Admission Pulse Pressure and in-hospital mortality in Type A Acute Aortic Dissection- Result from a Chinese study

Admission Pulse Pressure and in-hospital mortality in Type A Acute Aortic Dissection- Result from a Chinese study | 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 Admission Pulse Pressure and in-hospital mortality in Type A Acute Aortic Dissection- Result from a Chinese study Yuxin Liu, Liyuan Wang, Shijie Zhang, Jinzhang Li, Yuqi Cui, Yan Yun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4588632/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 9 You are reading this latest preprint version Abstract Objectives: In recent years, several epidemiologic studies have shown that pulse pressure (PP) is a powerful predictor of mortality from many cardiovascular diseases. However, few studies have reported the association between PP and adverse events during hospitalization in patients with type A acute aortic dissection (TAAAD). The aim of this study was to evaluate the relationship between admission PP and in-hospital all-cause mortality, in patients with TAAAD. Methods: Patients with TAAAD admitted from January 2015 to December 2021 were included and divided into four groups according to the PP values measured at the time of admission: reduced group (PP ≤ 40 mmHg), normal group (40 < PP ≤ 56 mmHg), mildly elevated group (56 75 mmHg). A multivariate binary logistic regression model was constructed, plotted using nomogram and evaluated with ROC curve. Results: Admission PP and in-hospital all-cause mortality showed a "J-curve" correlation and in-hospital all-cause mortality was significantly higher in the significantly elevated group and reduced group (P = 0.003), respectively. Multivariate binary logistic regression analysis showed that significantly elevated PP (PP > 75 mmHg) (P<0.001) and reduced PP (P = 0.043), D-dimer (P<0.001), ascending aortic diameter (P = 0.037), Abdominal visceral vessels involved (P = 0.019), and coronary atherosclerosis (P = 0.017) and emergent surgery (P < 0.001) were independent predictive factors for in-hospital all-cause mortality. The AUC of ROC plotted was 0.825 (95% CI, 0.780–0.870). Conclusions: Our findings demonstrated a "J-curve" association of admission PP with in-hospital all-cause mortality in TAAAD. Significantly elevated and reduced admission PP, D-dimer, ascending aortic diameter and coronary atherosclerosis were independent risk factors for in-hospital all-cause mortality in patients with TAAAD, and emergent surgery was a protective factor. Aortic dissection Pulse pressure J-curve In-hospital all-cause mortality Multivariate binary logistic regression analysis ROC curve Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Type A Acute Aortic Dissection (TAAAD) is widely accepted as an devastating aortic pathology in which aortic lesion involves the ascending aorta. For untreated TAAAD mortality increases to 50% during first 24 hours and emergent surgical intervention is indicated by principle for TAAAD for the purpose of timely repair and reconstruction of the impaired aorta. For decades cardiovascular surgeons across the globe have made the painstaking efforts for optimizing the surgical procedures. Although the short- and long-term outcomes of TAAAD have markedly improved worldwide, mortality remains high and is reported between 15% and 30%. Blood pressure control is the highest priority in the treatment of TAAAD. A retrospective study 【1】 that included 6,238 AAD patients registered in the International Registry of Acute Aortic Dissection (IRAD) from 1999 to 2016 analyzed the relationship between admission systolic blood pressure (SBP) and mortality during hospitalization. The results showed a significant inverse "J-curve" relationship between admission SBP and in-hospital mortality in patients with TAAAD, and a SBP of less than 80 mmHg was an independent risk factor for in-hospital mortality. A similar single-center retrospective study analyzed the relationship between admission SBP and in-hospital mortality in patients with AAD in China 【2】 . The results showed a non-linear correlation between admission SBP and in-hospital mortality in patients with AAD, and a negative correlation between SBP and in-hospital mortality when SBP was less than 120 mmHg. Systolic and diastolic blood pressure represent the two extremes of blood pressure fluctuations, and the difference between systolic and diastolic blood pressure is defined as pulse pressure (PP), which reflects the magnitude of this fluctuation in a cardiac cycle. In different patient populations, wide PP has been associated with adverse cardiovascular events and all-cause mortality 【3–6】 . Low admission PP is an independent predictor of mortality in patients with acute coronary syndrome 【7】 . However, few studies have reported on the relationship between admission PP and adverse events during hospitalization in patients with TAAAD. In this study, we retrospectively analyzed the clinical data of 488 TAAAD patients, with the aim of evaluating the correlation between admission PP and all-cause in-hospital mortality, in TAAAD patients. Subjects and Methods Subjects In this study, patients with TAAAD admitted between January 1, 2015, and December 31, 2022, were retrieved through the electronic medical record system of Shandong Provincial Hospital. This study was performed under the supervision of the Ethics Committee of Shandong Provincial Hospital affiliated to First Medical University. And this study was a retrospective cohort study (observational). Informed consent was waived because the study was retrospective according to the Ethics Committee (Human Ethics and Consent to Participate declarations: not applicable). The diagnosis of TAAAD was based on the 2022 ACC/AHA guidelines on the treatment and diagnosis of aortic disease 【8】 According to the guidelines, any dissection involving the ascending aorta that occurs within 14 days of symptom onset was defined as TAAAD, and the definitive diagnosis of dissection relied on imaging tests such as computed tomography (CT) or magnetic resonance imaging (MRI). 488 patients with TAAAD were categorized into four groups based on the initial PP value measured at the time of patient admission: reduced (PP ≤ 40 mmHg), normal (40 < PP ≤ 56 mmHg), mildly elevated (56 75 mmHg) 【9】 . Ethical approval for the study was provided by the hospital institutional review board (NSFC2018-002). As the study was retrospective, informed consent of patients was waived. The study was performed following the Good Clinical Practice (GCP) and principles of the Declaration of Helsinki. Inclusion and exclusion criteria Inclusion criteria (1) Patients presenting to the hospital within ≤ 14 days of symptom onset; (2) TAAAD clearly diagnosed by imaging examinations such as CT or MRI; (3) Patients with complete baseline data (e.g., age, gender, and vital signs, etc.) and complete perioperative data. Exclusion criteria (1) important clinical data was missing; (2) variants of typical aortic dissection such as intermural hematoma, penetrating aortic ulcer, intimal tear without hematoma, medical or traumatic AD, and periaortic hematoma; (3) Symptoms lasting > 14 days. Data collection of the study population The clinical data of the patients included in this study were obtained by searching the electronic medical record system of Shandong Provincial Hospital. The data included: baseline data, past history, clinical manifestations, imaging and laboratory findings, intraoperative and postoperative conditions, and etc. Statistical analysis Continuous variables were tested for normality and were expressed as mean ± standard deviation (normal distribution) or median (25th percentile, 75th percentile) (skewed distribution). Categorical variables were expressed as number of cases (percentage). One-way analysis of variance (ANOVA) was applied for continuous variables that conformed to a normal distribution, and Kruskal-Wallis test was used for data with a skewed distribution. Chi-square or Fisher's exact test was used to analyze the categorical variables. The total population was divided into two groups: the death group and the survival group, based on whether or not a death event occurred during hospitalization. Initially, study variables were screened using univariate analysis. Then, a multivariate binary logistic regression model was applied, using the backward stepwise method to introduce variables with a P-value of less than 0.10 from the univariate analysis. Risk adjustment was performed to determine the correlation between admission PP and in-hospital all-cause mortality, as well as to identify independent predictors of in-hospital all-cause mortality. The obtained results were expressed as odds ratio (OR) with a 95% confidence interval (CI). A disease predictive model for in-hospital all-cause mortality was constructed and presented in the form of a nomogram. The discriminatory power of predictive model was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). An AUC value greater than 0.75 was considered to indicate good discriminatory power. The model's calibration was evaluated using the Hosmer-Lemeshow test of goodness-of-fit. A P-value greater than 0.05 would suggest a better fit of the model. All statistical tests in this study were performed using two-sided tests, with P < 0.05 indicating a statistically significant difference. All statistical analyses were performed using SPSS Statistics 26.0 and RStudio 4.2.2 analysis software. Results Characteristics of patients Inclusion of patients From January 2015 to December 2021, 488 patients were recruited consecutively with TAAAD. 8 subjects were excluded because of diagnosis of intermural hematoma of the aorta (1 cases), missing clinical data (2 cases) and an onset of more than 14 days (5 cases) (Fig. 1 ). Detailed information of patients Demographics and past history The mean age of enrolled patients was 51.50 ± 11.01 years, and a total of 124 (25.41%) patients were older than 60 years. 344 (70.49%) were males and 144 (29.51%) were females. 107 patients were treated conservatively and 381 were treated surgically, and 100 (20.49%) died in hospital, of which 52 (48.60%) died after conservative treatment and 48 (12.60%) died after surgical treatment. As shown in Table 1 , the difference between the four groups of patients was statistically significant in terms of variables such as age and aortic aneurysm (P < 0.05). In the reduced group, the patients were older (55.52 ± 9.85 vs. 51.43 ± 10.59 vs. 50.15 ± 11.70 vs. 50.43 ± 10.77, P < 0.05), and the proportion of aortic aneurysms was greater [29 (34.52%) vs. 20 (16.13%) vs. 18 (11.54%) vs. 20 (16.95%). P < 0.001]. Table 1 Comparing clinical baseline data of four groups PP < 40mmHg 40 < PP < 56 mmHg 56 < PP 75mmHg Pvalue Number of cases, n 86 125 156 121 Age (yrs) 55.52 ± 9.85 51.43 ± 10.59 50.15 ± 11.70 50.43 ± 10.77 0.002 Male, n (%) 61 (70.93) 85 (68.00) 110 (70.51) 88 (72.73) 0.880 Age > 60years old, n (%) 32 (37.21) 32 (24.80) 33 (21.15) 29 (23.97) 0.480 Smoking, n (%) 37 (44.05) 45 (36.29) 65 (41.94) 42 (35.59) 0.494 Drinking, n (%) 27 (32.14) 33 (26.61) 41 (26.45) 41 (34.75) 0.387 Hypertension, n (%) 59 (70.24) 96 (76.80) 112 (71.79) 91 (76.47) 0.594 Diabetes, n (%) 4 (4.76) 4 (3.23) 7 (4.49) 6 (5.08) 0.903 Marfan syndrome, n (%) 4 (4.76) 3 (2.42) 8 (5.13) 5 (4.24) 0.708 Atherosclerosis, n (%) 35 (41.76) 57 (45.60) 52 (33.33) 43 (36.44) 0.174 Aortic aneurysm, n (%) 29 (34.52) 20 (16.13) 18 (11.54) 20 (16.95) < 0.001 Previous aortic dissection, n (%) 2 (2.38) 4 (3.23) 4 (2.56) 4 (3.39) 0.962 History of cardiac surgery, n (%) 3 (3.57) 4 (3.23) 3 (1.92) 3 (2.54) 0.863 Systolic blood pressure (mmHg) 109.28 ± 20.00 126.34 ± 14.91 140.21 ± 17.03 166.28 ± 23.23 < 0.001 Diastolic blood pressure (mmHg) 75.50 ± 18.07 77.50 ± 14.05 75.54 ± 16.14 78.40 ± 20.50 0.342 Pulse pressure (mmHg) 33.78 ± 4.91 48.84 ± 4.32 64.67 ± 5.00 87.88 ± 11.78 < 0.001 Mean arterial pressure (mmHg) 86.76 ± 18.58 93.78 ± 14.20 97.09 ± 16.27 107.70 ± 20.71 < 0.001 Chest pain, n (%) 62 (73.81) 98 (78.40) 121 (77.56) 97 (80.17) 0.754 Back pain, n (%) 34 (40.48) 57 (45.60) 70 (44.87) 68 (56.20) 0.115 Abdominal pain, n (%) 16 (19.05) 46 (36.80) 51 (32.69) 35 (28.93) 0.045 Syncope, n (%) 19 (22.62) 11 (8.80) 6 (3.85) 7 (5.79) < 0.001 Limb ischemia, n (%) 12 (14.81) 16 (13.01) 17 (10.97) 25 (21.19) 0.113 Ultrasound and imaging examination Bicuspid aortic valve, n (%) 6 (7.79) 0 (0) 10 ( 6.71) 1 (0.85) 0.002 Aortic valve insufficiency, n (%) 43 (55.13) 66 (54.55) 83 (55.70) 70 (60.87) 0.758 Pericardial effusion, n (%) 46 (58.23) 54 (45.00) 59 (39.86) 37 (32.17) 0.003 Pleural effusion, n (%) 29 (46.03) 34 (34.00) 39 (34.21) 25 (28.09) 0.150 Coronary artery involved, n (%) 8 (12.50) 7 (6.19) 10 (7.35) 9 (8.49) 0.506 Abdominal visceral vessels involved, n (%) 34 (40.35) 72 (58.00) 74 (47.32) 75 (61.84) 0.038 Three branch vessel involved, n (%) 49 (57.35) 65 (51.79) 104 (66.43) 87 (71.96) < 0.001 Iliac vessels involved, n (%) 24 (27.59) 51 (41.00) 52 (33.33) 64 (53.33) < 0.001 LVEF (%) 58.81 ± 5.56 59.73 ± 4.89 59.17 ± 5.25 59.42 ± 3.57 0.289 Ascending aorta diameter (cm) 5.35 ± 1.26 4.79 ± 1.24 4.73 ± 1.00 4.75 ± 0.98 0.001 Laboratory tests D-dimer (mg/L) 5.91 (1.99,11.63) 5.63 (2.24,12.51) 7.06 (2.47,13.83) 7.74 (2.95,17.33) 0.274 Cr (umol/L) 107.11 ± 66.06 103.47 ± 68.36 101.99 ± 62.73 112.61 ± 84.82 0.513 TG (mmol/L) 1.54 (0.89,2.09) 1.36 (0.96,1.90) 1.30 (0.94,1.92) 1.43 (0.96,2.25) 0.484 TC (mmol/L) 3.31 ± 1.05 3.55 ± 1.10 3.52 ± 1.10 3.78 ± 1.23 0.063 HDL-C (mmol/L) 1.08 ± 0.34 1.02 ± 0.33 1.03 ± 0.35 1.07 ± 0.45 0.822 LDL-C (mmol/L) 2.00 ± 0.83 2.12 ± 0.74 2.16 ± 0.78 2.27 ± 0.82 0.152 DBIL (umol/L) 5.40 (3.45,8.36) 4.50 (2.94,7.52) 5.07 (3.20,8.00) 4.57 (3.01,6.75) 0.113 IBIL (umol/L) 16.72 (11.98,21.23) 15.00 (9.70,22.42) 15.87 (11.50,23.06) 15.70 (10.60,20.37) 0.405 TBIL (umol/L) 23.10 (15.70,29.12) 19.80 (12.48,29.43) 22.27 (14.90,30.30) 20.20 (13.29,27.73) 0.320 HS-TnT (pg/ml) 51.69(14.23,322.48) 22.08 (7.39,283.45) 17.17 (8.39,281.66) 29.46 (10.77,239.93) 0.058 CK-MB (ng/ml) 5.64 (1.11,13.63) 3.05 (1.21,10.79) 2.80 (1.31,10.39) 3.78 (1.61,12.01) 0.260 MYO (ng/ml) 82.93 (21.35,452.61) 64.04 (25.62,427.96) 60.70 (24.67,412.05) 121.80 (38.07,427.96) 0.232 Normally distributed data are presented as the mean ± SD; non-normally distributed data are presented as median (IQR), and categorical variables are presented as n (%). P values were calculated based on t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher’s exact test for categorical variables. PP: pulse pressure; LVEF: left ventricular ejection fraction; Cr: creatinine; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoproteincholesterol; LDL-C: low-density lipoprotein cholesterol; DBIL: direct bilirubin; IBIL: indirect bilirubin; TBIL: total bilirubin; HS-TnT: hypersensitive troponin; CK-MB: creatine kinase-MB; MYO: myohemoglobin. Clinical manifestations Table 1 also showed the differences of clinical manifestation of four groups including abdominal pain [(19.05%) vs. 46 (36.80%) vs. 51 (32.69%) vs. 35 (28.93%), P = 0.045] and syncope [19 (22.62%) vs. 11 (8.80%) vs. 6 (3.85%) vs. 7 (5.79%), P < 0.001]. Preoperative ultrasound and imaging examination and lab tests Preoperative ultrasound and imaging examination and lab tests were summarized in Table 1 . The four groups were statistically significant in terms of bicuspid aortic valve (P = 0.002), pericardial effusion (P = 0.003), diameter of ascending aorta (P = 0.001), involvement of abdominal visceral arteries (P = 0.038), three branches of the arch (P < 0.001), and iliac arteries (P < 0.001). In the reduced group (PP ≤ 40 mmHg), the percentages of bicuspid aortic valve (7.79%) and pericardial effusion (58.23%) were highest, and mean diameter of ascending aorta (5.35 ± 1.26 cm) was largest. In the significantly elevated group (PP > 75 mmHg), involvement of abdominal visceral arteries (61.84%), aortic arch three-branches (71.96%), and iliac arteries (53.33%) were more common. Admission pulse pressure and in-hospital all-cause mortality and other complications As shown in Fig. 2 and Table 2 , PP and in-hospital all-cause mortality showed a "J-curve" correlation, with higher rates in the significantly elevated group and reduced group, which were 27.91% and 28.93%, respectively (P = 0.003); and rates in the normal group and mildly elevated group were 12.80% and 16.03%, respectively. Besides, the four groups showed significant differences in terms of cerebral infarction (P = 0.032), hepatic insufficiency (P = 0.039) and renal insufficiency (P = 0.020). Table 2 Treatment modalities and complications PP < 40mmHg 40 < PP < 56 mmHg 56 < PP 75mmHg P value Treatment Drug, n (%) 29 (33.72) 26 (20.80) 29 (18.59) 23 (19.01) 0.034 Emergent surgery, n (%) 57 (66.28) 99 (79.20) 127 (81.41) 98 (80.99) Aortic occlusion (min) 114.58 ± 37.07 111.44 ± 35.19 108.42 ± 26.72 113.49 ± 35.45 0.699 CPB (min) 209.63 ± 60.99 206.25 ± 63.08 207.87 ± 49.54 215.35 ± 62.68 0.570 Complications Cerebral infarction, n (%) 9 (12.33) 10 (9.01) 21 (14.58) 26 (22.61) 0.032 Hepatic insufficiency, n (%) 4 (5.48) 11 (9.91) 15 (10.42) 21 (18.42) 0.039 Renal insufficiency, n (%) 9 (12.33) 15 (13.27) 25 (17.36) 31 (27.19) 0.020 In-hospital all-cause mortality, n (%) 24 (27.91) 16 (12.80) 25 (16.03) 35 (28.93) 0.003 Normally distributed data are presented as the mean ± SD; non-normally distributed data are presented as median (IQR), and categorical variables are presented as n (%). P values were calculated based on t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher’s exact test for categorical variables. PP: pulse pressure; CPB: cardiopulmonary bypass. Risk factor screening and predictive modeling for in-hospital all-cause mortality Univariate logistic analysis Emergent surgical treatment was negatively associated with the in-hospital all-cause mortality (0.15, 0.09–0.25). In contrast, age (1.03, 1.01–1.05), age > 60 years (2.03, 1.27–3.25), PP (1.27, 1.04–1.55), coronary atherosclerosis (1.77, 1.10–2.73), syncope (2.74, 1.43–5.26), myocardial infarction/ischemia (4.16, 2.51–6.90), limb ischemia (2.10, 1.18–3.72), ascending aortic diameter (1.23, 1.01–1.50), coronary involvement (2.40, 1.09–5.28), abdominal visceral involvement (2.07, 1.11–3.87), triple-branch vessel involvement (2.28, 1.26–4.14), D-dimer (1.07, 1.04–1.10), Cr (1.01, 1.00-1.01 ), TG (1.41, 1.19–1.67), HS-TnT (1.00, 1.00–1.00), CK-MB (1.01, 1.00-1.02), cerebral infarction (5.25, 2.96–9.29), hepatic insufficiency (5.13, 2.75–9.55) and renal insufficiency (10.39, 5.93–18.20 ) were positively correlated with the in-hospital all-cause mortality (P < 0.05) (Table 3 ). Table 3 Univariate logistic analysis for in-hospital all-cause mortality OR value 95% Confidence interval P value Demographic information and medical history Age 1.03 1.01–1.05 0.012 Male 1.10 0.67–179 0.711 Age>60years 2.03 1.27–3.25 0.003 Smoking 1.15 0.74 ~ 1.80 0.542 Drinking 1.21 0.76–1.94 0.420 Hypertension 1.14 1.69–1.91 0.605 Diabetes 1.90 0.79–4.53 0.149 Marfan syndrome 1.03 0.34–3.17 0.954 Atherosclerosis 1.77 1.14–2.76 0.011 Aortic aneurysm 1.38 0.81–2.35 0.236 Aortic dissection 0.29 0.04–2.25 0.237 History of cardiac surgery 2.00 0.73–5.47 0.177 Clinical manifestations PP 1.27 1.04–1.55 0.022 Mean arterial pressure 1.00 0.99–1.01 0.656 Systolic blood pressure 1.00 0.99–1.01 0.654 Diastolic blood pressure 0.99 0.98–1.01 0.280 Chest pain 1.19 0.69–2.05 0.530 Back pain 1.28 0.82–1.99 0.275 Abdominal pain 0.72 0.44–1.19 0.195 Syncope 2.74 1.43–5.26 0.002 Myocardial ischemia 4.16 2.51–6.90 < 0.001 Heart failure 1.90 0.92–3.91 0.082 Ultrasound and imaging examination LVEF 0.96 0.92–0.99 0.042 Limb ischemia 1.95 1.11–3.42 0.019 Bicuspid aortic valve 2.64 1.11–3.42 0.028 Aortic valve insufficiency 1.45 0.92–2.28 0.112 Pericardial effusion 1.18 0.76–1.83 0.472 Ascending aorta diameter 1.22 1.02–1.47 0.03 Pleura effusion 1.04 0.66–1.66 0.854 Coronary artery involved 2.19 1.15–4.20 0.018 Abdominal visceral vessels involved 1.81 1.14–2.88 0.012 Three branch vessels involved 1.51 0.95–2.42 0.084 Iliac vessel involvement 1.56 1.01–2.43 0.047 Laboratory tests D-dimer 1.07 1.04–1.10 < 0.001 Cr 1.01 1.00-1.01 < 0.001 TG 1.41 1.19–1.67 < 0.001 TC 0.84 0.67–1.05 0.116 HDL 0.74 0.36–1.51 0.412 LDL 0.83 0.60–1.15 0.258 DBIL 1.02 0.99–1.04 0.234 IBIL 1.02 1.00-1.04 0.096 TBIL 1.01 1.00-1.02 0.115 TT 1.00 1.00–1.00 0.017 CKMB 1.01 1.00-1.02 0.018 MYO 1.00 1.00–1.00 0.298 Treatment modalities and complications Emergent surgery 0.15 0.09–0.25 < 0.001 Cerebral infarction 5.25 2.96–9.29 < 0.001 Hepatic insufficiency 5.13 2.75–9.55 < 0.001 Renal insufficiency 10.39 5.93–18.20 < 0.001 Study variables were screened using univariate analysis. OR value: odds ratio value; PP: pulse pressure; LVEF: left ventricular ejection fraction; Cr: creatinine; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoproteincholesterol; LDL-C: low-density lipoprotein cholesterol; DBIL: direct bilirubin; IBIL: indirect bilirubin; TBIL: total bilirubin; HS-TnT: hypersensitive troponin; TT: thrombin time; CK-MB: creatine kinase-MB; MYO: myohemoglobi Multivariate logistic analysis The multivariate binary logistic regression analysis (Table 4 ) showed that significantly elevated PP (PP > 75 mmHg) (P<0.001) and reduced PP (P = 0.043), D-dimer (P<0.001), ascending aortic diameter (P = 0.037), abdominal visceral vessels involved (P = 0.019), and coronary atherosclerosis (P = 0.017) and emergent surgery (P < 0.001) were independent predictive factors for in-hospital all-cause mortality. After plotting the ROC curve, and the AUC of the curve was 0.827 (95% CI: 0.774–0.880, P<0.001) and the Yoden index of this ROC curve was 0.227, which corresponded to a sensitivity and specificity of 78.7% and 75.3%, respectively (Fig. 3 ). The Hosmer-Lemeshow test of goodness-of-fit was χ = 2.285, P = 0.971>0.05. Establishment of nomogram Table 4 Multivariate logistic analysis for in-hospital all-cause mortality Variables β S.E Z P OR (95%CI) Ascending aortic diameter 0.21 0.11 1.89 0.058 1.24 (0.99–1.54) D-dimer 0.06 0.02 4.09 < .001 1.07 (1.03–1.10) Pulse Pressure Rating 2 1.00 (Reference) 1 1.19 0.43 2.79 0.005 3.28 (1.42–7.57) 3 0.39 0.42 0.93 0.352 1.48 (0.65–3.41) 4 1.20 0.42 2.89 0.004 3.33 (1.47–7.54) Coronary atherosclerosis 0 1.00 (Reference) 1 -0.52 0.27 -1.94 0.053 0.60 (0.35–1.01) Emergent surgery 1 1.00 (Reference) 0 2.00 0.30 6.78 < .001 7.39 (4.14–13.18) Abdominal visceral vessels involved 0 1.00 (Reference) 1 0.66 0.28 2.35 0.019 1.93 (1.11–3.34) A multivariate binary logistic regression model was applied, using the backward stepwise method to introduce variables with a P-value of less than 0.10 from the univariate analysis. S.E.: standard error; OR: odds ratio. Subsequently, column line plots were drawn to visualize the results of the prediction model. As shown in Fig. 4 , a total of six predictor variables were involved in the composition of the column line plot. Based on the level of influence of each predictor variable in the model, i.e., the magnitude of the regression coefficients, a score was assigned to the level of value of each variable, thus obtaining six individual scores, which were then summed up to obtain the total score. Finally, the predicted probability of in-hospital all-cause mortality of TAAAD patients was calculated by the functional transformation relationship between the total score and the probability of death event. The higher the total score, the higher the odds of in-hospital all-cause mortality. The calibration plot showed that the calibration curve (Apparent line) closely matched the diagonal (Ideal line), indicating good calibration of the predictive model. Additionally, internal validation of the predictive model using bootstrap resampling showed that the corrected curve (Bias-corrected line) almost overlapped with the diagonal, suggesting good accuracy of the nomogram model (Fig. 5 ). Finally, a DCA curve was drawn based on the constructed CPM. The horizontal axis represented the risk threshold, while the vertical axis showed the net benefit (NB) after accounting for benefits and harms. The DCA results indicated that the risk assessment model provided net benefit for patients within a threshold range of 0.01–0.72 (Fig. 6 ). Discussion In this study, we retrospectively analyzed the clinical data of 488 patients with TAAAD and there was a "J-shape" relationship between admission PP and in-hospital all-cause mortality. The mortality in the normal group (40 < PP ≤ 56 mmHg) was the lowest (12.80%), and in the elevated group (56 75 mmHg) the mortality increased with the increase of admission PP, which was 16.03% and 28.93%, respectively. When the PP value was lower than normal (PP ≤ 40 mmHg), the mortality increased inversely (27.91%). The "J-curve" phenomenon between blood pressure and cardiovascular outcomes is mainly observed in diastolic blood pressure 【10】 . In the treatment of hypertensive patients, the World Health Organization (WHO) recommends lowering blood pressure below 140/90 mmHg. However, numerous clinical studies and epidemiologic investigations have shown that hypertensive patients have a higher risk of adverse cardiovascular events than normal subjects, even after antihypertensive treatment and correction of other risk factors. At the same time, with the emergence of substantial clinical evidence of the benefits of blood pressure lowering, many scholars believe that a target blood pressure of 140/90 mmHg is no longer sufficient to achieve maximum benefit. As a result, the idea of "the lower the better" has become central to some physicians' approach to treating hypertension. However, a large number of studies have confirmed that when blood pressure, especially diastolic blood pressure (DBP), is lowered to a certain level, the risk of cardiovascular events increases, i.e., the phenomenon of the "J-curve" is generated. Stewart et al. reported for the first time that the relationship between DBP and myocardial infarction exists between DBP and myocardial infarction in patients with severe hypertension with a DBP of < 90 mmHg who receive antihypertensive treatment 【11】 . The study found that the incidence of myocardial infarction was more than five times higher in those whose diastolic blood pressure was reduced to less than 90 mmHg than in those whose diastolic blood pressure was in the range of 100–109 mmHg. This phenomenon has also been confirmed in studies published by Cruickshank and Farnett et al 【12–13】 . All these studies describe an interesting phenomenon, i.e., the incidence of myocardial infarction is increased when diastolic blood pressure falls below a certain level during antihypertensive therapy. The reason for this phenomenon might be that lower diastolic blood pressure reduces myocardial perfusion, which in turn increases the chance of myocardial infarction. Recent studies have found that systolic blood pressure < 130 mmHg or < 120 mmHg is also associated with cardiovascular outcomes and mortality in coronary artery disease, hypertension, or diabetes mellitus in a "J-curve" fashion. However, none of these studies reported a "J-curve" effect of blood pressure on stroke. McEvoy et al 【14】 further showed that in adults with systolic BP ≥ 120 mmHg, elevated PP and low diastolic BP were associated with subclinical myocardial injury and adverse coronary events. When antihypertensive therapy is administered to a systolic BP of less than 140 mmHg, care should be taken to ensure that diastolic BP levels do not fall below 70 mmHg, and in particular do not fall below 60 mmHg. As mentioned earlier, Eduardo et al 【1】 found a significant inverse "J-curve" relationship between admission systolic blood pressure and in-hospital mortality in patients with TAAAD. This study was the first to extend this specific paradigm of association between blood pressure and cardiovascular disease to AAD. In the present study, We for the first time demonstrated the "J-shaped" association between admission PP and in-hospital all-cause mortality in TAAAD. The reason for this "J-shaped" association might be related to the high "co-morbidity" rate in the group with significantly elevated PP and the group with reduced PP. In the group with significantly elevated PP, the proportions of involvement of three branches of the arch (71.96%), abdominal arteries (61.84%), and iliac arteries (53.33%) were significantly higher than those of the other three groups, and the proportions of cerebral infarctions (22.61%), hepatic insufficiency (18.42%), and renal insufficiency (27.19%) were also the highest in the group with significantly elevated PP. These complications, which were closely related to AAD, might led to fatal events. In the reduced group, the proportions of TAAAD patients with myocardial infarction/ischemia (25.58%) and pericardial effusion (58.23%) were significantly higher than those in the other three groups. In addition to this, the reduced group had the lowest percentage of patients undergoing surgical treatment (66.28%), which might also be an important rationale for its high mortality rate. After adjusting for confounders, multivariate binary logistic regression analysis showed that significantly elevated PP and reduced PP were independent risk factors for in-hospital all-cause mortality in patients with TAAAD. The causes of increased PP could be categorized as physiological and pathological. Common physiologic factors include increasing age, pregnancy and athletes. Ageing (> 55 years) is accompanied by aging of blood vessels, which could be manifested as a rise in vascular stiffness and a decrease in vascular elasticity and ultimately results in an increase in PP. Common pathologic factors contributing to elevated PP include aortic valve insufficiency (AI), atherosclerosis, hyperthyroidism, arteriovenous fistulae, and certain congenital heart diseases. In addition to this, some studies have found that genetic factors could contribute to elevated PP which might be a trait regulated by multiple genes 【15–18】 . The Framingham Heart Study showed that for every 10 mmHg increase in PP, there was a 23% increase in the risk of coronary heart disease 【19】 . The Multiple Risk Factor Intervention Trial (MRFIT) enrolled more than 340,000 men aged 35–57 years without prior diabetes or coronary heart disease and, during 22 years of follow-up, explored the relationship between PP and cardiovascular mortality 【20】 . The results showed that PP was independently associated with this risk of cardiovascular mortality, especially in older men (45–57 years). Mitchell et al 【21】 found that after adjusting for age, sex, baseline and time-dependent changes in mean arterial pressure, as well as common risk factors for atrial fibrillation (body mass index, smoking, valvular disease, diabetes mellitus, left ventricular hypertrophy, hypertension treatment, myocardial infarction, and heart failure), PP increase was associated with an increased risk of atrial fibrillation. In addition to cardiovascular diseases, PP has been strongly associated with progression of diabetes mellitus 【22–23】 and chronic kidney disease 【24–27】 . In the pharmacological treatment of aortic dissection, the current guideline recommendation is to control systolic blood pressure at 100–120 mmHg and heart rate at 60–80 beats/min, which does not sufficiently emphasize PP. This study found a "J-shaped" relationship between PP and the risk of in-hospital all-cause death in patients with TAAAD, and suggested that a PP > 75 mmHg or PP < 40mmHg is an independent risk factor for in-hospital death. Therefore, we suggested that appropriate antihypertensive medications should be chosen to lower PP while prescribing pharmacological treatment for aortic dissection. However, the traditional evaluation of effect of antihypertensive drugs relies on the detection of systolic as well as diastolic blood pressure, mainly systolic. And studies on the effects of different antihypertensive drugs on PP are extremely limited 【28】 . Further prospective studies are needed to determine whether PP could be used as a determining factor in the selection of antihypertensive drugs or as an indicator of the effectiveness of treatment. Although the techniques of surgical treatment of aortic dissection have been improved in recent years, its mortality rate is still at a relatively high level. The occurrence of in-hospital mortality events in patients with TAAAD is often the result of a combination of clinical factors. Therefore, on the basis of confirming the "J-type" correlation between PP and in-hospital mortality of TAAAD patients, the present study further performed a multivariate binary logistic regression on the clinical data, and the results showed that D-dimer, ascending aortic diameter, abdominal visceral vessels involved and coronary atherosclerosis were also independent predictive factors for in-hospital all-cause mortality, while surgery was a protective factor. Currently, there is no sufficient evidence on whether PP elevation increases the incidence of aortic dissection. PP increase might cause intimal damage and elastic fiber rupture, the initial event of aortic dissection. In the present study, the proportion of three-branch vessel involvement in the arch, abdominal vessel involvement, and iliac vessel involvement was significantly higher in the group with significantly elevated PP than in the other three groups. The group with significantly higher PP also had the highest rates of cerebral infarction, hepatic insufficiency, and renal insufficiency during hospitalization. The higher the PP, the more pronounced the pressure fluctuation, the greater the impact and damage to the artery wall, which could lead to an increase in the size and extent of the entrapment tear. Aortic branches could become poorly perfused due to the entrapment, which in turn affects the blood supply to the organs, leading to organ insufficiency and ultimately increasing the incidence of early fatal events 【29–31】 . On the contrary, PP reduction is more like a manifestation of myocardial dysfunction and pericardial tamponade. In support of this hypothesis, syncope, myocardial ischemia/infarction, and pericardial effusion were found to be more common in patients with reduced PP in this study. The AUC of the ROC curve constructed in this study was 0.827 (95% CI: 0.774–0.880, P 0.75, which indicated that the prediction model had a relatively good discriminatory degree. The result of the goodness-of-fit test was χ = 2.285, P = 0.971 > 0.05, which demonstrated that the model had a relatively good calibration. Limitations This study has some limitations. First, this study was a retrospective single-center study with inherent limitations of study design. Thus large, multicenter and prospective cohort studies are needed to validate our results and conclusions. Second, the PP values analyzed in this study were measured at a specific time point of admission to the hospital, whereas the PP could change dynamically. Third, some patients died before arriving at the hospital, thus altering the mortality rate of patients with very low/high PP. Conclusions Our results revealed a "J-curve" correlation between admission pulse pressure (PP) and in-hospital all-cause mortality in patients with type A acute aortic dissection (TAAAD). Elevated and reduced admission PP, along with D-dimer levels, ascending aortic diameter, and coronary atherosclerosis, were identified as independent risk factors for in-hospital all-cause mortality in TAAAD patients. Additionally, emergent surgery was found to be a protective factor. Large-scale, multicenter, and prospective cohort studies are required to confirm our findings and conclusions in the future. Declarations Funding This work was supported by the grants from the National Natural Science Foundation of China (81800255; 82201624), the Nature Science Foundation of Shandong Province (ZR2020MH044; ZR2021MH112; ZR2021QH016; ZR2023MH124; ZR2021QB122) and Jinan Science and Technology Plan Project (202225050). Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgement None. Human Ethics and Consent to Participate declarations Not applicable. References BOSSONE E, GORLA R, LABOUNTY T M, et al. Presenting Systolic Blood Pressure and Outcomes in Patients With Acute Aortic Dissection [J]. J Am Coll Cardiol, 2018, 71(13): 1432-1440. YANG G, PENG W, ZHOU Y, et al. Admission Systolic Blood Pressure and In-hospital Mortality in Acute Type A Aortic Dissection: A Retrospective Observational Study [J]. Front Med (Lausanne), 2021, 8(542212. MADHAVAN S, OOI W L, COHEN H, et al. Relation of pulse pressure and blood pressure reduction to the incidence of myocardial infarction [J]. Hypertension, 1994, 23(3): 395-401. DOMANSKI M, NORMAN J, WOLZ M, et al. Cardiovascular risk assessment using pulse pressure in the first national health and nutrition examination survey (NHANES I) [J]. Hypertension, 2001, 38(4): 793-797. SCHRAM M T, KOSTENSE P J, VAN DIJK R A, et al. Diabetes, pulse pressure and cardiovascular mortality: the Hoorn Study [J]. J Hypertens, 2002, 20(9): 1743-1751. PASTOR-BARRIUSO R, BANEGAS J R, DAMIáN J, et al. Systolic blood pressure, diastolic blood pressure, and pulse pressure: an evaluation of their joint effect on mortality [J]. Ann Intern Med, 2003, 139(9): 731-739 EL-MENYAR A, ZUBAID M, ALMAHMEED W, et al. Initial hospital pulse pressure and cardiovascular outcomes in acute coronary syndrome [J]. Arch Cardiovasc Dis, 2011, 104(8-9): 435-443 ISSELBACHER E M, PREVENTZA O, HAMILTON BLACK J, 3RD, et al. 2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines [J]. Circulation, 2022, 146(24): e334-e482. HOFF E, EAGLE T, PYERITZ R E, et al. Pulse pressure and type A acute aortic dissection in-hospital outcomes (from the International Registry of Acute Aortic Dissection) [J]. Am J Cardiol, 2014, 113(7): 1255-1259. MESSERLI F H, PANJRATH G S. The J-curve between blood pressure and coronary artery disease or essential hypertension: exactly how essential? [J]. J Am Coll Cardiol, 2009, 54(20): 1827-1834. STEWART I M. Relation of reduction in pressure to first myocardial infarction in patients receiving treatment for severe hypertension [J]. Lancet, 1979, 1(8121): 861-865. CRUICKSHANK J M, THORP J M, ZACHARIAS F J. Benefits and potential harm of lowering high blood pressure [J]. Lancet, 1987, 1(8533):581-584. FARNETT L, MULROW C D, LINN W D, et al. The J-curve phenomenon and the treatment of hypertension. Is there a point beyond which pressure reduction is dangerous[J]. Jama, 1991, 265(4): 489-495. MCEVOY J W, CHEN Y, RAWLINGS A, et al. Diastolic Blood Pressure, Subclinical Myocardial Damage, and Cardiac Events: Implications for Blood Pressure Control [J]. J Am Coll Cardiol, 2016, 68(16): 1713-1722. KATO N, TAKEUCHI F, TABARA Y, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians [J]. Nat Genet, 2011, 43(6): 531-538. WAIN L V, VERWOERT G C, O'REILLY P F, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure [J]. Nat Genet, 2011, 43(10): 1005-1011. BIELINSKI S J, LYNCH A I, MILLER M B, et al. Genome-wide linkage analysis for loci affecting pulse pressure: the Family Blood Pressure Program [J]. Hypertension, 2005, 46(6): 1286-1293. HSU F C, ZACCARO D J, LANGE L A, et al. The impact of pedigree structure on heritability estimates for pulse pressure in three studies [J]. Hum Hered, 2005, 60(2): 63-72. PETTERSSON-FERNHOLM K, FRöJDö S, FAGERUDD J, et al. The AT2 gene may have a gender-specific effect on kidney function and pulse pressure in type I diabetic patients [J]. Kidney Int, 2006, 69(10): 1880-1884. FRANKLIN S S, KHAN S A, WONG N D, et al. Is pulse pressure useful in predicting risk for coronary heart Disease? The Framingham heart study [J]. Circulation, 1999, 100(4): 354-360. DOMANSKI M, MITCHELL G, PFEFFER M, et al. Pulse pressure and cardiovascular disease-related mortality: follow-up study of the Multiple Risk Factor Intervention Trial (MRFIT) [J]. Jama, 2002, 287(20): 2677-2683 MITCHELL G F, VASAN R S, KEYES M J, et al. Pulse pressure and risk of new-onset atrial fibrillation [J]. Jama, 2007, 297(7): 709-715 YASUNO S, UESHIMA K, OBA K, et al. Is pulse pressure a predictor of new-onset diabetes in high-risk hypertensive patients?: a subanalysis of the Candesartan Antihypertensive Survival Evaluation in Japan (CASE-J) trial [J]. Diabetes Care, 2010, 33(5): 1122-1127. MULè G, NARDI E, COTTONE S, et al. Relationship of metabolic syndrome with pulse pressure in patients with essential hypertension [J]. Am J Hypertens, 2007, 20(2): 197-203. MUNTNER P, ANDERSON A, CHARLESTON J, et al. Hypertension awareness, treatment, and control in adults with CKD: results from the Chronic Renal Insufficiency Cohort (CRIC) Study [J]. Am J Kidney Dis, 2010, 55(3): 441-451. ARULKUMARAN N, DIWAKAR R, TAHIR Z, et al. Pulse pressure and progression of chronic kidney disease [J]. J Nephrol, 2010, 23(2): 189-193. TANAKA M, BABAZONO T, TAKEDA M, et al. Pulse pressure and chronic kidney disease in patients with type 2 diabetes [J]. Hypertens Res, 2006, 29(5): 345-352. GENG T T, TALAEI M, JAFAR T H, et al. Pulse Pressure and the Risk of End-Stage Renal Disease Among Chinese Adults in Singapore: The Singapore Chinese Health Study [J]. J Am Heart Assoc, 2019, 8(23): e013282. DOMANSKI M J, DAVIS B R, PFEFFER M A, et al. Isolated systolic hypertension : prognostic information provided by pulse pressure [J]. Hypertension, 1999, 34(3): 375-380 HAGAN P G, NIENABER C A, ISSELBACHER E M, et al. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease [J]. Jama, 2000, 283(7): 897-903. CAMBRIA R P, BREWSTER D C, GERTLER J, et al. Vascular complications associated with spontaneous aortic dissection [J]. J Vasc Surg, 1988, 7(2): 199-209. LAUTERBACH S R, CAMBRIA R P, BREWSTER D C, et al. Contemporary management of aortic branch compromise resulting from acute aortic dissection [J]. J Vasc Surg, 2001, 33(6): 1185-1192. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 24 Sep, 2024 Reviews received at journal 13 Aug, 2024 Reviews received at journal 07 Aug, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 21 Jul, 2024 Editor assigned by journal 18 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 16 Jun, 2024 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-4588632","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319252946,"identity":"6873844d-cfd7-44e7-ae15-223e371a3653","order_by":0,"name":"Yuxin Liu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Liu","suffix":""},{"id":319252948,"identity":"a48569fa-2e98-4038-97f4-7fcd51f2e896","order_by":1,"name":"Liyuan Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Wang","suffix":""},{"id":319252950,"identity":"84cfc519-a790-4746-a6d6-2662df133e13","order_by":2,"name":"Shijie Zhang","email":"","orcid":"","institution":"Provincial Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shijie","middleName":"","lastName":"Zhang","suffix":""},{"id":319252952,"identity":"c4a79251-8bdb-4caa-8bbe-3ba297466615","order_by":3,"name":"Jinzhang Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinzhang","middleName":"","lastName":"Li","suffix":""},{"id":319252953,"identity":"b9ea413a-c529-4a8f-a210-80e37e8e45b2","order_by":4,"name":"Yuqi Cui","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Cui","suffix":""},{"id":319252955,"identity":"174e617b-44b4-455d-b7a4-78f0594fd64a","order_by":5,"name":"Yan Yun","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yun","suffix":""},{"id":319252957,"identity":"bf22f76d-c286-4808-abec-e18ddb86757f","order_by":6,"name":"Xiaochun Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYPACGx429uYDBz78IF5Lmhw/z7HEgzN7iNdy2FhyRo7xYQ42ItTqzkh+upmnhjlxw5kzHw4z8DDI84sdwK/F7Mwxs5szjrElbjjeu+FwgQWD4czZCQS0HO9hu/GxgQdoy9kNh2fwMCQY3Cak5TAP243EBonEDTdyHgDZxGiB2GIA8j4DkVogfkkABbIBMJAliPDLjeRnt3lq/oOi8vGHDz9s5PmlCWhBBxKkKR8Fo2AUjIJRgB0AANLPTOi153wOAAAAAElFTkSuQmCC","orcid":"","institution":"Provincial Hospital Affiliated to Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaochun","middleName":"","lastName":"Ma","suffix":""},{"id":319252958,"identity":"a61863d8-332c-4ff1-beaa-a30dbcf79de6","order_by":7,"name":"Haizhou Zhang","email":"","orcid":"","institution":"Provincial Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Haizhou","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-16 06:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4588632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4588632/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-02475-w","type":"published","date":"2025-03-26T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60355037,"identity":"ef444565-8fb5-4935-bde4-55ba3a4a4604","added_by":"auto","created_at":"2024-07-15 23:58:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":534621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study patient selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePP: pulse pressure.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/c73a9a77065533b611eb4241.png"},{"id":60354016,"identity":"a9f5c401-83b2-4218-a0c2-e1421e9ff750","added_by":"auto","created_at":"2024-07-15 23:50:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between admission pulse pressure and in-hospital all-cause mortality in TAAAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePP and in-hospital all-cause mortality showed a \"J-curve\" correlation, with higher rates in the significantly elevated group and reduced group.\u003c/p\u003e\n\u003cp\u003ePP: pulse pressure; TAAAD: type A acute aortic dissection.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/7fa2805e6f14055b3d115a77.png"},{"id":60354015,"identity":"7a612bf6-765c-49da-9480-4b6d34e93a7d","added_by":"auto","created_at":"2024-07-15 23:50:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":357373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the model for predicting in-hospital all-cause mortality in TAAAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter plotting the ROC curve, and the AUC of the curve was 0.827 (95% CI: 0.774-0.880, P＜0.001).\u003c/p\u003e\n\u003cp\u003eROC: receiver operator characteristic curve; AUC: area under the curve.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/ea626a63cf03e23fd0f99b09.png"},{"id":60354020,"identity":"7a964a69-8806-438d-bd70-e4099a7b67d6","added_by":"auto","created_at":"2024-07-15 23:50:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":305044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting in-hospital all-cause mortality in TAAAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColumn line plots were drawn to visualize the results of the prediction model. A total of six predictor variables were involved in the composition of the column line plot. Based on the level of influence of each predictor variable in the model, i.e., the magnitude of the regression coefficients, a score was assigned to the level of value of each variable, thus obtaining six individual scores, which were then summed up to obtain the total score.TAAAD: type A acute aortic dissection.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/a978e0e06cd5ac80d6ff5b0d.png"},{"id":60355526,"identity":"b40486d0-8c6d-4647-9278-af4a6462461b","added_by":"auto","created_at":"2024-07-16 00:06:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":265268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the model for predicting in-hospital all-cause mortality in TAAAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe calibration plot showed that the calibration curve (Apparent line) closely matched the diagonal (Ideal line), indicating good calibration of the predictive model. Additionally, internal validation of the predictive model using bootstrap resampling showed that the corrected curve (Bias-corrected line) almost overlapped with the diagonal, suggesting good accuracy of the nomogram model.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/d52cb3812a6b44789d5d3494.png"},{"id":60355527,"identity":"cab45fbd-9cb0-4387-b8a9-2ea4e757ffd3","added_by":"auto","created_at":"2024-07-16 00:06:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":392862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical decision curve of the model for predicting in-hospital all-cause mortality in TAAAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA DCA curve was drawn based on the constructed CPM. The horizontal axis represented the risk threshold, while the vertical axis showed the net benefit (NB) after accounting for benefits and harms. The DCA results indicated that the risk assessment model provided net benefit for patients within a threshold range of 0.01-0.72.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/d587baef6d612020d0fc4540.png"},{"id":79605023,"identity":"7d067881-b7ea-49cf-957a-940e89de0824","added_by":"auto","created_at":"2025-03-31 16:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3683984,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4588632/v1/de5b5f83-299f-434d-b0fb-3c25f314987a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Admission Pulse Pressure and in-hospital mortality in Type A Acute Aortic Dissection- Result from a Chinese study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType A Acute Aortic Dissection (TAAAD) is widely accepted as an devastating aortic pathology in which aortic lesion involves the ascending aorta. For untreated TAAAD mortality increases to 50% during first 24 hours and emergent surgical intervention is indicated by principle for TAAAD for the purpose of timely repair and reconstruction of the impaired aorta. For decades cardiovascular surgeons across the globe have made the painstaking efforts for optimizing the surgical procedures. Although the short- and long-term outcomes of TAAAD have markedly improved worldwide, mortality remains high and is reported between 15% and 30%. Blood pressure control is the highest priority in the treatment of TAAAD. A retrospective study \u003csup\u003e【1】\u003c/sup\u003ethat included 6,238 AAD patients registered in the International Registry of Acute Aortic Dissection (IRAD) from 1999 to 2016 analyzed the relationship between admission systolic blood pressure (SBP) and mortality during hospitalization. The results showed a significant inverse \"J-curve\" relationship between admission SBP and in-hospital mortality in patients with TAAAD, and a SBP of less than 80 mmHg was an independent risk factor for in-hospital mortality. A similar single-center retrospective study analyzed the relationship between admission SBP and in-hospital mortality in patients with AAD in China \u003csup\u003e【2】\u003c/sup\u003e. The results showed a non-linear correlation between admission SBP and in-hospital mortality in patients with AAD, and a negative correlation between SBP and in-hospital mortality when SBP was less than 120 mmHg.\u003c/p\u003e \u003cp\u003eSystolic and diastolic blood pressure represent the two extremes of blood pressure fluctuations, and the difference between systolic and diastolic blood pressure is defined as pulse pressure (PP), which reflects the magnitude of this fluctuation in a cardiac cycle. In different patient populations, wide PP has been associated with adverse cardiovascular events and all-cause mortality \u003csup\u003e【3\u0026ndash;6】\u003c/sup\u003e. Low admission PP is an independent predictor of mortality in patients with acute coronary syndrome\u003csup\u003e【7】\u003c/sup\u003e. However, few studies have reported on the relationship between admission PP and adverse events during hospitalization in patients with TAAAD.\u003c/p\u003e \u003cp\u003eIn this study, we retrospectively analyzed the clinical data of 488 TAAAD patients, with the aim of evaluating the correlation between admission PP and all-cause in-hospital mortality, in TAAAD patients.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eIn this study, patients with TAAAD admitted between January 1, 2015, and December 31, 2022, were retrieved through the electronic medical record system of Shandong Provincial Hospital. This study was performed under the supervision of the Ethics Committee of Shandong Provincial Hospital affiliated to First Medical University. And this study was a retrospective cohort study (observational). Informed consent was waived because the study was retrospective according to the Ethics Committee (Human Ethics and Consent to Participate declarations: not applicable). The diagnosis of TAAAD was based on the 2022 ACC/AHA guidelines on the treatment and diagnosis of aortic disease\u003csup\u003e【8】\u003c/sup\u003e According to the guidelines, any dissection involving the ascending aorta that occurs within 14 days of symptom onset was defined as TAAAD, and the definitive diagnosis of dissection relied on imaging tests such as computed tomography (CT) or magnetic resonance imaging (MRI).\u003c/p\u003e \u003cp\u003e488 patients with TAAAD were categorized into four groups based on the initial PP value measured at the time of patient admission: reduced (PP\u0026thinsp;\u0026le;\u0026thinsp;40 mmHg), normal (40\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026le;\u0026thinsp;56 mmHg), mildly elevated (56\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026le;\u0026thinsp;75 mmHg), and significantly elevated (PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg) \u003csup\u003e【9】\u003c/sup\u003e. Ethical approval for the study was provided by the hospital institutional review board (NSFC2018-002). As the study was retrospective, informed consent of patients was waived. The study was performed following the Good Clinical Practice (GCP) and principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003cp\u003e(1) Patients presenting to the hospital within \u0026le;\u0026thinsp;14 days of symptom onset;\u003c/p\u003e \u003cp\u003e(2) TAAAD clearly diagnosed by imaging examinations such as CT or MRI;\u003c/p\u003e \u003cp\u003e(3) Patients with complete baseline data (e.g., age, gender, and vital signs, etc.) and complete perioperative data.\u003c/p\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003cp\u003e(1) important clinical data was missing;\u003c/p\u003e \u003cp\u003e(2) variants of typical aortic dissection such as intermural hematoma, penetrating aortic ulcer, intimal tear without hematoma, medical or traumatic AD, and periaortic hematoma;\u003c/p\u003e \u003cp\u003e(3) Symptoms lasting\u0026thinsp;\u0026gt;\u0026thinsp;14 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection of the study population\u003c/h2\u003e \u003cp\u003eThe clinical data of the patients included in this study were obtained by searching the electronic medical record system of Shandong Provincial Hospital. The data included: baseline data, past history, clinical manifestations, imaging and laboratory findings, intraoperative and postoperative conditions, and etc.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were tested for normality and were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (normal distribution) or median (25th percentile, 75th percentile) (skewed distribution). Categorical variables were expressed as number of cases (percentage). One-way analysis of variance (ANOVA) was applied for continuous variables that conformed to a normal distribution, and Kruskal-Wallis test was used for data with a skewed distribution. Chi-square or Fisher's exact test was used to analyze the categorical variables.\u003c/p\u003e \u003cp\u003eThe total population was divided into two groups: the death group and the survival group, based on whether or not a death event occurred during hospitalization. Initially, study variables were screened using univariate analysis. Then, a multivariate binary logistic regression model was applied, using the backward stepwise method to introduce variables with a P-value of less than 0.10 from the univariate analysis. Risk adjustment was performed to determine the correlation between admission PP and in-hospital all-cause mortality, as well as to identify independent predictors of in-hospital all-cause mortality. The obtained results were expressed as odds ratio (OR) with a 95% confidence interval (CI). A disease predictive model for in-hospital all-cause mortality was constructed and presented in the form of a nomogram. The discriminatory power of predictive model was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). An AUC value greater than 0.75 was considered to indicate good discriminatory power. The model's calibration was evaluated using the Hosmer-Lemeshow test of goodness-of-fit. A P-value greater than 0.05 would suggest a better fit of the model. All statistical tests in this study were performed using two-sided tests, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a statistically significant difference. All statistical analyses were performed using SPSS Statistics 26.0 and RStudio 4.2.2 analysis software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of patients\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eInclusion of patients\u003c/h2\u003e \u003cp\u003eFrom January 2015 to December 2021, 488 patients were recruited consecutively with TAAAD. 8 subjects were excluded because of diagnosis of intermural hematoma of the aorta (1 cases), missing clinical data (2 cases) and an onset of more than 14 days (5 cases) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDetailed information of patients\u003c/h2\u003e \u003cp\u003eDemographics and past history\u003c/p\u003e \u003cp\u003eThe mean age of enrolled patients was 51.50\u0026thinsp;\u0026plusmn;\u0026thinsp;11.01 years, and a total of 124 (25.41%) patients were older than 60 years. 344 (70.49%) were males and 144 (29.51%) were females. 107 patients were treated conservatively and 381 were treated surgically, and 100 (20.49%) died in hospital, of which 52 (48.60%) died after conservative treatment and 48 (12.60%) died after surgical treatment. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the difference between the four groups of patients was statistically significant in terms of variables such as age and aortic aneurysm (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the reduced group, the patients were older (55.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85 vs. 51.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.59 vs. 50.15\u0026thinsp;\u0026plusmn;\u0026thinsp;11.70 vs. 50.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the proportion of aortic aneurysms was greater [29 (34.52%) vs. 20 (16.13%) vs. 18 (11.54%) vs. 20 (16.95%). P\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparing clinical baseline data of four groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP\u0026thinsp;\u0026lt;\u0026thinsp;40mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026lt;\u0026thinsp;56 mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026lt;\u0026thinsp;75 mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP\u0026thinsp;\u0026gt;\u0026thinsp;75mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cases, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.15\u0026thinsp;\u0026plusmn;\u0026thinsp;11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (70.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (70.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (72.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;60years old, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (37.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (24.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (21.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (23.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (44.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (36.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (41.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (35.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (26.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (26.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (34.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (70.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (76.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (71.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (76.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarfan syndrome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherosclerosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (41.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (45.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (36.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic aneurysm, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (34.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (16.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (11.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (16.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious aortic dissection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiac surgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.28\u0026thinsp;\u0026plusmn;\u0026thinsp;20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.21\u0026thinsp;\u0026plusmn;\u0026thinsp;17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166.28\u0026thinsp;\u0026plusmn;\u0026thinsp;23.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.50\u0026thinsp;\u0026plusmn;\u0026thinsp;18.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.50\u0026thinsp;\u0026plusmn;\u0026thinsp;14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.54\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.40\u0026thinsp;\u0026plusmn;\u0026thinsp;20.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.76\u0026thinsp;\u0026plusmn;\u0026thinsp;18.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.78\u0026thinsp;\u0026plusmn;\u0026thinsp;14.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.09\u0026thinsp;\u0026plusmn;\u0026thinsp;16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.70\u0026thinsp;\u0026plusmn;\u0026thinsp;20.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (73.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (78.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (77.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (80.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBack pain, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (40.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (45.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (44.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (56.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal pain, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (19.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (36.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (32.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (28.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyncope, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (22.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb ischemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (14.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (13.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (21.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUltrasound and imaging examination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicuspid aortic valve, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 ( 6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic valve insufficiency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (55.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (55.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (60.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePericardial effusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (58.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (39.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (32.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural effusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (46.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (34.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (28.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery involved, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (7.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (8.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal visceral vessels involved, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (40.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (58.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (47.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (61.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree branch vessel involved, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (57.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (51.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (66.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (71.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIliac vessels involved, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (27.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (41.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (53.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscending aorta diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.91 (1.99,11.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.63 (2.24,12.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06 (2.47,13.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.74 (2.95,17.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107.11\u0026thinsp;\u0026plusmn;\u0026thinsp;66.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.47\u0026thinsp;\u0026plusmn;\u0026thinsp;68.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.99\u0026thinsp;\u0026plusmn;\u0026thinsp;62.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.61\u0026thinsp;\u0026plusmn;\u0026thinsp;84.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 (0.89,2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (0.96,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.94,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43 (0.96,2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.40 (3.45,8.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.50 (2.94,7.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.07 (3.20,8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.57 (3.01,6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBIL (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.72 (11.98,21.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.00 (9.70,22.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.87 (11.50,23.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.70 (10.60,20.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.10 (15.70,29.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.80 (12.48,29.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.27 (14.90,30.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.20 (13.29,27.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHS-TnT (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.69(14.23,322.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.08 (7.39,283.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.17 (8.39,281.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.46 (10.77,239.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.64 (1.11,13.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.05 (1.21,10.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.80 (1.31,10.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.78 (1.61,12.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYO (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.93 (21.35,452.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.04 (25.62,427.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.70 (24.67,412.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121.80 (38.07,427.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNormally distributed data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; non-normally distributed data are presented as median (IQR), and categorical variables are presented as n (%). P values were calculated based on t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePP: pulse pressure; LVEF: left ventricular ejection fraction; Cr: creatinine; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoproteincholesterol; LDL-C: low-density lipoprotein cholesterol; DBIL: direct bilirubin; IBIL: indirect bilirubin; TBIL: total bilirubin; HS-TnT: hypersensitive troponin; CK-MB: creatine kinase-MB; MYO: myohemoglobin.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eClinical manifestations\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also showed the differences of clinical manifestation of four groups including abdominal pain [(19.05%) vs. 46 (36.80%) vs. 51 (32.69%) vs. 35 (28.93%), P\u0026thinsp;=\u0026thinsp;0.045] and syncope [19 (22.62%) vs. 11 (8.80%) vs. 6 (3.85%) vs. 7 (5.79%), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e \u003cp\u003ePreoperative ultrasound and imaging examination and lab tests\u003c/p\u003e \u003cp\u003ePreoperative ultrasound and imaging examination and lab tests were summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The four groups were statistically significant in terms of bicuspid aortic valve (P\u0026thinsp;=\u0026thinsp;0.002), pericardial effusion (P\u0026thinsp;=\u0026thinsp;0.003), diameter of ascending aorta (P\u0026thinsp;=\u0026thinsp;0.001), involvement of abdominal visceral arteries (P\u0026thinsp;=\u0026thinsp;0.038), three branches of the arch (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and iliac arteries (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the reduced group (PP\u0026thinsp;\u0026le;\u0026thinsp;40 mmHg), the percentages of bicuspid aortic valve (7.79%) and pericardial effusion (58.23%) were highest, and mean diameter of ascending aorta (5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 cm) was largest. In the significantly elevated group (PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg), involvement of abdominal visceral arteries (61.84%), aortic arch three-branches (71.96%), and iliac arteries (53.33%) were more common.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAdmission pulse pressure and in-hospital all-cause mortality and other complications\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, PP and in-hospital all-cause mortality showed a \"J-curve\" correlation, with higher rates in the significantly elevated group and reduced group, which were 27.91% and 28.93%, respectively (P\u0026thinsp;=\u0026thinsp;0.003); and rates in the normal group and mildly elevated group were 12.80% and 16.03%, respectively. Besides, the four groups showed significant differences in terms of cerebral infarction (P\u0026thinsp;=\u0026thinsp;0.032), hepatic insufficiency (P\u0026thinsp;=\u0026thinsp;0.039) and renal insufficiency (P\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment modalities and complications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP\u0026thinsp;\u0026lt;\u0026thinsp;40mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026lt;\u0026thinsp;56 mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026lt;\u0026thinsp;75 mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP\u0026thinsp;\u0026gt;\u0026thinsp;75mmHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (33.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (20.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (18.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (19.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergent surgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (66.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (79.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127 (81.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98 (80.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic occlusion (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.58\u0026thinsp;\u0026plusmn;\u0026thinsp;37.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.44\u0026thinsp;\u0026plusmn;\u0026thinsp;35.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108.42\u0026thinsp;\u0026plusmn;\u0026thinsp;26.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113.49\u0026thinsp;\u0026plusmn;\u0026thinsp;35.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPB (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209.63\u0026thinsp;\u0026plusmn;\u0026thinsp;60.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206.25\u0026thinsp;\u0026plusmn;\u0026thinsp;63.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e207.87\u0026thinsp;\u0026plusmn;\u0026thinsp;49.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e215.35\u0026thinsp;\u0026plusmn;\u0026thinsp;62.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (12.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (9.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (14.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (22.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic insufficiency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (5.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (10.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (18.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal insufficiency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (12.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (13.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (17.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (27.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital all-cause mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (27.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (16.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35 (28.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNormally distributed data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; non-normally distributed data are presented as median (IQR), and categorical variables are presented as n (%). P values were calculated based on t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePP: pulse pressure; CPB: cardiopulmonary bypass.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk factor screening and predictive modeling for in-hospital all-cause mortality\u003c/h2\u003e \u003cp\u003eUnivariate logistic analysis\u003c/p\u003e \u003cp\u003eEmergent surgical treatment was negatively associated with the in-hospital all-cause mortality (0.15, 0.09\u0026ndash;0.25). In contrast, age (1.03, 1.01\u0026ndash;1.05), age\u0026thinsp;\u0026gt;\u0026thinsp;60 years (2.03, 1.27\u0026ndash;3.25), PP (1.27, 1.04\u0026ndash;1.55), coronary atherosclerosis (1.77, 1.10\u0026ndash;2.73), syncope (2.74, 1.43\u0026ndash;5.26), myocardial infarction/ischemia (4.16, 2.51\u0026ndash;6.90), limb ischemia (2.10, 1.18\u0026ndash;3.72), ascending aortic diameter (1.23, 1.01\u0026ndash;1.50), coronary involvement (2.40, 1.09\u0026ndash;5.28), abdominal visceral involvement (2.07, 1.11\u0026ndash;3.87), triple-branch vessel involvement (2.28, 1.26\u0026ndash;4.14), D-dimer (1.07, 1.04\u0026ndash;1.10), Cr (1.01, 1.00-1.01 ), TG (1.41, 1.19\u0026ndash;1.67), HS-TnT (1.00, 1.00\u0026ndash;1.00), CK-MB (1.01, 1.00-1.02), cerebral infarction (5.25, 2.96\u0026ndash;9.29), hepatic insufficiency (5.13, 2.75\u0026ndash;9.55) and renal insufficiency (10.39, 5.93\u0026ndash;18.20 ) were positively correlated with the in-hospital all-cause mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic analysis for in-hospital all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic information and medical history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u0026ndash;179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026gt;60years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026ndash;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026thinsp;~\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026ndash;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u0026ndash;4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarfan syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026ndash;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026ndash;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic aneurysm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic dissection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiac surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical manifestations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBack pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyncope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026ndash;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial ischemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.51\u0026ndash;6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026ndash;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUltrasound and imaging examination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb ischemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026ndash;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicuspid aortic valve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026ndash;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic valve insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePericardial effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscending aorta diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleura effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026ndash;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal visceral vessels involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026ndash;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree branch vessels involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIliac vessel involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment modalities and complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergent surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.96\u0026ndash;9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75\u0026ndash;9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.93\u0026ndash;18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eStudy variables were screened using univariate analysis. OR value: odds ratio value; PP: pulse pressure; LVEF: left ventricular ejection fraction; Cr: creatinine; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoproteincholesterol; LDL-C: low-density lipoprotein cholesterol; DBIL: direct bilirubin; IBIL: indirect bilirubin; TBIL: total bilirubin; HS-TnT: hypersensitive troponin; TT: thrombin time; CK-MB: creatine kinase-MB; MYO: myohemoglobi\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate logistic analysis\u003c/p\u003e \u003cp\u003eThe multivariate binary logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that significantly elevated PP (PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg) (P\u0026lt;0.001) and reduced PP (P\u0026thinsp;=\u0026thinsp;0.043), D-dimer (P\u0026lt;0.001), ascending aortic diameter (P\u0026thinsp;=\u0026thinsp;0.037), abdominal visceral vessels involved (P\u0026thinsp;=\u0026thinsp;0.019), and coronary atherosclerosis (P\u0026thinsp;=\u0026thinsp;0.017) and emergent surgery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent predictive factors for in-hospital all-cause mortality. After plotting the ROC curve, and the AUC of the curve was 0.827 (95% CI: 0.774\u0026ndash;0.880, P\u0026lt;0.001) and the Yoden index of this ROC curve was 0.227, which corresponded to a sensitivity and specificity of 78.7% and 75.3%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Hosmer-Lemeshow test of goodness-of-fit was χ\u0026thinsp;=\u0026thinsp;2.285, P\u0026thinsp;=\u0026thinsp;0.971\u0026gt;0.05. \u003cb\u003eEstablishment of nomogram\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic analysis for in-hospital all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscending aortic diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24 (0.99\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse Pressure Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.28 (1.42\u0026ndash;7.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48 (0.65\u0026ndash;3.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.33 (1.47\u0026ndash;7.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary atherosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.35\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergent surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.39 (4.14\u0026ndash;13.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal visceral vessels involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93 (1.11\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eA multivariate binary logistic regression model was applied, using the backward stepwise method to introduce variables with a P-value of less than 0.10 from the univariate analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eS.E.: standard error; OR: odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, column line plots were drawn to visualize the results of the prediction model. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a total of six predictor variables were involved in the composition of the column line plot. Based on the level of influence of each predictor variable in the model, i.e., the magnitude of the regression coefficients, a score was assigned to the level of value of each variable, thus obtaining six individual scores, which were then summed up to obtain the total score. Finally, the predicted probability of in-hospital all-cause mortality of TAAAD patients was calculated by the functional transformation relationship between the total score and the probability of death event. The higher the total score, the higher the odds of in-hospital all-cause mortality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe calibration plot showed that the calibration curve (Apparent line) closely matched the diagonal (Ideal line), indicating good calibration of the predictive model. Additionally, internal validation of the predictive model using bootstrap resampling showed that the corrected curve (Bias-corrected line) almost overlapped with the diagonal, suggesting good accuracy of the nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Finally, a DCA curve was drawn based on the constructed CPM. The horizontal axis represented the risk threshold, while the vertical axis showed the net benefit (NB) after accounting for benefits and harms. The DCA results indicated that the risk assessment model provided net benefit for patients within a threshold range of 0.01\u0026ndash;0.72 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we retrospectively analyzed the clinical data of 488 patients with TAAAD and there was a \"J-shape\" relationship between admission PP and in-hospital all-cause mortality. The mortality in the normal group (40\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026le;\u0026thinsp;56 mmHg) was the lowest (12.80%), and in the elevated group (56\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026le;\u0026thinsp;75 mmHg, PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg) the mortality increased with the increase of admission PP, which was 16.03% and 28.93%, respectively. When the PP value was lower than normal (PP\u0026thinsp;\u0026le;\u0026thinsp;40 mmHg), the mortality increased inversely (27.91%).\u003c/p\u003e \u003cp\u003eThe \"J-curve\" phenomenon between blood pressure and cardiovascular outcomes is mainly observed in diastolic blood pressure\u003csup\u003e【10】\u003c/sup\u003e. In the treatment of hypertensive patients, the World Health Organization (WHO) recommends lowering blood pressure below 140/90 mmHg. However, numerous clinical studies and epidemiologic investigations have shown that hypertensive patients have a higher risk of adverse cardiovascular events than normal subjects, even after antihypertensive treatment and correction of other risk factors. At the same time, with the emergence of substantial clinical evidence of the benefits of blood pressure lowering, many scholars believe that a target blood pressure of 140/90 mmHg is no longer sufficient to achieve maximum benefit. As a result, the idea of \"the lower the better\" has become central to some physicians' approach to treating hypertension. However, a large number of studies have confirmed that when blood pressure, especially diastolic blood pressure (DBP), is lowered to a certain level, the risk of cardiovascular events increases, i.e., the phenomenon of the \"J-curve\" is generated. Stewart et al. reported for the first time that the relationship between DBP and myocardial infarction exists between DBP and myocardial infarction in patients with severe hypertension with a DBP of \u0026lt;\u0026thinsp;90 mmHg who receive antihypertensive treatment\u003csup\u003e【11】\u003c/sup\u003e. The study found that the incidence of myocardial infarction was more than five times higher in those whose diastolic blood pressure was reduced to less than 90 mmHg than in those whose diastolic blood pressure was in the range of 100\u0026ndash;109 mmHg. This phenomenon has also been confirmed in studies published by Cruickshank and Farnett et al \u003csup\u003e【12\u0026ndash;13】\u003c/sup\u003e. All these studies describe an interesting phenomenon, i.e., the incidence of myocardial infarction is increased when diastolic blood pressure falls below a certain level during antihypertensive therapy. The reason for this phenomenon might be that lower diastolic blood pressure reduces myocardial perfusion, which in turn increases the chance of myocardial infarction. Recent studies have found that systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;130 mmHg or \u0026lt;\u0026thinsp;120 mmHg is also associated with cardiovascular outcomes and mortality in coronary artery disease, hypertension, or diabetes mellitus in a \"J-curve\" fashion. However, none of these studies reported a \"J-curve\" effect of blood pressure on stroke. McEvoy et al\u003csup\u003e【14】\u003c/sup\u003e further showed that in adults with systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;120 mmHg, elevated PP and low diastolic BP were associated with subclinical myocardial injury and adverse coronary events. When antihypertensive therapy is administered to a systolic BP of less than 140 mmHg, care should be taken to ensure that diastolic BP levels do not fall below 70 mmHg, and in particular do not fall below 60 mmHg.\u003c/p\u003e \u003cp\u003eAs mentioned earlier, Eduardo et al \u003csup\u003e【1】\u003c/sup\u003e found a significant inverse \"J-curve\" relationship between admission systolic blood pressure and in-hospital mortality in patients with TAAAD. This study was the first to extend this specific paradigm of association between blood pressure and cardiovascular disease to AAD. In the present study, We for the first time demonstrated the \"J-shaped\" association between admission PP and in-hospital all-cause mortality in TAAAD. The reason for this \"J-shaped\" association might be related to the high \"co-morbidity\" rate in the group with significantly elevated PP and the group with reduced PP. In the group with significantly elevated PP, the proportions of involvement of three branches of the arch (71.96%), abdominal arteries (61.84%), and iliac arteries (53.33%) were significantly higher than those of the other three groups, and the proportions of cerebral infarctions (22.61%), hepatic insufficiency (18.42%), and renal insufficiency (27.19%) were also the highest in the group with significantly elevated PP. These complications, which were closely related to AAD, might led to fatal events. In the reduced group, the proportions of TAAAD patients with myocardial infarction/ischemia (25.58%) and pericardial effusion (58.23%) were significantly higher than those in the other three groups. In addition to this, the reduced group had the lowest percentage of patients undergoing surgical treatment (66.28%), which might also be an important rationale for its high mortality rate. After adjusting for confounders, multivariate binary logistic regression analysis showed that significantly elevated PP and reduced PP were independent risk factors for in-hospital all-cause mortality in patients with TAAAD.\u003c/p\u003e \u003cp\u003eThe causes of increased PP could be categorized as physiological and pathological. Common physiologic factors include increasing age, pregnancy and athletes. Ageing (\u0026gt;\u0026thinsp;55 years) is accompanied by aging of blood vessels, which could be manifested as a rise in vascular stiffness and a decrease in vascular elasticity and ultimately results in an increase in PP. Common pathologic factors contributing to elevated PP include aortic valve insufficiency (AI), atherosclerosis, hyperthyroidism, arteriovenous fistulae, and certain congenital heart diseases. In addition to this, some studies have found that genetic factors could contribute to elevated PP which might be a trait regulated by multiple genes \u003csup\u003e【15\u0026ndash;18】\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Framingham Heart Study showed that for every 10 mmHg increase in PP, there was a 23% increase in the risk of coronary heart disease \u003csup\u003e【19】\u003c/sup\u003e. The Multiple Risk Factor Intervention Trial (MRFIT) enrolled more than 340,000 men aged 35\u0026ndash;57 years without prior diabetes or coronary heart disease and, during 22 years of follow-up, explored the relationship between PP and cardiovascular mortality\u003csup\u003e【20】\u003c/sup\u003e. The results showed that PP was independently associated with this risk of cardiovascular mortality, especially in older men (45\u0026ndash;57 years). Mitchell et al\u003csup\u003e【21】\u003c/sup\u003e found that after adjusting for age, sex, baseline and time-dependent changes in mean arterial pressure, as well as common risk factors for atrial fibrillation (body mass index, smoking, valvular disease, diabetes mellitus, left ventricular hypertrophy, hypertension treatment, myocardial infarction, and heart failure), PP increase was associated with an increased risk of atrial fibrillation. In addition to cardiovascular diseases, PP has been strongly associated with progression of diabetes mellitus \u003csup\u003e【22\u0026ndash;23】\u003c/sup\u003e and chronic kidney disease\u003csup\u003e【24\u0026ndash;27】\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e In the pharmacological treatment of aortic dissection, the current guideline recommendation is to control systolic blood pressure at 100\u0026ndash;120 mmHg and heart rate at 60\u0026ndash;80 beats/min, which does not sufficiently emphasize PP. This study found a \"J-shaped\" relationship between PP and the risk of in-hospital all-cause death in patients with TAAAD, and suggested that a PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg or PP\u0026thinsp;\u0026lt;\u0026thinsp;40mmHg is an independent risk factor for in-hospital death. Therefore, we suggested that appropriate antihypertensive medications should be chosen to lower PP while prescribing pharmacological treatment for aortic dissection. However, the traditional evaluation of effect of antihypertensive drugs relies on the detection of systolic as well as diastolic blood pressure, mainly systolic. And studies on the effects of different antihypertensive drugs on PP are extremely limited\u003csup\u003e【28】\u003c/sup\u003e. Further prospective studies are needed to determine whether PP could be used as a determining factor in the selection of antihypertensive drugs or as an indicator of the effectiveness of treatment.\u003c/p\u003e \u003cp\u003eAlthough the techniques of surgical treatment of aortic dissection have been improved in recent years, its mortality rate is still at a relatively high level. The occurrence of in-hospital mortality events in patients with TAAAD is often the result of a combination of clinical factors. Therefore, on the basis of confirming the \"J-type\" correlation between PP and in-hospital mortality of TAAAD patients, the present study further performed a multivariate binary logistic regression on the clinical data, and the results showed that D-dimer, ascending aortic diameter, abdominal visceral vessels involved and coronary atherosclerosis were also independent predictive factors for in-hospital all-cause mortality, while surgery was a protective factor.\u003c/p\u003e \u003cp\u003eCurrently, there is no sufficient evidence on whether PP elevation increases the incidence of aortic dissection. PP increase might cause intimal damage and elastic fiber rupture, the initial event of aortic dissection. In the present study, the proportion of three-branch vessel involvement in the arch, abdominal vessel involvement, and iliac vessel involvement was significantly higher in the group with significantly elevated PP than in the other three groups. The group with significantly higher PP also had the highest rates of cerebral infarction, hepatic insufficiency, and renal insufficiency during hospitalization. The higher the PP, the more pronounced the pressure fluctuation, the greater the impact and damage to the artery wall, which could lead to an increase in the size and extent of the entrapment tear. Aortic branches could become poorly perfused due to the entrapment, which in turn affects the blood supply to the organs, leading to organ insufficiency and ultimately increasing the incidence of early fatal events \u003csup\u003e【29\u0026ndash;31】\u003c/sup\u003e. On the contrary, PP reduction is more like a manifestation of myocardial dysfunction and pericardial tamponade. In support of this hypothesis, syncope, myocardial ischemia/infarction, and pericardial effusion were found to be more common in patients with reduced PP in this study.\u003c/p\u003e \u003cp\u003eThe AUC of the ROC curve constructed in this study was 0.827 (95% CI: 0.774\u0026ndash;0.880, P\u0026lt;0.001)\u0026thinsp;\u0026gt;\u0026thinsp;0.75, which indicated that the prediction model had a relatively good discriminatory degree. The result of the goodness-of-fit test was χ\u0026thinsp;=\u0026thinsp;2.285, P\u0026thinsp;=\u0026thinsp;0.971\u0026thinsp;\u0026gt;\u0026thinsp;0.05, which demonstrated that the model had a relatively good calibration.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, this study was a retrospective single-center study with inherent limitations of study design. Thus large, multicenter and prospective cohort studies are needed to validate our results and conclusions. Second, the PP values analyzed in this study were measured at a specific time point of admission to the hospital, whereas the PP could change dynamically. Third, some patients died before arriving at the hospital, thus altering the mortality rate of patients with very low/high PP.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results revealed a \"J-curve\" correlation between admission pulse pressure (PP) and in-hospital all-cause mortality in patients with type A acute aortic dissection (TAAAD). Elevated and reduced admission PP, along with D-dimer levels, ascending aortic diameter, and coronary atherosclerosis, were identified as independent risk factors for in-hospital all-cause mortality in TAAAD patients. Additionally, emergent surgery was found to be a protective factor. Large-scale, multicenter, and prospective cohort studies are required to confirm our findings and conclusions in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grants from the National Natural Science Foundation of China (81800255; 82201624), the Nature Science Foundation of Shandong Province (ZR2020MH044; ZR2021MH112; ZR2021QH016; ZR2023MH124; ZR2021QB122) and Jinan Science and Technology Plan Project (202225050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBOSSONE E, GORLA R, LABOUNTY T M, et al. 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Jama, 1991, 265(4): 489-495.\u003c/li\u003e\n\u003cli\u003eMCEVOY J W, CHEN Y, RAWLINGS A, et al. Diastolic Blood Pressure, Subclinical Myocardial Damage, and Cardiac Events: Implications for Blood Pressure Control [J]. J Am Coll Cardiol, 2016, 68(16): 1713-1722.\u003c/li\u003e\n\u003cli\u003eKATO N, TAKEUCHI F, TABARA Y, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians [J]. Nat Genet, 2011, 43(6): 531-538. \u003c/li\u003e\n\u003cli\u003eWAIN L V, VERWOERT G C, O\u0026apos;REILLY P F, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure [J]. Nat Genet, 2011, 43(10): 1005-1011. BIELINSKI S J, LYNCH A I, MILLER M B, et al. Genome-wide linkage analysis for loci affecting pulse pressure: the Family Blood Pressure Program [J]. Hypertension, 2005, 46(6): 1286-1293. \u003c/li\u003e\n\u003cli\u003eHSU F C, ZACCARO D J, LANGE L A, et al. The impact of pedigree structure on heritability estimates for pulse pressure in three studies [J]. Hum Hered, 2005, 60(2): 63-72. \u003c/li\u003e\n\u003cli\u003ePETTERSSON-FERNHOLM K, FR\u0026ouml;JD\u0026ouml; S, FAGERUDD J, et al. The AT2 gene may have a gender-specific effect on kidney function and pulse pressure in type I diabetic patients [J]. Kidney Int, 2006, 69(10): 1880-1884.\u003c/li\u003e\n\u003cli\u003eFRANKLIN S S, KHAN S A, WONG N D, et al. Is pulse pressure useful in predicting risk for coronary heart Disease? The Framingham heart study [J]. Circulation, 1999, 100(4): 354-360.\u003c/li\u003e\n\u003cli\u003eDOMANSKI M, MITCHELL G, PFEFFER M, et al. Pulse pressure and cardiovascular disease-related mortality: follow-up study of the Multiple Risk Factor Intervention Trial (MRFIT) [J]. Jama, 2002, 287(20): 2677-2683\u003c/li\u003e\n\u003cli\u003eMITCHELL G F, VASAN R S, KEYES M J, et al. Pulse pressure and risk of new-onset atrial fibrillation [J]. Jama, 2007, 297(7): 709-715\u003c/li\u003e\n\u003cli\u003eYASUNO S, UESHIMA K, OBA K, et al. Is pulse pressure a predictor of new-onset diabetes in high-risk hypertensive patients?: a subanalysis of the Candesartan Antihypertensive Survival Evaluation in Japan (CASE-J) trial [J]. Diabetes Care, 2010, 33(5): 1122-1127. \u003c/li\u003e\n\u003cli\u003eMUL\u0026egrave; G, NARDI E, COTTONE S, et al. Relationship of metabolic syndrome with pulse pressure in patients with essential hypertension [J]. Am J Hypertens, 2007, 20(2): 197-203.\u003c/li\u003e\n\u003cli\u003eMUNTNER P, ANDERSON A, CHARLESTON J, et al. Hypertension awareness, treatment, and control in adults with CKD: results from the Chronic Renal Insufficiency Cohort (CRIC) Study [J]. Am J Kidney Dis, 2010, 55(3): 441-451. \u003c/li\u003e\n\u003cli\u003eARULKUMARAN N, DIWAKAR R, TAHIR Z, et al. Pulse pressure and progression of chronic kidney disease [J]. J Nephrol, 2010, 23(2): 189-193. \u003c/li\u003e\n\u003cli\u003eTANAKA M, BABAZONO T, TAKEDA M, et al. Pulse pressure and chronic kidney disease in patients with type 2 diabetes [J]. Hypertens Res, 2006, 29(5): 345-352. \u003c/li\u003e\n\u003cli\u003eGENG T T, TALAEI M, JAFAR T H, et al. Pulse Pressure and the Risk of End-Stage Renal Disease Among Chinese Adults in Singapore: The Singapore Chinese Health Study [J]. J Am Heart Assoc, 2019, 8(23): e013282.\u003c/li\u003e\n\u003cli\u003eDOMANSKI M J, DAVIS B R, PFEFFER M A, et al. Isolated systolic hypertension : prognostic information provided by pulse pressure [J]. Hypertension, 1999, 34(3): 375-380\u003c/li\u003e\n\u003cli\u003eHAGAN P G, NIENABER C A, ISSELBACHER E M, et al. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease [J]. Jama, 2000, 283(7): 897-903. \u003c/li\u003e\n\u003cli\u003eCAMBRIA R P, BREWSTER D C, GERTLER J, et al. Vascular complications associated with spontaneous aortic dissection [J]. J Vasc Surg, 1988, 7(2): 199-209. \u003c/li\u003e\n\u003cli\u003eLAUTERBACH S R, CAMBRIA R P, BREWSTER D C, et al. Contemporary management of aortic branch compromise resulting from acute aortic dissection [J]. J Vasc Surg, 2001, 33(6): 1185-1192.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aortic dissection, Pulse pressure, J-curve, In-hospital all-cause mortality, Multivariate binary logistic regression analysis, ROC curve, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4588632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4588632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eIn recent years, several epidemiologic studies have shown that pulse pressure (PP) is a powerful predictor of mortality from many cardiovascular diseases. However, few studies have reported the association between PP and adverse events during hospitalization in patients with type A acute aortic dissection (TAAAD). The aim of this study was to evaluate the relationship between admission PP and in-hospital all-cause mortality, in patients with TAAAD.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003ePatients with TAAAD admitted from January 2015 to December 2021 were included and divided into four groups according to the PP values measured at the time of admission: reduced group (PP\u0026thinsp;\u0026le;\u0026thinsp;40 mmHg), normal group (40\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026le;\u0026thinsp;56 mmHg), mildly elevated group (56\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026le;\u0026thinsp;75 mmHg), and significantly elevated group (PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg). A multivariate binary logistic regression model was constructed, plotted using nomogram and evaluated with ROC curve.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAdmission PP and in-hospital all-cause mortality showed a \"J-curve\" correlation and in-hospital all-cause mortality was significantly higher in the significantly elevated group and reduced group (P\u0026thinsp;=\u0026thinsp;0.003), respectively. Multivariate binary logistic regression analysis showed that significantly elevated PP (PP\u0026thinsp;\u0026gt;\u0026thinsp;75 mmHg) (P\u0026lt;0.001) and reduced PP (P\u0026thinsp;=\u0026thinsp;0.043), D-dimer (P\u0026lt;0.001), ascending aortic diameter (P\u0026thinsp;=\u0026thinsp;0.037), Abdominal visceral vessels involved (P\u0026thinsp;=\u0026thinsp;0.019), and coronary atherosclerosis (P\u0026thinsp;=\u0026thinsp;0.017) and emergent surgery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent predictive factors for in-hospital all-cause mortality. The AUC of ROC plotted was 0.825 (95% CI, 0.780\u0026ndash;0.870).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eOur findings demonstrated a \"J-curve\" association of admission PP with in-hospital all-cause mortality in TAAAD. Significantly elevated and reduced admission PP, D-dimer, ascending aortic diameter and coronary atherosclerosis were independent risk factors for in-hospital all-cause mortality in patients with TAAAD, and emergent surgery was a protective factor.\u003c/p\u003e","manuscriptTitle":"Admission Pulse Pressure and in-hospital mortality in Type A Acute Aortic Dissection- Result from a Chinese study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:50:41","doi":"10.21203/rs.3.rs-4588632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-24T14:26:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-13T18:00:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T03:29:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198833384226058856613534424732883932470","date":"2024-07-24T08:12:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139584347637298512117814771351914893697","date":"2024-07-23T22:20:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-21T21:50:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-18T10:05:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-18T05:32:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2024-06-16T06:20:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcfb3a6c-8f4c-4ec2-8cc6-b1e1e447ab86","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:05:57+00:00","versionOfRecord":{"articleIdentity":"rs-4588632","link":"https://doi.org/10.1186/s40001-025-02475-w","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-03-26 15:57:47","publishedOnDateReadable":"March 26th, 2025"},"versionCreatedAt":"2024-07-15 23:50:41","video":"","vorDoi":"10.1186/s40001-025-02475-w","vorDoiUrl":"https://doi.org/10.1186/s40001-025-02475-w","workflowStages":[]},"version":"v1","identity":"rs-4588632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4588632","identity":"rs-4588632","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}