DOI: https://doi.org/10.21203/rs.3.rs-1580960/v1
Background: Blood pressure (BP) fluctuation is a common phenomenon in acute ischemic stroke (AIS), but the effect of fluctuations in BP during specific treatment on adverse events has received less attention. Therefore, we explored the relationship between both BP and 24-hour BP variation (BPV) and 90-day functional outcomes (assessed by modified Rankin Scale [mRS] score) in patients with AIS who underwent intravenous argatroban therapy within 24 hours of AIS onset.
Methods: A total of 214 patients with AIS who were hospitalized with neurological deficiency within 24 hours and who underwent intravenous argatroban therapy were included. BP was monitored using a cuff at fixed intervals of 1 hour, and mean systolic blood pressure (mean-SBP), maximum SBP (max-SBP), minimum SBP (min-SBP), mean diastolic blood pressure (mean-DBP), maximum DBP (max-DBP), minimum DBP (min-DBP), mean arterial pressure (MAP), standard deviation (SD), coefficient of variation (CV), successive variation (SV), and average real variability (ARV) were calculated. The correlation between both BP and BPV and 90-day mRS score was evaluated by logistic regression after adjusting for confounding variables.
Results: Two-hundred fourteen patients were included in the study, including 123 patients with a good prognosis (mRS score ≤ 2) (57.48%) and 91 patients with a poor prognosis (mRS score > 2) (42.52%). Age, National Institutes of Health Stroke Scale (NIHSS) score on admission, diabetes mellitus (DM), infarction location (anterior circulation), and prognosis were significantly related (P < 0.05). After one-way analysis of variance on BP and BPV, variables with a P value of <0.2 were used as conditions for a significant correlation with prognosis. BP and BPV parameters in the good prognosis group were higher than in the poor prognosis group. In the adjusted logistic regression model, mean-SBP in Model 1 (adjusted for age and NIHSS score) was significantly associated with 3-month mRS score (odds ratio [OR] = 1.068, 95% confidence interval [CI] 1.008 – 1.131, P = 0.025). In Model 2 (adjusted for age, NIHSS score, and DM), mean-SBP (OR = 1.061, 95% CI 1.001 – 1.123, P = 0.045) and max-SBP (OR = 0.951, 95% CI 0.906 – 0.998, P = 0.040) were associated with 3-month mRS score. In Model 3 (adjusted for age, NIHSS score, DM, and infarction location), max-SBP (OR = 0.952, 95% CI 0.906 – 1, P = 0.049) was associated with 3-month mRS score. There was no significant correlation between MAP or DBP and prognosis.
Conclusion: Twenty-four-hour BPV parameters (SD/CV/SV/ARV) in patients with AIS undergoing early argatroban therapy were not ideal prognostic predictors of 90-day functional outcomes, but 24-hour max-SBP and mean-SBP showed predictive value. In the future, more prospective studies are needed to further explore the association between short-term BPV and 90-day functional outcomes in patients with AIS undergoing early anticoagulation therapy.
Improving the prognosis of patients with acute ischemic stroke (AIS) is one of the most urgent clinical problems that needs to be solved. Currently, the clinical prognosis evaluation system for patients with AIS is not perfect. Therefore, it is of great significance and value to seek effective indicators of short-term and long-term prognosis. In recent years, many studies have shown that short-term blood pressure (BP) variation (BPV) has a greater influence on the prognosis of stroke patients than BP itself [1–3]. Moreover, BPV has shown good predictive value for the prognosis of patients with hemorrhagic stroke [4]. Studies in patients with AIS have suggested that short-term BPV is an independent predictor of outcomes at 14 days [3], 30 days [2], and 90 days [5]. Some studies have explored the relationship between BPV and 3-month functional prognosis in patients with AIS undergoing recombinant tissue-type plasminogen activator (rt-PA) thrombolytic therapy within 4.5 hours [6–7], as well as the relationship between BPV and the degree of recanalization, the occurrence of early neurological deterioration, and functional outcomes in emergency thrombectomy patients [8–10]. However, so far, no studies have explored the relationship between BP and BPV and 90-day functional outcomes in patients with AIS undergoing early (within 24 hours) intravenous anticoagulant therapy. During hospitalization, doctors usually consider BP values before administering intravenous anticoagulant therapy, but little attention has been paid to the effect of BP fluctuation on prognosis during anticoagulant therapy. In this study, we explored the relationship between 24-hour BP values, BPV, and 3-month functional prognosis in patients with AIS treated with intravenous argatroban within 24 hours of onset.
From January 2016 to February 2020, a total of 369 patients with AIS whose onset within 24-hour and were hospitalized at the Department of Neurology, Suzhou Ninth Hospital Affiliated to Soochow University in Suzhou, China.The follow-up period was 3 months. The study protocol was approved by the Ethics Committee of Suzhou Ninth Hospital Affiliated to Soochow University. Consent to use patient data for clinical analysis was obtained verbally by telephone from the patient or his or her family, and then an informed consent form was signed by the investigators and the patient or his or her family face to face. Patients satisfying the following criteria were enrolled: (1) age > 18 years; (2) new infarction confirmed by computed tomography (CT) or diffusion-weighted imaging (DWI) in accordance with AIS diagnostic criteria; (3) onset within 24 hours and intravenous argatroban therapy during hospitalization; (4) 24-hour continuous BP monitoring data (BP was recorded every hour, a total of 24 times); (5) receipt of informed consent.Patients were excluded based on the following criteria: (1) intravenous thrombolysis, thrombectomy, or bridging treatment after stroke; (2) BP intervention required for special reasons; (3) inability to undergo cranial CT or DWI; (4) severe liver or kidney disease; (5) a history of blood diseases or diseases with a bleeding tendency; (6) malignant tumor or anti-tumor therapy; (7) pregnancy, lactation, likely to become pregnant, or planning to become pregnant; (8) previous or current participation in other interventional clinical studies within 3 months prior to the date of obtaining informed consent; (9) unsuitability for participation. Finally, 214 patients were enrolled in this study.
Baseline data, laboratory indicators, and imaging indices of the included subjects were collected. BP values were measured in the non-paralytic upper arm using an electronic cuff at intervals of 1 hour with continuous monitoring for 24 hours, with a total of 24 BP values for each patient (IMEC 12 monitor; Mindary, Shenzhen Mindary Bioelectronics Co., Ltd., China). On the basis of the modified Rankin Scale (mRS) score at 3 months, patients were divided into a good prognosis group (mRS score ≤ 2) and a poor prognosis group (mRS score > 2).
Argatroban (Tianjin Institute of Pharmaceutical Research Co., Ltd., China; 10 mg/dose, total of 60 mg) was administered via intravenous drip for 2 days consecutively and then changed to 10 mg via intravenous drip twice per day for 5 days. The total course of treatment was 7 days.
R (version 4.0.2) was used for statistical analysis. Missing values of continuous variables other than BP were filled by multiple interpolation. The independent-samples t-test or the Wilcoxon rank-sum test was used to compare continuous variables between the baseline data groups. The chi-square test or Fisher’s exact probability method was used to compare the classified variables. A P value of < 0.05 was considered statistically significant. Variables with statistical significance at baseline were included as confounders in the multivariate logistic model for adjusting. Intergroup comparison of BP, BPV, and other parameters between groups with a good prognosis and a poor prognosis was performed using the Wilcoxon rank-sum test. Parameters with P values of < 0.2 for intergroup comparisons were included in the multivariate logistic regression model and were divided into four groups using the equal-proportion box method, and multivariate logistic regression was performed with the highest level as a reference. In Model 1, age and National Institutes of Health Stroke Scale (NIHSS) score were used as control variables, and the effects of BP and BPV on prognosis were assessed. In Model 2, diabetes mellitus (DM) was added as the control variable on the basis of Model 1, and the influence of BP, BPV, and other parameters on prognosis was assessed. In Model 3, which was based on Model 2, the influence of BP, BPV, and other parameters on prognosis was assessed by adding infarction location (anterior circulation) as a control variable.
Comparison of baseline characteristics in patients with a good/poor prognosis after acute ischemic stroke
Among the 214 patients with AIS who underwent intravenous anticoagulant therapy, 123 patients (57.48%) had a good prognosis (mRS score ≤ 2), and 91 patients (42.52%) had a poor prognosis (mRS score > 2). Age (P = 0.001), NIHSS score at admission (P < 0.001), accompanying DM (P = 0.011), and anterior circulation infarction (P = 0.027) were associated with prognosis. In the poor prognosis group, age, NIHSS score at admission, and the proportion of patients with accompanying DM and anterior circulation infarction were higher (P < 0.05) compared with the good prognosis group. Other factors showed no significant difference between the two groups (Table 1).
|
Total (n = 214) |
mRS ≤ 2 (n = 123) |
mRS > 2 (n = 91) |
P value |
|
|---|---|---|---|---|
|
Age (y, x ± s) |
68.02 ± 11.91 |
64.81 ± 11.61 |
70.40 ± 11.62 |
0.001 |
|
Female (%) |
80 (37.4) |
31 (14.5) |
49 (22.9) |
0.388 |
|
SBP at admission (mmHg, x ± s) |
160.43 ± 26.28 |
156.75 ± 24.94 |
163.16 ± 27.01 |
0.077 |
|
DBP at admission (mmHg, x ± s) |
88.24 ± 14.27 |
89.40 ± 13.25 |
87.38 ± 14.98 |
0.309 |
|
NIHSS score at admission (x ± s) |
6.75 ± 6.76 |
3.90 ± 3.44 |
8.85 ± 7.78 |
< 0.001 |
|
Platelet volume (fl, x ± s) |
10.46 ± 2.75 |
10.06 ± 2.18 |
10.72 ± 3.04 |
0.132 |
|
BNP (pg/ml, x ± s) |
209.02 ± 321.72 |
184.21 ± 314.94 |
222.55 ± 326.34 |
0.509 |
|
Platelet counting (×109/L, x ± s) |
192.30 ± 61.76 |
202 ± 56.07 |
186.32 ± 59.19 |
0.129 |
|
CTn-I (ng/ml, x ± s) |
0.11 ± 0.77 |
0.01 ± 0.01 |
0.18 ± 1.01 |
0.151 |
|
Scr (µmol/L, x ± s) |
68.26 ± 25.88 |
67.30 ± 27.8 |
68.98 ± 24.45 |
0.640 |
|
Plasma D-D polymers (ng/ml, x ± s) |
0.62 ± 1.09 |
0.58 ± 0.99 |
0.66 ± 1.15 |
0.580 |
|
FIB (g/L, x ± s) |
3.53 ± 8.30 |
4.53 ± 13.50 |
2.92 ± 0.65 |
0.290 |
|
CRP (mg/dl, x ± s) |
8.95 ± 19.15 |
5.53 ± 7.19 |
11.20 ± 23.76 |
0.062 |
|
HbA1c (%, x ± s) |
6.55 ± 1.69 |
6.39 ± 1.57 |
6.66 ± 1.76 |
0.382 |
|
Albumin (g/L, x ± s) |
36.63 ± 10.16 |
36.77 ± 11.08 |
36.54 ± 9.55 |
0.882 |
|
LDL (mmol/L, x ± s) |
2.74 ± 0.85 |
2.69 ± 0.85 |
2.78 ± 0.85 |
0.451 |
|
Hcy (µmol/L, x ± s) |
15.61 ± 11.92 |
16.67 ± 13.83 |
14.73 ± 10.05 |
0.280 |
|
Hypertension [n (%)] |
177 (82.7) |
71 (33.2) |
106 (49.5) |
0.119 |
|
Diabetes mellitus [n (%)] |
87 (40.7) |
28 (13.1) |
59 (27.6) |
0.011 |
|
Anterior circulation infarction [n (%)] |
149 (69.6) |
56 (26.2) |
93 (43.4) |
0.027 |
Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; BNP, brain natriuretic peptide; CTn-I, Troponin; Scr, serum creatinine; FIB, fibrinogen; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin A1c; LDL, low-density lipoprotein cholesterol; Hcy, homocysteine.
Comparison of BP and BPV-related parameters between the good and poor prognosis groups
Comparisons of BP and BPV-related parameters between the good prognosis group and the poor prognosis group are shown in Table 2. A P value of <0.2 was considered statistically significant. Among these parameters, mean systolic BP (mean-SBP) (P < 0.001), maximum SBP (max-SBP) (P = 0.013), minimum SBP (min-SBP) (P = 0.017), standard deviation (SD) of SBP (P = 0.081), SBP coefficient of variation (CV) (P = 0.06), average mean arterial pressure (MAP) (P = 0.017), maximum MAP (P = 0.067), and minimum MAP (P = 0.020) were significantly correlated with prognosis, and each parameter in the good prognosis group was higher compared with the poor prognosis group.
|
BP parameters |
Total (n = 214) |
mRS ≤ 2 (n = 123) |
mRS > 2 (n = 91) |
P- value |
|---|---|---|---|---|
|
SBP(mmHg) |
||||
|
mean |
143.83 ± 17.27 |
138.79 ± 15.73 |
147.56 ± 17.47 |
< 0.001 |
|
max |
172.18 ± 21.67 |
167.93 ± 20.02 |
175.33 ± 22.38 |
0.013 |
|
min |
116.96 ± 16.11 |
113.91 ± 13.96 |
119.22 ± 17.24 |
0.017 |
|
SD |
13.69 ± 4.51 |
13.06 ± 3.68 |
14.15 ± 4.99 |
0.081 |
|
CV |
13.71 ± 4.35 |
13.05 ± 3.62 |
14.19 ± 4.78 |
0.06 |
|
SV |
3.09 ± 0.84 |
3.07 ± 0.81 |
3.112 ± 0.87 |
0.696 |
|
ARV |
11.39 ± 2.95 |
11.25 ± 2.82 |
11.50 ± 3.06 |
0.541 |
|
DBP(mmHg) |
||||
|
mean |
80.99 ± 10.93 |
80.24 ± 10.44 |
81.54 ± 11.28 |
0.393 |
|
max |
99.57 ± 12.10 |
98.57 ± 11.33 |
100.32 ± 12.63 |
0.298 |
|
min |
62.35 ± 10.54 |
61.41 ± 9.72 |
63.04 ± 11.10 |
0.263 |
|
SD |
9.29 ± 2.56 |
9.23 ± 2.38 |
9.33 ± 2.70 |
0.784 |
|
CV |
9.16 ± 2.41 |
9.14 ± 2.28 |
9.18 ± 2.52 |
0.914 |
|
SV |
2.32 ± 0.64 |
2.30 ± 0.62 |
2.35 ± 0.67 |
0.600 |
|
ARV |
8.61 ± 2.39 |
8.43 ± 2.28 |
8.75 ± 2.48 |
0.348 |
|
MAP(mmHg) |
||||
|
mean |
101.94 ± 11.68 |
99.73 ± 10.90 |
103.58 ± 12.00 |
0.017 |
|
max |
121.43 ± 13.86 |
119.41 ± 12.84 |
122.92 ± 14.44 |
0.067 |
|
min |
82.76 ± 11.39 |
80.67 ± 10.28 |
84.32 ± 11.95 |
0.020 |
|
SD |
9.60 ± 2.89 |
9.49 ± 2.46 |
9.96 ± 3.18 |
0.619 |
|
CV |
9.47 ± 2.74 |
9.55 ± 2.40 |
9.42 ± 2.98 |
0.726 |
|
SV |
2.23 ± 0.58 |
2.22 ± 0.55 |
2.23 ± 0.60 |
0.952 |
|
ARV |
8.31 ± 2.17 |
8.27 ± 2.15 |
8.34 ± 2.19 |
0.801 |
Abbreviations: mRS: modified Rankin Scale; SBP, systolic blood pressure; SD, standard deviation; CV, coefficient of variation; SV, successive variation; ARV, average real variability; DBP, diastolic blood pressure; MAP, mean arterial pressure; max, maximum; min, minimum.
Multivariate logistic regression model adjusted for confounding factors to observe the influence of BP and BPV-related parameters on prognosis
The above BP values and BPV-related parameters were included in the adjusted logistic regression model with P values of <0.2 (Table 3). Considering no DBP relative parameters were associated with prognosis, DBP was excluded from the next model. In Model 1 (adjusted for age and NIHSS score), mean-SBP was significantly associated with prognosis (odds ratio [OR] = 1.068, 95% confidence interval [CI] 1.008–1.131, P = 0.025). Mean-SBP (OR = 1.061, 95% CI 1.001–1.123, P = 0.045) and max-SBP (OR = 0.951, 95% CI 0.906–0.998, P = 0.040) were significantly associated with prognosis. Mean-SBP (OR = 1.057, 95% CI 0.098–1.120, P = 0.059) and max-SBP (OR = 0.952, 95% CI 0.906–1, P = 0.049) were associated with prognosis. The OR values of mean-SBP and max-SBP decreased gradually with the increase in covariates in the model. Min-SBP, SBP SD, and SBP CV were the opposite. MAP and DBP BPV parameters did not show a good correlation with prognosis. Taking the SD of SBP as the key BPV index, the relationship between the SBP SD and prognosis was studied in the adjusted logistic regression model. The results show that the relationship between the SBP SD and prognosis was not significant (Fig. 1). In the adjusted logistic regression model, the variable mean-SBP was controlled. The relationship between SBP SD and prognosis was also not significant (Fig. 2).
|
Model 1 OR(95%CI) |
P-value |
Model 2 OR(95%CI) |
P-value |
Model 3 OR(95%CI) |
P-value |
|
|---|---|---|---|---|---|---|
|
mean-SBP |
1.068(1.008,1.131) |
0.025 |
1.061(1.001,1.123) |
0.045 |
1.057(0.998,1.120) |
0.059 |
|
max-SBP |
0.957(0.912,1.003) |
0.067 |
0.951(0.906,0.998) |
0.04 |
0.952(0.906,1) |
0.049 |
|
min-SBP |
1.007(0.958,1.058) |
0.793 |
1.018(0.966,1.072) |
0.506 |
1.019(0.966,1.074) |
0.49 |
|
SD-SBP |
0.999(0.640,1.557) |
0.995 |
1.017(0.647,1.598) |
0.943 |
1.079(0.677,1.720) |
0.749 |
|
CV-SBP |
1.153(0.741,1.793) |
0.528 |
1.175(0.750,1.840) |
0.482 |
1.117(0.706,1.768) |
0.637 |
|
Model 1 OR(95%CI) |
Model 2 OR(95%CI) |
P-value |
Model 3 OR(95%CI) |
P-value |
|
|---|---|---|---|---|---|
|
mean-MAP |
1.059(0.970, 1.156) |
1.047(0.957, 1.145) |
0.317 |
1.036(0.946, 1.135) |
0.45 |
|
max-MAP |
0.982(0.935, 1.033) |
0.988(0.938, 1.039) |
0.63 |
0.994(0.944, 1.047) |
0.831 |
|
min-MAP |
1.002(0.948, 1.059) |
1.007(0.951, 1.066) |
0.815 |
1.009(0.953, 1.069) |
0.75 |
Argatroban is a direct thrombin inhibitor that can rapidly inhibit thrombosis, and its safety and effectiveness have been confirmed in many clinical studies [11-15]. Early venous anticoagulation is an important strategy for individualized treatment of AIS.
The relationship between hyperacute BP, BPV, and short-term and long-term functional outcomes in patients with AIS has attracted increasing attention. Until now, no studies have explored the relationship between BP and BPV in the first 24 hours with 90-day functional prognosis in patients with AIS undergoing acute/early anticoagulation therapy (within 24 hours). Our center reviewed BP and BPV and further analyzed the relevance of 90-day functional outcomes, aiming to explore whether BP and BPV-related indicators could be effective prognostic indicators in patients with AIS undergoing acute/early argatroban anticoagulation therapy.
Although a meta-analysis [16] showed that the first 24-hour systolic BPV in patients with AIS was a good predictor of stroke outcome, the greater the systolic BPV, the worse the prognosis. Moreover, data from the VISTA study [17] showed that an elevated 24-hour SBP SD, DBP SD, and MAP SD were associated with 90-day adverse outcomes (mRS score of 3–6). Furthermore, the ECASS-II study [5] showed that 24-hour SBP SV was a risk factor for a poor prognosis at 90 days. Despite these previous observations, the results of our study suggest that systolic BPV, diastolic BPV, and MAP BPV indices (SD, CV, SV, and ARV) are not associated with prognosis. The results of this study are consistent with the results of Graff et al. [18] and the Samurai rt-PA registration study [19]. In an attempt to explain such discrepancies, Manning et al. [3] suggested that different BP detection methods might lead to different results. Different from the beat-to-beat finger BP detection method used by Graff et al. [18], our study and the Samurai rt-PA registration study [19] adopted fixed-interval and random cuff BP detection methods. Although the BP detection methods were different, the study results were consistent, which further suggests that 24-hour SD, CV, SV, and ARV have little predictive value for the prognosis of patients with AIS at 3 months. In this study, patients admitted to hospital had an average NIHSS score of 6 points, which means their condition was relatively mild with only slight BPV. To avoid interference of antihypertensive drugs on BPV, all patients who underwent intravenous thrombolysis and bridging therapy and all patients with related complications requiring emergency antihypertensive drug treatment were excluded. Moreover, early anticoagulant therapy can inhibit thrombus enlargement and cause partial dissolution of the thrombus, which can improve cerebral perfusion and reduce brain edema. Furthermore, anticoagulant therapy causes little fluctuation in various indicators of BPV among groups, and it has no significant effect on functional prognosis at 3 months. Both Graff et al.’s study [18] and our study had a small sample size (n = 75 and n = 214, respectively), and our study population underwent early intravenous argatroban anticoagulation therapy, so the three studies could not be analyzed in combination. Although the SD, CV, SV, and ARV of our study did not show significant differences between groups, more pronounced BPV was observed in the poor prognosis group than in the good prognosis group in this study.
Mean-SBP (OR = 1.068; 95% CI 1.008–1.131) showed some predictive value at 90 days, which is consistent with Yasuhiro’s study [20] (OR = 1.92; 95% CI 1.15–3.68) and the VISTA study [17] (OR = 1.24; 95% CI 1.10–1.38). The predictive value of max-SBP was second to mean-SBP, but min-SBP did not show predictive value.
It is worth mentioning that in our study, all DBP indicators (mean-DBP, max-DBP, min-DBP, and the corresponding SD, CV, SV, and ARV of DBP) showed no correlation with 90-day prognosis, which is consistent with the results of Manning et al. [16]. The reason may be that patients with AIS are older, and SBP has a greater influence on cerebral perfusion. Once stroke occurs, the self-regulation mechanism of cerebral blood flow is affected, and the survival of ischemic penumbra is greatly affected by systemic BP. Similarly, in our study, the predictive value of MAP-related indicators was similar to that of DBP.
Although earlier studies [21-22] showed that patients with a higher initial BP after stroke have a better prognosis, the ECASS-II study [17] and a study by Endo et al. [19] showed no correlation between initial BP and prognosis at 3 months. Our study also showed that initial BP at admission was not related to clinical outcomes at 90 days.
Delado-Mederos et al. [23] found that the influence of BPV on lesion enlargement and 90-day outcomes depends on whether blood vessels are recanalized in patients with AIS undergoing rt-PA thrombolysis. In patients with poor early recanalization, marked systolic BPV is an independent predictor of infarction enlargement and poor prognosis. However, in patients with good recanalization, this predictive value disappears. It is regrettable that our study did not consider infarct size and intracranial and extracranial vascular conditions in patients with AIS. The subtypes of stroke were not differentiated in accordance with Trial of Org 10172 in Acute Stroke Treatment or Oxfordshire Community Stroke Project classifications, but it may be more accurate to evaluate the association between BPV and prognosis by combining relevant imaging examinations and stroke subtypes, which is the direction of our future research.
This study has some limitations that should be noted. First, the study adopted a retrospective design and the sample size was small. Second, we used an electronic cuff to monitor BP, which is different from more accurate methods mentioned in the literature, such as beat-to-beat BP monitoring. Third, we did not consider the effects of heart rate on BP, and BPV was only monitored for 24 hours, rather than for 48–72 hours or 1 week. In addition, the fluctuation in BP in patients with AIS is most pronounced in the first few hours, but we did not perform a more detailed analysis at 0–6 hours of BPV or distinguish between diurnal and diurnal BPV and 90-day outcomes. Future studies are needed to explore the relationship between BPV and prognosis in patients undergoing early intravenous anticoagulation therapy.
Twenty-four-hour BPV (SD, CV, SV, and ARV) in patients with AIS undergoing early argatroban anticoagulation therapy has only minor predictive value for 90-day functional outcomes, but 24-hour max-SBP and mean-SBP may have some predictive value in this subgroup of patients. In the future, more prospective studies are needed to further explore the association between early BPV and 3-month prognosis and to clarify the BPV reference value to reduce BPV, which is of great significance for BP control and prognosis in patients with AIS.
BP: blood pressure; AIS:acute ischemic stroke; BPV: blood pressure variation; mRS: modified Rankin Scale; SBP: systolic blood pressure; DBP: diastolic blood pressure; mean-SBP: mean systolic blood pressure; max-SBP maximum SBP; min-SBP: minimum SBP; mean-DBP: mean diastolic blood pressure; max-DBP: maximum DBP; min-DBP: minimum DBP; MAP:mean arterial pressure; SD: standard deviation; CV: coefficient of variation; SV:successive variation; ARV: average real variability; DM:diabetes mellitus; NIHSS: National Institutes of Health Stroke Scale; BNP: brain natriuretic peptide; CTn-I: Troponin I; Scr: serum creatinine; FIB: fibrinogen; CRP: C-reactive protein; HbA1c: glycosylated hemoglobin A1c; LDL: low-density lipoprotein cholesterol; Hcy: homocysteine; rt-PA: recombinant tissue-type plasminogen activator; OR: odds ratio; CI: confidence interval.
Ethics approval and consent to participate
All methods were carried out in accordance with relevant guidelines and regulations. A statement to confirm that all experimental protocols were approved by the Ethics Committee of Suzhou Ninth Hospital Affiliated to Soochow University. We obtained verbal informed consent from the patient or his or her immediate family members by telephone and written Informed consent in a face-to-face situation.
Consent for publication
Not applicable.
Availability of data and materials
All the summarized and analysed data during this study are included in this published article
Competing interests
The authors declare that they have no competing interests.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Authors' contributions
LH and DJ had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis; drafting of the manuscript; and statistical analysis, their contributions are equal. YS was involved in the study conception and design; administrative, technical, and material support; study supervision; and critical revision of the manuscript for important intellectual content. WH and YX were involved in the acquisition, analysis, and interpretation of the data. All the authors read and approved the final manuscript.
Acknowledgements
We appreciate professor Yongjun Cao for giving valuable advice to enhance the integrity of this article during revision.