

aBinary logistic regression was performed to evaluate the associations between baseline risk factors and between 3-month functional outcomes, with NIHSS score, HT, or BPIs as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

bBinary logistic regression was performed to evaluate the associations between NIHSS score and between 3-month functional outcome, with BPIs as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

c24-hour SBP was included in the model as the BP recording divided by 10.

BP mediates the association between risk factors and clinical outcome

To further clarify how baseline risk factors affect clinical outcomes through BP after EVT, we used bootstrap analysis to determine the direct effects, indirect effects, and proportion of the indirect effects to total effects (Table 4). AF affected 3-month functional outcome by influencing the NIHSS score at the onset of AIS, but the direct effect was not significant (ACME: 0.0567, 95% CI: 0.0194, 0.1047; ADE: 0.0885, 95% CI: −0.0368, 0.2129). The direct effect of CHD on the outcome of the mRS score was 0.1420 (95% CI: 0.0091, 0.2636), while the indirect effect through the NIHSS score was 0.0741 (95% CI: 0.0287, 0.1263), constituting 34.29% of the total effect. According to the X-HT-mRS mediation model, no baseline risk factor had a significant indirect effect. When the BPIs were used as mediators, patients with a history of stroke were associated with greater SD (ACME: 0.0378, 95% CI: 0.0006, 0.0749) and VIM (ACME: 0.0340, 95% CI: −0.0015, 0.0716), mediating 72% and 71%, respectively, of the mediating effect. Furthermore, patients with a history of hypertension had higher SV (ACME: 0.0599, 95% CI: 0.0208, 0.1090), SD (ACME: 0.0475, 95% CI: 0.0145, 0.0897), VIM (ACME: 0.0369, 95% CI: 0.0085, 0.0748), and 24-hour SBP (ACME: 0.0374, 95% CI: 0.0058, 0.0777), with mediation proportions of 87.86%, 69.12%, 53.48%, and 46.19%, respectively, indicating that BP, particularly BPV, might completely mediate the impact of a history of hypertension on clinical outcome (Figure 2).

Table 4.
Open in new tab

The mediating effects of vascular risk factors, NIHSS score, and functional outcome examined by bootstrapping

MediatorBootstrapped estmates (n = 1,000)Proportion (%)
Direct effectIndirect effect
X → NIHSS → mRSa
 Stroke−0.0080 (−0.1298, 0.1146)−0.0057 (−0.0449, 0.0307)41.49
 Atrial Fibrillation0.0885 (−0.0368, 0.2129)0.0567 (0.0194, 0.1047)*39.05
 Hypertension0.0799 (−0.0458, 0.2044)−0.0129 (−0.0447, 0.0210)−16.15
 Diabetes0.1108 (−0.0043, 0.2348)−0.0034 (−0.0383, 0.0346)−3.07
 Dyslipidemia−0.0085 (−0.1942, 0.0312)−0.0196 (−0.0603, 0.0219)18.65
 CHD0.1420 (0.0091, 0.2636)*0.0741 (0.0287, 0.1263)*34.29*
X → HT → mRSa
 Stroke−0.0089 (−0.1333, 0.1204)0.0028 (−0.0252, 0.0274)−31.46
 Atrial Fibrillation0.0789 (−0.0409, 0.2061)0.0048 (−0.0198, 0.0326)5.78
 Hypertension0.0650 (−0.0511, 0.1842)0.0080 (−0.0155, 0.0342)10.99
 Diabetes0.1024 (-0.0091, 0.2218)0.0018 (−0.0226, 0.0247)1.70
 Dyslipidemia−0.0756 (−0.1857, 0.0460)−0.0018 (−0.0239, 0.0222)2.37
 CHD0.1349 (0.0040, 0.2604)*0.0069 (−0.0183, 0.0348)4.87
X → BPI → mRSa
 Stroke
  SV−0.0350 (−0.1590, 0.0953)0.0227 (−0.0172, 0.0630)−64.86
  SD−0.0525 (−0.1838, 0.0710)0.0378 (0.0006, 0.0749)*72.00*
  VIM−0.0472 (−0.1677, 0.0767)0.0344 (0.0018, 0.0716)*72.88*
  24-hour SBPc−0.0096 (−0.1334, 0.1085)0.0040 (−0.0187, 0.0266)−41.67
 Atrial Fibrillation
  SV0.0782 (−0.0428, 0.2003)0.0118 (−0.0325, 0.0521)13.12
  SD0.1004 (−0.0234, 0.2232)−0.0083 (−0.0456, 0.0223)−8.27
  VIM0.1003 (−0.0214, 0.2215)−0.0074 (−0.0427, 0.0244)−7.38
  24-hour SBP0.0843 (−0.0400, 0.2109)0.0008 (−0.0192, 0.0198)0.96
 Hypertension
  SV0.0083 (−0.1129, 0.1321)0.0599 (0.0208, 0.1090)*87.86*
  SD0.0212 (−0.1009, 0.1386)0.0475 (0.0145, 0.0897)*69.12*
  VIM0.0321 (−0.0916, 0.1502)0.0369 (0.0085, 0.0748)*53.47*
  24-hour SBP0.0435 (−0.0834, 0.1723)0.0374 (0.0058, 0.0777)*46.19*
 Diabetes
  SV0.1156 (0.0062, 0.2265)*−0.0072 (−0.0470, 0.0289)−6.23
  SD0.1119 (−0.0018, 0.2290)−0.0067 (−0.0422, 0.0237)−5.99
  VIM0.1153 (0.0006, 0.2331)*−0.0085 (−0.0430, 0.0210)−7.37
  24-hour SBP0.0903 (−0.0201, 0.2091)0.0125 (−0.0033, 0.0378)12.13
 Dyslipidemia
  SV−0.0586 (−0.1626, 0.0503)−0.0232 (−0.0621, 0.0151)28.35
  SD−0.0688 (−0.1746, 0.0409)−0.0211 (−0.0559, 0.0102)23.42
  VIM−0.0726 (−0.1788, 0.0377)−0.0181 (−0.0515, 0.0118)19.92
  24-hour SBP−0.0739 (−0.1855, 0.0447)−0.0066 (−0.0257, 0.0106)8.20
 CHD
  SV0.1343 (0.0053, 0.2525)*0.0199 (−0.0192, 0.0582)12.93
  SD0.1313 (−0.0016, 0.2467)0.0233 (−0.0086, 0.0601)15.06
  VIM0.1315 (−0.0031, 0.2493)0.0230 (−0.0072, 0.0596)14.91
  24-hour SBP0.1416 (0.0043, 0.2646)*−0.0012 (−0.0232, 0.0173)−0.85
NIHSS → BPI → mRSb
 SV0.0143 (0.0093, 0.0175)*0.0021 (0.0003, 0.0040)*12.82*
 SD0.0143 (0.0092, 0.0177)*0.0021 (0.0003, 0.0039)*12.62*
 VIM0.0142 (0.0091, 0.0176)*0.0022 (0.0005, 0.0040)*13.26*
 24-hour SBP0.0165 (0.0115, 0.0195)*−0.0006 (−0.0018, 0.0003)−3.64
NIHSS→ HT→ mRSb0.0134 (0.0080, 0.0171)*0.0030 (0.0010, 0.0054)*18.19*
MediatorBootstrapped estmates (n = 1,000)Proportion (%)
Direct effectIndirect effect
X → NIHSS → mRSa
 Stroke−0.0080 (−0.1298, 0.1146)−0.0057 (−0.0449, 0.0307)41.49
 Atrial Fibrillation0.0885 (−0.0368, 0.2129)0.0567 (0.0194, 0.1047)*39.05
 Hypertension0.0799 (−0.0458, 0.2044)−0.0129 (−0.0447, 0.0210)−16.15
 Diabetes0.1108 (−0.0043, 0.2348)−0.0034 (−0.0383, 0.0346)−3.07
 Dyslipidemia−0.0085 (−0.1942, 0.0312)−0.0196 (−0.0603, 0.0219)18.65
 CHD0.1420 (0.0091, 0.2636)*0.0741 (0.0287, 0.1263)*34.29*
X → HT → mRSa
 Stroke−0.0089 (−0.1333, 0.1204)0.0028 (−0.0252, 0.0274)−31.46
 Atrial Fibrillation0.0789 (−0.0409, 0.2061)0.0048 (−0.0198, 0.0326)5.78
 Hypertension0.0650 (−0.0511, 0.1842)0.0080 (−0.0155, 0.0342)10.99
 Diabetes0.1024 (-0.0091, 0.2218)0.0018 (−0.0226, 0.0247)1.70
 Dyslipidemia−0.0756 (−0.1857, 0.0460)−0.0018 (−0.0239, 0.0222)2.37
 CHD0.1349 (0.0040, 0.2604)*0.0069 (−0.0183, 0.0348)4.87
X → BPI → mRSa
 Stroke
  SV−0.0350 (−0.1590, 0.0953)0.0227 (−0.0172, 0.0630)−64.86
  SD−0.0525 (−0.1838, 0.0710)0.0378 (0.0006, 0.0749)*72.00*
  VIM−0.0472 (−0.1677, 0.0767)0.0344 (0.0018, 0.0716)*72.88*
  24-hour SBPc−0.0096 (−0.1334, 0.1085)0.0040 (−0.0187, 0.0266)−41.67
 Atrial Fibrillation
  SV0.0782 (−0.0428, 0.2003)0.0118 (−0.0325, 0.0521)13.12
  SD0.1004 (−0.0234, 0.2232)−0.0083 (−0.0456, 0.0223)−8.27
  VIM0.1003 (−0.0214, 0.2215)−0.0074 (−0.0427, 0.0244)−7.38
  24-hour SBP0.0843 (−0.0400, 0.2109)0.0008 (−0.0192, 0.0198)0.96
 Hypertension
  SV0.0083 (−0.1129, 0.1321)0.0599 (0.0208, 0.1090)*87.86*
  SD0.0212 (−0.1009, 0.1386)0.0475 (0.0145, 0.0897)*69.12*
  VIM0.0321 (−0.0916, 0.1502)0.0369 (0.0085, 0.0748)*53.47*
  24-hour SBP0.0435 (−0.0834, 0.1723)0.0374 (0.0058, 0.0777)*46.19*
 Diabetes
  SV0.1156 (0.0062, 0.2265)*−0.0072 (−0.0470, 0.0289)−6.23
  SD0.1119 (−0.0018, 0.2290)−0.0067 (−0.0422, 0.0237)−5.99
  VIM0.1153 (0.0006, 0.2331)*−0.0085 (−0.0430, 0.0210)−7.37
  24-hour SBP0.0903 (−0.0201, 0.2091)0.0125 (−0.0033, 0.0378)12.13
 Dyslipidemia
  SV−0.0586 (−0.1626, 0.0503)−0.0232 (−0.0621, 0.0151)28.35
  SD−0.0688 (−0.1746, 0.0409)−0.0211 (−0.0559, 0.0102)23.42
  VIM−0.0726 (−0.1788, 0.0377)−0.0181 (−0.0515, 0.0118)19.92
  24-hour SBP−0.0739 (−0.1855, 0.0447)−0.0066 (−0.0257, 0.0106)8.20
 CHD
  SV0.1343 (0.0053, 0.2525)*0.0199 (−0.0192, 0.0582)12.93
  SD0.1313 (−0.0016, 0.2467)0.0233 (−0.0086, 0.0601)15.06
  VIM0.1315 (−0.0031, 0.2493)0.0230 (−0.0072, 0.0596)14.91
  24-hour SBP0.1416 (0.0043, 0.2646)*−0.0012 (−0.0232, 0.0173)−0.85
NIHSS → BPI → mRSb
 SV0.0143 (0.0093, 0.0175)*0.0021 (0.0003, 0.0040)*12.82*
 SD0.0143 (0.0092, 0.0177)*0.0021 (0.0003, 0.0039)*12.62*
 VIM0.0142 (0.0091, 0.0176)*0.0022 (0.0005, 0.0040)*13.26*
 24-hour SBP0.0165 (0.0115, 0.0195)*−0.0006 (−0.0018, 0.0003)−3.64
NIHSS→ HT→ mRSb0.0134 (0.0080, 0.0171)*0.0030 (0.0010, 0.0054)*18.19*

Abbreviations: BPI, blood pressure index; CHD, coronary atherosclerotic heart disease; HT, hemorrhagic transformation; mRS, modified Rankin scale; mTICI, modified Thrombolysis in Cerebral Infarction Score; SBP, systolic blood pressure; SD, standard deviation; SV, successive variation; VIM, variability independent of mean blood pressure.

aBinary logistic regression was performed to evaluate the associations between baseline risk factors and between 3-month functional outcomes, with NIHSS score, HT, or BPIs as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

bBinary logistic regression was performed to evaluate the associations between NIHSS score and between 3-month functional outcome, with BPIs or HT as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

c24-hour SBP was included in the model as the BP recording divided by 10.

*P < 0.05.

Table 4.
Open in new tab

The mediating effects of vascular risk factors, NIHSS score, and functional outcome examined by bootstrapping

MediatorBootstrapped estmates (n = 1,000)Proportion (%)
Direct effectIndirect effect
X → NIHSS → mRSa
 Stroke−0.0080 (−0.1298, 0.1146)−0.0057 (−0.0449, 0.0307)41.49
 Atrial Fibrillation0.0885 (−0.0368, 0.2129)0.0567 (0.0194, 0.1047)*39.05
 Hypertension0.0799 (−0.0458, 0.2044)−0.0129 (−0.0447, 0.0210)−16.15
 Diabetes0.1108 (−0.0043, 0.2348)−0.0034 (−0.0383, 0.0346)−3.07
 Dyslipidemia−0.0085 (−0.1942, 0.0312)−0.0196 (−0.0603, 0.0219)18.65
 CHD0.1420 (0.0091, 0.2636)*0.0741 (0.0287, 0.1263)*34.29*
X → HT → mRSa
 Stroke−0.0089 (−0.1333, 0.1204)0.0028 (−0.0252, 0.0274)−31.46
 Atrial Fibrillation0.0789 (−0.0409, 0.2061)0.0048 (−0.0198, 0.0326)5.78
 Hypertension0.0650 (−0.0511, 0.1842)0.0080 (−0.0155, 0.0342)10.99
 Diabetes0.1024 (-0.0091, 0.2218)0.0018 (−0.0226, 0.0247)1.70
 Dyslipidemia−0.0756 (−0.1857, 0.0460)−0.0018 (−0.0239, 0.0222)2.37
 CHD0.1349 (0.0040, 0.2604)*0.0069 (−0.0183, 0.0348)4.87
X → BPI → mRSa
 Stroke
  SV−0.0350 (−0.1590, 0.0953)0.0227 (−0.0172, 0.0630)−64.86
  SD−0.0525 (−0.1838, 0.0710)0.0378 (0.0006, 0.0749)*72.00*
  VIM−0.0472 (−0.1677, 0.0767)0.0344 (0.0018, 0.0716)*72.88*
  24-hour SBPc−0.0096 (−0.1334, 0.1085)0.0040 (−0.0187, 0.0266)−41.67
 Atrial Fibrillation
  SV0.0782 (−0.0428, 0.2003)0.0118 (−0.0325, 0.0521)13.12
  SD0.1004 (−0.0234, 0.2232)−0.0083 (−0.0456, 0.0223)−8.27
  VIM0.1003 (−0.0214, 0.2215)−0.0074 (−0.0427, 0.0244)−7.38
  24-hour SBP0.0843 (−0.0400, 0.2109)0.0008 (−0.0192, 0.0198)0.96
 Hypertension
  SV0.0083 (−0.1129, 0.1321)0.0599 (0.0208, 0.1090)*87.86*
  SD0.0212 (−0.1009, 0.1386)0.0475 (0.0145, 0.0897)*69.12*
  VIM0.0321 (−0.0916, 0.1502)0.0369 (0.0085, 0.0748)*53.47*
  24-hour SBP0.0435 (−0.0834, 0.1723)0.0374 (0.0058, 0.0777)*46.19*
 Diabetes
  SV0.1156 (0.0062, 0.2265)*−0.0072 (−0.0470, 0.0289)−6.23
  SD0.1119 (−0.0018, 0.2290)−0.0067 (−0.0422, 0.0237)−5.99
  VIM0.1153 (0.0006, 0.2331)*−0.0085 (−0.0430, 0.0210)−7.37
  24-hour SBP0.0903 (−0.0201, 0.2091)0.0125 (−0.0033, 0.0378)12.13
 Dyslipidemia
  SV−0.0586 (−0.1626, 0.0503)−0.0232 (−0.0621, 0.0151)28.35
  SD−0.0688 (−0.1746, 0.0409)−0.0211 (−0.0559, 0.0102)23.42
  VIM−0.0726 (−0.1788, 0.0377)−0.0181 (−0.0515, 0.0118)19.92
  24-hour SBP−0.0739 (−0.1855, 0.0447)−0.0066 (−0.0257, 0.0106)8.20
 CHD
  SV0.1343 (0.0053, 0.2525)*0.0199 (−0.0192, 0.0582)12.93
  SD0.1313 (−0.0016, 0.2467)0.0233 (−0.0086, 0.0601)15.06
  VIM0.1315 (−0.0031, 0.2493)0.0230 (−0.0072, 0.0596)14.91
  24-hour SBP0.1416 (0.0043, 0.2646)*−0.0012 (−0.0232, 0.0173)−0.85
NIHSS → BPI → mRSb
 SV0.0143 (0.0093, 0.0175)*0.0021 (0.0003, 0.0040)*12.82*
 SD0.0143 (0.0092, 0.0177)*0.0021 (0.0003, 0.0039)*12.62*
 VIM0.0142 (0.0091, 0.0176)*0.0022 (0.0005, 0.0040)*13.26*
 24-hour SBP0.0165 (0.0115, 0.0195)*−0.0006 (−0.0018, 0.0003)−3.64
NIHSS→ HT→ mRSb0.0134 (0.0080, 0.0171)*0.0030 (0.0010, 0.0054)*18.19*
MediatorBootstrapped estmates (n = 1,000)Proportion (%)
Direct effectIndirect effect
X → NIHSS → mRSa
 Stroke−0.0080 (−0.1298, 0.1146)−0.0057 (−0.0449, 0.0307)41.49
 Atrial Fibrillation0.0885 (−0.0368, 0.2129)0.0567 (0.0194, 0.1047)*39.05
 Hypertension0.0799 (−0.0458, 0.2044)−0.0129 (−0.0447, 0.0210)−16.15
 Diabetes0.1108 (−0.0043, 0.2348)−0.0034 (−0.0383, 0.0346)−3.07
 Dyslipidemia−0.0085 (−0.1942, 0.0312)−0.0196 (−0.0603, 0.0219)18.65
 CHD0.1420 (0.0091, 0.2636)*0.0741 (0.0287, 0.1263)*34.29*
X → HT → mRSa
 Stroke−0.0089 (−0.1333, 0.1204)0.0028 (−0.0252, 0.0274)−31.46
 Atrial Fibrillation0.0789 (−0.0409, 0.2061)0.0048 (−0.0198, 0.0326)5.78
 Hypertension0.0650 (−0.0511, 0.1842)0.0080 (−0.0155, 0.0342)10.99
 Diabetes0.1024 (-0.0091, 0.2218)0.0018 (−0.0226, 0.0247)1.70
 Dyslipidemia−0.0756 (−0.1857, 0.0460)−0.0018 (−0.0239, 0.0222)2.37
 CHD0.1349 (0.0040, 0.2604)*0.0069 (−0.0183, 0.0348)4.87
X → BPI → mRSa
 Stroke
  SV−0.0350 (−0.1590, 0.0953)0.0227 (−0.0172, 0.0630)−64.86
  SD−0.0525 (−0.1838, 0.0710)0.0378 (0.0006, 0.0749)*72.00*
  VIM−0.0472 (−0.1677, 0.0767)0.0344 (0.0018, 0.0716)*72.88*
  24-hour SBPc−0.0096 (−0.1334, 0.1085)0.0040 (−0.0187, 0.0266)−41.67
 Atrial Fibrillation
  SV0.0782 (−0.0428, 0.2003)0.0118 (−0.0325, 0.0521)13.12
  SD0.1004 (−0.0234, 0.2232)−0.0083 (−0.0456, 0.0223)−8.27
  VIM0.1003 (−0.0214, 0.2215)−0.0074 (−0.0427, 0.0244)−7.38
  24-hour SBP0.0843 (−0.0400, 0.2109)0.0008 (−0.0192, 0.0198)0.96
 Hypertension
  SV0.0083 (−0.1129, 0.1321)0.0599 (0.0208, 0.1090)*87.86*
  SD0.0212 (−0.1009, 0.1386)0.0475 (0.0145, 0.0897)*69.12*
  VIM0.0321 (−0.0916, 0.1502)0.0369 (0.0085, 0.0748)*53.47*
  24-hour SBP0.0435 (−0.0834, 0.1723)0.0374 (0.0058, 0.0777)*46.19*
 Diabetes
  SV0.1156 (0.0062, 0.2265)*−0.0072 (−0.0470, 0.0289)−6.23
  SD0.1119 (−0.0018, 0.2290)−0.0067 (−0.0422, 0.0237)−5.99
  VIM0.1153 (0.0006, 0.2331)*−0.0085 (−0.0430, 0.0210)−7.37
  24-hour SBP0.0903 (−0.0201, 0.2091)0.0125 (−0.0033, 0.0378)12.13
 Dyslipidemia
  SV−0.0586 (−0.1626, 0.0503)−0.0232 (−0.0621, 0.0151)28.35
  SD−0.0688 (−0.1746, 0.0409)−0.0211 (−0.0559, 0.0102)23.42
  VIM−0.0726 (−0.1788, 0.0377)−0.0181 (−0.0515, 0.0118)19.92
  24-hour SBP−0.0739 (−0.1855, 0.0447)−0.0066 (−0.0257, 0.0106)8.20
 CHD
  SV0.1343 (0.0053, 0.2525)*0.0199 (−0.0192, 0.0582)12.93
  SD0.1313 (−0.0016, 0.2467)0.0233 (−0.0086, 0.0601)15.06
  VIM0.1315 (−0.0031, 0.2493)0.0230 (−0.0072, 0.0596)14.91
  24-hour SBP0.1416 (0.0043, 0.2646)*−0.0012 (−0.0232, 0.0173)−0.85
NIHSS → BPI → mRSb
 SV0.0143 (0.0093, 0.0175)*0.0021 (0.0003, 0.0040)*12.82*
 SD0.0143 (0.0092, 0.0177)*0.0021 (0.0003, 0.0039)*12.62*
 VIM0.0142 (0.0091, 0.0176)*0.0022 (0.0005, 0.0040)*13.26*
 24-hour SBP0.0165 (0.0115, 0.0195)*−0.0006 (−0.0018, 0.0003)−3.64
NIHSS→ HT→ mRSb0.0134 (0.0080, 0.0171)*0.0030 (0.0010, 0.0054)*18.19*

Abbreviations: BPI, blood pressure index; CHD, coronary atherosclerotic heart disease; HT, hemorrhagic transformation; mRS, modified Rankin scale; mTICI, modified Thrombolysis in Cerebral Infarction Score; SBP, systolic blood pressure; SD, standard deviation; SV, successive variation; VIM, variability independent of mean blood pressure.

aBinary logistic regression was performed to evaluate the associations between baseline risk factors and between 3-month functional outcomes, with NIHSS score, HT, or BPIs as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

bBinary logistic regression was performed to evaluate the associations between NIHSS score and between 3-month functional outcome, with BPIs or HT as the mediators. The model was adjusted for age, sex, BMI, atrial fibrillation, hypertension, diabetes, dyslipidemia, CHD, stroke history, IVtPA, mTICI grade, and NIHSS score.

c24-hour SBP was included in the model as the BP recording divided by 10.

*P < 0.05.

Schematic diagram of the mediation analysis for functional outcome.
Figure 2.

Schematic diagram of the mediation analysis for functional outcome.

Open in new tabDownload slide

According to the results of the construction of the NIHSS-BPI/HT-mRS mediation model, except for the 24-hour SBP, the indirect effects of the NIHSS score on functional outcome according to the SV, SD, and VIM were 0.0021 (95% CI: 0.0003, 0.0040), 0.0021 (95% CI: 0.0003, 0.0039), and 0.0022 (95% CI: 0.0005, 0.0040), respectively, and the proportions of mediating effects were 12.82%, 12.62%, and 13.26%, respectively, (Figure 2). In addition, a higher NIHSS score was associated with a greater probability of HT and a lower likelihood of favorable outcome (ACME: 0.0027, 95% CI: 0.0009, 0.0051; ADE: 0.0139, 95% CI: 0.0086, 0.0173).

DISCUSSION

This study investigated the mediating effects of baseline risk factors using the NIHSS score, BPIs, and HT as mediator variables on clinical outcomes. The findings were as follows: (i) Hypertensive patients were associated with greater BPV and a greater proportion of adverse functional outcomes. Patients with a previous history of stroke did not show a difference in the direct effect but may have experienced poor outcomes through greater BPV. (ii) AF and CHD were identified as mediators of poorer clinical outcomes through the NIHSS score, while CHD also affected the clinical outcome through direct effects. (iii) Patients with higher NIHSS scores were associated with more pronounced BPV and worse functional outcomes. (iv) The NIHSS score was associated with the probability of HT, while HT was still associated with worse clinical outcomes after adjusting for the NIHSS score. Our study provides confirmatory evidence of the associations between vascular risk factors and adverse functional outcomes of AIS patients.

Currently, BP is considered a comprehensive reflection of stroke rather than a direct cause of clinical outcome. Although there is no consensus on the goal of BP control after EVT, the necessity of managing poststroke BP after EVT has been acknowledged.22 A study in the MIMIC database revealed that stroke patients had greater BPV than did nonstroke patients and were generally associated with worse outcomes, suggesting that BPV may reflect damage to the central nervous system regulatory network.23 Our study showed that higher NIHSS scores are associated with greater BPV, which may mediate adverse clinical outcomes. Among the BP measurements, BPV exhibited stronger correlations with the independent variables and clinical outcome, which was also consistent with our previous findings. Elevated BP is also considered a factor associated with increased cardiovascular risk. However, in contrast to clinical trials, the fidelity of BP data in the real world varies. VIM, as an independent risk indicator of mean BP, can better predict stroke and cardiovascular risk.24 The BPV may be a better indicator than the mean BP. Although the impact of the NIHSS score on the unfavorable outcome of AIS is multifaceted, its contribution to adverse outcomes through BPV is approximately 13% (Figure 2). Therefore, controlling a well-regulated BPV rate can reduce the influence of the NIHSS score on adverse functional outcomes.

The optimal timing for initiating antihypertensive therapy after AIS remains uncertain. The ENCHANTED2/MT and OPTIMAL studies showed that intensive BP management for 24 hours resulted in a lower likelihood of functional independence at 3 months than did conventional BP management,3,4 suggesting that intensive antihypertensive therapy in the early stages of AIS may not improve functional outcome. Another study initiated antihypertensive treatment between 24 and 48 hours post-AIS, targeting BP within the range of 140–220 mm Hg. There were no significant differences between the 2 groups in terms of primary outcome on day 90 or in the incidence of recurrent stroke, major vascular events, or serious adverse events.25 Although a significant proportion of AIS patients have a history of hypertension, increased BP may promote cerebral perfusion after brain ischemia. In this study, hypertension mediated HT and adverse functional outcomes through BP level and BPV. Depending on the observational indicators, the contribution of a history of hypertension to unfavorable outcomes ranged from 46.19% of the 24-hour SBP levels to 87.86% of the SV (Figure 2). Therefore, hypertension may exert a greater influence on functional outcomes through BPV than through the 24-hour SBP. In clinical practice, a greater emphasis on BPV may be more advantageous.

A recent study suggested that a TIA within 96 hours may achieve a protective effect through intermittent ischemic stimulation of brain tissue and decreased systemic immune-inflammation index.9 In this study, a history of stroke was shown to mediate worse functional outcome at 3 months through greater BP variation, but there was no significant difference in the direct effect or total effect, suggesting that stroke may have a bidirectional effect on AIS, which can lead to a worse prognosis on the one hand and improve prognosis through ischemic preconditioning on the other hand. However, importantly, the medical histories taken for this study were mainly based on the statements of the patients and their families, which might have led to recall bias. Therefore, this conclusion still needs to be discussed with caution, and the impact of stroke on prognosis should be explored in the future based on larger cohorts and more strict onset times. The current understanding of how CHD contributes to worse outcomes in AIS patients is multifaceted. This study revealed direct and indirect effects of significant differences in the CHD-NIHSS-mRS score. These findings suggest that the impact of CHD on AIS outcome might extend beyond its influence solely during the onset of severe AIS.

There are several limitations in this study. First, this was a retrospective study with potential bias in patient selection, clinical practice, and procedural techniques, potentially limiting the representativeness of this cohort and affecting the generalizability of the results. Second, some disease-related data, such as onset-to-reperfusion time, ASPECTS, collateral status, and intraoperative stent use, antihypertensive medications were not recorded. Therefore, the results of this study need to be interpreted with caution, and larger cohorts are needed to confirm these results.

This study is the first to investigate the role of BP in the relationship between vascular risk factors and functional outcomes and to explore the potential causal relationship between risk factors and outcomes via BP correlations. We found that a history of stroke or hypertension and a higher NIHSS score at AIS onset was associated with an increase in BPV, leading to a greater proportion of unfavorable outcomes. Emphasizing the control of BPV after EVT may be a more critical approach than focusing solely on the 24-hour SBP levels. This study may provide new insights into the potential underlying mechanisms involved in the role of BP in vascular risk factors and functional outcomes in AIS patients.

Acknowledgment

We thank Dr Wanyi Sun (Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China) for statistical consultation for this article. This work was supported by the National Key Research and Development Program of China (2021YFE0204700), National High Level Hospital Clinical Research Funding (BJYY-2023-082), National Nature Science Foundation of China (82071329), and Natural Science Foundation of Hainan Province (823QN343).

Conflict of Interest

The authors declare no conflict of interest.

Data Availability

The data underlying this article will be shared on reasonable request to the corresponding author.

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Author notes

Dingkang Xu and Peng Qi contributed equally.

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Subject
Brain and CNS
Issue Section:
ORIGINAL CONTRIBUTIONS > Brain and Central Nervous System
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