Association of variability of blood pressure and glycemic with the risk of mortality and severe consciousness disturbance among critically ill patients with ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage: a retrospective cohort study from MIMIC-IV database

Association of variability of blood pressure and glycemic with the risk of mortality and severe consciousness disturbance among critically ill patients with ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage: a retrospective cohort study from MIMIC-IV database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of variability of blood pressure and glycemic with the risk of mortality and severe consciousness disturbance among critically ill patients with ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage: a retrospective cohort study from MIMIC-IV database Huiyuan Xue, Xiaofeng Wu, Juan Wang, Songsong Feng, Dong Teng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6772731/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Critically ill patients with stroke remains a major public health issue worldwide. Glycemic variability (GV) and systolic blood pressure variability (SBPV) are recognized as independent predictors of cardiovascular and cerebrovascular disease outcomes. This study aimed to explore the associations between GV and SBPV levels on the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance in different types of stroke patients including ischemic stroke (IS), subarachnoid hemorrhage (SAH), and intracerebral hemorrhage (ICH), and to further examine whether GV and SBPV exhibit interactive effects on these clinical outcomes. Methods: Data of this retrospective cohort study were extracted from the Medical Information Market for Intensive Care (MIMIC-IV) database. Severe consciousness disturbance was defined as Glasgow Coma Scale (GCS) points <8. Univariate and multivariate Cox proportional hazard models, logistics regression models, Kaplan-Meier (KM) analysis and restricted cubic splines (RCS) analysis were utilized to explore the associations of GV, SBPV, and their interactive effects on the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance, with hazard ratio (HR), odd ratio (OR) and 95% confidence interval (CI). Subgroup analyses were conducted based on different types of strokes. Results: Totally, 1,540 eligible patients were included, among them, 323 occurred in-hospital death, 523 occurred 1-year mortality and 463 occurred severe consciousness disturbance within 30-day after admission. High GV and high SBPV levels were associated with high in-hospital mortality risk (SBPV, HR=1.92, 95%CI: 1.41-2.59; GV, HR=1.84, 95%CI: 1.34-2.54) and high 1-year mortality risk (SBPV, HR=1.61, 95%CI: 1.24-2.08; GV, HR=1.57, 95%CI: 1.20-1.01). No significant associations were found between GV and SBPV with the risk of severe consciousness disturbance. High GV and high SBPV were exhibited a potential interactive effect on the risk of these outcomes we interested. We further observed that GV and SBPV demonstrated a significant interaction effect on the risk of in-hospital mortality (HR=and 6.80, 95%CI: 2.88-16.05, P <0.001) and 1-year mortality (HR=5.10, 95%CI: 2.54-10.27, P <0.001) among critically ill patients with ICH. The results of Kaplan-Meier analysis and RCS analysis demonstrated trends consistent with those observed in the multivariable Cox proportional hazard models. Conclusion: Our study suggested that in critically ill patients with IS, SAH and ICH, higher GV and SBPV levels were associated with higher risk of mortality. Furthermore, GV and SBPV exhibit interactive effects on both clinical prognosis and the development of severe consciousness disturbance in our study population. GV and SBPV can support doctors in identifying patients with high risk of mortality and making timely clinical decisions. Ischemic stroke subarachnoid hemorrhage intracerebral hemorrhage systolic blood pressure variability glycemic variability mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke, also known as cerebrovascular accident (CVA), is an acute vascular disorder characterized by cerebral ischemia due to vascular occlusion or brain tissue damage from cerebrovascular rupture [ 1 ]. Clinically, it is generally categorized into ischemic stroke (IS) and hemorrhagic stroke, with the latter further subdivided into intracranial hemorrhage (ICH) and subarachnoid hemorrhage (SAH) [ 2 ]. Worldwide, there are approximately 12 million new stroke cases annually, with 5.5 million deaths each year, contributing to 116 million disability-adjusted life years (DALYs) per year. It has now become the second leading cause of death worldwide [ 3 , 4 ]. In severe cases of stroke, extensive damage to the cerebral cortex occurs, impairing the patient’s arousal and conscious awareness. Consciousness disturbance serves as a critical marker of poor prognosis in stroke patients and is closely associated with high mortality rates, long-term disability, and substantial healthcare resource utilization [ 5 , 6 ]. Therefore, identifying potential risk factors for severe stroke holds significant importance for prognostic evaluation. The variability of metabolic parameters, such as glycemic variability (GV) and systolic blood pressure variability (SBPV), has been recognized as in independent cardiovascular and cerebrovascular risk factor, distinct form absolute glucose and blood pressure levels [ 7 ]. GV reflects a non-steady-state fluctuation of blood glucose, characterized by rapid transitions between peaks and troughs over short periods [ 8 ]. Acute glucose fluctuations may activate systemic oxidative stress responses, accelerating atherosclerosis and subsequent cerebrovascular stenosis, ultimately triggering stroke [ 9 ]. The human body exhibits complex spontaneous blood pressure fluctuations throughout a 24-h period. Excessive blood pressure variability (BPV) can damage vascular endothelium through mechanical stress, promoting plaque rupture, while hypotensive episodes may lead to insufficient coronary or cerebral perfusion, potentially triggering ischemic events [ 10 ]. Several previous epidemiological studies have indicated the association between GV and BPV and the risks of cardiovascular and cerebrovascular diseases [ 11 – 13 ]. Moreover, a study utilizing the Korena National Health Insurance System database demonstrated that elevated GV and SBPV serve as independent predictors of mortality and cardiovascular events in the general population, with evidence of synergistic effects between GV and SBPV on mortality risk [ 14 ]. Clinical evidence also supports the association between severe consciousness disturbance and increased mortality risk in stroke patients, with higher degrees of consciousness disturbance correlating with greater mortality risk [ 15 ]. This association may be attributed to physiological mechanisms such as ischemic or hemorrhagic lesions affecting the brainstem reticular activating system, consequently impairing the patient’s arousal function [ 16 ]. However, the studies on the association between GV and SBPV on the risk of mortality among different types of stroke patients remains lacking. Furthermore, it is still unclear whether GV and SBPV have an interactive effect on the risk of mortality in critically ill stroke patients. Therefore, this study investigated the association between GV and SBPV and the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance among critically ill patients with IS, ICH and SAH, and to further examine whether GV and SBPV exhibit synergistic effects on these clinical outcomes. Understanding this relationship may provide valuable insights into risk stratification in critically ill stroke patients and help clarify the potential role of GV and SBPV as prognostic indicators, thereby facilitating clinical decision-making and personalized treatment strategies for critically ill patients with stroke. Methods Study design and population Data of this retrospective cohort study were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). MIMIC-IV, a publicly accessible critical care database jointly developed by the MIT Laboratory for Computational Physiology at Harvard University and Beth Israel Deaconess Medical Center (BIDMC) in Boston, stands as one of the most authoritative ICU clinical databases worldwide. This repository comprises de-identified data from approximately 500,000 hospitalized patients (including 100,000 ICU admissions) between 2008 and 2019, encompassing multidimensional clinical parameters such as vital signs, laboratory tests, pharmacotherapeutic interventions, nursing documentation, and radiological reports. The ethics committee of Sanmenxia Hospital of the Yellow River exempted the study from ethical review. Informed consent for this study was waived due to the anonymity of patient information. A total of 6,538 patients with stroke and aged ≥ 18 years old were initially extracted from the database, including cases of IS, ICH, and SAH. Patients were excluded based on the following criteria: 1) those with ICU length of stay < 24 h (n = 881); 2) those have taken blood measurements < 3 times and SBP measurements < 10 times (n = 4,117). Ultimately, 1,540 eligible patients were included in our study (Fig. 1 ). Assessment of SBPV and GV Currently, there is no standardized clinical method for assessing GV and SBPV. In our study, we employed a widely accepted approach based on the coefficient of variation (CV) of glycemic measurements and SBP measurements obtained within the 0–24 h of hospitalization. Additionally, we conducted sensitivity analyses incorporating all available glycemic and SBP measurements throughout the entire hospitalization period to calculate GV and SBPV. SBP measurements were automatically recorded at least hourly by monitoring devices. We calculated 24-h BPV during the initial ICU admission using the standard deviation (SD) of all recorded values [ 8 ]. GV was evaluated based on methods established in previously published study [ 17 ]. The CV was calculated as the ratio of the SD to the mean (SD/mean × 100%). Based on X-tile, the continuous GV and SBPV were classified with their cut-off values being 31.46 and 17.70 respectively. Outcomes and follow-up The clinical outcomes of interest were classified as in-hospital death, 1-year death and severe consciousness disturbance. In-hospital death was defined as “the patients died during hospitalization”. The 1-year mortality was “the patient died within 1 year”. Furthermore, we also focused on the occurrence of severe consciousness disturbance with 30 days after hospital admission, defining as a Glasgow Coma Scale (GCS) score < 8 [ 18 ]. Patients was followed up from the initial 24 h after admission to the ICU until death occurred in the hospital or until the end of 1 year. Covariates We considered comprehensive baseline characteristics including: 1) demographic data: age (years), gender (female, male), race (White, Black, other); 2) vital signs: weight (kg), SBP (mmHg), and glucose (mg/dL); 3) comorbidities: diabetes (yes, no), congestive heart failure (yes, no), hypertension (yes, no) and sepsis (yes, no); 4) treatments: mechanical ventilation (yes, no), renal replacement therapy (yes, no) and antihypertensive drugs (yes, no); 5) lifestyle: smoking (yes, no). Statistical analysis Quantitative data were tested for normality using skewness and kurtosis, while homogeneity of variance was assessed using the Levene test. Normally distributed data were presented as a mean and standard deviation [Mean (± SD)] and compared between groups using the t-test for equal variances or the adjusted t' test for unequal variances. Non-normally distributed data were described as median and interquartile range [M (Q₁, Q₃)] and compared using the Wilcoxon rank-sum test. Categorical data were presented as number and percentage [n (%)], with between-group comparisons performed using Chi-square tests. Variables with > 20% missing values were excluded from analysis, while those with ≤ 20% missingness were handled via multiple imputation. Detailed results were presented in Table S1 . Univariate Cox proportional hazards models and logistics regression models were utilized to explore the association between all variables and the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance among critically patients with stroke, severing as a screening step for potential covariates. In Model 1, each covariate was analyzed by univariate analyses. Model 2 included all variables screened out from the Model 1, which were retained through a bidirectional stepwise regression process (Table S2 ). Then, univariate and multivariate Cox proportional hazards models and logistic regression models were conducted to explore the association between GV and SBPV and their interaction effect with the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance, with hazard ratio (HR), odds ratio (OR) and 95% confidence interval (CI). Model 1 was crude model; when in-hospital mortality was analyzed as the outcome, model 2 was adjusted race, mechanical ventilation, antihypertensive drugs, number of blood glucose measurements, SBP and glucose; when 1-year mortality as the outcome, model 3 adjusted age, gender, race, weight, congestive heart failure, hypertension, sepsis, mechanical ventilation, RRT, antihypertensive drugs, number pf blood glucose measurements, SBP and glucose; when severe consciousness disturbance as the outcome, model 4 adjusted age, race, diabetes, sepsis, mechanical ventilation, RRT, antihypertensive drugs, SBP and glucose. Subgroup analyses were conducted to further explore the association between GV and SBPV and their interaction effect on the association between in-hospital mortality, 1-year mortality and serve consciousness disturbance based on different types of strokes including ischemic stroke, SAH and ICH. The Kaplan-Meier survival curve illustrates the association between GV and SBPV on the risk of in-hospital mortality and 1-year mortality. Moreover, the restricted cubic splines (RCS) curve was performed to assess the nonlinear association between GV and SBPV with the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance. All statistical analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and R revision 4.2.2 (Institute for Statistics and Mathematics, Vienna, Austria). We chose P < 0.05 (two-sided) as our statistical significance level. Results Characteristics of included critically ill patients with different types of strokes This study ultimately included 1,540 eligible critically ill stroke patients, with a mean age of 76.89 years. Among these patients, 1,194 had IS, 115 had SAH, and 231 had ICH. A total of 323 patients died within ICU admission, 532 patients died within one year, and 463 patients developed severe consciousness disturbance within 30 days of admission. Table 1 summarized the baseline characteristics of the patients with IS, SAH and ICH. Stroke patients who experienced in-hospital mortality, 1-year mortality, or severe consciousness disturbance all demonstrated significant differences compared to unaffected patients in the following clinically relevant parameters: sepsis history, RRT utilization, antihypertensive medication use, number of blood glucose measurements, and baseline level of SBP and blood glucose (all P < 0.05). Table 1 Characteristics of patients with different types of strokes Variables Total (n = 1540) Different types of stokes Statistics P IS (n = 1194) SAH (n = 115) ICH (n = 231) Age, years, Mean ± SD 67.89 ± 15.04 69.70 ± 14.68 58.70 ± 13.79 63.13 ± 14.87 F = 44.016 < 0.001 Gender, n (%) χ 2 = 10.160 0.006 Female 659 (42.79) 503 (42.13) 65 (56.52) 91 (39.39) Male 881 (57.21) 691 (57.87) 50 (43.48) 140 (60.61) Race, n (%) χ 2 = 27.872 < 0.001 White 982 (63.77) 801 (67.09) 59 (51.30) 122 (52.81) Black 108 (7.01) 79 (6.62) 7 (6.09) 22 (9.52) Other 450 (29.22) 314 (26.30) 49 (42.61) 87 (37.66) Weight, kg, Mean ± SD 81.06 ± 22.28 80.83 ± 21.90 82.02 ± 24.12 81.74 ± 23.32 F = 0.276 0.759 Smoking status, n (%) χ 2 = 7.758 0.021 No 1418 (92.08) 1110 (92.96) 99 (86.09) 209 (90.48) Yes 122 (7.92) 84 (7.04) 16 (13.91) 22 (9.52) Diabetes, n (%) χ 2 = 40.710 < 0.001 No 1034 (67.14) 756 (63.32) 102 (88.70) 176 (76.19) Yes 506 (32.86) 438 (36.68) 13 (11.30) 55 (23.81) Congestive heart failure, n (%) χ 2 = 63.818 < 0.001 No 1113 (72.27) 807 (67.59) 111 (96.52) 195 (84.42) Yes 427 (27.73) 387 (32.41) 4 (3.48) 36 (15.58) Hypertension, n (%) χ 2 = 10.889 0.004 No 720 (46.75) 566 (47.40) 65 (56.52) 89 (38.53) Yes 820 (53.25) 628 (52.60) 50 (43.48) 142 (61.47) Sepsis, n (%) χ 2 = 1.013 0.603 No 460 (29.87) 364 (30.49) 31 (26.96) 65 (28.14) Yes 1080 (70.13) 830 (69.51) 84 (73.04) 166 (71.86) Mechanical ventilation, n (%) χ 2 = 20.059 < 0.001 No 330 (21.43) 284 (23.79) 21 (18.26) 25 (10.82) Yes 1210 (78.57) 910 (76.21) 94 (81.74) 206 (89.18) RRT, n (%) χ 2 = 4.115 0.128 No 1426 (92.60) 1097 (91.88) 110 (95.65) 219 (94.81) Yes 114 (7.40) 97 (8.12) 5 (4.35) 12 (5.19) Antihypertensive drugs, n (%) χ 2 = 171.133 < 0.001 No 314 (20.39) 160 (13.40) 63 (54.78) 91 (39.39) Yes 1226 (79.61) 1034 (86.60) 52 (45.22) 140 (60.61) Number of blood glucose measurements, M (Q 1 , Q 3 ) 5.00 (3.00, 7.00) 5.00 (3.00, 8.00) 4.00 (3.00, 5.00) 4.00 (3.00, 5.00) χ 2 = 113.250# < 0.001 SBP, baseline, mmHg, Mean ± SD 126.13 ± 26.50 123.12 ± 25.52 130.83 ± 26.18 139.39 ± 27.29 F = 40.409 < 0.001 Glucose, baseline, mg/dL, M (Q 1 , Q 3 ) 140.50 (116.00, 177.50) 139.00 (115.00, 177.00) 147.00 (120.00, 195.00) 143.00 (119.00, 176.00) χ 2 = 3.803# 0.149 GV, M (Q 1 , Q 3 ) 17.21 (10.71, 25.16) 18.33 (11.61, 26.33) 13.26 (7.96, 21.52) 12.50 (7.62, 21.92) χ 2 = 47.109# < 0.001 SBPV, M (Q 1 , Q 3 ) 12.45 (10.07, 15.41) 12.44 (10.20, 15.39) 13.03 (10.59, 16.64) 12.32 (9.59, 15.01) χ 2 = 4.765# 0.092 GV, n (%) χ 2 = 2.073 0.355 Low 1317 (85.52) 1013 (84.84) 102 (88.70) 202 (87.45) High 223 (14.48) 181 (15.16) 13 (11.30) 29 (12.55) SBPV, n (%) χ 2 = 3.306 0.191 Low 1341 (87.08) 1043 (87.35) 94 (81.74) 204 (88.31) High 199 (12.92) 151 (12.65) 21 (18.26) 27 (11.69) GV and SBPV, n (%) χ 2 = 11.598 0.072 LOW GV and LOW SBPV 1156 (75.06) 888 (74.37) 84 (73.04) 184 (79.65) LOW GV and High SBPV 161 (10.45) 125 (10.47) 18 (15.65) 18 (7.79) High GV and LOW SBPV 185 (12.01) 155 (12.98) 10 (8.70) 20 (8.66) High GV and High SBPV 38 (2.47) 26 (2.18) 3 (2.61) 9 (3.90) In-hospital mortality, n (%) χ 2 = 59.068 < 0.001 Alive 1217 (79.03) 994 (83.25) 79 (68.70) 144 (62.34) Dead 323 (20.97) 200 (16.75) 36 (31.30) 87 (37.66) Follow-up time, hospital, day, M (Q 1 , Q 3 ) 10.66 (6.26, 17.84) 10.34 (6.43, 17.45) 12.55 (5.84, 20.95) 10.90 (4.85, 18.94) χ 2 = 0.765# 0.682 1y-ear mortality, n (%) χ 2 = 45.501 < 0.001 Alive 1008 (65.45) 830 (69.51) 70 (60.87) 108 (46.75) Dead 532 (34.55) 364 (30.49) 45 (39.13) 123 (53.25) Folow-up time, 1 year, M (Q 1 , Q 3 ) 365.00 (43.00, 365.00) 365.00 (115.00, 365.00) 365.00 (8.00, 365.00) 249.00 (7.00, 365.00) χ 2 = 59.657# < 0.001 Severe consciousness disturbance, n (%) χ 2 = 34.373 < 0.001 No 1077 (69.94) 879 (73.62) 67 (58.26) 131 (56.71) Yes 463 (30.06) 315 (26.38) 48 (41.74) 100 (43.29) IS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; RRT, renal replacement therapy; SBP, systolic blood pressure;GV, glycemic variability; SBPV, systolic blood pressure variability; SD, standard deviation; Q1, quartile 1; Q3, quartile 3; M, median; Z, Z-test; t, t-test; χ 2, Chi-square test. Association of SBPV and GV and their interaction effect on the risk of in-hospital mortality Two Cox proportional hazards models were employed to examine the associations of SBPV and GV with the risk of in-hospital mortality among patients with three types of strokes (IS, SAH, and ICH), as well as their potential interaction effects on mortality. The results were presented in Table 2 . The fully adjusted Model 2 which accounted for race, mechanical ventilation, antihypertensive drugs, number of blood glucose measurements, SBP and glucose suggested that compared to low SBPV levels, high SBPV levels were associated with high risk of in-hospital mortality in patients with stroke (HR = 1.91, 95%CI: 1.41–2.59, P < 0.001), particularly in patients with IS (HR = 1.88, 95%CI: 1.28–2.76, P < 0.001) and ICH (HR = 3.67, 95%CI: 2.02–6.69, P < 0.001). High GV levels were also associated with high risk of in-hospital mortality (HR = 1.84, 95%CI: 1.34–2.54, P < 0.001), these associations were more observably in patients with SAH (HR = 3.94, 95%CI: 1.72–8.99, P = 0.001) and ICH (HR = 3.94, 95%CI: 2.12–7.33, P < 0.001). Moreover, we also observed that patients with concomitantly high levels of both GV and SBPV exhibited a significantly highest risk of in-hospital mortality compared to those with low levels of GV and SBPV (HR = 3.04, 95%CI: 1.66–5.58, P < 0.001) (Table 4 ), suggesting a potential interactive effect between GV and SBPV on in-hospital mortality in critically ill patients with stroke, and these interaction effects were more significant in patients with ICH (HR = 6.80, 95%CI: 2.88–16.05, P < 0.001) (Fig. 2 ). Table 2 The association of GV, SBPV and their interaction effects with the risk of in-hospital mortality among patients different types of strokes Variables Overall populations of stroke IS (n = 1194) SAH (n = 115) ICH (n = 231) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P SBPV Low Ref Ref Ref Ref Ref Ref Ref Ref High 2.13 (1.61–2.81) < 0.001 1.91 (1.41–2.59) < 0.001 2.00 (1.42–2.84) < 0.001 1.88 (1.28–2.76) 0.001 1.47 (0.62–3.49) 0.383 1.27 (0.54–3.02) 0.582 4.06 (2.30–7.20) < 0.001 3.67 (2.02–6.69) < 0.001 GV Low Ref Ref Ref Ref Ref Ref Ref Ref High 1.71 (1.31–2.24) < 0.001 1.84 (1.34–2.54) < 0.001 1.35 (0.96–1.90) 0.083 1.22 (0.82–1.83) 0.327 2.09 (0.89–4.92) 0.092 3.94 (1.72–8.99) 0.001 4.17 (2.45–7.08) < 0.001 3.94 (2.12–7.33) < 0.001 GV and SBPV Low GV and Low SBPV Ref Ref Ref Ref Ref Ref Ref Ref Low GV and High SBPV 2.20 (1.61–3.01) < 0.001 1.91 (1.35–2.71) < 0.001 2.19 (1.49–3.22) < 0.001 2.07 (1.35–3.16) < 0.001 1.32 (0.49–3.54) 0.587 1.18 (0.39–3.53) 0.772 4.65 (2.32–9.29) < 0.001 3.61 (1.66–7.86) 0.001 High GV and Low SBPV 1.75 (1.30–2.37) < 0.001 1.83 (1.27–2.62) 0.001 1.48 (1.01–2.16) 0.045 1.35 (0.86–2.11) 0.190 1.82 (0.67–4.95) 0.238 4.72 (1.85–12.05) 0.001 4.64 (2.51–8.59) < 0.001 3.78 (1.75–8.16) < 0.001 High GV and High SBPV 3.02 (1.74–5.26) < 0.001 3.04 (1.66–5.58) < 0.001 2.03 (0.97–4.22) 0.059 1.67 (0.72–3.87) 0.231 3.69 (0.78–17.42) 0.099 3.19 (0.89–11.44) 0.075 5.99 (2.28–15.71) < 0.001 6.80 (2.88–16.05) < 0.001 IS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; SBP, systolic blood pressure;GV, glycemic variability; SBPV, systolic blood pressure variability; Ref, reference; HR, hazard ratio; CI, confidence intervals; Model 1, crude model; Model 2, adjusted race, mechanical ventilation, antihypertensive drugs, number of blood glucose measurements, SBP and glucose. Association of SBPV and GV and their interaction effect on the risk of 1-year mortality As shown in Table 3 , after adjusting age, gender, race, weight, congestive heart failure, hypertension, sepsis, mechanical ventilation, RRT, antihypertension drug, number of blood glucose measurements, SBP and glucose, we found high SBPV levels were also associated with high risk of 1-year mortality (HR = 1.61, 95%CI: 1.24–2.08, P < 0.001), and these associations were significant in patients with IS (HR = 1.61, 95%CI: 1.18–2.19, P = 0.002) and ICH (HR = 2.88, 95%CI: 1.68–4.93, P < 0.001). High GV levels were associated with high risk of 1-year mortality in critically ill patients with stroke (HR = 1.57, 95%CI: 1.20–2.04, P < 0.001). These associations were more significant in patients with ICH (HR = 2.98, 95%CI: 1.61–5.53, P < 0.001). Notably, patients with concomitantly high GV and SBPV exhibited the highest 1-year mortality risk compared to those with low levels of both GV and SBPV (HR = 2.66, 95%CI: 1.59–4.47, P < 0.001). Figure 3 exhibited the interactive effect of GV and SBPV on the risk of 1-year mortality among critically ill patients with stroke. These interaction effects were pronounced in patients with SAH (HR = 3.16, 95%CI: 1.25–8.03, P = 0.015) and ICH (HR = 5.10, 95%CI: 2.54–10.27, P < 0.001). Table 3 The association of GV, SBPV and their interaction effects with the risk of 1-year mortality among patients different types of strokes Variables Overall populations of stroke IS (n = 1194) SAH (n = 115) ICH (n = 231) Model 1 Model 3 Model 1 Model 3 Model 1 Model 3 Model 1 Model 2 Model 1 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P SBPV Low Ref Ref Ref Ref Ref Ref Ref Ref High 1.82 (1.45–2.30) < 0.001 1.61 (1.24–2.08) < 0.001 1.77 (1.34–2.33) < 0.001 1.61 (1.18–2.19) 0.002 1.36 (0.63–2.94) 0.440 1.03 (0.43–2.43) 0.952 3.03 (1.80–5.12) < 0.001 2.88 (1.68–4.93) < 0.001 GV Low Ref Ref Ref Ref Ref Ref Ref Ref High 1.63 (1.31–2.03) < 0.001 1.57 (1.20–2.04) < 0.001 1.54 (1.19–1.99) < 0.001 1.27 (0.93–1.74) 0.125 1.37 (0.57–3.27) 0.477 1.91 (0.88–4.14) 0.104 3.28 (1.97–5.46) < 0.001 2.98 (1.61–5.53) < 0.001 GV and SBPV Low GV and Low SBPV Ref Ref Ref Ref Ref Ref Ref Ref Low GV and High SBPV 1.82 (1.40–2.37) < 0.001 1.52 (1.14–2.04) 0.004 1.89 (1.39–2.57) < 0.001 1.67 (1.19–2.33) 0.003 1.20 (0.50–2.85) 0.682 0.86 (0.32–2.30) 0.764 3.04 (1.59–5.81) < 0.001 2.62 (1.33–5.16) 0.005 High GV and Low SBPV 1.62 (1.27–2.07) < 0.001 1.48 (1.11–1.98) 0.008 1.63 (1.23–2.15) < 0.001 1.31 (0.94–1.82) 0.110 1.13 (0.40–3.17) 0.813 1.45 (0.55–3.82) 0.450 3.34 (1.81–6.14) < 0.001 2.64 (1.21–5.75) 0.014 High GV and High SBPV 2.64 (1.65–4.22) < 0.001 2.66 (1.59–4.47) < 0.001 2.08 (1.14–3.78) 0.017 1.79 (0.90–3.58) 0.098 2.82 (0.58–13.63) 0.197 3.16 (1.25–8.03) 0.015 4.51 (1.91–10.64) < 0.001 5.10 (2.54–10.27) < 0.001 IS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; SBP, systolic blood pressure; GV, glycemic variability; SBPV, systolic blood pressure variability; Ref, reference; HR, hazard ratio; CI, confidence intervals; Model 1, crude model; Model 3, adjusted age, gender, race, weight, congestive heart failure, hypertension, sepsis, mechanical ventilation, RRT, antihypertensive drugs, number of blood glucose measurements, SBP and glucose. Association of SBPV and GV and their interaction effect on the risk of severe consciousness disturbance We also focused on the clinical outcome of severe consciousness disturbance occurring within 30 days of ICU admission in critically ill stroke patients. After adjusting for age, race, diabetes, sepsis, mechanical ventilation, RRT, antihypertensive drugs, SBP and glucose, we found neither GV nor SBPV alone showed a statistically significant association with the risk of severe consciousness disturbance ( P > 0.05) (Table 4 ). Among the three different types of stroke patients, we are still unobserved a statistically significant association between GV and SBPV with the risk of severe consciousness disturbance (all P > 0.05). However, the interactive effect of GV and SBPV demonstrated marginal significance in relation to this outcome in total stroke populations (OR = 1.94, 95%CI: 0.97–3.90, P = 0.062) (Fig. 4 ). Table 4 The association of GV, SBPV and their interaction effects with the risk of severe consciousness disturbance among patients different types of strokes Variables Overall populations of stroke IS (n = 1194) SAH (n = 115) ICH (n = 231) Model 1 Model 4 Model 1 Model 4 Model 1 Model 4 Model 1 Model 4 Model 1 Model 4 HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P SBPV Low Ref Ref Ref Ref Ref Ref Ref Ref High 1.45 (1.06–1.98) 0.019 0.97 (0.69–1.36) 0.863 1.51 (1.04–2.17) 0.028 1.02 (0.69–1.52) 0.914 1.34 (0.52–3.47) 0.546 0.81 (0.27–2.43) 0.705 1.25 (0.56–2.79) 0.588 0.83 (0.33–2.06) 0.686 GV Low Ref Ref Ref Ref Ref Ref Ref Ref High 1.36 (1.01–1.84) 0.041 1.12 (0.79–1.58) 0.540 1.35 (0.95–1.90) 0.091 1.03 (0.68–1.54) 0.902 1.22 (0.38–3.90) 0.732 1.40 (0.32–6.05) 0.656 2.03 (0.92–4.48) 0.079 2.08 (0.80–5.43) 0.133 GV and SBPV Low GV and Low SBPV Ref Ref Ref Ref Ref Ref Ref Ref Low GV and High SBPV 1.23 (0.86–1.75) 0.253 0.80 (0.55–1.17) 0.248 1.45 (0.96–2.17) 0.075 0.95 (0.61–1.47) 0.809 1.18 (0.42–3.28) 0.756 0.80 (0.24–2.60) 0.709 0.52 (0.18–1.53) 0.236 0.36 (0.11–1.14) 0.082 High GV and Low SBPV 1.17 (0.84–1.64) 0.352 0.92 (0.62–1.37) 0.689 1.30 (0.89–1.89) 0.176 0.95 (0.61–1.49) 0.838 0.98 (0.26–3.74) 0.977 1.51 (0.27–8.28) 0.638 1.11 (0.44–2.81) 0.823 1.13 (0.37–3.50) 0.831 High GV and High SBPV 3.09 (1.61–5.94) < .001 1.94 (0.97–3.90) 0.062 2.26 (1.02–4.98) 0.044 1.37 (0.58–3.20) 0.472 2.94 (0.26–33.73) 0.386 1.03 (0.07–14.37) 0.982 10.87 (1.33–88.72) 0.026 7.47 (0.82–68.41) 0.075 IS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; SBP, systolic blood pressure; GV, glycemic variability; SBPV, systolic blood pressure variability; Ref, reference; HR, hazard ratio; CI, confidence intervals; Model 1, crude model; Model 4, adjusted age, race, diabetes, sepsis, mechanical ventilation, RRT, antihypertensive drugs, SBP, and glucose. The Kaplan-Meier curve and RCS curve analysis of the association between GV and SBPV and the risk of mortality among patients with IS, SAH and ICH Kaplan-Meier curves further illustrated the associations of GV and SBPV with in-hospital mortality and 1-year mortality in critically ill stroke patients. Over the follow-up period, all four patient groups experienced in-hospital or 1-year mortality events. However, patients with concomitantly high GV and high SBPV levels exhibited the highest risk for both outcomes ( P < 0.001) (Supplementary Fig. 1). The Kaplan-Meier survival analysis curves of GV and SBPV for IS, ICH and SAH patients and the risk of in-hospital mortality and 1-year mortality were plotted in Supplementary Fig. 2. The RCS results for the associations of GV and SBPV with in-hospital mortality, 1-year mortality, and severe consciousness disturbance in critically ill stroke patients were presented in Supplementary Fig. 3. The RCS analyses revealed that as GV and SBPV levels increased, the risks of both mortality outcomes and severe consciousness disturbance generally exhibited a gradual upward trend. Discussion Utilizing the data from the MIMIC-IV database, this retrospective cohort study explored the association between GV and SBPV with the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance among critically ill patients with different types of strokes. Moreover, we further explored whether GV and SBPV have interactive effect on these clinical outcomes we interested. After adjusting for a series of confounding factors in multivariate Cox proportional hazards models, we found both elevated levels of GV and SBPV were significantly associated with increased risk of in-hospital mortality and 1-year mortality. Notably, these two metabolic parameters exhibited significant interactive effects on mortality risk, with particularly pronounced synergistic impacts observed in patients with ICH, where their combined variability shown markedly greater predictive value for prognosis. Persistent hyperglycemia promotes the glycation process, leading to a close correlation between hemoglobin A1c (HbA1c) levels and blood glucose concentrations. However, HbA1c—considered the "gold standard" for assessing hyperglycemia severity—only reflects long-term glycemic control and fails to capture glucose variability [ 19 ]. Numerous epidemiological studies have now investigated the association between glucose fluctuations and prognosis in various diseases. Liu et al. [ 20 ] aimed to explore the association between mean blood glucose (MBG) and GV with the ICU mortality of sepsis patients and found that MBG and GV were significantly associated with the higher ICU mortality. Moreover, these impacts on death were increased with the severity of sepsis. Another study conducted at a leading cancer treatment center in Latin America also demonstrated a significant association between higher GV and increased mortality among patients in the oncology ICU [ 21 ]. Cai et al. [ 17 ] extracted from MIMIC-IV database exploring the association of GV with mortality and severe consciousness disturbance among critically ill patients with cerebrovascular disease and found high GV is an independent risk factor for severe cognitive decline and in-hospital mortality. The association between GV and mortality risk can be explained through multi-level pathophysiological mechanisms. First, acute glucose fluctuations may lead to excessive production of reactive oxygen species (ROS) from mitochondrial electron transport chains and activate the NF-κB pathway, accelerating the release of pro-inflammatory factors and ultimately causing vascular endothelial dysfunction [ 22 ]. Second, significant glucose variability may disrupt the blood-brain barrier and impair cerebral autoregulation. Additionally, elevated GV levels can promote coagulation abnormalities and suppress neutrophil function, resulting in systemic metabolic disturbances [ 23 ]. SBPV, an indicator reflecting spontaneous blood pressure fluctuations, is closely associated with left ventricular hypertrophy, arterial vascular remodeling, and stroke risk. Numerous studies have demonstrated that both long-term and short-term increases in SBPV significantly elevate the risk of cardiovascular and cerebrovascular diseases, as well as all-cause mortality. Dong et al. [ 24 ] reported a positive correlation between increased interdialytic home SBPV and higher mortality risk on maintenance hemodialysis patients. Choo et al. [ 25 ] also suggested that SBPV improved the predictive ability of the Global Registry of Acute Coronary Events (GRACE) risk score for all-cause mortality and major cardiovascular events in patients after myocardial infarction. Our study observed associations consistent with previous clinical research, demonstrating that elevated GV and SBPV were significantly associated with increased mortality risk in critically ill stroke patients. Notably, this relationship exhibits a synergistic effect—patients with concurrently high GV and SBPV face the highest mortality risk. Such synergistic interaction has also been reported in prior studies [ 11 , 12 , 26 , 27 ]. The physiological mechanism by which higher GV and SBPV synergistically increase the risk of mortality involves multiple system interactions. Firstly, sharp fluctuations in blood glucose and blood pressure may respectively cause excessive mitochondrial ROS and increased mechanical stress on the vascular wall. ROS and shear stress jointly activate the NF-kB pathway, leading to the massive release of pro-inflammatory factors and ultimately resulting in the synergistic amplification of vascular endothelial injury. Secondly, fluctuations in blood sugar and blood pressure may jointly cause disorders in cerebral blood flow regulation and cardiac microcirculation. Thirdly, high GV and SBPV may lead to abnormal coagulation function and weakened immune defense capacity, thereby activating the thrombosis-inflammation network. In summary, the interplay between high GV and SBPV drives a pathophysiological cascade (endothelial injury →perfusion deficits →metabolic derangement →organ failure), significantly increasing mortality risk in critically ill stroke patients. This necessitates integrated clinical management targeting both variabilities. To our knowledge, this represents the first study utilizing the large-scale public MIMIC-IV database to investigate the associations of GV and SBPV with mortality risk in different types of strokes patients. Compared to traditional scoring systems primarily relying on static indicators, GV and SBPV serve as dynamic physiological markers that reflect cardiovascular/metabolic system decompensation, providing more sensitive prognostic information for critically ill patients. These metrics may assist clinicians in identifying high-risk populations and implementing personalized healthcare strategies. However, several limitations warrant cautious interpretation. First, the retrospective cohort design inherently carries risks of recall bias and selection bias, precluding causal inferences between GV/SBPV and mortality risk. Second, despite adjusting for numerous stroke-related prognostic factors, unmeasured confounding may persist due to data availability constraints. Third, as a single-center study, generalizing our findings to other ethnic populations or healthcare settings requires prudence. Future well-designed, prospective, multicenter cohort studies are needed to validate these observations. Conclusion Our study suggested GV and SBPV exhibited a significant association with the risk of mortality among critically ill patients with stroke, and these associations were shown a synergistic effect. The association of GV and SBPV with mortality risk demonstrated subtle variations across different stroke subtypes. Notably, a significant interaction between GV and SBPV was observed specifically ill patients with ICH. GV and SBPV are independent prognostic predictors distinct from mean glucose/blood pressure levels, reflecting the body’s homeostatic regulatory capacity. Clinical practice should prioritize the management of “dual variability” through precise monitoring, personalized treatment, and patient education to improve long-term clinical outcomes. Abbreviations CVA: cerebrovascular accident; IS: ischemic stroke; ICH: intracranial hemorrhage; SAH: subarachnoid hemorrhage; DALYs: disability-adjusted life years; GV: glycemic variability; SBPV: systolic blood pressure variability; MIMIC-IV: Medical Information Mart for Intensive Care-IV; BIDMC: Beth Israel Deaconess Medical Center; CV: coefficient of variation; SD: standard deviation; GCS: Glasgow Coma Scale; OR: odds ratio; HR: hazard ratio; CI: confidence interval; RCS: restricted cubic splines; HbA1c: hemoglobin A1c. Declarations Ethics approval and consent to participate The ethics committee of Sanmenxia Hospital of the Yellow River exempted the study from ethical review. Informed consent for this study was waived due to the anonymity of patient information. The project was conducted by the Helsinki Declaration. Consent for publication Not applicable. Availability of data and materials The datasets generated during and/or analyzed during the current study are available in the MIMIC-IV database, https://mimic.physionet.org/iv/. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions Liwei Chen designed the study. Huiyuan Xue and Xiaofeng Wu wrote the manuscript. Huiyuan Xue, Xiaofeng Wu, Juan Wang, Songsong Feng, Dong Teng, Hao Zhang and Huifang Yao collected, analyzed and interpreted the data. Liwei Chen critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Hilkens NA, et al. Stroke Lancet. 2024;403(10446):2820–36. Amarenco P, et al. Classification of stroke subtypes. Cerebrovasc Dis. 2009;27(5):493–501. Global. regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol, 2021. 20(10): pp. 795–820. Ding C, et al. Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990–2019. Front Public Health. 2022;10:952161. Global. regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol, 2024. 23(10): pp. 973–1003. Tsuboi H, et al. Effect of early mobilization in patients with stroke and severe disturbance of consciousness: Retrospective study. J Stroke Cerebrovasc Dis. 2022;31(10):106698. Lim S, et al. Effects of metabolic parameters' variability on cardiovascular outcomes in diabetic patients. Cardiovasc Diabetol. 2023;22(1):114. Zhang Z, et al. Blood pressure variability associated with in-hospital and 30-day mortality in heart failure patients: a multicenter cohort study. Sci Rep. 2025;15(1):9911. Yuan C, et al. The Stress Hyperglycemia Ratio is Associated with Hemorrhagic Transformation in Patients with Acute Ischemic Stroke. Clin Interv Aging. 2021;16:431–42. Stevens SL, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354:i4098. Zhou JJ, Nuyujukian DS, Reaven PD. New Insights into the Role of Visit-to-Visit Glycemic Variability and Blood Pressure Variability in Cardiovascular Disease Risk. Curr Cardiol Rep. 2021;23(4):25. He HM, et al. Associations of variability in blood glucose and systolic blood pressure with mortality in patients with coronary artery disease: A retrospective cohort study from the MIMIC-IV database. Diabetes Res Clin Pract. 2024;209:111595. Chiriacò M, et al. Association between blood pressure variability, cardiovascular disease and mortality in type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab. 2019;21(12):2587–98. Kim MK, et al. Associations of Variability in Blood Pressure, Glucose and Cholesterol Concentrations, and Body Mass Index With Mortality and Cardiovascular Outcomes in the General Population. Circulation. 2018;138(23):2627–37. Dostović Z et al. Stroke and disorders of consciousness. Cardiovasc Psychiatry Neurol, 2012. 2012: p. 429108. Ishida A, et al. Dynamic Interaction between Cortico-Brainstem Pathways during Training-Induced Recovery in Stroke Model Rats. J Neurosci. 2019;39(37):7306–20. Cai W, et al. Association of glycemic variability with death and severe consciousness disturbance among critically ill patients with cerebrovascular disease: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):315. Chen T, Qian Y, Deng X. Triglyceride glucose index is a significant predictor of severe disturbance of consciousness and all-cause mortality in critical cerebrovascular disease patients. Cardiovasc Diabetol. 2023;22(1):156. Jang JY, et al. Visit-to-visit HbA1c and glucose variability and the risks of macrovascular and microvascular events in the general population. Sci Rep. 2019;9(1):1374. Lu Z, et al. Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization. Front Public Health. 2022;10:857368. Oliveira AP, Castro MDS, Lima DVM. Glycemic variability and mortality in oncologic intensive care units. Rev Bras Enferm. 2023;76(4):e20220812. Hoffman RP, et al. Glycemic variability predicts inflammation in adolescents with type 1 diabetes. J Pediatr Endocrinol Metab. 2016;29(10):1129–33. Habtu BF, et al. Assessments of coagulation profile among good glycemic control and poor glycemic control type 2 diabetic patient attending at Wolkite University specialized hospital, Central Ethiopia: a comparative study. BMC Endocr Disord. 2024;24(1):204. Dong L, et al. Interdialytic home systolic blood pressure variability increases all-cause mortality in hemodialysis patients. Clin Cardiol. 2024;47(4):e24259. Choo EH, et al. Visit-to-visit blood pressure variability and mortality and cardiovascular outcomes after acute myocardial infarction. J Hum Hypertens. 2022;36(11):960–7. Di Flaviani A, et al. Impact of glycemic and blood pressure variability on surrogate measures of cardiovascular outcomes in type 2 diabetic patients. Diabetes Care. 2011;34(7):1605–9. Sezer H, et al. The relationship between glycemic variability and blood pressure variability in normoglycemic normotensive individuals. Blood Press Monit. 2021;26(2):102–7. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx SupplementaryFigure1.png Supplementary Fig.1. Kaplan-Meier analysis of mortality in total stroke patients; a: in-hospital mortality; b: 1-year mortality. SupplementaryFigure2.png Supplementary Fig.2. Kaplan-Meier analysis of mortality risks among different stroke populations; a. in-hospital mortality in IS patients; b. in-hospital mortality in SAH patients; c. in-hospital mortality in ICH patients; d: 1-year mortality in IS patients; e. 1-year mortality in SAH patients; f. 1-year mortality in ICH patients. SupplementaryFigure3.png Supplementary Fig.3. RCS analysis for the association between GV and SBPV with the risk of mortality and severe consciousness disturbance in different strokes patients; a. in-hospital mortality and GV; b.1-year mortality and GV; c. severe consciousness disturbance and GV; d. in-hospital mortality and SBPV; e. 1-year mortality and SBPV; f. severe consciousness disturbance and SBPV. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 27 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor invited by journal 05 Jun, 2025 Editor assigned by journal 30 May, 2025 Submission checks completed at journal 30 May, 2025 First submitted to journal 29 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6772731","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472218327,"identity":"fa29246d-7bb8-446c-ac4e-9535dacb4e5b","order_by":0,"name":"Huiyuan Xue","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Huiyuan","middleName":"","lastName":"Xue","suffix":""},{"id":472218328,"identity":"21d56c27-1e7c-4837-9f9e-a70c63128e0c","order_by":1,"name":"Xiaofeng Wu","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Wu","suffix":""},{"id":472218329,"identity":"e3bbc466-8ad8-4e20-b5b8-3424a2fa1c29","order_by":2,"name":"Juan Wang","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Wang","suffix":""},{"id":472218330,"identity":"0ec8cccb-5a1f-4c67-8794-5eeee6932355","order_by":3,"name":"Songsong Feng","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Songsong","middleName":"","lastName":"Feng","suffix":""},{"id":472218331,"identity":"f5c3f47d-a1b3-4798-bf55-b3df88af333e","order_by":4,"name":"Dong Teng","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Teng","suffix":""},{"id":472218332,"identity":"69358027-8f3a-45db-a784-0230100f7672","order_by":5,"name":"Hao Zhang","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhang","suffix":""},{"id":472218333,"identity":"ee2e29fa-b9fe-4797-9aca-337f2056cb3d","order_by":6,"name":"Huifen Yao","email":"","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":false,"prefix":"","firstName":"Huifen","middleName":"","lastName":"Yao","suffix":""},{"id":472218334,"identity":"3472af9e-86dc-4271-8ed6-09437ee520b1","order_by":7,"name":"Liwei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie2QvwrCMBCHUwp1ie1aUOwrpHR08VGui53cnfyDkC7i7uIzONU1JeAUfAEdOjkrLkFETBVBB6NugvmGywV+H7kLQgbDD+LaqsCtt5gqDaeSMq3iPCiojEYuFvAqfVMeL6USz/wW0SsVHBYF2jTImjO2O/KE+upV2c00g+GIANpGZNWGfDrhHVobMmss1hqlmvmAeDwXmPDqWCl1BrZFtcpCKqV/VxLHB/JOydTsHEipYMnhA8U7lIOFU+GoXQZJSNUn57pdPG8Z7yXigSvsvNidmkGQpnkhu6+VK9b53tDR9WT6/BOn3hdhg8Fg+BcuLV9YphOwUP0AAAAASUVORK5CYII=","orcid":"","institution":"Sanmenxia Hospital of the Yellow River","correspondingAuthor":true,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-05-29 04:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6772731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6772731/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85346492,"identity":"2c9f43c3-2c14-4b17-9165-e1d706502c88","added_by":"auto","created_at":"2025-06-25 02:10:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18000,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of selecting eligible patients. ICU, intensive care unit; MIMIC, Medical Information Mart for Intensive Care.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/0673e6fa64579b6a0ec16c6b.png"},{"id":85346497,"identity":"98eba36b-88ca-4750-bf32-7caba379145e","added_by":"auto","created_at":"2025-06-25 02:10:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334942,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction effect of GV and SBPV on the risk of in-hospital mortality.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/2f288571aed9bb4ed1935fd7.png"},{"id":85346503,"identity":"b34fe0b9-8d07-4336-b086-3d2609960e02","added_by":"auto","created_at":"2025-06-25 02:10:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":357366,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction effect of GV and SBPV on the risk of 1-year mortality.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/25cd33f3b75bdeb2a3a70a23.png"},{"id":85346496,"identity":"24f29a76-00de-45f5-acf6-b8e108a46bbe","added_by":"auto","created_at":"2025-06-25 02:10:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":323597,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction effect of GV and SBPV on the risk of severe consciousness distribution.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/79c72a8543bd1b2d53dd9339.png"},{"id":85347209,"identity":"66eec739-beaf-42c7-9325-4bb1a3a016d0","added_by":"auto","created_at":"2025-06-25 02:18:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2230510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/6037ba9f-6d07-4a70-8e97-b73b0d15c889.pdf"},{"id":85346493,"identity":"35f3101b-d716-4ea0-ad05-86a6bb59b326","added_by":"auto","created_at":"2025-06-25 02:10:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21911,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/3ad196b746ef86ad441e621c.docx"},{"id":85346510,"identity":"60e0c2b2-e0d3-4ae3-ba5a-05dca9a3da10","added_by":"auto","created_at":"2025-06-25 02:10:33","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":215351,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig.1. Kaplan-Meier analysis of mortality in total stroke patients;\u003c/p\u003e\n\u003cp\u003ea: in-hospital mortality; b: 1-year mortality.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/d1d4e74188d186799ff369af.png"},{"id":85346508,"identity":"82cb1cb8-9cb5-4e0e-83e1-d15b3b02df49","added_by":"auto","created_at":"2025-06-25 02:10:32","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":470981,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig.2. Kaplan-Meier analysis of mortality risks among different stroke populations;\u003c/p\u003e\n\u003cp\u003ea. in-hospital mortality in IS patients; b. in-hospital mortality in SAH patients; c. in-hospital mortality in ICH patients; d: 1-year mortality in IS patients; e. 1-year mortality in SAH patients; f. 1-year mortality in ICH patients.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/3da51fc570d4dbea6c682b01.png"},{"id":85346512,"identity":"5fff1939-7ff7-4f5b-99af-b30ce0fceb12","added_by":"auto","created_at":"2025-06-25 02:10:33","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":391085,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig.3. RCS analysis for the association between GV and SBPV with the risk of mortality and severe consciousness disturbance in different strokes patients;\u003c/p\u003e\n\u003cp\u003ea. in-hospital mortality and GV; b.1-year mortality and GV; c. severe consciousness disturbance and GV; d. in-hospital mortality and SBPV; e. 1-year mortality and SBPV; f. severe consciousness disturbance and SBPV.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6772731/v1/36c5eb108e4eaf5d15b28fb6.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of variability of blood pressure and glycemic with the risk of mortality and severe consciousness disturbance among critically ill patients with ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage: a retrospective cohort study from MIMIC-IV database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke, also known as cerebrovascular accident (CVA), is an acute vascular disorder characterized by cerebral ischemia due to vascular occlusion or brain tissue damage from cerebrovascular rupture [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Clinically, it is generally categorized into ischemic stroke (IS) and hemorrhagic stroke, with the latter further subdivided into intracranial hemorrhage (ICH) and subarachnoid hemorrhage (SAH) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Worldwide, there are approximately 12\u0026nbsp;million new stroke cases annually, with 5.5\u0026nbsp;million deaths each year, contributing to 116\u0026nbsp;million disability-adjusted life years (DALYs) per year. It has now become the second leading cause of death worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In severe cases of stroke, extensive damage to the cerebral cortex occurs, impairing the patient\u0026rsquo;s arousal and conscious awareness. Consciousness disturbance serves as a critical marker of poor prognosis in stroke patients and is closely associated with high mortality rates, long-term disability, and substantial healthcare resource utilization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, identifying potential risk factors for severe stroke holds significant importance for prognostic evaluation.\u003c/p\u003e \u003cp\u003eThe variability of metabolic parameters, such as glycemic variability (GV) and systolic blood pressure variability (SBPV), has been recognized as in independent cardiovascular and cerebrovascular risk factor, distinct form absolute glucose and blood pressure levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. GV reflects a non-steady-state fluctuation of blood glucose, characterized by rapid transitions between peaks and troughs over short periods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Acute glucose fluctuations may activate systemic oxidative stress responses, accelerating atherosclerosis and subsequent cerebrovascular stenosis, ultimately triggering stroke [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The human body exhibits complex spontaneous blood pressure fluctuations throughout a 24-h period. Excessive blood pressure variability (BPV) can damage vascular endothelium through mechanical stress, promoting plaque rupture, while hypotensive episodes may lead to insufficient coronary or cerebral perfusion, potentially triggering ischemic events [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Several previous epidemiological studies have indicated the association between GV and BPV and the risks of cardiovascular and cerebrovascular diseases [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, a study utilizing the Korena National Health Insurance System database demonstrated that elevated GV and SBPV serve as independent predictors of mortality and cardiovascular events in the general population, with evidence of synergistic effects between GV and SBPV on mortality risk [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Clinical evidence also supports the association between severe consciousness disturbance and increased mortality risk in stroke patients, with higher degrees of consciousness disturbance correlating with greater mortality risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This association may be attributed to physiological mechanisms such as ischemic or hemorrhagic lesions affecting the brainstem reticular activating system, consequently impairing the patient\u0026rsquo;s arousal function [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the studies on the association between GV and SBPV on the risk of mortality among different types of stroke patients remains lacking. Furthermore, it is still unclear whether GV and SBPV have an interactive effect on the risk of mortality in critically ill stroke patients.\u003c/p\u003e \u003cp\u003eTherefore, this study investigated the association between GV and SBPV and the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance among critically ill patients with IS, ICH and SAH, and to further examine whether GV and SBPV exhibit synergistic effects on these clinical outcomes. Understanding this relationship may provide valuable insights into risk stratification in critically ill stroke patients and help clarify the potential role of GV and SBPV as prognostic indicators, thereby facilitating clinical decision-making and personalized treatment strategies for critically ill patients with stroke.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eData of this retrospective cohort study were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). MIMIC-IV, a publicly accessible critical care database jointly developed by the MIT Laboratory for Computational Physiology at Harvard University and Beth Israel Deaconess Medical Center (BIDMC) in Boston, stands as one of the most authoritative ICU clinical databases worldwide. This repository comprises de-identified data from approximately 500,000 hospitalized patients (including 100,000 ICU admissions) between 2008 and 2019, encompassing multidimensional clinical parameters such as vital signs, laboratory tests, pharmacotherapeutic interventions, nursing documentation, and radiological reports. The ethics committee of Sanmenxia Hospital of the Yellow River exempted the study from ethical review. Informed consent for this study was waived due to the anonymity of patient information.\u003c/p\u003e \u003cp\u003eA total of 6,538 patients with stroke and aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years old were initially extracted from the database, including cases of IS, ICH, and SAH. Patients were excluded based on the following criteria: 1) those with ICU length of stay\u0026thinsp;\u0026lt;\u0026thinsp;24 h (n\u0026thinsp;=\u0026thinsp;881); 2) those have taken blood measurements\u0026thinsp;\u0026lt;\u0026thinsp;3 times and SBP measurements\u0026thinsp;\u0026lt;\u0026thinsp;10 times (n\u0026thinsp;=\u0026thinsp;4,117). Ultimately, 1,540 eligible patients were included in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of SBPV and GV\u003c/h3\u003e\n\u003cp\u003eCurrently, there is no standardized clinical method for assessing GV and SBPV. In our study, we employed a widely accepted approach based on the coefficient of variation (CV) of glycemic measurements and SBP measurements obtained within the 0\u0026ndash;24 h of hospitalization. Additionally, we conducted sensitivity analyses incorporating all available glycemic and SBP measurements throughout the entire hospitalization period to calculate GV and SBPV. SBP measurements were automatically recorded at least hourly by monitoring devices. We calculated 24-h BPV during the initial ICU admission using the standard deviation (SD) of all recorded values [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. GV was evaluated based on methods established in previously published study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The CV was calculated as the ratio of the SD to the mean (SD/mean \u0026times; 100%). Based on X-tile, the continuous GV and SBPV were classified with their cut-off values being 31.46 and 17.70 respectively.\u003c/p\u003e\n\u003ch3\u003eOutcomes and follow-up\u003c/h3\u003e\n\u003cp\u003eThe clinical outcomes of interest were classified as in-hospital death, 1-year death and severe consciousness disturbance. In-hospital death was defined as \u0026ldquo;the patients died during hospitalization\u0026rdquo;. The 1-year mortality was \u0026ldquo;the patient died within 1 year\u0026rdquo;. Furthermore, we also focused on the occurrence of severe consciousness disturbance with 30 days after hospital admission, defining as a Glasgow Coma Scale (GCS) score\u0026thinsp;\u0026lt;\u0026thinsp;8 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Patients was followed up from the initial 24 h after admission to the ICU until death occurred in the hospital or until the end of 1 year.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eWe considered comprehensive baseline characteristics including: 1) demographic data: age (years), gender (female, male), race (White, Black, other); 2) vital signs: weight (kg), SBP (mmHg), and glucose (mg/dL); 3) comorbidities: diabetes (yes, no), congestive heart failure (yes, no), hypertension (yes, no) and sepsis (yes, no); 4) treatments: mechanical ventilation (yes, no), renal replacement therapy (yes, no) and antihypertensive drugs (yes, no); 5) lifestyle: smoking (yes, no).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eQuantitative data were tested for normality using skewness and kurtosis, while homogeneity of variance was assessed using the Levene test. Normally distributed data were presented as a mean and standard deviation [Mean (\u0026plusmn;\u0026thinsp;SD)] and compared between groups using the t-test for equal variances or the adjusted t' test for unequal variances. Non-normally distributed data were described as median and interquartile range [M (Q₁, Q₃)] and compared using the Wilcoxon rank-sum test. Categorical data were presented as number and percentage [n (%)], with between-group comparisons performed using Chi-square tests. Variables with \u0026gt;\u0026thinsp;20% missing values were excluded from analysis, while those with \u0026le;\u0026thinsp;20% missingness were handled via multiple imputation. Detailed results were presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUnivariate Cox proportional hazards models and logistics regression models were utilized to explore the association between all variables and the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance among critically patients with stroke, severing as a screening step for potential covariates. In Model 1, each covariate was analyzed by univariate analyses. Model 2 included all variables screened out from the Model 1, which were retained through a bidirectional stepwise regression process (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Then, univariate and multivariate Cox proportional hazards models and logistic regression models were conducted to explore the association between GV and SBPV and their interaction effect with the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance, with hazard ratio (HR), odds ratio (OR) and 95% confidence interval (CI). Model 1 was crude model; when in-hospital mortality was analyzed as the outcome, model 2 was adjusted race, mechanical ventilation, antihypertensive drugs, number of blood glucose measurements, SBP and glucose; when 1-year mortality as the outcome, model 3 adjusted age, gender, race, weight, congestive heart failure, hypertension, sepsis, mechanical ventilation, RRT, antihypertensive drugs, number pf blood glucose measurements, SBP and glucose; when severe consciousness disturbance as the outcome, model 4 adjusted age, race, diabetes, sepsis, mechanical ventilation, RRT, antihypertensive drugs, SBP and glucose. Subgroup analyses were conducted to further explore the association between GV and SBPV and their interaction effect on the association between in-hospital mortality, 1-year mortality and serve consciousness disturbance based on different types of strokes including ischemic stroke, SAH and ICH. The Kaplan-Meier survival curve illustrates the association between GV and SBPV on the risk of in-hospital mortality and 1-year mortality. Moreover, the restricted cubic splines (RCS) curve was performed to assess the nonlinear association between GV and SBPV with the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance. All statistical analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and R revision 4.2.2 (Institute for Statistics and Mathematics, Vienna, Austria). We chose \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) as our statistical significance level.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of included critically ill patients with different types of strokes\u003c/h2\u003e\n \u003cp\u003eThis study ultimately included 1,540 eligible critically ill stroke patients, with a mean age of 76.89 years. Among these patients, 1,194 had IS, 115 had SAH, and 231 had ICH. A total of 323 patients died within ICU admission, 532 patients died within one year, and 463 patients developed severe consciousness disturbance within 30 days of admission. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the baseline characteristics of the patients with IS, SAH and ICH. Stroke patients who experienced in-hospital mortality, 1-year mortality, or severe consciousness disturbance all demonstrated significant differences compared to unaffected patients in the following clinically relevant parameters: sepsis history, RRT utilization, antihypertensive medication use, number of blood glucose measurements, and baseline level of SBP and blood glucose (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of patients with different types of strokes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1540)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eDifferent types of stokes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIS\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSAH\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICH\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.70\u0026thinsp;\u0026plusmn;\u0026thinsp;14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.70\u0026thinsp;\u0026plusmn;\u0026thinsp;13.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.13\u0026thinsp;\u0026plusmn;\u0026thinsp;14.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;44.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e659 (42.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e503 (42.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (56.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (39.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e881 (57.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e691 (57.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (43.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (60.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;27.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e982 (63.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e801 (67.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (51.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (52.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (7.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (6.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (9.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450 (29.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314 (26.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (42.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (37.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight, kg, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.06\u0026thinsp;\u0026plusmn;\u0026thinsp;22.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.83\u0026thinsp;\u0026plusmn;\u0026thinsp;21.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.02\u0026thinsp;\u0026plusmn;\u0026thinsp;24.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.74\u0026thinsp;\u0026plusmn;\u0026thinsp;23.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1418 (92.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1110 (92.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (86.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209 (90.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (13.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (9.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;40.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1034 (67.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e756 (63.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (88.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176 (76.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e506 (32.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438 (36.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (11.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (23.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;63.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1113 (72.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807 (67.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (96.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195 (84.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427 (27.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e387 (32.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (15.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e720 (46.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e566 (47.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (56.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (38.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e820 (53.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e628 (52.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (43.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (61.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e460 (29.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364 (30.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (26.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (28.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1080 (70.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e830 (69.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (73.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (71.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;20.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284 (23.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (18.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (10.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1210 (78.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e910 (76.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (81.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (89.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRRT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1426 (92.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1097 (91.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (95.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219 (94.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (5.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntihypertensive drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;171.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314 (20.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (13.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (54.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (39.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1226 (79.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1034 (86.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (45.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (60.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of blood glucose measurements, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00 (3.00, 7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00 (3.00, 5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00 (3.00, 5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;113.250#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP, baseline, mmHg, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.13\u0026thinsp;\u0026plusmn;\u0026thinsp;26.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.12\u0026thinsp;\u0026plusmn;\u0026thinsp;25.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.83\u0026thinsp;\u0026plusmn;\u0026thinsp;26.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.39\u0026thinsp;\u0026plusmn;\u0026thinsp;27.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;40.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose, baseline, mg/dL, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.50 (116.00, 177.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.00 (115.00, 177.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.00 (120.00, 195.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.00 (119.00, 176.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.803#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.21 (10.71, 25.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.33 (11.61, 26.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.26 (7.96, 21.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.50 (7.62, 21.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;47.109#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBPV, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.45 (10.07, 15.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.44 (10.20, 15.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.03 (10.59, 16.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.32 (9.59, 15.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.765#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1317 (85.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1013 (84.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (88.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202 (87.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223 (14.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181 (15.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (11.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (12.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBPV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1341 (87.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1043 (87.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (81.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (88.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (12.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (12.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (18.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (11.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV and SBPV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOW GV and LOW SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1156 (75.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e888 (74.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (73.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (79.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOW GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (10.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125 (10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (15.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (7.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and LOW SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185 (12.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 (12.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (8.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (8.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;59.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1217 (79.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e994 (83.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (68.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (62.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (20.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (16.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (31.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (37.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFollow-up time, hospital, day, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.66 (6.26, 17.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.34 (6.43, 17.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.55 (5.84, 20.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.90 (4.85, 18.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.765#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1y-ear mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;45.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1008 (65.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e830 (69.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (60.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (46.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e532 (34.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364 (30.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (39.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (53.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFolow-up time, 1 year, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365.00 (43.00, 365.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365.00 (115.00, 365.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365.00 (8.00, 365.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.00 (7.00, 365.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;59.657#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere consciousness disturbance, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;34.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1077 (69.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e879 (73.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (58.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (56.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e463 (30.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315 (26.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (41.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (43.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eIS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; RRT, renal replacement therapy; SBP, systolic blood pressure;GV, glycemic variability; SBPV, systolic blood pressure variability; SD, standard deviation; Q1, quartile 1; Q3, quartile 3; M, median; Z, Z-test; t, t-test; \u0026chi;\u003csup\u003e2,\u003c/sup\u003e Chi-square test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation of SBPV and GV and their interaction effect on the risk of in-hospital mortality\u003c/h3\u003e\n\u003cp\u003eTwo Cox proportional hazards models were employed to examine the associations of SBPV and GV with the risk of in-hospital mortality among patients with three types of strokes (IS, SAH, and ICH), as well as their potential interaction effects on mortality. The results were presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The fully adjusted Model 2 which accounted for race, mechanical ventilation, antihypertensive drugs, number of blood glucose measurements, SBP and glucose suggested that compared to low SBPV levels, high SBPV levels were associated with high risk of in-hospital mortality in patients with stroke (HR\u0026thinsp;=\u0026thinsp;1.91, 95%CI: 1.41\u0026ndash;2.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), particularly in patients with IS (HR\u0026thinsp;=\u0026thinsp;1.88, 95%CI: 1.28\u0026ndash;2.76, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and ICH (HR\u0026thinsp;=\u0026thinsp;3.67, 95%CI: 2.02\u0026ndash;6.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High GV levels were also associated with high risk of in-hospital mortality (HR\u0026thinsp;=\u0026thinsp;1.84, 95%CI: 1.34\u0026ndash;2.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), these associations were more observably in patients with SAH (HR\u0026thinsp;=\u0026thinsp;3.94, 95%CI: 1.72\u0026ndash;8.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and ICH (HR\u0026thinsp;=\u0026thinsp;3.94, 95%CI: 2.12\u0026ndash;7.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, we also observed that patients with concomitantly high levels of both GV and SBPV exhibited a significantly highest risk of in-hospital mortality compared to those with low levels of GV and SBPV (HR\u0026thinsp;=\u0026thinsp;3.04, 95%CI: 1.66\u0026ndash;5.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a potential interactive effect between GV and SBPV on in-hospital mortality in critically ill patients with stroke, and these interaction effects were more significant in patients with ICH (HR\u0026thinsp;=\u0026thinsp;6.80, 95%CI: 2.88\u0026ndash;16.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe association of GV, SBPV and their interaction effects with the risk of in-hospital mortality among patients different types of strokes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eOverall populations of stroke\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eIS (n\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eSAH (n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eICH (n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13 (1.61\u0026ndash;2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91 (1.41\u0026ndash;2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 (1.42\u0026ndash;2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88 (1.28\u0026ndash;2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (0.62\u0026ndash;3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (0.54\u0026ndash;3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06 (2.30\u0026ndash;7.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67 (2.02\u0026ndash;6.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71 (1.31\u0026ndash;2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84 (1.34\u0026ndash;2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.96\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.82\u0026ndash;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09 (0.89\u0026ndash;4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94 (1.72\u0026ndash;8.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17 (2.45\u0026ndash;7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94 (2.12\u0026ndash;7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV and SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GV and Low SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20 (1.61\u0026ndash;3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91 (1.35\u0026ndash;2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19 (1.49\u0026ndash;3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07 (1.35\u0026ndash;3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (0.49\u0026ndash;3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.39\u0026ndash;3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65 (2.32\u0026ndash;9.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61 (1.66\u0026ndash;7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and Low SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75 (1.30\u0026ndash;2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83 (1.27\u0026ndash;2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48 (1.01\u0026ndash;2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.86\u0026ndash;2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (0.67\u0026ndash;4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72 (1.85\u0026ndash;12.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.64 (2.51\u0026ndash;8.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.78 (1.75\u0026ndash;8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02 (1.74\u0026ndash;5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.04 (1.66\u0026ndash;5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (0.97\u0026ndash;4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67 (0.72\u0026ndash;3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.69 (0.78\u0026ndash;17.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19 (0.89\u0026ndash;11.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.99 (2.28\u0026ndash;15.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.80 (2.88\u0026ndash;16.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eIS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; SBP, systolic blood pressure;GV, glycemic variability; SBPV, systolic blood pressure variability;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eRef, reference; HR, hazard ratio; CI, confidence intervals;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eModel 1, crude model;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eModel 2, adjusted race, mechanical ventilation, antihypertensive drugs, number of blood glucose measurements, SBP and glucose.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\u0026nbsp;\u003c/caption\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of SBPV and GV and their interaction effect on the risk of 1-year mortality\u003c/h2\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, after adjusting age, gender, race, weight, congestive heart failure, hypertension, sepsis, mechanical ventilation, RRT, antihypertension drug, number of blood glucose measurements, SBP and glucose, we found high SBPV levels were also associated with high risk of 1-year mortality (HR\u0026thinsp;=\u0026thinsp;1.61, 95%CI: 1.24\u0026ndash;2.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and these associations were significant in patients with IS (HR\u0026thinsp;=\u0026thinsp;1.61, 95%CI: 1.18\u0026ndash;2.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and ICH (HR\u0026thinsp;=\u0026thinsp;2.88, 95%CI: 1.68\u0026ndash;4.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High GV levels were associated with high risk of 1-year mortality in critically ill patients with stroke (HR\u0026thinsp;=\u0026thinsp;1.57, 95%CI: 1.20\u0026ndash;2.04, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These associations were more significant in patients with ICH (HR\u0026thinsp;=\u0026thinsp;2.98, 95%CI: 1.61\u0026ndash;5.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, patients with concomitantly high GV and SBPV exhibited the highest 1-year mortality risk compared to those with low levels of both GV and SBPV (HR\u0026thinsp;=\u0026thinsp;2.66, 95%CI: 1.59\u0026ndash;4.47, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e exhibited the interactive effect of GV and SBPV on the risk of 1-year mortality among critically ill patients with stroke. These interaction effects were pronounced in patients with SAH (HR\u0026thinsp;=\u0026thinsp;3.16, 95%CI: 1.25\u0026ndash;8.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and ICH (HR\u0026thinsp;=\u0026thinsp;5.10, 95%CI: 2.54\u0026ndash;10.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe association of GV, SBPV and their interaction effects with the risk of 1-year mortality among patients different types of strokes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eOverall populations of stroke\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eIS (n\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eSAH (n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eICH (n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (1.45\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61 (1.24\u0026ndash;2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77 (1.34\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61 (1.18\u0026ndash;2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (0.63\u0026ndash;2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.43\u0026ndash;2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03 (1.80\u0026ndash;5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88 (1.68\u0026ndash;4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63 (1.31\u0026ndash;2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 (1.20\u0026ndash;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54 (1.19\u0026ndash;1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (0.93\u0026ndash;1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37 (0.57\u0026ndash;3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91 (0.88\u0026ndash;4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28 (1.97\u0026ndash;5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98 (1.61\u0026ndash;5.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV and SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GV and Low SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (1.40\u0026ndash;2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52 (1.14\u0026ndash;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89 (1.39\u0026ndash;2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67 (1.19\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (0.50\u0026ndash;2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.32\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.04 (1.59\u0026ndash;5.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (1.33\u0026ndash;5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and Low SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62 (1.27\u0026ndash;2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48 (1.11\u0026ndash;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63 (1.23\u0026ndash;2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (0.94\u0026ndash;1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (0.40\u0026ndash;3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (0.55\u0026ndash;3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34 (1.81\u0026ndash;6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.21\u0026ndash;5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.65\u0026ndash;4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66 (1.59\u0026ndash;4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08 (1.14\u0026ndash;3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79 (0.90\u0026ndash;3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82 (0.58\u0026ndash;13.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16 (1.25\u0026ndash;8.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51 (1.91\u0026ndash;10.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10 (2.54\u0026ndash;10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eIS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; SBP, systolic blood pressure; GV, glycemic variability; SBPV, systolic blood pressure variability; Ref, reference; HR, hazard ratio; CI, confidence intervals;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eModel 1, crude model;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eModel 3, adjusted age, gender, race, weight, congestive heart failure, hypertension, sepsis, mechanical ventilation, RRT, antihypertensive drugs, number of blood glucose measurements, SBP and glucose.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation of SBPV and GV and their interaction effect on the risk of severe consciousness disturbance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe also focused on the clinical outcome of severe consciousness disturbance occurring within 30 days of ICU admission in critically ill stroke patients. After adjusting for age, race, diabetes, sepsis, mechanical ventilation, RRT, antihypertensive drugs, SBP and glucose, we found neither GV nor SBPV alone showed a statistically significant association with the risk of severe consciousness disturbance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the three different types of stroke patients, we are still unobserved a statistically significant association between GV and SBPV with the risk of severe consciousness disturbance (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the interactive effect of GV and SBPV demonstrated marginal significance in relation to this outcome in total stroke populations (OR\u0026thinsp;=\u0026thinsp;1.94, 95%CI: 0.97\u0026ndash;3.90, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.062) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe association of GV, SBPV and their interaction effects with the risk of severe consciousness disturbance among patients different types of strokes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eOverall populations of stroke\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eIS (n\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eSAH (n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eICH (n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (1.06\u0026ndash;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.69\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (1.04\u0026ndash;2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.69\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (0.52\u0026ndash;3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.27\u0026ndash;2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (0.56\u0026ndash;2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.33\u0026ndash;2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (1.01\u0026ndash;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.79\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.95\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.68\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.38\u0026ndash;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.32\u0026ndash;6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (0.92\u0026ndash;4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08 (0.80\u0026ndash;5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV and SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GV and Low SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.86\u0026ndash;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.55\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (0.96\u0026ndash;2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.61\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.42\u0026ndash;3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.24\u0026ndash;2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (0.18\u0026ndash;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36 (0.11\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and Low SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (0.84\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.62\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30 (0.89\u0026ndash;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.61\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.26\u0026ndash;3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (0.27\u0026ndash;8.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.44\u0026ndash;2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (0.37\u0026ndash;3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GV and High SBPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09 (1.61\u0026ndash;5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (0.97\u0026ndash;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26 (1.02\u0026ndash;4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37 (0.58\u0026ndash;3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94 (0.26\u0026ndash;33.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.07\u0026ndash;14.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.87 (1.33\u0026ndash;88.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.47 (0.82\u0026ndash;68.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eIS, ischemic stroke; SAH, subarachnoid hemorrhage; ICH, intracranial hemorrhage; SBP, systolic blood pressure; GV, glycemic variability; SBPV, systolic blood pressure variability; Ref, reference; HR, hazard ratio; CI, confidence intervals;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eModel 1, crude model;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eModel 4, adjusted age, race, diabetes, sepsis, mechanical ventilation, RRT, antihypertensive drugs, SBP, and glucose.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eThe Kaplan-Meier curve and RCS curve analysis of the association between GV and SBPV and the risk of mortality among patients with IS, SAH and ICH\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eKaplan-Meier curves further illustrated the associations of GV and SBPV with in-hospital mortality and 1-year mortality in critically ill stroke patients. Over the follow-up period, all four patient groups experienced in-hospital or 1-year mortality events. However, patients with concomitantly high GV and high SBPV levels exhibited the highest risk for both outcomes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Fig.\u0026nbsp;1). The Kaplan-Meier survival analysis curves of GV and SBPV for IS, ICH and SAH patients and the risk of in-hospital mortality and 1-year mortality were plotted in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e\n \u003cp\u003eThe RCS results for the associations of GV and SBPV with in-hospital mortality, 1-year mortality, and severe consciousness disturbance in critically ill stroke patients were presented in Supplementary Fig.\u0026nbsp;3. The RCS analyses revealed that as GV and SBPV levels increased, the risks of both mortality outcomes and severe consciousness disturbance generally exhibited a gradual upward trend.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUtilizing the data from the MIMIC-IV database, this retrospective cohort study explored the association between GV and SBPV with the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance among critically ill patients with different types of strokes. Moreover, we further explored whether GV and SBPV have interactive effect on these clinical outcomes we interested. After adjusting for a series of confounding factors in multivariate Cox proportional hazards models, we found both elevated levels of GV and SBPV were significantly associated with increased risk of in-hospital mortality and 1-year mortality. Notably, these two metabolic parameters exhibited significant interactive effects on mortality risk, with particularly pronounced synergistic impacts observed in patients with ICH, where their combined variability shown markedly greater predictive value for prognosis.\u003c/p\u003e \u003cp\u003ePersistent hyperglycemia promotes the glycation process, leading to a close correlation between hemoglobin A1c (HbA1c) levels and blood glucose concentrations. However, HbA1c\u0026mdash;considered the \"gold standard\" for assessing hyperglycemia severity\u0026mdash;only reflects long-term glycemic control and fails to capture glucose variability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Numerous epidemiological studies have now investigated the association between glucose fluctuations and prognosis in various diseases. Liu et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] aimed to explore the association between mean blood glucose (MBG) and GV with the ICU mortality of sepsis patients and found that MBG and GV were significantly associated with the higher ICU mortality. Moreover, these impacts on death were increased with the severity of sepsis. Another study conducted at a leading cancer treatment center in Latin America also demonstrated a significant association between higher GV and increased mortality among patients in the oncology ICU [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Cai et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] extracted from MIMIC-IV database exploring the association of GV with mortality and severe consciousness disturbance among critically ill patients with cerebrovascular disease and found high GV is an independent risk factor for severe cognitive decline and in-hospital mortality. The association between GV and mortality risk can be explained through multi-level pathophysiological mechanisms. First, acute glucose fluctuations may lead to excessive production of reactive oxygen species (ROS) from mitochondrial electron transport chains and activate the NF-κB pathway, accelerating the release of pro-inflammatory factors and ultimately causing vascular endothelial dysfunction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Second, significant glucose variability may disrupt the blood-brain barrier and impair cerebral autoregulation. Additionally, elevated GV levels can promote coagulation abnormalities and suppress neutrophil function, resulting in systemic metabolic disturbances [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSBPV, an indicator reflecting spontaneous blood pressure fluctuations, is closely associated with left ventricular hypertrophy, arterial vascular remodeling, and stroke risk. Numerous studies have demonstrated that both long-term and short-term increases in SBPV significantly elevate the risk of cardiovascular and cerebrovascular diseases, as well as all-cause mortality. Dong et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported a positive correlation between increased interdialytic home SBPV and higher mortality risk on maintenance hemodialysis patients. Choo et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] also suggested that SBPV improved the predictive ability of the Global Registry of Acute Coronary Events (GRACE) risk score for all-cause mortality and major cardiovascular events in patients after myocardial infarction. Our study observed associations consistent with previous clinical research, demonstrating that elevated GV and SBPV were significantly associated with increased mortality risk in critically ill stroke patients. Notably, this relationship exhibits a synergistic effect\u0026mdash;patients with concurrently high GV and SBPV face the highest mortality risk. Such synergistic interaction has also been reported in prior studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The physiological mechanism by which higher GV and SBPV synergistically increase the risk of mortality involves multiple system interactions. Firstly, sharp fluctuations in blood glucose and blood pressure may respectively cause excessive mitochondrial ROS and increased mechanical stress on the vascular wall. ROS and shear stress jointly activate the NF-kB pathway, leading to the massive release of pro-inflammatory factors and ultimately resulting in the synergistic amplification of vascular endothelial injury. Secondly, fluctuations in blood sugar and blood pressure may jointly cause disorders in cerebral blood flow regulation and cardiac microcirculation. Thirdly, high GV and SBPV may lead to abnormal coagulation function and weakened immune defense capacity, thereby activating the thrombosis-inflammation network. In summary, the interplay between high GV and SBPV drives a pathophysiological cascade (endothelial injury \u0026rarr;perfusion deficits \u0026rarr;metabolic derangement \u0026rarr;organ failure), significantly increasing mortality risk in critically ill stroke patients. This necessitates integrated clinical management targeting both variabilities.\u003c/p\u003e \u003cp\u003eTo our knowledge, this represents the first study utilizing the large-scale public MIMIC-IV database to investigate the associations of GV and SBPV with mortality risk in different types of strokes patients. Compared to traditional scoring systems primarily relying on static indicators, GV and SBPV serve as dynamic physiological markers that reflect cardiovascular/metabolic system decompensation, providing more sensitive prognostic information for critically ill patients. These metrics may assist clinicians in identifying high-risk populations and implementing personalized healthcare strategies. However, several limitations warrant cautious interpretation. First, the retrospective cohort design inherently carries risks of recall bias and selection bias, precluding causal inferences between GV/SBPV and mortality risk. Second, despite adjusting for numerous stroke-related prognostic factors, unmeasured confounding may persist due to data availability constraints. Third, as a single-center study, generalizing our findings to other ethnic populations or healthcare settings requires prudence. Future well-designed, prospective, multicenter cohort studies are needed to validate these observations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study suggested GV and SBPV exhibited a significant association with the risk of mortality among critically ill patients with stroke, and these associations were shown a synergistic effect. The association of GV and SBPV with mortality risk demonstrated subtle variations across different stroke subtypes. Notably, a significant interaction between GV and SBPV was observed specifically ill patients with ICH. GV and SBPV are independent prognostic predictors distinct from mean glucose/blood pressure levels, reflecting the body\u0026rsquo;s homeostatic regulatory capacity. Clinical practice should prioritize the management of \u0026ldquo;dual variability\u0026rdquo; through precise monitoring, personalized treatment, and patient education to improve long-term clinical outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCVA: cerebrovascular accident; IS: ischemic stroke; ICH: intracranial hemorrhage; SAH: subarachnoid hemorrhage; DALYs: disability-adjusted life years; GV: glycemic variability; SBPV: systolic blood pressure variability; MIMIC-IV: Medical Information Mart for Intensive Care-IV; BIDMC: Beth Israel Deaconess Medical Center; CV: coefficient of variation; SD: standard deviation; GCS: Glasgow Coma Scale; OR: odds ratio; HR: hazard ratio; CI: confidence interval; RCS: restricted cubic splines; HbA1c: hemoglobin A1c.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committee of Sanmenxia Hospital of the Yellow River exempted the study from ethical review. Informed consent for this study was waived due to the anonymity of patient information. The project was conducted by the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available in the MIMIC-IV database, https://mimic.physionet.org/iv/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiwei Chen designed the study. Huiyuan Xue and Xiaofeng Wu wrote the manuscript. Huiyuan Xue, Xiaofeng Wu, Juan Wang, Songsong Feng, Dong Teng, Hao Zhang and Huifang Yao collected, analyzed and interpreted the data. Liwei Chen critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHilkens NA, et al. Stroke Lancet. 2024;403(10446):2820\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmarenco P, et al. Classification of stroke subtypes. Cerebrovasc Dis. 2009;27(5):493\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal. regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol, 2021. 20(10): pp. 795\u0026ndash;820.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing C, et al. Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990\u0026ndash;2019. Front Public Health. 2022;10:952161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal. regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol, 2024. 23(10): pp. 973\u0026ndash;1003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuboi H, et al. Effect of early mobilization in patients with stroke and severe disturbance of consciousness: Retrospective study. J Stroke Cerebrovasc Dis. 2022;31(10):106698.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim S, et al. Effects of metabolic parameters' variability on cardiovascular outcomes in diabetic patients. Cardiovasc Diabetol. 2023;22(1):114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, et al. Blood pressure variability associated with in-hospital and 30-day mortality in heart failure patients: a multicenter cohort study. Sci Rep. 2025;15(1):9911.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan C, et al. The Stress Hyperglycemia Ratio is Associated with Hemorrhagic Transformation in Patients with Acute Ischemic Stroke. Clin Interv Aging. 2021;16:431\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens SL, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354:i4098.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou JJ, Nuyujukian DS, Reaven PD. New Insights into the Role of Visit-to-Visit Glycemic Variability and Blood Pressure Variability in Cardiovascular Disease Risk. Curr Cardiol Rep. 2021;23(4):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe HM, et al. Associations of variability in blood glucose and systolic blood pressure with mortality in patients with coronary artery disease: A retrospective cohort study from the MIMIC-IV database. Diabetes Res Clin Pract. 2024;209:111595.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiriac\u0026ograve; M, et al. Association between blood pressure variability, cardiovascular disease and mortality in type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab. 2019;21(12):2587\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim MK, et al. Associations of Variability in Blood Pressure, Glucose and Cholesterol Concentrations, and Body Mass Index With Mortality and Cardiovascular Outcomes in the General Population. Circulation. 2018;138(23):2627\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDostović Z et al. \u003cem\u003eStroke and disorders of consciousness.\u003c/em\u003e Cardiovasc Psychiatry Neurol, 2012. 2012: p. 429108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshida A, et al. Dynamic Interaction between Cortico-Brainstem Pathways during Training-Induced Recovery in Stroke Model Rats. J Neurosci. 2019;39(37):7306\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai W, et al. Association of glycemic variability with death and severe consciousness disturbance among critically ill patients with cerebrovascular disease: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Qian Y, Deng X. Triglyceride glucose index is a significant predictor of severe disturbance of consciousness and all-cause mortality in critical cerebrovascular disease patients. Cardiovasc Diabetol. 2023;22(1):156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang JY, et al. Visit-to-visit HbA1c and glucose variability and the risks of macrovascular and microvascular events in the general population. Sci Rep. 2019;9(1):1374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Z, et al. Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization. Front Public Health. 2022;10:857368.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira AP, Castro MDS, Lima DVM. Glycemic variability and mortality in oncologic intensive care units. Rev Bras Enferm. 2023;76(4):e20220812.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman RP, et al. Glycemic variability predicts inflammation in adolescents with type 1 diabetes. J Pediatr Endocrinol Metab. 2016;29(10):1129\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabtu BF, et al. Assessments of coagulation profile among good glycemic control and poor glycemic control type 2 diabetic patient attending at Wolkite University specialized hospital, Central Ethiopia: a comparative study. BMC Endocr Disord. 2024;24(1):204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong L, et al. Interdialytic home systolic blood pressure variability increases all-cause mortality in hemodialysis patients. Clin Cardiol. 2024;47(4):e24259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoo EH, et al. Visit-to-visit blood pressure variability and mortality and cardiovascular outcomes after acute myocardial infarction. J Hum Hypertens. 2022;36(11):960\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Flaviani A, et al. Impact of glycemic and blood pressure variability on surrogate measures of cardiovascular outcomes in type 2 diabetic patients. Diabetes Care. 2011;34(7):1605\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSezer H, et al. The relationship between glycemic variability and blood pressure variability in normoglycemic normotensive individuals. Blood Press Monit. 2021;26(2):102\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ischemic stroke, subarachnoid hemorrhage, intracerebral hemorrhage, systolic blood pressure variability, glycemic variability, mortality","lastPublishedDoi":"10.21203/rs.3.rs-6772731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6772731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCritically ill patients with stroke remains a major public health issue worldwide. Glycemic variability (GV) and systolic blood pressure variability (SBPV) are recognized as independent predictors of cardiovascular and cerebrovascular disease outcomes. This study aimed to explore the associations between GV and SBPV levels on the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance in different types of stroke patients including ischemic stroke (IS), subarachnoid hemorrhage (SAH), and intracerebral hemorrhage (ICH), and to further examine whether GV and SBPV exhibit interactive effects on these clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData of this retrospective cohort study were extracted from the Medical Information Market for Intensive Care (MIMIC-IV) database. Severe consciousness disturbance was defined as Glasgow Coma Scale (GCS) points \u0026lt;8. Univariate and multivariate Cox proportional hazard models, logistics regression models, Kaplan-Meier (KM) analysis and restricted cubic splines (RCS) analysis were utilized to explore the associations of GV, SBPV, and their interactive effects on the risk of in-hospital mortality, 1-year mortality and severe consciousness disturbance, with hazard ratio (HR), odd ratio (OR) and 95% confidence interval (CI). Subgroup analyses were conducted based on different types of strokes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eTotally, 1,540 eligible patients were included, among them, 323 occurred in-hospital death, 523 occurred 1-year mortality and 463 occurred severe consciousness disturbance within 30-day after admission. High GV and high SBPV levels were associated with high in-hospital mortality risk (SBPV, HR=1.92, 95%CI: 1.41-2.59; GV, HR=1.84, 95%CI: 1.34-2.54) and high 1-year mortality risk (SBPV, HR=1.61, 95%CI: 1.24-2.08; GV, HR=1.57, 95%CI: 1.20-1.01). No significant associations were found between GV and SBPV with the risk of severe consciousness disturbance. High GV and high SBPV were exhibited a potential interactive effect on the risk of these outcomes we interested. We further observed that GV and SBPV demonstrated a significant interaction effect on the risk of in-hospital mortality (HR=and 6.80, 95%CI: 2.88-16.05, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and 1-year mortality (HR=5.10, 95%CI: 2.54-10.27, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) among critically ill patients with ICH. The results of Kaplan-Meier analysis and RCS analysis demonstrated trends consistent with those observed in the multivariable Cox proportional hazard models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur study suggested that in critically ill patients with IS, SAH and ICH, higher GV and SBPV levels were associated with higher risk of mortality. Furthermore, GV and SBPV exhibit interactive effects on both clinical prognosis and the development of severe consciousness disturbance in our study population. GV and SBPV can support doctors in identifying patients with high risk of mortality and making timely clinical decisions.\u003c/p\u003e","manuscriptTitle":"Association of variability of blood pressure and glycemic with the risk of mortality and severe consciousness disturbance among critically ill patients with ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage: a retrospective cohort study from MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:10:23","doi":"10.21203/rs.3.rs-6772731/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T17:25:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T17:12:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206862452864224610415724047521558320166","date":"2025-09-26T10:59:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229861835706574517550330083497675745528","date":"2025-09-10T12:05:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T08:36:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228707419973454834473839580687397313900","date":"2025-06-21T19:55:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T18:14:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-05T12:52:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-30T14:08:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-30T14:01:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-05-29T04:44:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dcdde18-d3dc-4ca6-922f-8455d0c4262d","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T19:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:10:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6772731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6772731","identity":"rs-6772731","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}