Association of Wide Pulse Pressure with Coronary Collateral Flow in Patients with ST- Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention

Association of Wide Pulse Pressure with Coronary Collateral Flow in Patients with ST- Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention | 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 Association of Wide Pulse Pressure with Coronary Collateral Flow in Patients with ST- Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention Cemalettin Yılmaz, Büşra Güvendi Şengör, Ahmet Karaduman, Muhammet Tiryaki, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4363861/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2024 Read the published version in Journal of Human Hypertension → Version 1 posted 10 You are reading this latest preprint version Abstract Coronary collateral flow (CCF) plays a protective role in myocardial viability. Pulse pressure (PP) is defined as the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), has been associated with various cardiovascular diseases. However, the relationship between wide PP (WPP) and CCF in ST elevation myocardial infarction (STEMI) patients remains limited. Our objective was to assess how WPP impacts CCF in patients with STEMI undergoing primary percutaneous coronary intervention (p-PCI). This retrospective, single center study included 1180 STEMI patients underwent p-PCI in a tertiary healthcare center between 2021 and 2023. Patients were classified into two groups (good and poor CCF) based on the CCF status (Rentrop 0 and 1: poor CCF; Rentrop 2 and 3: good CCF). WPP was defined as PP ≥ 65 mmHg. Multivariable logistic regression included two distinct models was used to identify independent predictors of good CCF. A total of 272 patients (23.1%) were assigned to good CCF group while 908 patients (76.9%) were categorized into the poor CCF group. WPP was identified a negative independent predictor for good CCF (OR: 0.511, 95% CI: 0.334–0.783, p = 0.002). Moreover, diabetes mellitus, pre-infarction angina, Killip class III/IV, multivessel disease, and pre-TIMI (thrombolysis in myocardial infarction) flow 0 were also found to be independent predictors of CCF. WPP, derived from blood pressure measurements was associated with CCF in STEMI patients undergoing p-PCI. Moreover, in contrast to SBP, DBP, mean arterial pressure, and even PP, WPP was found to predict poor CCF. Health sciences/Diseases/Cardiovascular diseases/Acute coronary syndromes/Myocardial infarction Health sciences/Diseases/Cardiovascular diseases/Hypertension Figures Figure 1 Figure 2 Introduction ST-segment elevation myocardial infarction (STEMI) is a severe and life-threatening manifestation of coronary artery disease (CAD). One of the critical determinants of mortality in patients with STEMI is the size of the infarct [ 1 ]. When ischemia, resulting from severely stenosed or occluded coronary arteries, threatens myocardial viability, the coronary collateral flow (CCF) acts as a backup blood supply, attempting to protect the infarct area through retrograde perfusion. Approximately 40% of STEMI patients have collaterals that can be seen angiographically [ 2 ]. This CCF has been associated with reduced infarct area, prevention of no-reflow, preserved ventricular function and lower mortality in STEMI patients who underwent primary percutaneous coronary intervention (p-PCI) [ 3 ]. The difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), known as pulse pressure (PP), represents the maximal and minimum pressure alterations during blood circulation [ 4 ]. The normal PP value is approximately 40 mmHg, while PP \(\ge\) 65 mmHg is considered as wide PP (WPP) [ 5 ]. WPP has been shown to be associated with chronic disease such as diabetes mellitus (DM), stroke and chronic kidney disease (CKD) [ 6 – 8 ]. Additionally, WPP has been independently linked to extensive cardiovascular disease and all-cause of mortality [ 9 – 11 ]. Considering these findings, it can be inferred that pulse pressure, influenced by age-related changes in blood pressure (BP), may reflect cardiovascular status. However, evidence regarding the impact of WPP on CCF is limited. Therefore, our study aimed to evaluate the effect of WPP on CCF in patients with STEMI undergoing p-PCI. Materials and Methods Study design and population Our study which has a retrospective design included 1,382 patients who had undergone p-PCI in a tertiary center due to STEMI between January, 2021 and September, 2023. Detailed demographic, clinical and laboratory information was collected from patients’ medical records. We selected patients who met the inclusion criteria, i.e., those who underwent p-PCI within 12 hours of symptom onset and were aged above 18 years. Patients with specific conditions such as any degree of aortic regurgitation, severe valvular regurgitation or stenosis, valvular operation history, coronary artery bypass grafting (CABG), peripheral artery disease, aortic dissection, untreated aortic coarctation or stage 5 CKD (estimated glomerular filtration rate (eGFR) < 15 ml/min/1.73m² or requiring hemodialysis) were excluded from study. Finally, a total of 1,180 eligible patients formed the study population, as depicted in the consort diagram (Fig. 1 ). This study adhered to the principles of the Declaration of Helsinki and received ethical approval from the Kartal Kosuyolu Training and Research Hospital. Due to the retrospective nature of this study, which utilized patient medical record data, written informed consent was not obtained from the participants. Angiographic procedure and CCF assessment All patients with STEMI were administered a loading dose of dual antiplatelet therapy (acetylsalicylic acid 300 mg and clopidogrel 600 mg) before the procedure. Coronary angiography was performed via the femoral approach and unfractionated heparin was administered to achieve systemic anticoagulation (targeting activated clotting time > 250 seconds). All coronary angiograms included at least two views of the right coronary artery and four views of the left coronary arteries. The cine time was sufficient for accurate assessment of coronary collateral flow. Treatment of culprit lesions in study cohort involved standard PCI techniques using a 7-French guiding catheter (Launcher; Medtronic, Minneapolis, Minnesota, USA). Drug eluting stents were implanted and the administration of glycoprotein IIb/IIIa antagonists at the discretion of the interventional cardiologist. Subsequently, all patients were transferred to the coronary care unit following the completion of the procedure. A daily maintenance dose of dual antiplatelet therapy (clopidogrel 75 mg and acetylsalicylic acid 100 mg) was prescribed for all patients throughout their hospital stay. CCF was evaluated using the Rentrop classification system (Rentrop 0: absent, Rentrop 1: weak collateral flow, Rentrop 2: partial collateral flow, Rentrop 3: complete collateral flow) by two experienced interventional cardiologists [ 12 ]. We defined Rentrop 0 and 1 as the poor CCF and Rentrop 2 and 3 as good CCF. Antegrade coronary flow after PCI was classified according to Thrombolysis in Myocardial Infarction (TIMI) criteria (TIMI 0: no antegrade flow, TIMI I: penetration without perfusion, TIMI II: partial perfusion, TIMI III: complete perfusion). Pre-procedural TIMI flow was defined as pre-TIMI flow. Blood pressure measurement BP measurements were obtained using a validated BP device (Omron HEM-7001-E; Omron Corp, Tokyo, Japan) at the left and right brachial arteries for all enrolled patients at initial presentation to the emergency unit. A standard tourniquet with a width of 12–13 and a length of 35 cm, following the current guidelines, was applied [ 13 ]. When necessary, a larger or smaller size tourniquet was used accordingly. Prior to measurement, patients were instructed to sit and rest for 5 minutes. Subsequently, BP was recorded three times at 5-minute intervals by a trained medical professional. During measurement, the patient's arms were positioned at the heart level. The final SBP and DBP were calculated as the average values obtained from both arms. PP was derived using the formula: PP = SBP – DBP [ 14 ]. Mean arterial pressure (MAP) is calculated by adding one-third of the PP to the DBP: MAP = DBP + (PP/3) [ 15 ]. Definitions Hypertension (HT) was defined as SBP ≥ 140 mmHg or DBP ≥ 90 mmHg or the use of antihypertensive medications [ 16 ]. Clinically significant WPP was identified as PP ≥ 65 mmHg [ 5 ]. Current smokers or individuals with a history of tobacco use were classified as smokers. DM was defined based on diagnostic criteria of American Diabetes Association, which included fasting plasma glucose ≥ 126 mg/dl or glycosylated hemoglobin (HbA1C) ≥ 6.5% or random plasma glucose ≥ 200 mg/dl, or the use of antidiabetic medications [ 17 ]. Hyperlipidemia (HL) was defined as total cholesterol levels > 200 mg/dl, or low-density lipoprotein cholesterol (LDL-C) levels > 116 mg/dl, or triglyceride levels > 150 mg/dl, or the use of lipid-lowering drugs [ 18 ]. STEMI was defined as the presence of ST-segment elevation of at least 1 mm in two or more contiguous leads, with the exception of leads V1-V3, where the criteria for ST-segment elevation were ≥ 2 mm. In leads V3R, V4R and V7-V9, the ST-segment elevation was defined as at least 0.5 mm. Additionally, new onset left bundle branch block was included in the criteria for diagnosing STEMI. The manifestation of acute myocardial infarction was classified according to the Killip classification: Killip I, no evidence of heart failure; Killip II: heart failure; Killip III, severe heart failure or acute pulmonary edema; Killip IV, cardiogenic shock [ 19 ]. Chest discomfort or pain occurring within one month before a myocardial infarction, lasting for less than 30 minutes, was classified as pre-infarction angina [ 20 ]. Statistical analysis Continuous research data were expressed as median and interquartile (IQR) values, whereas categorical data were expressed as absolute and percentage values. Independent samples t-test and Mann–Whitney U test were used for the comparisons of independent continuous data groups, and Pearson’s chi-squared or Fisher’s exact test was used for the comparisons of categorical data groups. Crude univariable and adjusted two distinct multivariable regression analyses were used to determine the independent predictors of the dependent (good CCF) variable. Model’s coefficients were represented using odds ratio (OR), and confidence interval (CI) was taken as 95%. For all statistical analyses, 2-tailed probability (p) values less than .05 were deemed to indicate statistical significance. All statistical analyses were performed using Jamovi and R 4.01 software (Vienna, Austria) with “ggplot”, “Hmisc”, “rms” packages. Results A total of 1,180 patients were ultimately included in this retrospective study, with 805 males (68.2%) and 375 females (31.8%). According to the classification based on CCF, 272 patients (23.1%) were assigned to good CCF group while 908 patients (76.9%) were categorized into the poor CCF group. The baseline and clinical characteristics of two patient groups were shown in Table 1. The median age showed no significant difference between the good CCF group and the poor CCF group (61 (56-68) years vs 61.5 (54.5-67.5) years, respectively). In the poor CCF group, Killip class III/IV was more common, and the duration of ICU stay was longer compared to the good collateral group (p=0.047 and p=0.024, respectively). The prevalence of HT was similar in both groups (p=0.251). In addition, SBP, DBP, and MAP values obtained at hospital admission did not differ between the groups (all p> 0.05, respectively). However, in the good and poor CCF groups, respectively: PP was notably higher in the good CCF group (57 (52-60) vs. 54 (47-62), p=0.001), whereas wide PP was significantly higher in the poor CCF group (33 (12.1) vs. 166 (18.3), p=0.017). The procedural data of the study population according to CCF was presented in Table 2. Pre-infarction angina was more prevalent in the good CCF group, whereas multivessel disease and pre-TIMI flow 0 were more prevalent in the poor CCF group (p=0.029, p=0.015, and p=0.026, respectively). Laboratory parameters were outlined in Table 3. Total protein and peak troponin levels were significantly higher in the poor CCF group compared to the good CCF group (p=0.042 and p<0.001, respectively). For the prediction of good CCF, a basic logistic regression model was developed using covariates identified as associated with good CCF in univariate analysis (p<0.1). This model was adjusted for traditional risk factors including gender, age, DM, smoking, and previous PCI. Subsequently, PP and WPP were individually incorporated into the base model, resulting in the creation of two distinct multivariable models (Model 1 and Model 2, respectively) as shown in Table 4. In both Model 1 and Model 2, DM, pre-infarction angina, Killip class III/IV, and multivessel disease independently served as predictors of good CCF (OR: 0.053, 95% CI: 0.014 – 0.201, p<0.001 and OR: 0.051, 95% CI: 0.013–0.194, p<0.001; OR: 21.507, 95% CI: 5.833 – 70.299, p<0.001 and OR:24.259, 95% CI: 6.526-90.171, p<0.001; OR: 0.559, 95% CI: 0.325-0.61, p=0.035 and OR: 0.515, 95% CI: 0.299-0.888, p=0.017; OR: 0.693, 95% CI: 0.512-0.939, p=0.018 and OR: 0.653, 95% CI: 0.482-0.885, p=0.006; respectively). In Model 1, PP was not identified as an independent predictor for good CCF, whereas in Model 2, WPP was identified as a negative independent predictor for good CCF (OR: 0.549, 95% CI: 0.360-0.836, p=0.005). Furthermore, the forest plot illustrating independent predictors of CCF was presented in Figure 2. Discussion In this study, we investigated the potential association between WPP and CCF in patients with STEMI who underwent p-PCI. Our findings revealed a significant correlation between WPP and poor CCF. Moreover, through multivariate logistic regression analysis, we identified WPP as an independent predictor of poor collateral flow in this patient population. Moreover, DM, pre-infarction angina, Killip class III/IV, and multivessel disease were also found to be independent predictors of CCF. When a coronary artery becomes occluded or severely stenosed in patients with STEMI, a remodeling process is triggered, leading to the enlargement of pre-existing, non-functional arterioles. This process initiates the development of CCF in response to the redistribution of blood flow and increased shear stress [ 21 , 22 ]. The formation of CCF occurs in two distinct stages known as angiogenesis and arteriogenesis [ 23 ]. Various factors, including hypoxia, hypoperfusion, shear stress, cytokines, time of occlusion, DM, and severity of CAD, play critical roles in stimulating these stages [ 24 ]. In our study, we observed that DM and multivessel disease are predictive for poor CCF in patients with STEMI. Despite some findings suggesting that DM increases coronary collateral formation, our study indicates that the collaterals formed in diabetic patients are inadequate compared to those without diabetes, and even coronary collaterals are negatively affected by DM. Additionally, the insufficiency of antegrade coronary flow in multivessel disease may impede the effective development of CCF, which serves as a protective mechanism against cardiac ischemia. HT, a well-known risk factor of CAD, can contribute to reduced myocardial flow reserve. Interestingly, an inverse relationship between CCF and SBP and PP was shown for the first time by Koersalman et al.[ 25 ] In their study including 237 patients, patients with high preintervention SBP had fewer collaterals, and also the group with good collaterals had lower SBP and PP [ 25 ]. It is well known that SBP and DBP which increase concomitantly due to age related changes in arterial stiffness, begin to diverge after approximately 50–55 years of age, resulting an increase in SBP and a decrease in DBP [ 26 ]. Factors such as elastin thinning, degradation and replacement by collagen in the arterial wall are thought to contribute to this process [ 26 ]. These changes present as wide pulse pressure in cardiac examination. Although the pathophysiologic pathways are not fully understood, current researches suggest that WPP related to extensive cardiovascular disease [ 9 , 10 ]. However, the literature exploring the relationship between WPP and CCF is currently limited. Therefore, our study aimed to investigate the potential association between WPP and CCF in patients with STEMI. Our study showed that WPP is a negative predictor of CCF, unlike SBP, DBP, MAP and even PP. In our study, the observed association between WPP and poor collateral development may be attributed to several underlying mechanisms. The inverse association currently found between WPP and good CCF may be described by functional and structural remodeling, termed microvascular rarefaction of coronary arterioles, in response to increased PP [ 27 , 28 ]. This process comprises obliteration of pre-existing blood vessels, particularly arteriolar vessels 100–150 mm in diameter. Moreover, Boudier et al.[ 29 ] suggested that the resulting decrease in micro vascularity would increase both peripheral vascular resistance and pulse pressure. In addition, increased PP reflects greater arterial stiffness, impaired endothelial function, and altered vasomotor tone, which may hinder the development and recruitment of collateral vessels. These findings support the notion that arterial stiffness may act as a barrier to collateral growth, thereby reducing the capacity for collateral-dependent perfusion during myocardial infarction. Although this complex and bidirectional relationship between PP and CCF remains unclear, WPP appears to be a predictor of poor CCF. Previous studies have emphasized the importance of coronary collateral development in limiting the extent of myocardial damage and improving clinical outcomes following STEMI.[ 30 – 32 ] Aslanjari et al.[ 2 ] have demonstrated that patients without collaterals are at a higher risk of developing cardiogenic shock. In addition, they observed a significant protective effect against cardiogenic shock, even with the presence of smallest degree of collateral flow to the ischemia related artery. Our study, which reveals that Killip class III/IV heart failure is a predictor of poor CCF, supports previous research and underscores the impact of CCF on the development of acute heart failure. However, contrary to previous findings, cardiogenic shock rates were similar between the two groups in our study. The lower proportion of patients with cardiogenic shock (Killip class IV) in our study compared to the study of Aslanjari et al.[ 2 ], as well as differences in the classification of CCF, may have influenced the results. Further studies are warranted to explore this topic further. The development of such collaterals is time dependent process. A previous study found that history of pre-infarct angina can provide stimuli resulted in collateral development [ 33 ]. Similar to these findings, pre-infarction angina was also significantly more common in the good CCF group in this study. Furthermore, the presence of pre-infarction angina was identified as an independent predictor of good CCF. This finding is intriguing as pre-infarction angina may signify ongoing ischemia and a state of chronic vascular dysfunction. Such a milieu might influence the development of collaterals, leading to compensatory mechanisms in response to acute ischemic events. Overall, our study emphasizes the clinical importance of WPP as a potential indicator of poor CCF in patients with STEMI undergoing p-PCI. This parameter, easily obtainable from BP measurement, may help identify individuals at higher risk of impaired collateralization and, therefore, enable interventional cardiologists to tailor treatment strategies for improved outcomes. Nonetheless, our study contributes valuable insights into understanding the complex interplay between hemodynamic factors and coronary collateral formation. However, additional research is necessary to corroborate our findings and elucidate the precise mechanisms linking WPP to coronary collateralization. Limitations Our study had several limitations. Firstly, the measurement of BP at hospital admission may be influenced by preprocedural stress responses and concurrent drug therapy, which may not align with current guideline [ 16 ]. Secondly our study protocol only encompassed angiographically visualized coronary collaterals with a diameter exceeding 100 µm. Lastly, in our study, the WPP was not categorized into subgroups such as high systolic-low diastolic BP (isolated systolic hypertension), low systolic-low diastolic BP and high systolic-high diastolic BP. Consequently, the association of these subgroups with collateralization could not be evaluated. Conclusion WPP, derived from BP measurements at hospital admission, was associated with CCF in STEMI patients undergoing p-PCI. Moreover, in contrast to SBP, DBP, MAP, and even PP, WPP was found to predict poor CCF in these patients. The measurement of PP is a straightforward and cost-effective parameter that can provide valuable insights into CCF in patients with STEMI. Summary Table What is known about the topic WPP has been linked to chronic diseases such as DM, stroke, and CKD. WPP has been independently associated with extensive cardiovascular disease and increased all-cause mortality. Previous studies have suggested that PP influenced by age-related changes in blood pressure, may serve as an indicator of cardiovascular health. What this study adds This study uncovers a connection between WPP and coronary collateral flow. WPP emerges as a potentially valuable predictor of poor collateral flow. The impact of wide pulse pressure on coronary collateral flow surpasses that of SBP, DBP, MAP, and even PP. Declarations Availability of Data and Material The data of the research is available in our University Hospital archive. A de-identified dataset will be shared with other researchers upon reasonable request from the corresponding author. Acknowledgement None. Author contribution Conceptualization and design: CY, BGŞ, AK. Data collection: AK, CY, BK, MMT. Data curation and analysis: BK, MMT, CY. Writing original draft: CY. Writing reviewing and editing; TU, RZ, CY, MMT, AK, BGŞ. Corresponding author Correspondence to Cemalettin Yılmaz Funding None. Ethical approval This study adhered to the principles of the Declaration of Helsinki and received ethical approval from the Kartal Kosuyolu Training and Research Hospital. Competing Interests The Authors declare that they have no conflict of interests. References Yoon SJ, Ko YG, Kim JS, Moon JY, Kim YJ, Park S, et al. 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Impact of coronary artery collaterals on infarct size assessed by serial cardiac magnetic resonance imaging after primary percutaneous coronary intervention in patients with acute myocardial infarction. Coron Artery Dis 2009;20:440–5. https://doi.org/10.1097/MCA.0B013E328330C930 . Nicolau JC, Pinto MAFV, Nogueira PR, Lorga AM, Jacob JLB, Garzon SAC. The role of antegrade and collateral flow in relation to left ventricular function post-thrombolysis. Int J Cardiol 1997;61:47–54. https://doi.org/10.1016/S0167-5273(97)00134-4 . Kim EK, Choi JH, Song Y Bin, Hahn JY, Chang SA, Park SJ, et al. A protective role of early collateral blood flow in patients with ST-segment elevation myocardial infarction. Am Heart J 2016;171:56–63. https://doi.org/10.1016/J.AHJ.2015.10.016 . Tables Table 1 . Baseline and clinical characteristics of patients based on CCF Variable Good CCF n=272 (23.1%) Poor CCF n= 908 (76.9%) p Baseline characteristics Age (years), median (IQR) 61 (56-68) 61.5 (54.5-67.5) 0.352 Gender (male), n (%) 174 (64) 631 (69.5) 0.086 Body mass index (kg/m 2 ), median (IQR) 27.7 (25.1-29.4) 26.9 (25.0-29.4) 0.483 DM, n (%) 62 (22.8) 190 (20.9) 0.509 HT, n (%) 171 (62.9) 605 (66.6) 0.251 HL, n (%) 132 (48.5) 416 (45.8) 0.431 Previous PCI, n (%) 44 (16.2) 128 (14.1) 0.394 COPD, n (%) 32 (11.8) 88 (9.7) 0.321 CVD, n (%) 16 (5.9) 36 (4) 0.176 Smoking, n (%) 145 (53.3) 535 (58.9) 0.100 History of AF, n (%) 23 (8.5) 61 (6.7) 0.328 On admission AF on admission, n (%) 17 (6.3) 79 (8.7) 0.195 SBP (mmHg), median (IQR) 144 (136-151) 144 (133-152) 0.589 DBP (mmHg), median (IQR) 88 (80-94) 87.5 (79-95) 0.675 MAP (mmHg), median (IQR) 106 (99-113) 106 (96.9-114) 0.891 PP (mmHg), median (IQR) 57 (52-60) 54 (47-62) 0.001 Wide PP, n (%) 33 (12.1) 166 (18.3) 0.017 During hospital Stent thrombosis, n (%) 4 (1.5) 20 (2.2) 0.453 Post-PCI EF (%), median (IQR) 50 (40-55) 48 (40-55) 0.159 Killip class III/IV, n (%) 18 (6.6) 97 (10.7) 0.047 Cardiogenic shock, n (%) 5 (1.8) 23 (2.5) 0.509 Inotropic need, n (%) 24 (8.8) 52 (5.7) 0.068 CPR, n (%) 5 (1.8) 19 (2.1) 0.794 Duration of ICU stay (hour), median (IQR) 24 (20-34) 27 (20.8-36) 0.024 Hospitalization duration (day), median (IQR) 4 (3-4) 4 (3-4) 0.610 In-hospital mortality, n (%) 8 (2.9) 24 (2.6) 0.791 Bold values denote statistical significance at the p<0.05 level. CCF, coronary collateral flow; DM, diabetes mellitus; HT, hypertension; HL, hyperlipidemia; PAD, peripheral arterial disease; COPD, chronic obstructive pulmonary disease; CVD, cerebrovascular disease, AF, atrial fibrillation; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; post-PCI EF, post-percutaneous coronary intervention ejection fraction; PCI, percutaneous coronary intervention; CPR, cardiopulmonary resuscitation; ICU duration, intensive care unit duration; IQR, interquartile ranges. Table 2. Procedural data of study population according to CCF Variable Good CCF n=272 (23.1%) Poor CCF n= 908 (76.9%) p Pre-procedural data Anterior infarction, n (%) 80 (29.4) 300 (33) 0.261 Pre-infarction angina, n (%) 74 (27.2) 190 (20.9) 0.029 Symptom to reperfusion time (minute), median (IQR) 278 (120-600) 283 (131-600) 0.826 During procedure Multivessel disease, n (%) 82 (30.1) 347 (38.2) 0.015 Pre-TIMI flow 0, n (%) 165 (60.7) 617 (68.0) 0.026 Final TIMI flow, n (%) 1 2 3 9 (3.3) 24 (8.8) 239 (87.9) 47 (5.2) 84 (9.3) 777 (85.6) 0.426 No-reflow, n (%) 31 (11.4) 125 (13.8) 0.312 Amount of contrast media (mL), median (IQR) 194 (144-290) 209 (144-300) 0.823 Bold values denote statistical significance at the p<0.05 level. CCF, coronary collateral flow; TIMI, thrombolysis in myocardial infarction; pre-TIMI flow, pre-procedural TIMI flow; IQR, interquartile ranges. Table 3. Laboratory findings of patients based on CCF Variable Good CCF n=272 (23.1%) Poor CCF n= 908 (76.9%) p WBC (10 3 /μL) 9.9 (8.2-12.1) 10.4 (8.6-13.1) 0.111 Hemoglobin (g/dl) 13.7 (12.7-14.7) 13.8 (12.7-14.9) 0.581 Platelet (10 3 / μL) 255 (214-314) 257 (214-319) 0.561 Total cholesterol (mg/dl) 200 (165-246) 193 (111-253) 0.234 LDL (mg/dl) 126 (95.8-152) 127 102-152) 0.530 HDL (mg/dl) 32 (22-39) 31 (19-39) 0.177 TG (mg/dl) 193 (111-253) 184 (124-240) 0.747 Total protein (g/L) 6.59 (6.00-7.40) 6.80 (6-7.6) 0.042 Albumin (g/L) 4.03 (3.70-4.25) 4 (3.7-4.2) 0.404 Peak troponin (ng/mL) 1.10 (0.38-5.05) 1.91 (0.62-5.2) <0.001 Urea (mg/dL) 24 (16-38) 26 (17.1-35) 0.321 Basal creatinin (mg/dl) 0.80 (0.65-1.03) 0.82 (0.71-1.00) 0.308 Uric acid (mg/ dL) 6 (4.80-6.90) 6 (5.0-6.82) 0.654 CRP (mg/L) 5.6 (2.5-14) 5.7 (2.6-16) 0.306 TSH (μIU/L) 1.3 (1.0-2.1) 1.2 (1.0-2.1) 0.112 Bold values denote statistical significance at the p<0.05 level. CCF, coronary collateral flow; WBC, white blood cell; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, triglyceride; CRP; C reactive protein; TSH, thyroid-stimulating hormone. Table 4. Multivariable logistic regression analysis at Model 1 and Model 2 for prediction of good CCF Model 1 Model 2 Variables Odds Ratio 95 % CI (lower-upper) p Odds Ratio 95 % CI (lower-upper) p Age (years) 1.008 0.993-1.024 0.287 1.007 0.992-1.023 0.351 Gender (male) 0.776 0.569-1.033 0.081 0.780 0.579-1.052 0.104 DM 0.053 0.014-0.201 < 0.001 0.051 0.013-0.194 <0 .001 Smoking 0.832 0.626-1.107 0.207 0.852 0.640-1.134 0.272 Pre-infarction angina 21.507 5.833-70.299 < 0.001 24.259 6.526-90.171 < 0.001 Killip class III/IV 0.559 0.325-0.961 0.035 0.515 0.299-0.888 0.017 Multivessel disease 0.693 0.512-0.939 0.018 0.653 0.482-0.885 0.006 Peak troponin 0.953 0.906-1.004 0.071 0.964 0.916-1.015 0.164 Pre-TIMI flow 0 0.881 0.632-1.227 0.454 0.811 0.582-1.130 0.216 Previous PCI 1.163 0.792-1.707 0.440 1.195 0.814-1.756 0.362 PP 1.011 0.997-1.025 0.115 - - - Wide PP - - - 0.549 0.360-0.836 0.005 Bold values denote statistical significance at the p<0.05 level. CCF, coronary collateral flow; DM, diabetes mellitus; CVD, cerebrovascular disease; pre-TIMI flow, pre-procedural TIMI flow; PCI, percutaneous coronary intervention; PP, pulse pressure; CI, confidence interval. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 17 Dec, 2024 Read the published version in Journal of Human Hypertension → Version 1 posted Editorial decision: revise 08 Oct, 2024 Review # 2 received at journal 05 Oct, 2024 Reviewer # 2 agreed at journal 05 Oct, 2024 Review # 1 received at journal 17 Jul, 2024 Reviewer # 1 agreed at journal 08 Jul, 2024 Reviewers invited by journal 17 May, 2024 Editor assigned by journal 15 May, 2024 Submission checks completed at journal 09 May, 2024 First submitted to journal 07 May, 2024 Unknown event 07 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4363861","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":303537382,"identity":"5543013a-25a7-44e0-93a2-871749ea00f0","order_by":0,"name":"Cemalettin Yılmaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYFAC5gYJHgYGORDzwAPitDCCtRiDtSSQoiWxAcQmSgv/tIONN9623UmfH3b4IdAWOzndBgJaJG4nNlvObXuWu/F2mgFQS7Kx2QECWgykE9ukedsO526cnQDSciBxG7Fa0g1np38gTUuCvHQOkbaA/TLn3GHDDdI5BQcSDIjwC//s5IM33pQdlpefnb75w4cKOzmCWsCAkQ3oQrBKA2KUg8EfBgb5BqJVj4JRMApGwUgDALZ4SQFt8dkvAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4140-9139","institution":"Malazgirt State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Cemalettin","middleName":"","lastName":"Yılmaz","suffix":""},{"id":303537383,"identity":"5c96a3cf-6c8b-408a-9ee0-6a0c1981ebd1","order_by":1,"name":"Büşra Güvendi Şengör","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Büşra","middleName":"Güvendi","lastName":"Şengör","suffix":""},{"id":303537384,"identity":"6e7f17f1-cab7-48c3-a445-09f51b057151","order_by":2,"name":"Ahmet Karaduman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Karaduman","suffix":""},{"id":303537385,"identity":"0d7f72bd-f46f-4f6c-9a23-e56a5d3fbc56","order_by":3,"name":"Muhammet Tiryaki","email":"","orcid":"","institution":"Bulanık State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Muhammet","middleName":"","lastName":"Tiryaki","suffix":""},{"id":303537386,"identity":"2a8ff81c-ba59-46b1-a396-c50729103e21","order_by":4,"name":"Barkın Kültürsay","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Barkın","middleName":"","lastName":"Kültürsay","suffix":""},{"id":303537387,"identity":"b3953b1e-f25f-4398-b8cd-87fb65d833c7","order_by":5,"name":"tuba unkun","email":"","orcid":"","institution":"artal kosuyolu education and research hospital","correspondingAuthor":false,"prefix":"","firstName":"tuba","middleName":"","lastName":"unkun","suffix":""},{"id":303537388,"identity":"e0e69944-1cfe-4a8e-8719-604506136c04","order_by":6,"name":"Regayip Zehir","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Regayip","middleName":"","lastName":"Zehir","suffix":""}],"badges":[],"createdAt":"2024-05-03 11:35:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4363861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4363861/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41371-024-00986-3","type":"published","date":"2024-12-17T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57628097,"identity":"2d69e97a-e925-4c35-8917-c3b27a0c191f","added_by":"auto","created_at":"2024-06-03 14:25:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205422,"visible":true,"origin":"","legend":"\u003cp\u003eConsort diagram of study population. (STEMI, ST-elevation myocardial infarction; p-PCI, primary percutaneous coronary intervention; eGFR, estimated glomerular filtration rate; CCF, coronary collateral flow)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4363861/v1/e9065b37a82e5bdf5dba4226.png"},{"id":57628099,"identity":"1a3b0087-ab7a-4c5d-a2a4-bcbba9cd48e4","added_by":"auto","created_at":"2024-06-03 14:25:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115105,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot demonstrating predictors of good coronary collateral circulation\u003c/p\u003e","description":"","filename":"Figure2.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-4363861/v1/1b965dbdace22781b7c88fe3.png"},{"id":71740345,"identity":"8611b94f-e549-4a3d-b72f-20c857624a20","added_by":"auto","created_at":"2024-12-18 08:06:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1013746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4363861/v1/0c46379c-1df7-466a-a79b-9fa3a312b6e1.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Association of Wide Pulse Pressure with Coronary Collateral Flow in Patients with ST- Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eST-segment elevation myocardial infarction (STEMI) is a severe and life-threatening manifestation of coronary artery disease (CAD). One of the critical determinants of mortality in patients with STEMI is the size of the infarct [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. When ischemia, resulting from severely stenosed or occluded coronary arteries, threatens myocardial viability, the coronary collateral flow (CCF) acts as a backup blood supply, attempting to protect the infarct area through retrograde perfusion. Approximately 40% of STEMI patients have collaterals that can be seen angiographically [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This CCF has been associated with reduced infarct area, prevention of no-reflow, preserved ventricular function and lower mortality in STEMI patients who underwent primary percutaneous coronary intervention (p-PCI) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), known as pulse pressure (PP), represents the maximal and minimum pressure alterations during blood circulation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The normal PP value is approximately 40 mmHg, while PP\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e65 mmHg is considered as wide PP (WPP) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. WPP has been shown to be associated with chronic disease such as diabetes mellitus (DM), stroke and chronic kidney disease (CKD) [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, WPP has been independently linked to extensive cardiovascular disease and all-cause of mortality [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering these findings, it can be inferred that pulse pressure, influenced by age-related changes in blood pressure (BP), may reflect cardiovascular status. However, evidence regarding the impact of WPP on CCF is limited. Therefore, our study aimed to evaluate the effect of WPP on CCF in patients with STEMI undergoing p-PCI.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eOur study which has a retrospective design included 1,382 patients who had undergone p-PCI in a tertiary center due to STEMI between January, 2021 and September, 2023. Detailed demographic, clinical and laboratory information was collected from patients\u0026rsquo; medical records. We selected patients who met the inclusion criteria, i.e., those who underwent p-PCI within 12 hours of symptom onset and were aged above 18 years. Patients with specific conditions such as any degree of aortic regurgitation, severe valvular regurgitation or stenosis, valvular operation history, coronary artery bypass grafting (CABG), peripheral artery disease, aortic dissection, untreated aortic coarctation or stage 5 CKD (estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;15 ml/min/1.73m\u0026sup2; or requiring hemodialysis) were excluded from study. Finally, a total of 1,180 eligible patients formed the study population, as depicted in the consort diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study adhered to the principles of the Declaration of Helsinki and received ethical approval from the Kartal Kosuyolu Training and Research Hospital. Due to the retrospective nature of this study, which utilized patient medical record data, written informed consent was not obtained from the participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAngiographic procedure and CCF assessment\u003c/h2\u003e \u003cp\u003eAll patients with STEMI were administered a loading dose of dual antiplatelet therapy (acetylsalicylic acid 300 mg and clopidogrel 600 mg) before the procedure. Coronary angiography was performed via the femoral approach and unfractionated heparin was administered to achieve systemic anticoagulation (targeting activated clotting time\u0026thinsp;\u0026gt;\u0026thinsp;250 seconds). All coronary angiograms included at least two views of the right coronary artery and four views of the left coronary arteries. The cine time was sufficient for accurate assessment of coronary collateral flow. Treatment of culprit lesions in study cohort involved standard PCI techniques using a 7-French guiding catheter (Launcher; Medtronic, Minneapolis, Minnesota, USA). Drug eluting stents were implanted and the administration of glycoprotein IIb/IIIa antagonists at the discretion of the interventional cardiologist. Subsequently, all patients were transferred to the coronary care unit following the completion of the procedure. A daily maintenance dose of dual antiplatelet therapy (clopidogrel 75 mg and acetylsalicylic acid 100 mg) was prescribed for all patients throughout their hospital stay.\u003c/p\u003e \u003cp\u003eCCF was evaluated using the Rentrop classification system (Rentrop 0: absent, Rentrop 1: weak collateral flow, Rentrop 2: partial collateral flow, Rentrop 3: complete collateral flow) by two experienced interventional cardiologists [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We defined Rentrop 0 and 1 as the poor CCF and Rentrop 2 and 3 as good CCF. Antegrade coronary flow after PCI was classified according to Thrombolysis in Myocardial Infarction (TIMI) criteria (TIMI 0: no antegrade flow, TIMI I: penetration without perfusion, TIMI II: partial perfusion, TIMI III: complete perfusion). Pre-procedural TIMI flow was defined as pre-TIMI flow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBlood pressure measurement\u003c/h2\u003e \u003cp\u003eBP measurements were obtained using a validated BP device (Omron HEM-7001-E; Omron Corp, Tokyo, Japan) at the left and right brachial arteries for all enrolled patients at initial presentation to the emergency unit. A standard tourniquet with a width of 12\u0026ndash;13 and a length of 35 cm, following the current guidelines, was applied [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. When necessary, a larger or smaller size tourniquet was used accordingly. Prior to measurement, patients were instructed to sit and rest for 5 minutes. Subsequently, BP was recorded three times at 5-minute intervals by a trained medical professional. During measurement, the patient's arms were positioned at the heart level. The final SBP and DBP were calculated as the average values obtained from both arms. PP was derived using the formula: PP\u0026thinsp;=\u0026thinsp;SBP \u0026ndash; DBP [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Mean arterial pressure (MAP) is calculated by adding one-third of the PP to the DBP: MAP\u0026thinsp;=\u0026thinsp;DBP + (PP/3) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions\u003c/h2\u003e \u003cp\u003eHypertension (HT) was defined as SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg or the use of antihypertensive medications [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Clinically significant WPP was identified as PP\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Current smokers or individuals with a history of tobacco use were classified as smokers. DM was defined based on diagnostic criteria of American Diabetes Association, which included fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dl or glycosylated hemoglobin (HbA1C)\u0026thinsp;\u0026ge;\u0026thinsp;6.5% or random plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dl, or the use of antidiabetic medications [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Hyperlipidemia (HL) was defined as total cholesterol levels\u0026thinsp;\u0026gt;\u0026thinsp;200 mg/dl, or low-density lipoprotein cholesterol (LDL-C) levels\u0026thinsp;\u0026gt;\u0026thinsp;116 mg/dl, or triglyceride levels\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/dl, or the use of lipid-lowering drugs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSTEMI was defined as the presence of ST-segment elevation of at least 1 mm in two or more contiguous leads, with the exception of leads V1-V3, where the criteria for ST-segment elevation were \u0026ge;\u0026thinsp;2 mm. In leads V3R, V4R and V7-V9, the ST-segment elevation was defined as at least 0.5 mm. Additionally, new onset left bundle branch block was included in the criteria for diagnosing STEMI. The manifestation of acute myocardial infarction was classified according to the Killip classification: Killip I, no evidence of heart failure; Killip II: heart failure; Killip III, severe heart failure or acute pulmonary edema; Killip IV, cardiogenic shock [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Chest discomfort or pain occurring within one month before a myocardial infarction, lasting for less than 30 minutes, was classified as pre-infarction angina [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous research data were expressed as median and interquartile (IQR) values, whereas categorical data were expressed as absolute and percentage values. Independent samples t-test and Mann\u0026ndash;Whitney U test were used for the comparisons of independent continuous data groups, and Pearson\u0026rsquo;s chi-squared or Fisher\u0026rsquo;s exact test was used for the comparisons of categorical data groups. Crude univariable and adjusted two distinct multivariable regression analyses were used to determine the independent predictors of the dependent (good CCF) variable. Model\u0026rsquo;s coefficients were represented using odds ratio (OR), and confidence interval (CI) was taken as 95%. For all statistical analyses, 2-tailed probability (p) values less than .05 were deemed to indicate statistical significance. All statistical analyses were performed using Jamovi and R 4.01 software (Vienna, Austria) with \u0026ldquo;ggplot\u0026rdquo;, \u0026ldquo;Hmisc\u0026rdquo;, \u0026ldquo;rms\u0026rdquo; packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,180 patients were ultimately included in this retrospective study, with 805 males (68.2%) and 375 females (31.8%). According to the classification based on CCF, 272 patients (23.1%) were assigned to good CCF group while 908 patients (76.9%) were categorized into the poor CCF group. The baseline and clinical characteristics of two patient groups were shown in Table 1. The median age showed no significant difference between the good CCF group and the poor CCF group (61 (56-68) years \u003cem\u003evs\u003c/em\u003e 61.5 (54.5-67.5) years, respectively).\u0026nbsp;In the poor CCF group, Killip class III/IV was more common, and the duration of ICU stay was longer compared to the good collateral group (p=0.047 and p=0.024, respectively). The prevalence of HT was similar in both groups (p=0.251). In addition, SBP, DBP, and MAP values obtained at hospital admission did not differ between the groups (all p\u0026gt; 0.05, respectively). However, in the good and poor CCF groups, respectively: PP was notably higher in the good CCF group (57 (52-60) vs. 54 (47-62), p=0.001), whereas wide PP was significantly higher in the poor CCF group (33 (12.1) vs. 166 (18.3), p=0.017).\u0026nbsp;The procedural data of the study population according to CCF was presented in Table 2. Pre-infarction angina was more prevalent in the good CCF group, whereas multivessel disease and pre-TIMI flow 0 were more prevalent in the poor CCF group (p=0.029, p=0.015, and p=0.026, respectively).\u0026nbsp;Laboratory parameters were outlined in Table 3. Total protein and peak troponin levels were significantly higher in the poor CCF group compared to the good CCF group (p=0.042 and p\u0026lt;0.001, respectively).\u003c/p\u003e\n\u003cp\u003eFor the prediction of good CCF, a basic logistic regression model was developed using covariates identified as associated with good CCF in univariate analysis (p\u0026lt;0.1). This model was adjusted for traditional risk factors including gender, age, DM, smoking, and previous PCI. Subsequently, PP and WPP were individually incorporated into the base model, resulting in the creation of two distinct multivariable models (Model 1 and Model 2, respectively) as shown in Table 4. In both Model 1 and Model 2, DM, pre-infarction angina, Killip class III/IV, and multivessel disease independently served as predictors of good CCF (OR: 0.053, 95% CI: 0.014 \u0026ndash; 0.201, p\u0026lt;0.001 and OR: 0.051, 95% CI: 0.013\u0026ndash;0.194, p\u0026lt;0.001; OR: 21.507, 95% CI: 5.833 \u0026ndash; 70.299, p\u0026lt;0.001 and OR:24.259, 95% CI: 6.526-90.171, p\u0026lt;0.001; OR: 0.559, 95% CI: 0.325-0.61, p=0.035 and OR: 0.515, 95% CI: 0.299-0.888, p=0.017; OR: 0.693, 95% CI: 0.512-0.939, p=0.018 and OR: 0.653, 95% CI: 0.482-0.885, p=0.006; respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Model 1, PP was not identified as an independent predictor for good CCF, whereas in Model 2, WPP was identified as a negative independent predictor for good CCF (OR: 0.549, 95% CI: 0.360-0.836, p=0.005). Furthermore, the forest plot illustrating independent predictors of CCF was presented in Figure 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the potential association between WPP and CCF in patients with STEMI who underwent p-PCI. Our findings revealed a significant correlation between WPP and poor CCF. Moreover, through multivariate logistic regression analysis, we identified WPP as an independent predictor of poor collateral flow in this patient population. Moreover, DM, pre-infarction angina, Killip class III/IV, and multivessel disease were also found to be independent predictors of CCF.\u003c/p\u003e \u003cp\u003eWhen a coronary artery becomes occluded or severely stenosed in patients with STEMI, a remodeling process is triggered, leading to the enlargement of pre-existing, non-functional arterioles. This process initiates the development of CCF in response to the redistribution of blood flow and increased shear stress [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The formation of CCF occurs in two distinct stages known as angiogenesis and arteriogenesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Various factors, including hypoxia, hypoperfusion, shear stress, cytokines, time of occlusion, DM, and severity of CAD, play critical roles in stimulating these stages [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study, we observed that DM and multivessel disease are predictive for poor CCF in patients with STEMI. Despite some findings suggesting that DM increases coronary collateral formation, our study indicates that the collaterals formed in diabetic patients are inadequate compared to those without diabetes, and even coronary collaterals are negatively affected by DM. Additionally, the insufficiency of antegrade coronary flow in multivessel disease may impede the effective development of CCF, which serves as a protective mechanism against cardiac ischemia.\u003c/p\u003e \u003cp\u003eHT, a well-known risk factor of CAD, can contribute to reduced myocardial flow reserve. Interestingly, an inverse relationship between CCF and SBP and PP was shown for the first time by Koersalman et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] In their study including 237 patients, patients with high preintervention SBP had fewer collaterals, and also the group with good collaterals had lower SBP and PP [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is well known that SBP and DBP which increase concomitantly due to age related changes in arterial stiffness, begin to diverge after approximately 50\u0026ndash;55 years of age, resulting an increase in SBP and a decrease in DBP [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Factors such as elastin thinning, degradation and replacement by collagen in the arterial wall are thought to contribute to this process [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These changes present as wide pulse pressure in cardiac examination. Although the pathophysiologic pathways are not fully understood, current researches suggest that WPP related to extensive cardiovascular disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the literature exploring the relationship between WPP and CCF is currently limited. Therefore, our study aimed to investigate the potential association between WPP and CCF in patients with STEMI. Our study showed that WPP is a negative predictor of CCF, unlike SBP, DBP, MAP and even PP. In our study, the observed association between WPP and poor collateral development may be attributed to several underlying mechanisms. The inverse association currently found between WPP and good CCF may be described by functional and structural remodeling, termed microvascular rarefaction of coronary arterioles, in response to increased PP [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This process comprises obliteration of pre-existing blood vessels, particularly arteriolar vessels 100\u0026ndash;150 mm in diameter. Moreover, Boudier et al.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] suggested that the resulting decrease in micro vascularity would increase both peripheral vascular resistance and pulse pressure. In addition, increased PP reflects greater arterial stiffness, impaired endothelial function, and altered vasomotor tone, which may hinder the development and recruitment of collateral vessels. These findings support the notion that arterial stiffness may act as a barrier to collateral growth, thereby reducing the capacity for collateral-dependent perfusion during myocardial infarction. Although this complex and bidirectional relationship between PP and CCF remains unclear, WPP appears to be a predictor of poor CCF.\u003c/p\u003e \u003cp\u003ePrevious studies have emphasized the importance of coronary collateral development in limiting the extent of myocardial damage and improving clinical outcomes following STEMI.[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Aslanjari et al.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] have demonstrated that patients without collaterals are at a higher risk of developing cardiogenic shock. In addition, they observed a significant protective effect against cardiogenic shock, even with the presence of smallest degree of collateral flow to the ischemia related artery. Our study, which reveals that Killip class III/IV heart failure is a predictor of poor CCF, supports previous research and underscores the impact of CCF on the development of acute heart failure. However, contrary to previous findings, cardiogenic shock rates were similar between the two groups in our study. The lower proportion of patients with cardiogenic shock (Killip class IV) in our study compared to the study of Aslanjari et al.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], as well as differences in the classification of CCF, may have influenced the results. Further studies are warranted to explore this topic further.\u003c/p\u003e \u003cp\u003eThe development of such collaterals is time dependent process. A previous study found that history of pre-infarct angina can provide stimuli resulted in collateral development [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similar to these findings, pre-infarction angina was also significantly more common in the good CCF group in this study. Furthermore, the presence of pre-infarction angina was identified as an independent predictor of good CCF. This finding is intriguing as pre-infarction angina may signify ongoing ischemia and a state of chronic vascular dysfunction. Such a milieu might influence the development of collaterals, leading to compensatory mechanisms in response to acute ischemic events.\u003c/p\u003e \u003cp\u003eOverall, our study emphasizes the clinical importance of WPP as a potential indicator of poor CCF in patients with STEMI undergoing p-PCI. This parameter, easily obtainable from BP measurement, may help identify individuals at higher risk of impaired collateralization and, therefore, enable interventional cardiologists to tailor treatment strategies for improved outcomes. Nonetheless, our study contributes valuable insights into understanding the complex interplay between hemodynamic factors and coronary collateral formation. However, additional research is necessary to corroborate our findings and elucidate the precise mechanisms linking WPP to coronary collateralization.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study had several limitations. Firstly, the measurement of BP at hospital admission may be influenced by preprocedural stress responses and concurrent drug therapy, which may not align with current guideline [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Secondly our study protocol only encompassed angiographically visualized coronary collaterals with a diameter exceeding 100 \u0026micro;m. Lastly, in our study, the WPP was not categorized into subgroups such as high systolic-low diastolic BP (isolated systolic hypertension), low systolic-low diastolic BP and high systolic-high diastolic BP. Consequently, the association of these subgroups with collateralization could not be evaluated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWPP, derived from BP measurements at hospital admission, was associated with CCF in STEMI patients undergoing p-PCI. Moreover, in contrast to SBP, DBP, MAP, and even PP, WPP was found to predict poor CCF in these patients. The measurement of PP is a straightforward and cost-effective parameter that can provide valuable insights into CCF in patients with STEMI.\u003c/p\u003e"},{"header":"Summary Table","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is known about the topic\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eWPP has been linked to chronic diseases such as DM, stroke, and CKD.\u003c/li\u003e\n \u003cli\u003eWPP has been independently associated with extensive cardiovascular disease and increased all-cause mortality.\u003c/li\u003e\n \u003cli\u003ePrevious studies have suggested that PP influenced by age-related changes in blood pressure, may serve as an indicator of cardiovascular health.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eThis study uncovers a connection between WPP and coronary collateral flow.\u003c/li\u003e\n \u003cli\u003eWPP emerges as a potentially valuable predictor of poor collateral flow.\u003c/li\u003e\n \u003cli\u003eThe impact of wide pulse pressure on coronary collateral flow surpasses that of SBP, DBP, MAP, and even PP.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of the research is available in our University Hospital archive. A de-identified dataset will be shared with other researchers upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and design: CY, BGŞ, AK. Data collection: AK, CY, BK, MMT. Data curation and analysis: BK, MMT, CY. Writing original draft: CY. Writing reviewing and editing; TU, RZ, CY, MMT, AK, BGŞ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Cemalettin Yılmaz\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the principles of the Declaration of Helsinki and received ethical approval from the Kartal Kosuyolu Training and Research Hospital.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors declare that they have no conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYoon SJ, Ko YG, Kim JS, Moon JY, Kim YJ, Park S, et al. 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A protective role of early collateral blood flow in patients with ST-segment elevation myocardial infarction. Am Heart J 2016;171:56\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.AHJ.2015.10.016\u003c/span\u003e\u003cspan address=\"10.1016/J.AHJ.2015.10.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e \u0026nbsp;Baseline and clinical characteristics of patients based on CCF\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood CCF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=272 (23.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor CCF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en= 908 (76.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e61 (56-68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e61.5 (54.5-67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eGender (male), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e174 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e631 (69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e27.7 (25.1-29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e26.9 (25.0-29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e62 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e190 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eHT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e171 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e605 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eHL, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e132 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e416 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003ePrevious PCI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e44 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e128 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eCOPD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e32 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e88 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e16 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e36 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e145 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e535 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eHistory of AF, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e23 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e61 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOn admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eAF on admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e17 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e79 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eSBP (mmHg), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e144 (136-151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e144 (133-152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eDBP (mmHg), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e88 (80-94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e87.5 (79-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eMAP (mmHg), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e106 (99-113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e106 (96.9-114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003ePP (mmHg), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e57 (52-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e54 (47-62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003eWide PP,\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e33 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e166 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuring hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eStent thrombosis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e20 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003ePost-PCI EF (%), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e50 (40-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e48 (40-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eKillip class III/IV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e18 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e97 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eCardiogenic shock, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e23 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eInotropic need, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e24 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e52 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eCPR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e19 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of ICU stay (hour), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e24 (20-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e27 (20.8-36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eHospitalization duration (day), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.166666666666664%\" valign=\"top\"\u003e\n \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.5%\" valign=\"top\"\u003e\n \u003cp\u003e24 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold values denote statistical significance at the p\u0026lt;0.05 level.\u003c/p\u003e\n\u003cp\u003eCCF, coronary collateral flow; DM, diabetes mellitus; HT, hypertension; HL, hyperlipidemia; PAD, peripheral arterial disease; COPD, chronic obstructive pulmonary disease; CVD, cerebrovascular disease, AF, atrial fibrillation; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; post-PCI EF, post-percutaneous coronary intervention ejection fraction; PCI, percutaneous coronary intervention; CPR, cardiopulmonary resuscitation; ICU duration, intensive care unit duration; IQR, interquartile ranges.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eProcedural data of study population according to CCF\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"598\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood CCF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=272 (23.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor CCF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en= 908 (76.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-procedural data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003eAnterior infarction, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e80 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e300 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003ePre-infarction angina, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e74 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e190 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003eSymptom to reperfusion time (minute), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e278 (120-600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e283 (131-600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuring procedure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003eMultivessel disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e82 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e347 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003ePre-TIMI flow 0, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e165 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e617 (68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003eFinal TIMI flow, n (%)\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (3.3)\u003c/p\u003e\n \u003cp\u003e24 (8.8)\u003c/p\u003e\n \u003cp\u003e239 (87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47 (5.2)\u003c/p\u003e\n \u003cp\u003e84 (9.3)\u003c/p\u003e\n \u003cp\u003e777 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003eNo-reflow, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e31 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e125 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.15719063545151%\" valign=\"top\"\u003e\n \u003cp\u003eAmount of contrast media (mL), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.073578595317727%\" valign=\"top\"\u003e\n \u003cp\u003e194 (144-290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e209 (144-300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200668896321071%\" valign=\"top\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold values denote statistical significance at the p\u0026lt;0.05 level.\u003c/p\u003e\n\u003cp\u003eCCF, coronary collateral flow; TIMI, thrombolysis in myocardial infarction; pre-TIMI flow, pre-procedural TIMI flow; IQR, interquartile ranges.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Laboratory findings of patients based on CCF\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood CCF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=272 (23.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor CCF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en= 908 (76.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e9.9 (8.2-12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e10.4 (8.6-13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e13.7 (12.7-14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e13.8 (12.7-14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003ePlatelet (10\u003csup\u003e3\u003c/sup\u003e/ \u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e255 (214-314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e257 (214-319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e200 (165-246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e193 (111-253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eLDL (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e126 (95.8-152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e127 102-152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eHDL (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e32 (22-39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e31 (19-39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e193 (111-253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e184 (124-240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eTotal protein (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e6.59 (6.00-7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e6.80 (6-7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e4.03 (3.70-4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e4 (3.7-4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003ePeak troponin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e1.10 (0.38-5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e1.91 (0.62-5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eUrea (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e24 (16-38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e26 (17.1-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eBasal creatinin (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e0.80 (0.65-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e0.82 (0.71-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eUric acid (mg/ dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e6 (4.80-6.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e6 (5.0-6.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e5.6 (2.5-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e5.7 (2.6-16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.166666666666664%\"\u003e\n \u003cp\u003eTSH (\u0026mu;IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003e1.3 (1.0-2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.166666666666668%\"\u003e\n \u003cp\u003e1.2 (1.0-2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.333333333333334%\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold values denote statistical significance at the p\u0026lt;0.05 level.\u003c/p\u003e\n\u003cp\u003eCCF, coronary collateral flow; WBC, white blood cell; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, triglyceride; CRP; C reactive protein; TSH, thyroid-stimulating hormone. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Multivariable logistic regression analysis at Model 1 and Model 2 for prediction of good CCF\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.124792013311147%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.272878535773714%\" colspan=\"3\" style=\"width: 33.9337%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.44093178036606%\" colspan=\"3\" style=\"width: 32.9998%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95 % CI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(lower-upper)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95 % CI\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(lower-upper)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.993-1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.992-1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.569-1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.579-1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.014-0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.013-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.626-1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.640-1.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003ePre-infarction angina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e21.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e5.833-70.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e24.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e6.526-90.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eKillip class III/IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.325-0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.299-0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eMultivessel disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.512-0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.482-0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003ePeak troponin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.906-1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.916-1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003ePre-TIMI flow 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.632-1.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.582-1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003ePrevious PCI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.792-1.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.814-1.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e0.997-1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.166666666666668%\" style=\"width: 23.5046%;\"\u003e\n \u003cp\u003eWide PP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.333333333333332%\" style=\"width: 16.1886%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.5%\" style=\"width: 8.8726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.333333333333334%\" style=\"width: 8.7169%;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.833333333333334%\" style=\"width: 14.7876%;\"\u003e\n \u003cp\u003e0.360-0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" style=\"width: 9.4952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold values denote statistical significance at the p\u0026lt;0.05 level.\u003c/p\u003e\n\u003cp\u003eCCF, coronary collateral flow; DM, diabetes mellitus; CVD, cerebrovascular disease; pre-TIMI flow, pre-procedural TIMI flow; PCI, percutaneous coronary intervention; PP, pulse pressure; CI, confidence interval.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-human-hypertension","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jhh","sideBox":"Learn more about [Journal of Human Hypertension](http://www.nature.com/jhh/)","snPcode":"41371","submissionUrl":"https://mts-jhh.nature.com/cgi-bin/main.plex","title":"Journal of Human Hypertension","twitterHandle":"@jhhypertension","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4363861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4363861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoronary collateral flow (CCF) plays a protective role in myocardial viability. Pulse pressure (PP) is defined as the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), has been associated with various cardiovascular diseases. However, the relationship between wide PP (WPP) and CCF in ST elevation myocardial infarction (STEMI) patients remains limited. Our objective was to assess how WPP impacts CCF in patients with STEMI undergoing primary percutaneous coronary intervention (p-PCI). This retrospective, single center study included 1180 STEMI patients underwent p-PCI in a tertiary healthcare center between 2021 and 2023. Patients were classified into two groups (good and poor CCF) based on the CCF status (Rentrop 0 and 1: poor CCF; Rentrop 2 and 3: good CCF). WPP was defined as PP\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg. Multivariable logistic regression included two distinct models was used to identify independent predictors of good CCF. A total of 272 patients (23.1%) were assigned to good CCF group while 908 patients (76.9%) were categorized into the poor CCF group. WPP was identified a negative independent predictor for good CCF (OR: 0.511, 95% CI: 0.334\u0026ndash;0.783, p\u0026thinsp;=\u0026thinsp;0.002). Moreover, diabetes mellitus, pre-infarction angina, Killip class III/IV, multivessel disease, and pre-TIMI (thrombolysis in myocardial infarction) flow 0 were also found to be independent predictors of CCF. WPP, derived from blood pressure measurements was associated with CCF in STEMI patients undergoing p-PCI. Moreover, in contrast to SBP, DBP, mean arterial pressure, and even PP, WPP was found to predict poor CCF.\u003c/p\u003e","manuscriptTitle":"Association of Wide Pulse Pressure with Coronary Collateral Flow in Patients with ST- Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 14:25:35","doi":"10.21203/rs.3.rs-4363861/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-10-08T11:49:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-10-05T17:03:18+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-10-05T16:42:23+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-17T09:32:11+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-09T02:49:11+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-05-17T06:45:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-15T22:57:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-09T09:02:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Human Hypertension","date":"2024-05-07T12:33:23+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-05-07T09:19:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-human-hypertension","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jhh","sideBox":"Learn more about [Journal of Human Hypertension](http://www.nature.com/jhh/)","snPcode":"41371","submissionUrl":"https://mts-jhh.nature.com/cgi-bin/main.plex","title":"Journal of Human Hypertension","twitterHandle":"@jhhypertension","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"96a1ae10-630f-4eae-8fae-4239e431c3f2","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32041694,"name":"Health sciences/Diseases/Cardiovascular diseases/Acute coronary syndromes/Myocardial infarction"},{"id":32041695,"name":"Health sciences/Diseases/Cardiovascular diseases/Hypertension"}],"tags":[],"updatedAt":"2024-12-18T08:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-4363861","link":"https://doi.org/10.1038/s41371-024-00986-3","journal":{"identity":"journal-of-human-hypertension","isVorOnly":false,"title":"Journal of Human Hypertension"},"publishedOn":"2024-12-17 05:00:00","publishedOnDateReadable":"December 17th, 2024"},"versionCreatedAt":"2024-06-03 14:25:35","video":"","vorDoi":"10.1038/s41371-024-00986-3","vorDoiUrl":"https://doi.org/10.1038/s41371-024-00986-3","workflowStages":[]},"version":"v1","identity":"rs-4363861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4363861","identity":"rs-4363861","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}