Predictive Value of Cardiopulmonary Exercise Testing Parameters in Patients under PCI with High Pulse Pressure

Predictive Value of Cardiopulmonary Exercise Testing Parameters in Patients under PCI with High Pulse Pressure | 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 Article Predictive Value of Cardiopulmonary Exercise Testing Parameters in Patients under PCI with High Pulse Pressure Qiang Ren, Xing-Bo Mu, Yu-Shan Li, Jian Zhang, Yan-Chun Liang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4065804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and objective : The correlation of cardiopulmonary exercise testing (CPET) parameters and the prognosis of coronary artery disease (CAD) patients with high pulse pressure (PP) is uncertain. Present study evaluated the association and prognosis value of CPET parameters in high PP patients. Methods : Patients with CAD who underwent percutaneous coronary intervention (PCI) and CPET were enrolled. Enrolled patients were divided into two groups according to PP after admission: high PP group and normal PP group. The primary endpoint was major adverse cardiovascular events (MACE). Cox regression analysis and univariate receiver operating characteristic (ROC) curves were used to identify optimal predictors of MACE. Results : 111 patients with MACE occurred. Compared with the normal PP group, participants in the high PP group showed higher incidence of MACE (4.86% vs. 2.68%, P =0.005). In high PP group, patients had significantly lower peak heart rate, lower peak oxygen pulse, lower breathing reserve whereas higher ventilatory equivalents for carbon dioxide (VE/VCO 2 ). Peak VO 2 (HR 0.94, 95% CI 0.88 to 1.00, P = 0.038) and VE/VCO 2 (HR 1.08, 95% CI 1.02 to 1.15, P = 0.007) were identified as significant predictive factors through multifactorial analysis. The area under the curve (AUC) of VE/VCO 2 and peak VO 2 were 0.62 (95% CI 0.557 to 0.673) and 0.58 (95% CI 0.515 to 0.634), respectively. Conclusions : The prognosis of CAD patients with high PP was worse compared to the patients with normal PP. The peak VO 2 and VE/VCO 2 were predictors of prognosis of CAD patients with high PP. Health sciences/Cardiology Health sciences/Cardiology/Interventional cardiology coronary artery disease cardiopulmonary exercise testing high pulse pressure major adverse cardiovascular events Figures Figure 1 Figure 2 Introduction Coronary artery disease (CAD) is one of the most prevalent cardiovascular diseases worldwide, which is also the leading cause of death in both developed and developing countries [ 1 , 2 ]. Although several commonly used techniques, such as the electrocardiography (ECG), routine non-invasive imaging tests, and non-invasive stress imaging, etc. [ 3 ]. are currently accepted as an effective method of review for patients who have undergone percutaneous coronary intervention (PCI). It is still unclear that which method should be used in clinical practice in a specific situation to assess the prognosis of this disease. Comparing to other invasive or non-invasive methods, cardiopulmonary exercise testing (CPET) is efficient, cost-less, and even more convenient, which uses no radiation for patients and does not require operators to undergo extensive professional training aiming to meet extremely high standards [2] . What’s more, comparing with ECG, regardless of the method between routine static ECG and exercise ECG, the sensitivity and/or specificity of the ECG is lower than that of CPET [ 4 , 5 ]. Furthermore, CPET parameters such as oxygen pulse, peak oxygen uptake (peak VO 2 ) and ventilatory equivalents for carbon dioxide (VE/VCO 2 ) are closely related to cardiac prognosis [ 6 – 9 ]. To sum up, CPET has the characteristics of objective assess, quantifiable, dynamic monitoring and non-invasiveness [ 10 ]. Pulse pressure (PP), an index of arterial stiffening, measured as the D-value between systolic blood pressure (SBP) and diastolic blood pressure (DBP). Currently, there is increasing evidence that PP is an independent predictor of CAD risk in middle-aged and older individuals, associated with increased cardiovascular disease risk [ 11 , 12 ]. High PP is a negative prognostic factor for acute CAD and heart failure, confirmed by Haider et al. [ 13 ]. Therefore, further research is needed to identify the optimal parameters of CPET as predictors of adverse outcomes in patients with high PP. The present study aims at finding the optimal parameters of CPET as predictors of adverse outcomes in patients with high PP. Methods Study design and population This single-center, retrospective, population-based cohort study including consecutive patients with CAD age over 18 years from the General Hospital of the Northern Theater Command was performed. Inclusion criteria: (a) one was admitted for confirmed CAD; (b) at least 18 years old; (c) undergone PCI between November 1, 2015 and September 30, 2021 and got CPET within a week after PCI during hospitalization. The criteria for grouping in this study was determined by referring to the findings of previous research, and it was established that a PP ≥ 50mmHg in men and ≥ 60mmHg in women is considered high PP [ 14 ]. Therefore, patients in the present study were divided into two groups according to their PP on admission: high PP group (PP of male ≥ 50 mmHg; PP of female ≥ 60 mmHg) and normal PP group (PP of male < 50 mmHg; PP of female < 60 mmHg). Cardiopulmonary exercise testing (CPET) CPET was conducted following PCI, with patients under standard medications [ 13 ]. The dynamic pulmonary function indicators were assessed using bicycle ergometers (SCHILLER, Baar, Switzerland). Meanwhile, baseline metabolic data were collected. The patient then performed an unloaded exercise at 60 rpm for 2–3 minutes. As the equilibrium oxygen uptake and carbon dioxide excretion reached [ 15 ], the load of exercise started to increase continuously (progressively increased in accordance with the patient's age, height, and weight by 10% of the expected exercise power) until the test termination which is defined by the scientific statement from the American Heart Association. After the procedure, the patient would rest for about 3 minutes in the recovery phase, and the rehabilitation technicians would record the CPET test result. The anaerobic threshold was determined based on the ventilatory equivalent for VO 2 nadir while maintaining a consistent ventilatory equivalent for VCO 2 [ 16 ]. VE/VCO 2 was calculated from the patient's breath-by-breath data of VE and VCO 2 over the course of the exercise. Clinical data collection and follow-up The medical records of each included patient were obtained by retrospective review of clinical follow-up records and hospital computerized data. Baseline data included demographic information, medical history, clinical diagnosis, medications at discharge, imaging examination results, procedural information, laboratory indexes and CPET results. It should be emphasized that to reduce measurement error, BP in the right brachial artery was taken repeated twice, after a 5-minute break in the clinic, and then averaged. In addition, strenuous exercise was prohibited 1 hour before BP measurement. And smoking, drinking strong tea and coffee were prohibited for 30 minutes before the measurement, and there was a 2-minutes interval between each measurement. PP on admission of each patient was calculated by using the following equation: PP on admission = SBP on admission - DBP on admission. BP was measured and PP was calculated in accordance with the Japanese Society of Hypertension's recommendations [ 17 ]. In present study, clinical outcome data were obtained by via telephone interviews. Follow-up was conducted at 1, 6, 12, 24, 36, 48, and 60 months, or until the present study endpoint occurred or the trial was terminated. Outcomes The present study endpoint was defined as the occurrence of a major adverse cardiovascular events (MACE), including all-cause death, myocardial infarction (MI), and stroke. All-cause death occurred as a result of an evident cardiac event, unexplained sudden death, or noncardiac cause. MI included acute myocardial infarction (AMI), coronary procedure-related MI and prior or silent/unrecognized MI. Stroke was sudden onset and rapid development of clinical signs including vertigo, numbness, dysphasia, weakness, visual field defects, dysarthria, or other focal neurologic deficits due to nothing but vascular origin. Statistical analysis Continuous variables are expressed as mean ± standard deviation (SD) and categorical variables were displayed as frequencies with percentages. Baseline information for the high PP group versus those in the normal PP group was compared by means of independent group Student’s t-tests and one-way analysis of variance (ANOVA), with correction for unequal variance when necessary. As appropriate, categorical variables was compared by means of Chi-square tests or Fisher exact tests. Comparison of the CPET parameters between the MACE and MACE-free groups is performed by means of independent group Student’s t-tests and ANOVA, with correction for unequal variance when necessary. A two-sided P-value < 0.05 was considered statistically significant. Survival free from MACE in 5 years following PCI was estimated by Kaplan-Meier survival methodology. And the results for the high PP group and the normal PP group were compared by log-rank tests. Univariate and multivariate Cox proportional hazards regressions for predictors of MACE in each group were performed to obtain hazard ratios (HR) for the prognostic impact of CPET parameters on MACE. A forward stepwise selection process was used. Univariate receiver-operating characteristics (ROC) curves at 5 years displayed the discriminative capability of unadjusted individual CPET parameters, with corresponding area under the curve (AUC) displayed. All statistical analyses were performed by using R version 4.1.2 and SPSS 27.0 (IBM). Result Study Population and Clinical Characteristics A total 2,785 patients meeting the inclusion criteria were registered in the study. Among them, 1,665 (59.78%) patients meeting the high PP grouping criteria that PP of male ≥ 50 mmHg and PP of female ≥ 60 mmHg were enrolled in high PP group. And the other 1,120 (40.22%) patients who didn’t meet the high PP grouping were enrolled in normal PP group. Demographic information, medical history, indications for PCI, medication at discharge in both groups were shown in Table 1 . The average age of all patients was 57.03 ± 8.90 years, and 79.89% in which were male. Proportion of males (82.94% vs. 75.36%, P <0.001), proportion of hypertension (67.81% vs. 48.66%, P <0.001), proportion of diabetes mellitus (DM, 31.35% vs. 23.13%, P <0.001), the synergy between percutaneous coronary intervention with taxus and cardiac surgery (SYNTAX) score [11.00 (7.00, 16.13) vs. 12.00 (8.00, 17.00), P = 0.004], SBP on admission (146.08 ± 15.61 vs. 123.48 ± 13.53, P < 0.001) and left ventricular ejection fractions (LVEF, 61.43 ± 5.76 vs. 60.88 ± 6.18, P = 0.018) were significantly higher in high PP group compared with normal PP group. While heart rate on admission (75.29 ± 11.73 vs. 77.73 ± 11.90, P < 0.001) was lower in high PP group. Patients in high PP group had a higher rate of ticagrelor (18.80% vs. 26.34%, P <0.001), calcium channel blockers (CCB, 31.53% vs. 22.86%, P <0.001), angiotensin-converting enzyme inhibitors (ACEI, 27.87% vs. 21.01%, P <0.001) and angiotonin receptor blocker (ARB, 34.95% vs. 29.11%, P = 0.001). Table 1 Clinical Characteristics of Patients Clinical Characteristics Overall (n = 2785) High PP (n = 1665) Normal PP (n = 1120) P-value Age (years) 57.33 ± 8.35 57.54 ± 8.44 57.02 ± 8.22 0.106 Male, no. (%) 2,225 (79.89%) 1,381 (82.94%) 844 (75.36%) < 0.001 c BMI (Kg/m 2 ) 25.51 ± 2.84 25.45 ± 2.78 25.59 ± 2.94 0.201 Smoking, no. (%) 1,139 (40.90%) 702 (42.16%) 437 (39.02%) 0.106 Drinking, no. (%) 671 (24.09%) 405 (24.32%) 266 (23.75%) 0.934 Medical history, no. (%) Hypertension 1,674 (60.11%) 1,129 (67.81%) 545 (48.66%) < 0.001 Hypertension classification < 0.001 Ⅰ 158 (5.67%) 107 (6.43%) 51 (4.55%) Ⅱ 451 (16.19%) 298 (17.90%) 153 (13.66%) Ⅲ 1,081 (38.82%) 732 (43.96%) 349 (31.16%) DM 781 (28.04%) 522 (31.35%) 259 (23.13%) < 0.001 CKD 16 (0.57%) 11 (0.66%) 5 (0.45%) 0.633 Previous PCI 793 (28.47%) 469 (28.17%) 324 (28.93%) 0.694 Previous stroke 294 (10.56%) 192 (11.53%) 102 (9.11%) 0.048 Previous MI 521 (18.71%) 304 (18.26%) 217 (19.38%) 0.489 Heart rate on admission (beats/min) 76.27 ± 11.86 75.29 ± 11.73 77.73 ± 11.90 < 0.001 SBP on admission (mmHg) 136.99 ± 18.50 146.08 ± 15.61 123.48 ± 13.53 < 0.001 DBP on admission (mmHg) 81.06 ± 11.43 80.97 ± 11.26 81.18 ± 11.70 0.643 LVEF (%) 61.21 ± 5.94 61.43 ± 5.76 60.88 ± 6.19 0.018 Indications for coronary angiography (%) 0.064 Unstable angina 2,199 (78.96%) 1,326 (79.64%) 873 (77.95%) NSTEMI 324 (11.63%) 202 (12.13%) 122 (10.89%) Stable angina 2 (0.07%) 1 (0.06%) 1 (0.09%) STEMI 260 (9.34%) 136 (8.17%) 124 (11.07%) SYNTAX score 12.00(7.00,17.0) 11.00 (7.00,16.13) 12.00 (8.00,17.00) 0.004 Medication at Discharge, no. (%) Aspirin 2,770 (99.46%) 1,657 (99.52%) 1,113 (99.38%) 0.805 Clopidogrel 2,175 (78.10%) 1,351 (81.14%) 824 (73.57%) < 0.001 Ticagrelor 608 (21.83%) 313 (18.80%) 295 (26.34%) < 0.001 Statin 2,747 (98.64%) 1,647 (98.92%) 1,100 (98.21%) 0.160 β-Blocker 1,739 (62.44%) 1,049 (63.00%) 690 (61.61%) 0.480 CCB 781 (28.04%) 525 (31.53%) 256 (22.86%) < 0.001 Nitrates 1,830 (65.71%) 1,103 (66.25%) 727 (64.91%) 0.492 PPI 1,292 (46.39%) 771 (46.31%) 521 (46.52%) 0.943 ACEI 700 (25.13%) 464 (27.87%) 236 (21.07%) < 0.001 ARB 908 (32.60%) 582 (34.95%) 326 (29.11%) 0.001 Diuretic 132 (4.74%) 71 (4.26%) 61 (5.45%) 0.177 Laboratory Indicators Hemoglobin (g/L) 140.08 ± 13.61 139.81 ± 13.43 140.49 ± 13.85 0.199 NT-proBNP (pg/ml) 87.31(38.68,234.00) 92.24 (41.97,232.60) 78.18 (34.37,239.43) 0.181 Triglycerides (mmol/L) 1.42 (1.01,1.98) 1.39 (1.00,1.98) 1.45 (1.03,1.98) 0.126 LDL (mmol/L) 2.16 ± 0.77 2.16 ± 0.75 2.17 ± 0.80 0.728 HDL (mmol/L) 1.08 ± 0.23 1.08 ± 0.23 1.08 ± 0.24 0.312 Total cholesterol (mmol/L) 3.89 ± 1.06 3.88 ± 1.03 3.92 ± 1.10 0.392 Troponin (ng/mL) 0.01 (0.01,0.03) 0.01 (0.01,0.03) 0.01 (0.01,0.02) 0.085 CKMB (U/L) 12.20 (10.00,16.00) 12.00 (10.00,16.00) 12.20 (10.00,16.00) 0.163 Effects of High PP on CPET Parameters Parameters of CPET were shown in Table 2 . Compared with normal PP group, peak heart rate (116.91 ± 18.70 vs. 119.98 ± 19.01, P < 0.001), peak oxygen pulse (9.55 ± 2.46 vs. 9.82 ± 2.44, P = 0.004), breathing reserve (BR, 60.38 ± 12.16 vs. 62.12 ± 11.66, P < 0.001) were lower, while VE/VCO 2 (31.29 ± 3.72 vs. 30.97 ± 3.70, P = 0.025), SBPcpet (171.80 ± 28.39 vs. 160.10 ± 27.78, P < 0.001), DBPcpet [84.00 (73.00, 91.00) vs. 82.00 (72.00, 91.00), P = 0.017] was higher in high PP group. Table 2 Comparison CPET in Groups with Normal and High Pulse Pressure CPET parameters Overall (n = 2785) High PP (n = 1665) Normal PP (n = 1120) P-value Peak heart rate (time/min) 118.15 ± 18.88 116.91 ± 18.70 119.98 ± 19.01 < 0.001 Peak MET 4.72 ± 1.11 4.73 ± 1.10 4.72 ± 1.12 0.909 MET AT 3.10 ± 0.59 3.11 ± 0.60 3.09 ± 0.59 0.332 Peak VO 2 (ml/kg/min) 15.49 ± 3.87 15.50 ± 3.84 15.47 ± 3.92 0.867 VO 2 AT (ml/kg/min) 10.85 ± 2.08 10.88 ± 2.10 10.81 ± 2.06 0.333 Peak Oxygen pulse (ml/beat) 9.71 ± 2.45 9.55 ± 2.46 9.82 ± 2.44 0.004 VE/VO 2 29.28 ± 3.79 29.36 ± 3.75 29.17 ± 3.85 0.191 VE/VCO 2 31.16 ± 3.71 31.29 ± 3.72 30.97 ± 3.70 0.025 SBPcpet 167.10 ± 28.72 171.80 ± 28.39 160.10 ± 27.78 < 0.001 DBPcpet 82.00 (73.00,91.00) 84.00 (73.00,92.00) 82.00 (72.00,91.00) 0.017 HRR (time/min) 44.34 ± 18.97 43.92 ± 18.76 44.97 ± 19.28 0.157 BR (time/min) 61.08 ± 11.99 60.38 ± 12.16 62.12 ± 11.66 < 0.001 Follow-Up Among all patients, MACE occurred in 111 (3.99%) patients, including 44 patients with deaths, 39 patients with MI and 40 patients with strokes, over a median follow-up duration of 1,215 (687-1,586) days (Table 3 ). And the incidence of MACE in high PP group was significantly higher than the normal PP group (4.86% vs. 2.68%, P = 0.005). Detailly, comparing with normal PP group, there were higher proportion of death (2.16% vs. 0.71%, P = 0.004) in high PP group. Table 3 Clinical outcomes in groups with normal and high pulse pressure Overall (n = 2785) High PP (n = 1665) Normal PP (n = 1120) P-value MACE (%) 111 (3.99%) 81 (4.86%) 30 (2.68%) 0.005 death 44 (1.58%) 36 (2.16%) 8 (0.71%) 0.004 MI 39 (1.40%) 26 (1.56%) 13 (1.16%) 0.473 stroke 40 (1.44%) 30 (1.80%) 10 (0.89%) 0.07 Judging from the above results, we found that patients in the high PP group had worse CPET results. Univariate Cox Regression Analysis of MACE in total population Univariate Cox analysis was used to analyze the relationship between clinical and CPET parameters and prognosis in total population. The univariate Cox analysis showed that age, DM, peak heart rate, peak MET, METAT, peak VO 2 , VO 2 AT, VE/VO 2 , VE/VCO 2 and were significantly associated with patient prognosis (Table 4 ). Peak heart rate, peak MET, METAT, peak VO 2 , VO 2 AT and peak oxygen pulse were protective factors. VE/VO 2 and VE/VCO 2 were risk factors. Table 4 Univariable Cox Model in total population Characteristic Hazard Ratio (95% CI) P-value Gender 0.89 (0.57, 1.40) 0.626 Age 1.03 (1.01, 1.05) 0.013 BMI 1.01 (0.94, 1.09) 0.689 hypertension 1.43 (0.96, 2.13) 0.074 DM 1.69 (1.15, 2.47) 0.009 Smoking 1.35 (0.93, 1.96) 0.114 Drinking 0.9 (0.72, 1.13) 0.373 Triglycerides 0.97 (0.82, 1.15) 0.762 LDL 1.08 (0.86, 1.36) 0.517 HDL 1.31 (0.60, 2.88) 0.505 Total cholesterol 1.04 (0.87, 1.23) 0.694 Peak heart rate 0.99 (0.98, 1.00) 0.01 Peak MET 0.66 (0.55, 0.79) < 0.001 MET AT 0.57 (0.41, 0.78) < 0.001 Peak VO 2 0.89 (0.84, 0.93) < 0.001 VO 2 AT 0.85 (0.77, 0.93) < 0.001 Peak oxygen pulse 0.89 (0.82, 0.96) 0.002 VE/VO 2 1.1 (1.05, 1.14) < 0.001 VE/VCO 2 1.12 (1.08, 1.17) < 0.001 SBPcpet 1 (0.99, 1.01) 0.761 DBPcpet 0.99 (0.98, 1.00) 0.104 HRR 1.01 (1.00, 1.02) 0.148 BR 1 (0.98, 1.01) 0.701 Univariate and Multivariate Cox Regression Analysis of MACE in patients with high PP Univariate Cox analysis was used to analyze the relationship between clinical and CPET parameters and prognosis in patients with high PP. The Univariate Cox analysis showed that DM, smoking, peak heart rate, peak MET, METAT, peak VO 2 , VO 2 AT, VE/VO 2 , VE/VCO 2 and were significantly associated with patient prognosis (Table 5 ). Peak heart rate, peak MET, METAT, peak VO 2 , VO 2 AT, were protective factors. DM, smoking, VE/VO 2 , VE/VCO 2 were risk factors. Consequently, they were included in the multifactor analysis, and the results showed that DM (HR 1.75, 95% CI 1.12 to 2.72, P = 0.015), smoking (HR 1.61, 95% CI 1.04 to 2.49, P = 0.033), peak VO 2 (HR 0.94, 95% CI 0.88 to 1.00, P = 0.038) and VE/VCO 2 (HR 1.08, 95% CI 1.02 to 1.15, P = 0.007) could be used as independent predictors of patient prognosis (Table 5 ). The multivariable regression analysis did not find independent variables as predictors of MACE in normal PP. Univariate ROC curves display the discriminative capability of individual CPET parameters, with corresponding AUC displayed. The AUC of VE/VCO 2 and peak VO 2 were 0.62 (95% CI 0.557 to 0.673, Figure Ⅰ) and 0.58 (95% CI 0.515 to 0.634, Figure Ⅱ), respectively. Table 5 Univariable Cox Model in High PP Patients Characteristic Univariate Cox Regression Analysis Multivariate Cox Regression Analysis HR (95% CI) P-value HR (95% CI) P-value Gender 1.16 (0.64, 2.11) 0.609 - - Age 1.02 (0.99, 1.04) 0.213 - - BMI 1.05 (0.97, 1.14) 0.256 - - hypertension 1.26 (0.77, 2.06) 0.339 - - DM 1.84 (1.18, 2.85) 0.008 1.75 (1.12, 2.72) 0.015 Smoking 1.57 (1.01, 2.43) 0.043 1.61 (1.04, 2.49) 0.033 Drinking 0.96 (0.74, 1.24) 0.759 - - Triglycerides 1.04 (0.87, 1.24) 0.701 - - LDL 1.05 (0.79, 1.39) 0.745 - - HDL 1.46 (0.59, 3.64) 0.416 - - Total cholesterol 1.01 (0.82, 1.24) 0.954 - - Peak heart rate 0.99 (0.97, 1.00) 0.019 - - Peak MET 0.71 (0.57, 0.87) < 0.001 - - METAT 0.6 (0.42, 0.88) 0.007 - - Peak VO 2 0.9 (0.85, 0.96) < 0.001 0.94 (0.88, 1.00) 0.038 VO 2 AT 0.87 (0.78, 0.96) 0.007 - - Peak oxygen pulse 0.92 (0.84, 1.01) 0.084 - - VE/VO 2 1.09 (1.03, 1.15) 0.003 - - VE/VCO 2 1.11 (1.05, 1.16) < 0.001 1.08 (1.02, 1.15) 0.007 SBPcpet 1 (0.99, 1.00) 0.469 - - DBPcpet 0.99 (0.97, 1.00) 0.099 - - HRR 1.01 (1.00, 1.02) 0.079 - - BR 1 (0.98, 1.01) 0.758 - - Discussion In present retrospective analysis of high PP, we analyzed a total of 2,785 patients with varying degrees of PP who underwent PCI for CAD. The median follow-up duration was 1,215 (687-1,586) days. The main findings of this study were that patients with high PP had a worse prognosis compared to normal PP. Different from the normal PP group, all ages had characteristics of high risk for MACE in high PP group, while incidence of MACE increases with age in normal PP group. Additionally, we found that lower peak VO 2 and higher VE/VCO 2 were risk predictors of MACE in patients with high PP, but not in the normal PP group. These data suggests that in the CAD patients with high PP, special attention should be paid to peak VO 2 and VE/VCO 2 during CPET. CPET provides a noninvasive method for assessing the cardiovascular, pulmonary, and skeletal muscle components of exercise performance. In cardiovascular disease, CPET has many applications, including the evaluation of patients with systolic and diastolic heart failure (HF), pulmonary hypertension, dilated cardiomyopathy, and congenital heart disease. The innovation of this study lay in identifying the association between CPET and the prognosis of patients with concomitant high blood pressure who have undergone PCI for CAD. High PP has been reported to be independently associated with adverse cardiovascular outcome in the cohort of the Strong Heart Study [ 18 ]. More recent evidence suggests that increased brachial PP is associated with adverse cardiovascular outcome, independently of mean arterial pressure, in high-risk patients with atherosclerosis [ 19 ]. Furthermore, an elevated PP is also linked to chronic heart failure in older adults and can be considered a marker of high risk [ 13 , 20 ]. The above conclusion may be associated with alterations in large arterial compliance. With advancing age, elastin degeneration and collagen deposition decrease the compliance and elasticity of the conduit vessels, resulting in, increased PP [ 21 ]. In the presence of normal arterial compliance, the systolic-diastolic, ventricular vascular coupling reduces left ventricle afterload by storing potential energy in systole and transforming the pulsatile cardiac ejection into a mostly continuous aortic flow, thus maintaining diastolic aortic pressure and improving coronary blood flow. With progressive arterial stiffening, there is an increase in the afterload on the heart, as the arterial wave reflection accelerates back during systole, elevating the systolic pressure, exerting additional pressure on the left ventricle, and reducing coronary blood flow. This viewpoint has also been corroborated by the studies of Cesare Russo and H. Watanabe [ 22 , 23 ]. In conclusion, the blood pressure response during exercise provides a window for assessing cardiovascular function in heart failure patients. In summary, based on the pathophysiological mechanisms of high PP and relevant findings of this study, the best CPET predictors of MACE in high PP patients are closely related to heart failure. This study included patients with comorbid hypertension and CAD, which are major risk factors for the occurrence and progression of heart failure. These risk factors frequently coexist and have a synergistic effect [ 24 – 27 ]. The identification of prognostic biomarkers to guide the prognosis of cardiovascular disease patients remains an area of intense research. As such, many studies have sought to identify other parameters measured during CPET testing as prognostic markers [ 28 , 29 ]. VE/VCO 2 and Peak VO 2 are the two predictive factors identified in this study, with VE/VCO 2 reflecting pulmonary gas exchange and blood flow matching efficiency and exerting strong predictive abilities for both preserved and reduced ejection fraction heart failure [ 30 ]. Based on the alveolar gas equation, a low arterial carbon dioxide partial pressure, an abnormally high tidal volume dead space fraction, or both could contribute to an increased VE/VCO 2 ratio [ 31 ]. The partial pressure of arterial carbon dioxide only slightly varies during exercise in these patients with HF that was a poor progression of large-arterial stiffness or high PP [ 32 ]. What’s more, one of the characteristics of large-arterial stiffness was abnormal left ventricular systolic, which can lead to a reduction in cardiac output and, consequently, to an imbalance in ventilation and perfusion of the lungs. The current analysis indicates that VE/VCO 2 has the highest ROC. Anuradha Lala et al.'s study has corrected for circulatory power and peak Borg score, confirming that VE/VCO 2 is the strongest predictor of advanced heart failure. Peak VO 2 reflects the patient's maximal exercise capacity and holds significant value in the diagnosis and prediction of myocardial ischemia [ 33 ]. A diagnostic study conducted by Romualdo Belardinelli et al. on patients with chest pain has confirmed that CPET is more accurate than the electrocardiogram stress test in diagnosing myocardial ischemia [ 33 ]. Patients with both peak VO 2 91% of predicted peak VO 2 and absence of VO 2 -related signs of myocardial ischemia had no evidence of obstructive CAD in 100% of cases. Although peak VO 2 is not the optimal predictor for heart failure, it remains highly valuable as a reference value. Studies have shown that diastolic dysfunction is associated with a decline in peak VO 2 [ 34 ]. This suggests that assessing peak VO 2 can provide evidence of diastolic dysfunction. In conclusion, through the above analysis, the combined effect of VE/VCO 2 and peak VO 2 can be used to preliminarily assess the prognosis of patients with combined hypertension and CAD. The present study found that age was related to MACE incidence in the general population and normal PP group, while no such correlation in the high PP group. Therefore, in the CAD patients with high PP, any age has the same high risk of MACE while there is currently no confirmed evidence. This observation had important implications for clinical practice. Regardless of age, CAD patients with high PP should be focused on the evaluation and treatment. The average age of population in the present study was 57.03 ± 8.90 years, which is lower than population in previous studies [ 35 ]. Due to concerns about potential risks associated with CPET in elder, physicians in our center were conservative in recommending CPET. Previous studies have demonstrated that CPET is an efficient and safe method for CAD patients [ 36 , 37 ], which benefits far outweigh their potential risks. In future clinical practice, physicians should be encouraged to adopt a proactive strategy towards conducting CPET on elderly patients. In present study, multivariate Cox regression analysis revealed that lower peak VO 2 and higher VE/VCO 2 were adverse factors for the long-term prognosis in the high PP group. However, in the normal PP group, no parameters were found to be associated with prognosis in the multivariate Cox regression analysis. Steven J. Keteyia and Wataru Fujimoto et al. reported that VE/VCO 2 and peak VO 2 were both significantly related to all-cause death and cardiovascular death in CAD patients [ 38 , 39 ]. The difference from present study may be attributable to the inconsistent in enrollment. In the above previous research, there was a high proportion of patients with AMI. While the study participants in present study were patients undergoing elective PCI. To sum up, physicians should focus on peak VO 2 and VE/VCO 2 during CPET on patients undergoing elective PCI to accurately assess the prognosis of patients. Limitation The present study is limited by the following facts: (a) This is a retrospective study, and there is a certain recall bias and selection bias. (b) The patients included in present study were younger compared to previous research. Therefore, our results need to be confirmed in CAD patients of all ages. (c) Although peak VO 2 and VE/VCO 2 are the best prediction parameter, their AUC are relatively small. Consequently, follow-up research could try to investigate the other indicators derived from them. Conclusion In the CAD patients with high PP, lower peak VO 2 and higher VE/VCO 2 were risk predictors of MACE, but not in the normal PP group. Additionally, the patients with high PP had a worse prognosis compared to normal PP. All ages had characteristics of high risk for MACE in high PP group, while incidence of MACE increases with age in normal PP group. Declarations Author contributions Conceptualization: Ya-Ling Han; Data curation: Qiang Ren Xing-Bo Mu, Yu-Shan Li; Formal analysis: Xing-Bo Mu, Yu-Shan Li; Funding acquisition: Quan-Yu Zhang; Investigation: Qiang Ren, Xing-Bo Mu, Yu-Shan Li; Methodology: Ya-Ling Han, Quan-Yu Zhang; Project administration: Jian Zhang, Yan-Chun Liang; Software: Xing-Bo Mu, Yu-Shan Li; Supervision: Quan-Yu Zhang; Validation and Visualization: Xing-Bo Mu; Writing - original draft: Qiang Ren, Xing-Bo Mu; Writing - review & editing: Quan-Yu Zhang, Qiang Ren Competing interests The authors declare no competing interests. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics declarations This retrospective study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of General Hospital of Northern Theater Command, and an exemption for informed consent was approved simultaneously [Y (2023)021] on February 1, 2023. Funding This study, the Rapid Service Fee and the Open Access fee were funded by the National Natural Science Foundation of China (NSFC: 32071116) and Applied Basic Research Project of Liaoning Province (2022JH2/101500028). References Malakar, A. K., Choudhury, D., Halder, B., Paul, P., Uddin, A., & Chakraborty, S. A review on coronary artery disease, its risk factors, and therapeutics. J Cell Physiol . 234 (10) , 16812–16823 (2019). 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Peak aerobic capacity predicts prognosis in patients with coronary heart disease. Am Heart J . 156 (2) , 292–300 (2008). Lavie, C. J., Milani, R. V., & Mehra, M. R. Peak exercise oxygen pulse and prognosis in chronic heart failure. Am J Cardiol . 93 (5) , 588–593 (2004). Mejhert, M., Linder-Klingsell, E., Edner, M., Kahan, T., & Persson, H. Ventilatory variables are strong prognostic markers in elderly patients with heart failure. Heart . 88 (3) , 239–243 (2002). Han, B.et al. Characteristics of cardiopulmonary exercise testing and its prognostic value in patients with old myocardial infarction. Clinical Journal of Medical Officers. 51 (10) , 1013-1017+1023 (2023). Akıncı Özyürek, B., Savaş Bozbaş, Ş., Aydınalp, A., Bozbaş, H., & Ulubay, G. Value of cardiopulmonary exercise testing in the diagnosis of coronary artery disease. Kardiyopulmoner egzersiz testlerinin koroner arter hastalığındaki tanısal değeri. Tuberk Toraks . 67 (2) , 102–107(2019). Mitchell, G. F. et al. Arterial stiffness and cardiovascular events: the Framingham Heart Study. Circulation . 121 (4) , 505–511 (2010). Benetos, A. et al. Pulse pressure: a predictor of long-term cardiovascular mortality in a French male population. Hypertension . 30 (6) , 1410–1415 (1997). Haider, A. W., Larson, M. G., Franklin, S. S., Levy, D., & Framingham Heart Study. Systolic blood pressure, diastolic blood pressure, and pulse pressure as predictors of risk for congestive heart failure in the Framingham Heart Study. Ann Intern Med . 138 (1) , 10–16 (2003). Si, Y. Q. et al. Correlation between elevation of brachial artery pulse pressure increased and coronary heart disease in different genders. Zhonghua yi xue za zhi . 100 (23) , 1816–1819 (2020). Clinical exercise testing with reference to lung diseases: indications, standardization and interpretation strategies. ERS Task Force on Standardization of Clinical Exercise Testing. European Respiratory Society. Eur Respir J . 10 (11) , 2662–2689 (1997). Wasserman, K., Whipp, B. J., Koyl, S. N., & Beaver, W. L. Anaerobic threshold and respiratory gas exchange during exercise. J Appl Physiol. 35 (2) , 236–243 (1973) Umemura, S. et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019). Hypertens Res . 42 (9) , 1235–1481 (2019). Mancusi, C. et al. Higher pulse pressure and risk for cardiovascular events in patients with essential hypertension: The Campania Salute Network. Eur J Prev Cardiol . 25 (3) , 235–243 (2018). Selvaraj, S. et al. Pulse Pressure and Risk for Cardiovascular Events in Patients With Atherothrombosis: From the REACH Registry. J Am Coll Cardiol. 67 (4) , 392–403 (2016). Chae, C. U., Pfeffer, M. A., Glynn, R. J., Mitchell, G. F., Taylor, J. O., & Hennekens, C. H. Increased pulse pressure and risk of heart failure in the elderly. JAMA . 281 (7) , 634–639 (1999). Dart, A. M., & Kingwell, B. A. Pulse pressure--a review of mechanisms and clinical relevance. J Am Coll Cardiol. 37 (4) , 975–984 (2001). Watanabe, H., Ohtsuka, S., Kakihana, M., & Sugishita, Y. Coronary circulation in dogs with an experimental decrease in aortic compliance. J Am Coll Cardiol. 21 (6) , 1497–1506 (1993). Russo, C. et al. Arterial stiffness and wave reflection: sex differences and relationship with left ventricular diastolic function. Hypertension. 60 (2) , 362–368 (2012). Brucks, S. et al. Contribution of left ventricular diastolic dysfunction to heart failure regardless of ejection fraction. Am J Cardiol. 95 (5) , 603–606 (2005). Felker, G. M., Shaw, L. K., & O'Connor, C. M. A standardized definition of ischemic cardiomyopathy for use in clinical research. J Am Coll Cardiol. 39 (2) , 210–218 (2002). Qian, J. Y. et al. Identification of syndrome X using intravascular ultrasound imaging and Doppler flow mapping. Chin Med J (Engl). 117 (4) , 521–527 (2004). Velagaleti, R. S., & Vasan, R. S. Heart failure in the twenty-first century: is it a coronary artery disease or hypertension problem? Cardiol Clin. 25 (4) , 487–v (2007). Aaronson, K. D., & Mancini, D. M. Is percentage of predicted maximal exercise oxygen consumption a better predictor of survival than peak exercise oxygen consumption for patients with severe heart failure? J Heart Lung Transplant. 14 (5) , 981–989 (1995). Cohen-Solal, A., Tabet, J. Y., Logeart, D., Bourgoin, P., Tokmakova, M., & Dahan, M. A non-invasively determined surrogate of cardiac power ('circulatory power') at peak exercise is a powerful prognostic factor in chronic heart failure. Eur Heart J . 23 (10), 806–814 (2002). Lala, A. et al. Predictive Value of Cardiopulmonary Exercise Testing Parameters in Ambulatory Advanced Heart Failure. JACC. Heart failure . 9 (3) , 226–236 (2021). Ingle, L., Sloan, R., Carroll, S., Goode, K., Cleland, J. G., & Clark, A. L. Prognostic significance of different measures of the ventilation-carbon dioxide relation in patients with suspected heart failure. Eur J Heart Fail. 13 (5) , 537–542 (2011). Aguilaniu, B., Page, E., Peronnet, F., & Perrault, H. Lung function and exercise gas exchange in chronic heart failure. Circulation . 98 (10) , 1043–1044(1998). Belardinelli, R., Lacalaprice, F., Tiano, L., Muçai, A., & Perna, G. P. Cardiopulmonary exercise testing is more accurate than ECG-stress testing in diagnosing myocardial ischemia in subjects with chest pain. Int J Cardiol. 174 (2) , 337–342 (2014). Smart, N., Haluska, B., Leano, R., Case, C., Mottram, P. M., & Marwick, T. H. Determinants of functional capacity in patients with chronic heart failure: role of filling pressure and systolic and diastolic function. Am Heart J. 149 (1) , 152–158 (2005). Ganesananthan, S. et al. Cardiopulmonary exercise testing and efficacy of percutaneous coronary intervention: a substudy of the ORBITA trial. Eur Heart J. 43 (33) , 3132–3145 (2022). Tanaka, R. et al. Reproducibility of cardiopulmonary exercise testing between one after and 1-3 weeks after elective percutaneous coronary intervention. J Exerc Rehabil . 19 (5) , 268–274 (2023). American Thoracic Society, & American College of Chest Physicians. ATS/ACCP Statement on cardiopulmonary exercise testing. Am J Respir Crit Care Med. 167 (2) , 211–277 (2003). Fujimoto W, Oishi S, Kawai H. The Prognostic Significance of Cardiopulmonary Exercise Testing at Discharge for the Patients with Acute Myocardial Infarction. J CARD FAIL . 22 (9) , 174 (2016). Keteyian, S. J. et al. Peak aerobic capacity predicts prognosis in patients with coronary heart disease. Am Heart J. 156 (2) , 292–300 (2008). Additional Declarations No competing interests reported. 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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-4065804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286243780,"identity":"d1f03ff2-bab2-4aa6-a3f0-baad07796d99","order_by":0,"name":"Qiang Ren","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Ren","suffix":""},{"id":286243782,"identity":"8581e74d-7165-46fe-bd91-73e49be78e52","order_by":1,"name":"Xing-Bo Mu","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Xing-Bo","middleName":"","lastName":"Mu","suffix":""},{"id":286243783,"identity":"93d0d183-f620-4e11-a39b-161bcf5ecca6","order_by":2,"name":"Yu-Shan Li","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Yu-Shan","middleName":"","lastName":"Li","suffix":""},{"id":286243785,"identity":"25e772bf-bdb5-404f-be79-5f24e7558873","order_by":3,"name":"Jian Zhang","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhang","suffix":""},{"id":286243786,"identity":"ec86cf8a-573a-445a-9d22-10c297950058","order_by":4,"name":"Yan-Chun Liang","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Yan-Chun","middleName":"","lastName":"Liang","suffix":""},{"id":286243787,"identity":"b2ed98f4-882b-4d9d-baa9-f4aca5202897","order_by":5,"name":"Ya-Ling Han","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Ya-Ling","middleName":"","lastName":"Han","suffix":""},{"id":286243788,"identity":"5d3759fc-d6ce-49c8-a65b-a363c33be85e","order_by":6,"name":"Quan-Yu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJCCD0Asx94AYhpYEKWDcQaQMOY5ANYiQbyWxB6wFgYitMi35xg23cw5nN7D3mO64UeBBAN/e3cCfit63hg25247nNvDcyztZg/QYRJnzm7Aq4VZIsf8MUjLfonkYzd4gFoMJHLxa2GTyAHbks4jkdh28w8xWnigWhJ4gLbcJsoWCZ5nhUAt6YYgv9yWMZDgIegX+fbkjUAt1vI87D1mN9/8sZHjb+/Fr4WBIcMA1aUElINA+gMiFI2CUTAKRsGIBgBD00Zg3pxioQAAAABJRU5ErkJggg==","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":true,"prefix":"","firstName":"Quan-Yu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-03-10 14:45:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4065804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4065804/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53966261,"identity":"df1aaf62-8745-4060-ad72-c2c212a1a6b9","added_by":"auto","created_at":"2024-04-02 19:40:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81599,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC of VE/VCO\u003csub\u003e2\u003c/sub\u003e for Patients with High Pulse Pressure.\u003c/p\u003e","description":"","filename":"Figure1.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-4065804/v1/e1247804e354231ea5cea7ca.png"},{"id":53966262,"identity":"4235e90b-0d1d-4954-b117-86f1560825af","added_by":"auto","created_at":"2024-04-02 19:40:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83849,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC of peak VO\u003csub\u003e2\u003c/sub\u003e for Patients with High Pulse Pressure.\u003c/p\u003e","description":"","filename":"Figure2.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-4065804/v1/d0213110c803185cdfc1fa87.png"},{"id":57006474,"identity":"08c3fea1-e15a-40ee-bfea-866ed61e0e41","added_by":"auto","created_at":"2024-05-23 10:10:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4065804/v1/36e12620-c4f1-45b2-bd79-9b7ddaf1abb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Cardiopulmonary Exercise Testing Parameters in Patients under PCI with High Pulse Pressure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) is one of the most prevalent cardiovascular diseases worldwide, which is also the leading cause of death in both developed and developing countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although several commonly used techniques, such as the electrocardiography (ECG), routine non-invasive imaging tests, and non-invasive stress imaging, etc. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. are currently accepted as an effective method of review for patients who have undergone percutaneous coronary intervention (PCI). It is still unclear that which method should be used in clinical practice in a specific situation to assess the prognosis of this disease. Comparing to other invasive or non-invasive methods, cardiopulmonary exercise testing (CPET) is efficient, cost-less, and even more convenient, which uses no radiation for patients and does not require operators to undergo extensive professional training aiming to meet extremely high standards \u003csup\u003e[2]\u003c/sup\u003e. What\u0026rsquo;s more, comparing with ECG, regardless of the method between routine static ECG and exercise ECG, the sensitivity and/or specificity of the ECG is lower than that of CPET [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, CPET parameters such as oxygen pulse, peak oxygen uptake (peak VO\u003csub\u003e2\u003c/sub\u003e) and ventilatory equivalents for carbon dioxide (VE/VCO\u003csub\u003e2\u003c/sub\u003e) are closely related to cardiac prognosis [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To sum up, CPET has the characteristics of objective assess, quantifiable, dynamic monitoring and non-invasiveness [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePulse pressure (PP), an index of arterial stiffening, measured as the D-value between systolic blood pressure (SBP) and diastolic blood pressure (DBP). Currently, there is increasing evidence that PP is an independent predictor of CAD risk in middle-aged and older individuals, associated with increased cardiovascular disease risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. High PP is a negative prognostic factor for acute CAD and heart failure, confirmed by Haider et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, further research is needed to identify the optimal parameters of CPET as predictors of adverse outcomes in patients with high PP.\u003c/p\u003e \u003cp\u003eThe present study aims at finding the optimal parameters of CPET as predictors of adverse outcomes in patients with high PP.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis single-center, retrospective, population-based cohort study including consecutive patients with CAD age over 18 years from the General Hospital of the Northern Theater Command was performed. Inclusion criteria: (a) one was admitted for confirmed CAD; (b) at least 18 years old; (c) undergone PCI between November 1, 2015 and September 30, 2021 and got CPET within a week after PCI during hospitalization.\u003c/p\u003e \u003cp\u003eThe criteria for grouping in this study was determined by referring to the findings of previous research, and it was established that a PP\u0026thinsp;\u0026ge;\u0026thinsp;50mmHg in men and \u0026ge;\u0026thinsp;60mmHg in women is considered high PP [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, patients in the present study were divided into two groups according to their PP on admission: high PP group (PP of male\u0026thinsp;\u0026ge;\u0026thinsp;50 mmHg; PP of female\u0026thinsp;\u0026ge;\u0026thinsp;60 mmHg) and normal PP group (PP of male\u0026thinsp;\u0026lt;\u0026thinsp;50 mmHg; PP of female\u0026thinsp;\u0026lt;\u0026thinsp;60 mmHg).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCardiopulmonary exercise testing (CPET)\u003c/h2\u003e \u003cp\u003eCPET was conducted following PCI, with patients under standard medications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The dynamic pulmonary function indicators were assessed using bicycle ergometers (SCHILLER, Baar, Switzerland). Meanwhile, baseline metabolic data were collected. The patient then performed an unloaded exercise at 60 rpm for 2\u0026ndash;3 minutes. As the equilibrium oxygen uptake and carbon dioxide excretion reached [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the load of exercise started to increase continuously (progressively increased in accordance with the patient's age, height, and weight by 10% of the expected exercise power) until the test termination which is defined by the scientific statement from the American Heart Association. After the procedure, the patient would rest for about 3 minutes in the recovery phase, and the rehabilitation technicians would record the CPET test result. The anaerobic threshold was determined based on the ventilatory equivalent for VO\u003csub\u003e2\u003c/sub\u003e nadir while maintaining a consistent ventilatory equivalent for VCO\u003csub\u003e2\u003c/sub\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. VE/VCO\u003csub\u003e2\u003c/sub\u003e was calculated from the patient's breath-by-breath data of VE and VCO\u003csub\u003e2\u003c/sub\u003e over the course of the exercise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eClinical data collection and follow-up\u003c/h2\u003e \u003cp\u003eThe medical records of each included patient were obtained by retrospective review of clinical follow-up records and hospital computerized data. Baseline data included demographic information, medical history, clinical diagnosis, medications at discharge, imaging examination results, procedural information, laboratory indexes and CPET results.\u003c/p\u003e \u003cp\u003eIt should be emphasized that to reduce measurement error, BP in the right brachial artery was taken repeated twice, after a 5-minute break in the clinic, and then averaged. In addition, strenuous exercise was prohibited 1 hour before BP measurement. And smoking, drinking strong tea and coffee were prohibited for 30 minutes before the measurement, and there was a 2-minutes interval between each measurement. PP on admission of each patient was calculated by using the following equation: PP on admission\u0026thinsp;=\u0026thinsp;SBP on admission - DBP on admission. BP was measured and PP was calculated in accordance with the Japanese Society of Hypertension's recommendations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn present study, clinical outcome data were obtained by via telephone interviews. Follow-up was conducted at 1, 6, 12, 24, 36, 48, and 60 months, or until the present study endpoint occurred or the trial was terminated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe present study endpoint was defined as the occurrence of a major adverse cardiovascular events (MACE), including all-cause death, myocardial infarction (MI), and stroke. All-cause death occurred as a result of an evident cardiac event, unexplained sudden death, or noncardiac cause. MI included acute myocardial infarction (AMI), coronary procedure-related MI and prior or silent/unrecognized MI. Stroke was sudden onset and rapid development of clinical signs including vertigo, numbness, dysphasia, weakness, visual field defects, dysarthria, or other focal neurologic deficits due to nothing but vascular origin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and categorical variables were displayed as frequencies with percentages. Baseline information for the high PP group versus those in the normal PP group was compared by means of independent group Student\u0026rsquo;s t-tests and one-way analysis of variance (ANOVA), with correction for unequal variance when necessary. As appropriate, categorical variables was compared by means of Chi-square tests or Fisher exact tests. Comparison of the CPET parameters between the MACE and MACE-free groups is performed by means of independent group Student\u0026rsquo;s t-tests and ANOVA, with correction for unequal variance when necessary. A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Survival free from MACE in 5 years following PCI was estimated by Kaplan-Meier survival methodology. And the results for the high PP group and the normal PP group were compared by log-rank tests. Univariate and multivariate Cox proportional hazards regressions for predictors of MACE in each group were performed to obtain hazard ratios (HR) for the prognostic impact of CPET parameters on MACE. A forward stepwise selection process was used. Univariate receiver-operating characteristics (ROC) curves at 5 years displayed the discriminative capability of unadjusted individual CPET parameters, with corresponding area under the curve (AUC) displayed. All statistical analyses were performed by using R version 4.1.2 and SPSS 27.0 (IBM).\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eA total 2,785 patients meeting the inclusion criteria were registered in the study. Among them, 1,665 (59.78%) patients meeting the high PP grouping criteria that PP of male\u0026thinsp;\u0026ge;\u0026thinsp;50 mmHg and PP of female\u0026thinsp;\u0026ge;\u0026thinsp;60 mmHg were enrolled in high PP group. And the other 1,120 (40.22%) patients who didn\u0026rsquo;t meet the high PP grouping were enrolled in normal PP group. Demographic information, medical history, indications for PCI, medication at discharge in both groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of all patients was 57.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90 years, and 79.89% in which were male. Proportion of males (82.94% vs. 75.36%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), proportion of hypertension (67.81% vs. 48.66%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), proportion of diabetes mellitus (DM, 31.35% vs. 23.13%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), the synergy between percutaneous coronary intervention with taxus and cardiac surgery (SYNTAX) score [11.00 (7.00, 16.13) vs. 12.00 (8.00, 17.00), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004], SBP on admission (146.08\u0026thinsp;\u0026plusmn;\u0026thinsp;15.61 vs. 123.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and left ventricular ejection fractions (LVEF, 61.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76 vs. 60.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) were significantly higher in high PP group compared with normal PP group. While heart rate on admission (75.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.73 vs. 77.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was lower in high PP group. Patients in high PP group had a higher rate of ticagrelor (18.80% vs. 26.34%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), calcium channel blockers (CCB, 31.53% vs. 22.86%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), angiotensin-converting enzyme inhibitors (ACEI, 27.87% vs. 21.01%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and angiotonin receptor blocker (ARB, 34.95% vs. 29.11%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\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\u003eClinical Characteristics of Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;2785)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh PP (n\u0026thinsp;=\u0026thinsp;1665)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal PP (n\u0026thinsp;=\u0026thinsp;1120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,225 (79.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,381 (82.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e844 (75.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,139 (40.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e702 (42.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437 (39.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e671 (24.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (24.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266 (23.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical history, no. (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,674 (60.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,129 (67.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (48.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eHypertension classification\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (5.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (6.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (4.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e451 (16.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (17.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (13.66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,081 (38.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e732 (43.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349 (31.16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e781 (28.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e522 (31.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259 (23.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (0.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (0.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e793 (28.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (28.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324 (28.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294 (10.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (11.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (9.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e521 (18.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304 (18.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217 (19.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate on admission (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.27\u0026thinsp;\u0026plusmn;\u0026thinsp;11.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eSBP on admission (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.99\u0026thinsp;\u0026plusmn;\u0026thinsp;18.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.08\u0026thinsp;\u0026plusmn;\u0026thinsp;15.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eDBP on admission (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.06\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.97\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.18\u0026thinsp;\u0026plusmn;\u0026thinsp;11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\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\u003e61.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndications for coronary angiography (%)\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 \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstable angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,199 (78.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,326 (79.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e873 (77.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSTEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324 (11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (12.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (10.89%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260 (9.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (8.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (11.07%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.00(7.00,17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00 (7.00,16.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.00 (8.00,17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication at Discharge, no. (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,770 (99.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,657 (99.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,113 (99.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,175 (78.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,351 (81.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e824 (73.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eTicagrelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e608 (21.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (18.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295 (26.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,747 (98.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,647 (98.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,100 (98.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,739 (62.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,049 (63.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e690 (61.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e781 (28.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e525 (31.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256 (22.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eNitrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,830 (65.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,103 (66.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e727 (64.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,292 (46.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e771 (46.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e521 (46.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700 (25.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464 (27.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 (21.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e908 (32.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582 (34.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e326 (29.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (4.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (4.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (5.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory Indicators\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.08\u0026thinsp;\u0026plusmn;\u0026thinsp;13.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.81\u0026thinsp;\u0026plusmn;\u0026thinsp;13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.49\u0026thinsp;\u0026plusmn;\u0026thinsp;13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.31(38.68,234.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.24 (41.97,232.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.18 (34.37,239.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (1.01,1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.00,1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.03,1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 (0.01,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.01,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (0.01,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKMB (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.20 (10.00,16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.00 (10.00,16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.20 (10.00,16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEffects of High PP on CPET Parameters\u003c/h2\u003e \u003cp\u003eParameters of CPET were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared with normal PP group, peak heart rate (116.91\u0026thinsp;\u0026plusmn;\u0026thinsp;18.70 vs. 119.98\u0026thinsp;\u0026plusmn;\u0026thinsp;19.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), peak oxygen pulse (9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46 vs. 9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), breathing reserve (BR, 60.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16 vs. 62.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were lower, while VE/VCO\u003csub\u003e2\u003c/sub\u003e (31.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72 vs. 30.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), SBPcpet (171.80\u0026thinsp;\u0026plusmn;\u0026thinsp;28.39 vs. 160.10\u0026thinsp;\u0026plusmn;\u0026thinsp;27.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DBPcpet [84.00 (73.00, 91.00) vs. 82.00 (72.00, 91.00), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017] was higher in high PP group.\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\u003eComparison CPET in Groups with Normal and High Pulse Pressure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPET parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;2785)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh PP (n\u0026thinsp;=\u0026thinsp;1665)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal PP (n\u0026thinsp;=\u0026thinsp;1120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003ePeak heart rate (time/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.15\u0026thinsp;\u0026plusmn;\u0026thinsp;18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.91\u0026thinsp;\u0026plusmn;\u0026thinsp;18.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.98\u0026thinsp;\u0026plusmn;\u0026thinsp;19.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePeak MET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMET AT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak VO\u003csub\u003e2\u003c/sub\u003e (ml/kg/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e AT (ml/kg/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak Oxygen pulse (ml/beat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPcpet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167.10\u0026thinsp;\u0026plusmn;\u0026thinsp;28.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.80\u0026thinsp;\u0026plusmn;\u0026thinsp;28.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.10\u0026thinsp;\u0026plusmn;\u0026thinsp;27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDBPcpet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.00 (73.00,91.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.00 (73.00,92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.00 (72.00,91.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRR (time/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.34\u0026thinsp;\u0026plusmn;\u0026thinsp;18.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.92\u0026thinsp;\u0026plusmn;\u0026thinsp;18.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.97\u0026thinsp;\u0026plusmn;\u0026thinsp;19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR (time/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFollow-Up\u003c/h2\u003e \u003cp\u003eAmong all patients, MACE occurred in 111 (3.99%) patients, including 44 patients with deaths, 39 patients with MI and 40 patients with strokes, over a median follow-up duration of 1,215 (687-1,586) days (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). And the incidence of MACE in high PP group was significantly higher than the normal PP group (4.86% vs. 2.68%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Detailly, comparing with normal PP group, there were higher proportion of death (2.16% vs. 0.71%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) in high PP group.\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\u003eClinical outcomes in groups with normal and high pulse pressure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;2785)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh PP (n\u0026thinsp;=\u0026thinsp;1665)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal PP (n\u0026thinsp;=\u0026thinsp;1120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eMACE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111 (3.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81 (4.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (2.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (1.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (2.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (0.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (1.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (1.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (1.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (1.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (1.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (0.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eJudging from the above results, we found that patients in the high PP group had worse CPET results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Cox Regression Analysis of MACE in total population\u003c/h2\u003e \u003cp\u003eUnivariate Cox analysis was used to analyze the relationship between clinical and CPET parameters and prognosis in total population. The univariate Cox analysis showed that age, DM, peak heart rate, peak MET, METAT, peak VO\u003csub\u003e2\u003c/sub\u003e, VO\u003csub\u003e2\u003c/sub\u003eAT, VE/VO\u003csub\u003e2\u003c/sub\u003e, VE/VCO\u003csub\u003e2\u003c/sub\u003e and were significantly associated with patient prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Peak heart rate, peak MET, METAT, peak VO\u003csub\u003e2\u003c/sub\u003e, VO\u003csub\u003e2\u003c/sub\u003eAT and peak oxygen pulse were protective factors. VE/VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e were risk factors.\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\u003eUnivariable Cox Model in total population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.57, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \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 (1.01, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.94, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\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\u003e1.43 (0.96, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69 (1.15, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\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\u003e1.35 (0.93, 1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114\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\u003e0.9 (0.72, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.82, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.762\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\u003e1.08 (0.86, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.517\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\u003e1.31 (0.60, 2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.87, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak heart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak MET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.55, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eMET AT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.41, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003ePeak VO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.84, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eVO\u003csub\u003e2\u003c/sub\u003e AT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.77, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003ePeak oxygen pulse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.82, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1 (1.05, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eVE/VCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.08, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eSBPcpet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.99, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBPcpet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.98, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and Multivariate Cox Regression Analysis of MACE in patients with high PP\u003c/h2\u003e \u003cp\u003eUnivariate Cox analysis was used to analyze the relationship between clinical and CPET parameters and prognosis in patients with high PP. The Univariate Cox analysis showed that DM, smoking, peak heart rate, peak MET, METAT, peak VO\u003csub\u003e2\u003c/sub\u003e, VO\u003csub\u003e2\u003c/sub\u003eAT, VE/VO\u003csub\u003e2\u003c/sub\u003e, VE/VCO\u003csub\u003e2\u003c/sub\u003e and were significantly associated with patient prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Peak heart rate, peak MET, METAT, peak VO\u003csub\u003e2\u003c/sub\u003e, VO\u003csub\u003e2\u003c/sub\u003eAT, were protective factors. DM, smoking, VE/VO\u003csub\u003e2\u003c/sub\u003e, VE/VCO\u003csub\u003e2\u003c/sub\u003e were risk factors. Consequently, they were included in the multifactor analysis, and the results showed that DM (HR 1.75, 95% CI 1.12 to 2.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), smoking (HR 1.61, 95% CI 1.04 to 2.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), peak VO\u003csub\u003e2\u003c/sub\u003e (HR 0.94, 95% CI 0.88 to 1.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and VE/VCO\u003csub\u003e2\u003c/sub\u003e (HR 1.08, 95% CI 1.02 to 1.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) could be used as independent predictors of patient prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The multivariable regression analysis did not find independent variables as predictors of MACE in normal PP. Univariate ROC curves display the discriminative capability of individual CPET parameters, with corresponding AUC displayed. The AUC of VE/VCO\u003csub\u003e2\u003c/sub\u003e and peak VO\u003csub\u003e2\u003c/sub\u003e were 0.62 (95% CI 0.557 to 0.673, Figure Ⅰ) and 0.58 (95% CI 0.515 to 0.634, Figure Ⅱ), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Cox Model in High PP Patients\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Cox Regression Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate Cox Regression Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\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\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.64, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \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\u003e1.02 (0.99, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.97, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\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\u003e1.26 (0.77, 2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.84 (1.18, 2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75 (1.12, 2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\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\u003e1.57 (1.01, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61 (1.04, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\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\u003e0.96 (0.74, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.87, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\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\u003e1.05 (0.79, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\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\u003e1.46 (0.59, 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.82, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak heart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak MET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.57, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.42, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak VO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.85, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94 (0.88, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.78, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak oxygen pulse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.84, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.03, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.05, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (1.02, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPcpet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.99, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBPcpet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.98, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn present retrospective analysis of high PP, we analyzed a total of 2,785 patients with varying degrees of PP who underwent PCI for CAD. The median follow-up duration was 1,215 (687-1,586) days. The main findings of this study were that patients with high PP had a worse prognosis compared to normal PP. Different from the normal PP group, all ages had characteristics of high risk for MACE in high PP group, while incidence of MACE increases with age in normal PP group. Additionally, we found that lower peak VO\u003csub\u003e2\u003c/sub\u003e and higher VE/VCO\u003csub\u003e2\u003c/sub\u003e were risk predictors of MACE in patients with high PP, but not in the normal PP group. These data suggests that in the CAD patients with high PP, special attention should be paid to peak VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e during CPET.\u003c/p\u003e \u003cp\u003eCPET provides a noninvasive method for assessing the cardiovascular, pulmonary, and skeletal muscle components of exercise performance. In cardiovascular disease, CPET has many applications, including the evaluation of patients with systolic and diastolic heart failure (HF), pulmonary hypertension, dilated cardiomyopathy, and congenital heart disease. The innovation of this study lay in identifying the association between CPET and the prognosis of patients with concomitant high blood pressure who have undergone PCI for CAD. High PP has been reported to be independently associated with adverse cardiovascular outcome in the cohort of the Strong Heart Study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. More recent evidence suggests that increased brachial PP is associated with adverse cardiovascular outcome, independently of mean arterial pressure, in high-risk patients with atherosclerosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, an elevated PP is also linked to chronic heart failure in older adults and can be considered a marker of high risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The above conclusion may be associated with alterations in large arterial compliance. With advancing age, elastin degeneration and collagen deposition decrease the compliance and elasticity of the conduit vessels, resulting in, increased PP [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the presence of normal arterial compliance, the systolic-diastolic, ventricular vascular coupling reduces left ventricle afterload by storing potential energy in systole and transforming the pulsatile cardiac ejection into a mostly continuous aortic flow, thus maintaining diastolic aortic pressure and improving coronary blood flow. With progressive arterial stiffening, there is an increase in the afterload on the heart, as the arterial wave reflection accelerates back during systole, elevating the systolic pressure, exerting additional pressure on the left ventricle, and reducing coronary blood flow. This viewpoint has also been corroborated by the studies of Cesare Russo and H. Watanabe [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In conclusion, the blood pressure response during exercise provides a window for assessing cardiovascular function in heart failure patients. In summary, based on the pathophysiological mechanisms of high PP and relevant findings of this study, the best CPET predictors of MACE in high PP patients are closely related to heart failure.\u003c/p\u003e \u003cp\u003eThis study included patients with comorbid hypertension and CAD, which are major risk factors for the occurrence and progression of heart failure. These risk factors frequently coexist and have a synergistic effect [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The identification of prognostic biomarkers to guide the prognosis of cardiovascular disease patients remains an area of intense research. As such, many studies have sought to identify other parameters measured during CPET testing as prognostic markers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. VE/VCO\u003csub\u003e2\u003c/sub\u003e and Peak VO\u003csub\u003e2\u003c/sub\u003e are the two predictive factors identified in this study, with VE/VCO\u003csub\u003e2\u003c/sub\u003e reflecting pulmonary gas exchange and blood flow matching efficiency and exerting strong predictive abilities for both preserved and reduced ejection fraction heart failure [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on the alveolar gas equation, a low arterial carbon dioxide partial pressure, an abnormally high tidal volume dead space fraction, or both could contribute to an increased VE/VCO\u003csub\u003e2\u003c/sub\u003e ratio [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The partial pressure of arterial carbon dioxide only slightly varies during exercise in these patients with HF that was a poor progression of large-arterial stiffness or high PP [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. What\u0026rsquo;s more, one of the characteristics of large-arterial stiffness was abnormal left ventricular systolic, which can lead to a reduction in cardiac output and, consequently, to an imbalance in ventilation and perfusion of the lungs. The current analysis indicates that VE/VCO\u003csub\u003e2\u003c/sub\u003e has the highest ROC. Anuradha Lala et al.'s study has corrected for circulatory power and peak Borg score, confirming that VE/VCO\u003csub\u003e2\u003c/sub\u003e is the strongest predictor of advanced heart failure.\u003c/p\u003e \u003cp\u003ePeak VO\u003csub\u003e2\u003c/sub\u003e reflects the patient's maximal exercise capacity and holds significant value in the diagnosis and prediction of myocardial ischemia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A diagnostic study conducted by Romualdo Belardinelli et al. on patients with chest pain has confirmed that CPET is more accurate than the electrocardiogram stress test in diagnosing myocardial ischemia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Patients with both peak VO\u003csub\u003e2\u003c/sub\u003e 91% of predicted peak VO\u003csub\u003e2\u003c/sub\u003e and absence of VO\u003csub\u003e2\u003c/sub\u003e-related signs of myocardial ischemia had no evidence of obstructive CAD in 100% of cases. Although peak VO\u003csub\u003e2\u003c/sub\u003e is not the optimal predictor for heart failure, it remains highly valuable as a reference value. Studies have shown that diastolic dysfunction is associated with a decline in peak VO\u003csub\u003e2\u003c/sub\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This suggests that assessing peak VO\u003csub\u003e2\u003c/sub\u003e can provide evidence of diastolic dysfunction. In conclusion, through the above analysis, the combined effect of VE/VCO\u003csub\u003e2\u003c/sub\u003e and peak VO\u003csub\u003e2\u003c/sub\u003e can be used to preliminarily assess the prognosis of patients with combined hypertension and CAD.\u003c/p\u003e \u003cp\u003eThe present study found that age was related to MACE incidence in the general population and normal PP group, while no such correlation in the high PP group. Therefore, in the CAD patients with high PP, any age has the same high risk of MACE while there is currently no confirmed evidence. This observation had important implications for clinical practice. Regardless of age, CAD patients with high PP should be focused on the evaluation and treatment. The average age of population in the present study was 57.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90 years, which is lower than population in previous studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Due to concerns about potential risks associated with CPET in elder, physicians in our center were conservative in recommending CPET. Previous studies have demonstrated that CPET is an efficient and safe method for CAD patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which benefits far outweigh their potential risks. In future clinical practice, physicians should be encouraged to adopt a proactive strategy towards conducting CPET on elderly patients.\u003c/p\u003e \u003cp\u003eIn present study, multivariate Cox regression analysis revealed that lower peak VO\u003csub\u003e2\u003c/sub\u003e and higher VE/VCO\u003csub\u003e2\u003c/sub\u003e were adverse factors for the long-term prognosis in the high PP group. However, in the normal PP group, no parameters were found to be associated with prognosis in the multivariate Cox regression analysis. Steven J. Keteyia and Wataru Fujimoto et al. reported that VE/VCO\u003csub\u003e2\u003c/sub\u003e and peak VO\u003csub\u003e2\u003c/sub\u003e were both significantly related to all-cause death and cardiovascular death in CAD patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The difference from present study may be attributable to the inconsistent in enrollment. In the above previous research, there was a high proportion of patients with AMI. While the study participants in present study were patients undergoing elective PCI. To sum up, physicians should focus on peak VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e during CPET on patients undergoing elective PCI to accurately assess the prognosis of patients.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThe present study is limited by the following facts: (a) This is a retrospective study, and there is a certain recall bias and selection bias. (b) The patients included in present study were younger compared to previous research. Therefore, our results need to be confirmed in CAD patients of all ages. (c) Although peak VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e are the best prediction parameter, their AUC are relatively small. Consequently, follow-up research could try to investigate the other indicators derived from them.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the CAD patients with high PP, lower peak VO\u003csub\u003e2\u003c/sub\u003e and higher VE/VCO\u003csub\u003e2\u003c/sub\u003e were risk predictors of MACE, but not in the normal PP group. Additionally, the patients with high PP had a worse prognosis compared to normal PP. All ages had characteristics of high risk for MACE in high PP group, while incidence of MACE increases with age in normal PP group.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Ya-Ling Han; Data curation: Qiang Ren Xing-Bo Mu, Yu-Shan Li; Formal analysis: Xing-Bo Mu, Yu-Shan Li; Funding acquisition: Quan-Yu Zhang; Investigation: Qiang Ren, Xing-Bo Mu, Yu-Shan Li; Methodology: Ya-Ling Han, Quan-Yu Zhang; Project administration: Jian Zhang, Yan-Chun Liang; Software: Xing-Bo Mu, Yu-Shan Li; Supervision: Quan-Yu Zhang; Validation and Visualization: Xing-Bo Mu; Writing - original draft: Qiang Ren, Xing-Bo Mu; Writing - review \u0026amp; editing: Quan-Yu Zhang, Qiang Ren\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of General Hospital of Northern Theater Command, and an exemption for informed consent was approved simultaneously [Y (2023)021] on February 1, 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, the Rapid Service Fee and the Open Access fee were funded by the National Natural Science Foundation of China (NSFC: 32071116) and Applied Basic Research Project of Liaoning Province (2022JH2/101500028).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMalakar, A. 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Peak aerobic capacity predicts prognosis in patients with coronary heart disease. \u003cem\u003eAm Heart J.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003e156\u003c/em\u003e\u003c/strong\u003e(2)\u003cstrong\u003e,\u003c/strong\u003e 292\u0026ndash;300 (2008). \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coronary artery disease, cardiopulmonary exercise testing, high pulse pressure, major adverse cardiovascular events","lastPublishedDoi":"10.21203/rs.3.rs-4065804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4065804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and objective\u003c/strong\u003e: The correlation of cardiopulmonary exercise testing (CPET) parameters and the prognosis of coronary artery disease (CAD) patients with high pulse pressure (PP) is uncertain. Present study evaluated the association and prognosis value of CPET parameters in high PP patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Patients with CAD who underwent percutaneous coronary intervention (PCI) and CPET were enrolled. Enrolled patients were divided into two groups according to PP after admission: high PP group and normal PP group. The primary endpoint was major adverse cardiovascular events (MACE). Cox regression analysis and univariate receiver operating characteristic (ROC) curves were used to identify optimal predictors of MACE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 111 patients with MACE occurred. Compared with the normal PP group, participants in the high PP group showed higher incidence of MACE (4.86% vs. 2.68%, \u003cem\u003eP\u003c/em\u003e =0.005). In high PP group, patients had significantly lower peak heart rate, lower peak oxygen pulse, lower breathing reserve whereas higher ventilatory equivalents for carbon dioxide (VE/VCO\u003csub\u003e2\u003c/sub\u003e). Peak VO\u003csub\u003e2\u003c/sub\u003e (HR 0.94, 95% CI 0.88 to 1.00, \u003cem\u003eP\u003c/em\u003e = 0.038) and VE/VCO\u003csub\u003e2\u003c/sub\u003e (HR 1.08, 95% CI 1.02 to 1.15, \u003cem\u003eP\u003c/em\u003e = 0.007) were identified as significant predictive factors through multifactorial analysis. The area under the curve (AUC) of VE/VCO\u003csub\u003e2\u003c/sub\u003e and peak VO\u003csub\u003e2\u003c/sub\u003e were 0.62 (95% CI 0.557 to 0.673) and 0.58 (95% CI 0.515 to 0.634), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The prognosis of CAD patients with high PP was worse compared to the patients with normal PP. The peak VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e were predictors of prognosis of CAD patients with high PP.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Cardiopulmonary Exercise Testing Parameters in Patients under PCI with High Pulse Pressure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-02 19:40:37","doi":"10.21203/rs.3.rs-4065804/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"082aaae8-5589-4cc9-917d-405b2910f1c5","owner":[],"postedDate":"April 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30127942,"name":"Health sciences/Cardiology"},{"id":30127943,"name":"Health sciences/Cardiology/Interventional cardiology"}],"tags":[],"updatedAt":"2024-05-23T10:02:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-02 19:40:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4065804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4065804","identity":"rs-4065804","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}