Stress hyperglycaemia ratio is an independent predictor of in-hospital heart failure among patients with anterior ST-segment elevation myocardial infarction

Stress hyperglycaemia ratio is an independent predictor of in-hospital heart failure among patients with anterior ST-segment elevation myocardial infarction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stress hyperglycaemia ratio is an independent predictor of in-hospital heart failure among patients with anterior ST-segment elevation myocardial infarction Zia ul Sabah, Saif Aboud M Alqahtani, Javed Iqbal, Shahid Aziz, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5028884/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 20 You are reading this latest preprint version Abstract Background Stress hyperglycaemia ratio (SHR) has been reported to be independently and significantly associated with various adverse cardiovascular events as well as mortality. Moreover, in-hospital heart failure following acute myocardial infarction has been demonstrated to account for majority of all heart failure (HF) cases with anterior myocardial infarction showing higher rates of HF. However, the association between SHR and in-hospital HF following an anterior ST-elevation myocardial infarction (STEMI) has not been reported earlier. Therefore, the present study aimed at identifying the relationship between SHR and in-hospital HF post STEMI. Methods In this retrospective study electronic health records of 512 patients who presented with anterior STEMI from 01 January 2022 to 31 January 2024 were analysed. Based on the development of in-hospital HF, the enrolled patients were stratified into two groups: Group I, comprising of 290 patients who developed in-hospital HF and Group II comprising of 222 patients who did not develop in-hospital HF. ROC and Multivariable logistic regression analyses were performed to assess the relationship between SHR and in-hospital HF. Results The results revealed that SHR is a significant independent predictor of in-hospital HF (OR: 3.53; 95%CI: 2.02–6.15; p < 0.001). Apart from SHR, the results also identified age, nosocomial pneumonia, ventricular fibrillation, LVEF, and NT-pro-BNP levels as other independent predictors. ROC analysis showed that SHR independently had a moderate discriminative power with AUC: 0.683, 95% CI 0.605–0.762; p = 0.04, which was almost comparable to the combined predictive value of other independent risk factors (AUC: 0.726, 95% CI 0.677–0.784). Noticeably, combining SHR and other identified independent predictors demonstrated a significant predictive power (AUC: 0.813, 95% CI 0.757–0.881; p = 0.01). Conclusion SHR is an independent predictor for in-hospital HF in anterior wall STEMI patients. Stress hyperglycaemia ratio anterior wall STEMI In-hospital heart-failure NT-pro-BNP Nosocomial pneumonia Figures Figure 1 Introduction The most common sequalae of acute ST-elevation myocardial infarction (STEMI) is new-onset left ventricular systolic dysfunction that poses increased risks for sudden death and heat failure (HF). Although the use of primary percutaneous coronary intervention (pPCI) results in better prognosis [ 1 , 2 ] as it can limit both the infarct size as well as preserve the left ventricular systolic function [ 3 , 4 ]. However, previous studies have demonstrated that timely PCI in not helpful in preserving or maintaining heart function in all STEMI patients and despite successful pPCI, 4.7–8.6% of STEMI patients still experience significant depression in heart function [ 5 , 6 ]. Despite the rapid advances in interventional and pharmacological treatment modalities, STEMI still is the leading cause of HF and mortality. As per an estimate the incidence of HF in patients with STEMI even after pPCI is 4.6%, 4.7%, and 5.1% at 1 month, 1 year, and at 2 years respectively[ 5 ]. Post-acute STEMI incidence of new-onset HF has been reported to range from 10%-45% [ 7 – 13 ]. Previous investigations have suggested that in-hospital HF after acute myocardial infarction (MI) is a major contributor for all HF cases [ 14 ]. Previous investigations studying the significant predictors of HF following STEMI have shown high heterogeneity among the study subjects in terms of type of acute MI (STEMI or non-STEMI), reperfusion modalities (thrombolysis, pPCI, and PCI after thrombolysis), exclusion of patients with cardiogenic shock, and infarct location[ 15 – 19 ]. Studies have demonstrated higher rates of HF [ 5 , 16 , 19 ], lower left-ventricular ejection fraction [ 20 ], and higher mortality [ 21 ] in patients with anterior MI compared to patients with other infarct locations. Acute hyperglycaemia in response to physiological stress because of an acute illness has been associated with significantly higher risks of morbidity and mortality in critically ill patients[ 22 – 26 ]. Studies have reported this correlation between stress hyperglycaemia and morbidity and mortality irrespective of prior status of diabetes and can be more significant in non-diabetic subjects [ 22 – 24 ]. Recently a novel indicator of relative hyperglycaemic status, the stress hyperglycaemia ratio (SHR) has been proposed [ 27 ]. It is defined as random glucose levels at admission divided by the estimated average glucose levels using glycosylated haemoglobin (HbA1c). It has been demonstrated that SHR is a better marker of stress hyperglycaemia and a significant predictor of critical illness compared to absolute hyperglycaemia in patients with acute illness[ 27 ]. Previous studies have shown a significant association between SHR and poor prognosis in acute coronary syndrome (ACS) patients [ 28 – 30 ]. However, these studies have primarily focussed on unstable angina and non-STEMI [ 28 ]. Furthermore, the association between SHR and HF in STEMI patients treated with PCI remains unexplored, therefore necessitating an investigation. Since in-hospital HF after acute MI has been demonstrated to account for majority of all HF cases [ 14 ], and anterior MI showing higher rates of HF [ 5 , 16 , 19 ], and mortality [ 21 ], the present study is therefore aimed at assessing the significance of SHR (calculated from HbA1c) in predicting in-hospital HF in anterior STEMI patients undergoing PCI. Methods Study design and setting This retrospective study was conducted at Prince Faisal-Bin-Khalid Cardiac Centre, Abha, Kingdom of Saud Arabia. Study subjects and duration From 01 January 2022 to 31 January 2024, the electronic health records (EHR’s) of a total of 600 patients with anterior wall STEMI were perused. Out of 600, only 512 patients met our set inclusion / exclusion criteria, hence were finally enrolled. Amongst the enrolled 512 patients, 290 with in-hospital heart failure (HF) were included in Group I, and 222 patients who had not developed in-hospital heart failure were included in Group II. The study was approved by the ethics committee at King Khalid University (HAPO-06-B-001), Abha, Saudi Arabia vide approval number ECM# 2024 − 1901 and carried out in-line with the 2013 revision of the principles of Declaration of Helsinki[ 31 ]. Written consent was obtained from each subject enrolled. Inclusion Criteria Patients were only included in the study if: (1) they had been diagnosed with acute anterior wall STEMI (2) Underwent primary percutaneous intervention (pPCI) within 1.5 hours of symptom onset. Exclusion Criteria Exclusion criteria were: (1) Any history of previous myocardial infarction, congenital heart disease, cardiomyopathy, chronic heart failure, or severe valvular disease (2) Incomplete clinical data. Data and Definitions The following clinical variables were recorded for all patients enrolled: (1) age, gender, smoking status, heart rate, systolic blood pressure, diastolic blood pressure, and past medical history (Diabetes, hypertension, past myocardial infarction (MI) (2) Laboratory variates (WBC, blood sugar, glycosylated haemoglobin (HBA1c), serum creatinine, cystatin C, low density lipoprotein (LDL), biomarkers (highest peak from repeated samples during hospitalization)- creatine kinase, creatine kinase-MB, N-Terminal pro- brain natriuretic peptide (NT-pro BNP), and cardiac troponin I (3) In-hospital complications- Atrial Fibrillation (AF), ventricular fibrillation (VF), sustained ventricular tachycardia, and nosocomial pneumonia (NP) (4) data from first transthoracic echocardiography-left ventricular ejection fraction(LVEF), left ventricular end diastolic diameter, left atrial diameter, mitral and aortic regurgitation, left ventricular diastolic dysfunction, and regional wall motion abnormality (RWMA) of left ventricular wall (5) Coronary angiographic data- pre-procedural thrombolysis in myocardial infarction (TIMI) flow grade in the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA), post-procedural TIMI flow grade < 3 in infarct related artery, lesions in the proximal part of LAD, single vessel disease, three vessel diseases, and total artery occlusion. The diagnosis of acute STEMI was confirmed as per the criteria established by the European Society of Cardiology[ 32 ]. The infarct region was detected by electrocardiogram and confirmed by angiography. In-hospital HF was defined as documented evidence of dyspnoea upon exertion or fluid retention, and in patients with Killip class ≥ II upon signs of HF like rales, jugular venous distention, or pulmonary edema and was only confirmed once excluding other conditions that can cause similar signs and symptoms. Patients were stratified to one of the Killip classes, as per the set criteria[ 33 ]. Stress hyperglycaemia ratio (SHR) was calculated as, SHR = (Fasting blood glucose (FBG) (mmol/L)) / (1.59×HbA1c (%)—2.59) [ 28 ]. The decision of using fasting glucose (FBG) instead of admission glucose (ABG) levels as the numerator in the above equation was grounded in its comparatively better prognostic significance in patients with acute cardiovascular disease [ 34 , 35 ]. It has also been reported to be insensitivity to food or other sugar infusions, and limited inter-individual variability [36,37 ]. P-value < 0.05 Statistical Analysis All statistical analysis was performed using R[ 38 ] scripting language and the accompanying R studio [ 39 ] (Version 1.2.5033, Orange Blossom). Sample size calculation was done using Cochran’s equation [ 40 ]. Continuous variables with Gaussian distribution were reported as mean ± standard deviation (SD), whereas continuous variables with non-Gaussian distribution were reported as median (interquartile interval), and categorical data in numbers and percentages (%). Continuous variables following Gaussian distribution were compared using student’s t-test, whereas continuous variables with non-Gaussian distribution were compared using Mann-Whitney U-test. Chi-Square tests were used to compare categorical variables. The odds ratio (OR) and confidence interval (CI) were obtained by multivariable logistic regression. Variates with p < 0.05 in univariate analysis were considered significant and hence were included in multivariable logistic regression analysis to uncover independent predictors ( p < 0.05). The predictive value of each significant independent predictor was assessed utilizing the area under receiver operating characteristic curve (AUROC). Results Baseline characteristics of the study cohort This study involved a total of 512 anterior-wall STEMI patients. The mean age of the study population was 62 ± 14.2. The study cohort was predominantly male (79.8%). The study subjects were stratified into two groups: one comprising of subjects who developed in-hospital HF (Group I) and the other comprising of subjects not developing in-hospital HF (Group II). The primary analysis of data extracted (demographic, clinical, echocardiographic, and angiographic characteristics of the enrolled study subjects) revealed that subjects in Group I in comparison to subjects in Group II presented with older age ( p < 0.001), increased heart rate ( p < 0.001), higher fasting blood glucose ( p < 0.001), higher glycated hemoglobin (HbA1c) ( p = . 001 ) , higher SHR ( p < 0.001), low blood pressure, low LVEF ( p < 0.001), new-onset AF ( p < 0.001), VF ( p < 0.001), ventricular tachycardia ( p < 0.001), NP ( p < 0.001), increased left-ventricular end-diastolic diameter, mitral regurgitation ( p < 0.001), regional-wall motion abnormality (RWMA) in apical ( p = 0.010), anterior ( p < 0.001) and rest of the left-ventricular wall ( p < 0.001), higher WBC count, higher serum creatinine ( p < 0.001), higher cystatin C levels ( p < 0.001), increased fibrinogen levels, higher peaks of D-dimer ( p < 0.001) and NT-pro-BNP ( p < 0.001). Additionally, 3-vessel disease, pre-procedural TIMI flow grade ≤ 1 in LCX and RCA, post-procedural TIMI flow grade ≥ 3 in infarct-related artery were significantly associated with the in-hospital HF (Table 1 ). Table 1 Baseline demographic and patient characteristics stratified by in-hospital HF Variate Group I (n = 290) Group II (n = 222) p -value Demographic and clinical variates Age (years), mean ± SD 65.2 ± 9.6 57.6 ± 10.5 < 0.001 Male, n (%) 218 (75) 191 (86) 0.002 Hypertensive, n (%) 154 (53) 107 (48) 0.262 Diabetic, n (%) 96 (33) 58 (26) 0.086 Smoker, n (%) 142 (49) 118 (53) 0.370 SBP (mmHg), mean ± SD 129.6 ± 23.7 135.32 ± 22.6 0.005 DBP (mmHg), mean ± SD 77.3 ± 14.8 80.4 ± 14.9 0.019 HR (bpm), median (Q1, Q3) 87(74,101) 81(72, 93) < 0.001 New-onset Atrial Fibrillation, n (%) 26 (9) 7 (3) 0.006 Sustained Ventricular Tachycardia, n (%) 18 (6) 1 (0.4) < 0.001 Ventricular Fibrillation, n (%) 35 (12) 7 (3) < 0.001 Nosocomial Pneumonia, n (%) 73 (25) 9 (4) < 0.001 Laboratory variates Fasting Blood Glucose, mmol/L 9.29 ± 4.10 6.02 ± 1.64 < 0.001 HbA1c (%), mean ± SD 6.98 ± 1.81 6.43 ± 1.38 0.001 SHR, median (Q1, Q3) 0.98(0.87, 1.19) 0.72(0.61, 0.83) < 0.001 WBC (10 9 /L), median (Q1, Q3) 11.8(8.6, 15.6) 10.6(7.5, 10.9) 0.031 LDL (mmol/L), (mean ± SD) 3.6 ± 1.3 3.4 ± 1.2 0.072 Cystatin C (mg/L), median (Q1, Q3) 1.2(1.0, 1.7) 1.0(0.8, 1.3) < 0.001 Serum Creatinine (µmol/L), median (IQR interval) 83.0(68.6, 112) 75.2(61.0, 86.8) < 0.001 Peak CK (IU/L), median (Q1, Q3) 1006.3(268.5, 2898) 1012.7(278.0, 2383.2) 0.623 Peak CK-MB (IU/L) 90.2(32.2, 271.0) 83.4(34.7, 200.6) 0.415 Fibrinogen (g/L), mean ± SD 4.2 ± 1.6 3.9 ± 1.1 0.017 Peak D-dimer (µg/mL), median (Q1, Q3) 1143(541.2, 1696.3) 500(287.2, 1478) < 0.001 Peak Troponin I (µg/L), mean ± SD 18.8 ± 16.9 16.7 ± 15.3) 0.147 Peak NT-pro-BNP (pg/mL), median (Q1, Q3) 2168(887, 4937) 693(313, 2158) < 0.001 Echocardiographic variates LVEF (%), median (Q1, Q3) 45(38, 56) 56(49, 60) < 0.001 RWMA Apex, n(%) 201(69) 129(58) 0.010 Anterior wall, n(%) 250(86) 164(74) < 0.001 Rest of anterior wall, n(%) 154(53) 82(37) < 0.001 Table 1 continued Variate Group I (n = 290) Group II (n = 222) p -value Left-ventricular diastolic dysfunction, n(%) 119(41) 80(36) 0.250 Left-atrial diameter (mm), median(Q1, Q3) 36(31, 39) 35(32, 40) 0.121 Left-ventricular end-diastolic diameter (mm), Median(Q1, Q3) 46(41, 49) 45(43, 48) 0.041 Mitral regurgitation, n(%) 116(40) 56(25) < 0.001 Coronary-angiographic variates Pre-procedural TIMI flow grade ≤ 1 in LAD, n(%) 148(51) 109(49) 0.654 LCX, n(%) 27 (9) 7(3) 0.006 RCA, n(%) 32(11) 11(5) 0.015 1-vessel disease, n(%) 87(30) 89(40) 0.018 3-vessel disease, n(%) 119(41) 66(30) 0.010 Presence of lesion in proximal LAD, n(%) 215(74) 154(69) 0.213 Total artery occlusion, n(%) 139(48) 102(46) 0.653 Post-procedural TIMI flow grade < 3, n(%) 29(10) 11(5) 0.037 SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; HbA1c, glycosylated haemoglobin; SHR, stress hyperglycaemia ratio; WBC, white blood cells; LDL, low density lipoprotein; CK, creatine kinase; CK-MB, creatine kinase-myocardial band; NT-pro-BNP, N-terminal pro-brain natriuretic peptide; LVEF, left-ventricular ejection fraction; RWMA, regional-wall motion abnormality; TIMI, thrombolysis in myocardial infarction; LAD, left-anterior descending artery; LCX, left-circumflex artery; RCA, right-coronary artery; mean ± SD, mean ± standard deviation; p < 0.05, significant. Association of SHR and in-hospital heart failure post anterior-wall STEMI To find the association between SHR and in-hospital HF following anterior-wall STEMI, parameters with statistically significant differences between the two groups were further analysed with univariate and multivariate logistic regression analyses, with in-hospital HF as dependent variable. The results revealed that stress hyperglycaemia ratio (SHR) is a significant independent predictor of in-hospital HF (OR: 3.53; 95%CI: 2.02–6.15; p < 0.001), as presented in Table 2 . Table 2 Association of SHR with in-hospital HF after anterior-wall STEMI SHR ≥ 0.85 OR 95%CI \(\:P\) SHR (unadjusted) 4.38 2.60–9.27 0.001 SHR (adjusted)* 3.53 2.02–6.15 < 0.001 *, adjusted for age, gender, left-ventricular ejection fraction, ventricular fibrillation, nosocomial pneumonia, and N-terminal pro-brain natriuretic peptide levels; p < 0.05, significant. Apart from SHR, the other independent predictors of in-hospital HF identified by multivariate logistic regression are presented in Table 3 . Table 3 Other Independent Predictors of in-hospital HF based on Multivariate Logistic Regression NP, nosocomial pneumonia; SHR, stress hyperglycaemia ratio; LVEF, left-ventricular ejection fraction; NT-pro-BNP, N-terminal pro-brain natriuretic peptide; p < 0.05, significant. Independent Predictor OR 95% CI p-value Age (≥ 63 Years) 1.02 0.96–1.10 0.037 NP 3.48 2.12–8.87 < 0.001 Ventricular Fibrillation 4.32 2.18–10.36 < 0.001 LVEF (< 50%) 1.08 0.89–1.15 0.024 Peak NT-pro-BNP (≥ 832 pg/mL) 1.04 0.91–1.21 0.010 Cut-off value and predictive power of SHR Using Youden’s index [ 41 ], the optimal cut-off value of SHR was calculated to be 0.85. Furthermore, the ROC curve analysis (Fig. 1 ) showed that SHR independently (Model 2) had a moderate predictive potential for in-hospital HF in anterior wall STEMI undergoing PCI (AUC: 0.683, 95% CI 0.605–0.762), which was almost comparable to the combined predictive value of other independent risk factors (Model 1) (AUC: 0.726, 95% CI 0.677–0.784). Noticeably, Model 3 (a composite of Model 1 and Model 2) demonstrated a significant predictive power (AUC: 0.813, 95% CI 0.757–0.881) compared to Model 1 alone (Table 4 ). Table 4 AUROC for Model 1, Model 2, and Model 3 Predictive factors AUROC (95% CI) p -value Model 1 (Age + NP + VF + LVEF + NT-pro-BNP) 0.726 (0.677–0.784) Reference Model 2 (SHR) 0.683 (0.605–0.762) 0.04 Model 3 (Model 1 + Model 2) 0.813 (0.757–0.881) 0.01 NP, nosocomial pneumonia; VF, ventricular fibrillation; LVEF, left-ventricular ejection fraction; NT-pro-BNP, N-terminal pro-brain natriuretic peptide; SHR, stress hyperglycaemia ratio; p < 0.05, significant. Discussion To our knowledge this is the first study to investigate the impact of SHR calculated from glycosylated haemoglobin (HbA1c) on in-hospital HF following an anterior wall STEMI. The results strongly suggest that apart from conventional risk parameters-age, VF, nosocomial pneumonia, LVEF, and NT-pro-BNP levels, SHR is an independent predictor of in-hospital HF in patients with anterior wall STEMI (OR: 3.53, 95% CI: 2.02–6.15, p < 0.001). It has been suggested that the hypothalamic-pituitary-adrenal axis is responsible for stress hyperglycaemia by elevating cortisol and adrenaline secretion[ 42 ]. Pro-inflammatory cytokines-interleukin-1, interleukin-6, and tumor necrosis factor-α have been suggested to be responsible for impairment of insulin secretion and insulin resistance, and stress hyperglycaemia has been reported to be responsible for overexpression of these pro-inflammatory cytokines [ 36 , 37 , 43 ], thereby indicating a transitive relationship between stress hyperglycaemia and insulin resistance. Moreover, hypercoagulable state may be attributed to stress induced hyperglycaemia as it contributes to increased thrombogenic activity [ 44 , 45 ]. Earlier investigations have shown significant associations between increased SHR and lager thrombus burden and decreased TIMI flow grade in angiography[ 46 , 47 ]. The results of the present study are partially in line with various earlier studies. Stress hyperglycaemia has been reported to be a significant predictor of poor prognosis in acute coronary syndrome (ACS) patients in general and acute myocardial patients in particular [ 23 , 28 , 29 , 30 ]. Stress hyperglycaemia has not only been established as a significant indicator of the severity of an acute emergent condition, but it has also been demonstrated to complicate and catalyse obstruction in microvasculature[ 48 ], weaken endothelial vasodilatory mechanisms[ 49 ], hinder platelet nitric oxide response[ 50 ], and augment vascular damage. However, admission blood glucose levels do not truly reflect the stress state as the actual stress hyperglycaemic state is masked by the chronic glucose levels [ 51 ]. Hence, as a sequel to the previous argument, Roberts et al. [ 52 ], proposed SHR as an indicator of real stress hyperglycaemia by filtering out the chronic glycaemic state from the absolute hyperglycaemia on admission. They further proved that SHR is a robust and better biomarker of critical disease than hyperglycaemia on admission. In a retrospective study involving 905 STEMI patients, it was demonstrated that SHR was a strong predictor of no-reflow after primary PCI (pPCI) [ 53 ]. In another study on 1553 AMI patients, SHR has been reported to be a better prognostic indicator of in-hospital mortality [ 54 ] than absolute glucose levels on admission. Yang et al. [ 55 ], in a retrospective study involving 4362 subjects from the Catholic medical centre percutaneous coronary intervention (COACT) registry who underwent PCI, reported that the hazard ratio (HR) for upper SHR quartile (quartile 4) for long-term MACCE was 1.31 (95% CI 1.05–1.64) in comparison to lower SHR quartiles (quartiles 1–30). The present study also demonstrated that prior diabetic status has no influence on association of SHR and in-hospital HF following an anterior wall STEMI. This result is in line with an earlier study, wherein, a significant association was found between SHR and in-hospital death among patients following myocardial infarction (MI), irrespective of their prior diabetic status[ 29 ]. However, in a study SHR was shown to have no substantial association on all-cause mortality and cardiovascular death among non-diabetic ACS patients[ 56 ]. TIMI risk score [ 57 , 58 ] is the most used prognosis stratification model for STEMI patients but despite all its strengths and significance some previous studies have reported that in majority of cases its predictive utility is limited[ 59 , 60 ]. This may be attributable to the fact that the TIMI risk score model takes only cardiovascular risk factors into consideration and ignores the inclusion of all important metabolic factors. Hence, a few studies have suggested inclusion of SHR to the risk scoring system may aid in early risk profiling [ 30 , 41 ]. The present study discovered that SHR is a significant independent risk and predictive factor for in-hospital HF post anterior wall STEMI. This assumes more importance given the ease and low cost associated with obtaining fasting blood glucose and HbA1c in clinical settings. The results of this study also demonstrate that integrating SHR with conventional risk factors augment the risk prognosis of in-hospital HF following an anterior wall STEMI. This is strongly indicative of including SHR as a significant predictive factor in constructing new prognostic models for STEMI in general. Limitations Single centre study, limited sample size, demographic component largely drawn from a particular geography (Aseer region, Kingdom of Saudi Arabia), might limit the generalization of the results. All these constitute the main limitations of this study. Conclusion SHR is an independent predictor for in-hospital HF in anterior wall STEMI patients treated with pPCI. Adding SHR to already established conventional risk factors (age, LVEF, NT-pro-BNP levels) for in-hospital HF significantly augments the discriminative ability of conventional risk factors. Declarations Ethical Approval This study was approved by the Research Ethics Committee at King Khalid University (HAPO-06-B-001) vide approval No: ECM# 2024-1901, and written informed consent was obtained from all the participants. Consent for Publication Not Applicable Availability of Data The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflict of Interest None Funding and Acknowledgements The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia for funding this work through Large Group Project under grant number [RPG 2/562/44] Zia-ul-Sabah, Javed Iqbal, Shahid Aziz, and Humayoun Khan Durrani majorly contributed to the conceptualization and Design. Saif Aboud M Alqahtani, Ayyub Ali Patel, and Imran Rangreze contributed towards data acquisition, analysis, and interpretation of data. Rasha Mirdad, Sara Shahrani, and Muad Ali Alfayea contributed to manuscript writing. All authors read and approved the final manuscript. Acknowledgements The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for encouragement and funding this work. References Mehta RH, Parsons L, Rao SV, Peterson ED, National Registry of Myocardial Infarction (NRMI) Investigators. Association of bleeding and in-hospital mortality in black and white patients with st-segment-elevation myocardial infarction receiving reperfusion. Circulation. 2012;125(14):1727–34. 10.1161/CIRCULATIONAHA.111.068668 . Keeley EC, Boura JA, Grines CL. Primary angioplasty versus intravenous thrombolytic therapy for acute myocardial infarction: a quantitative review of 23 randomised trials. 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Short and long-term prognosis of admission hyperglycemia in patients with and without diabetes after acute myocardial infarction: a retrospective cohort study. Cardiovasc Diabetol. 2022;21(1):114. 10.1186/s12933-022-01550-4 . Published 2022 Jun 23. Mizock BA. Alterations in fuel metabolism in critical illness: hyperglycaemia. Best Pract Res Clin Endocrinol Metab. 2001;15(4):533–51. 10.1053/beem.2001.0168 . Heesen M, Bloemeke B, Heussen N, Kunz D. Can the interleukin-6 response to endotoxin be predicted? Studies of the influence of a promoter polymorphism of the interleukin-6 gene, gender, the density of the endotoxin receptor CD14, and inflammatory cytokines. Crit Care Med. 2002;30(3):664–9. 10.1097/00003246-200203000-00028 . R Core Team. R: a language and environment for statistical computing. R Foundation for statistical computing V, Austria. URL; 2020. https://www.R-project.org/ . RStudio T, RStudio, RStudio. PBC, Boston, MA URL http://www.rstudio.com/ . City;2020. Cochran WG. 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Hyperglycemia enhances coagulation and reduces neutrophil degranulation, whereas hyperinsulinemia inhibits fibrinolysis during human endotoxemia. Blood. 2008;112(1):82–9. 10.1182/blood-2007-11-121723 . Chu J, Tang J, Lai Y, et al. Association of stress hyperglycemia ratio with intracoronary thrombus burden in diabetic patients with ST-segment elevation myocardial infarction. J Thorac Dis. 2020;12(11):6598–608. 10.21037/jtd-20-2111 . Stalikas N, Papazoglou AS, Karagiannidis E et al. Association of stress induced hyperglycemia with angiographic findings and clinical outcomes in patients with ST-elevation myocardial infarction. Cardiovasc Diabetol . 2022;21(1):140. Published 2022 Jul 26. 10.1186/s12933-022-01578-6 Jensen CJ, Eberle HC, Nassenstein K, et al. Impact of hyperglycemia at admission in patients with acute ST-segment elevation myocardial infarction as assessed by contrast-enhanced MRI. Clin Res Cardiol. 2011;100(8):649–59. 10.1007/s00392-011-0290-7 . Williams SB, Goldfine AB, Timimi FK, et al. Acute hyperglycemia attenuates endothelium dependent vasodilation in humans in vivo. Circulation. 1998;97:1695–701. Worthley MI, Holmes AS, Willoughby SR, et al. The deleterious effects of hyperglycemia on platelet function in diabetic patients with acute coronary syndromes mediation by superoxide pro duction, resolution with intensive insulin administration. J Am Coll Cardiol. 2007;49:304–10. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D. A1c-Derived Average Glucose Study Group. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31:1473–8. Roberts GW, Quinn SJ, Valentine N, et al. Relative hyperglycemia, a marker of critical illness: introducing the stress hyperglycemia ratio. J Clin Endocrinol Metab. 2015;100:4490–7. Simsek B, Cõnar T, Tanõk VO, et al. The association of acute–to–chronic glycemic ratio with no-reflow in patients with ST–segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Kardiol Pol. 2021;79:170–8. Marenzi G, Cosentino N, Milazzo V, et al. Prognostic value of the acute-to-chronic glycemic ratio at admission in acute myocardial infarction: a prospective study. Diabetes Care. 2018;41:847–53. Yang Y, Kim TH, Yoon KH, et al. The stress hyperglycemia ratio, an index of relative hyperglycemia, as a predictor of clinical outcomes after percutaneous coronary intervention. Int J Cardiol. 2017;241:57–63. Liu Y, Yang YM, Zhu J, Tan HQ, Liang Y, Li JD. Prognostic signifi cance of hemoglobin A1c level in patients hospitalized with coronary artery disease. A systematic review and meta-analysis. Cardiovasc Diabetol. 2011;10:98. 10.1186/1475-2840-10-98 . Morrow DA, Antman EM, Parsons L, de Lemos JA, Cannon CP, Giugliano RP, et al. Application of the TIMI risk score for ST-elevation MI in the National Registry of Myocardial Infarction 3. JAMA. 2001;286:1356–9. Morrow DA, Antman EM, Charlesworth A, Cairns R, Murphy SA, de Lemos JA, et al. TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation. 2000;102:2031–7. Khan SQ, Quinn P, Davies JE, Ng LL. N-terminal pro-B-type natriuretic peptide is better than TIMI risk score at predicting death after acute myocardial infarction. Heart. 2008;94:40–3. Yanqiao L, Shen L, Yutong M, Linghong S, Ben H. Comparison of GRACE and TIMI risk scores in the prediction of in-hospital and long-term out comes among East Asian non-ST-elevation myocardial infarction patients. BMC Cardiovasc Disord. 2022;22:4. Chen Q, Su H, Yu X, Chen Y, Ding X, Xiong B, et al. The stress hyper glycemia ratio improves the predictive ability of the GRACE score for in-hospital mortality in patients with acute myocardial infarction. Hellenic J Cardiol. 2023;70:36–45. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5028884","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360777806,"identity":"cacf6421-6988-4f89-bcb1-c9d14be77fa0","order_by":0,"name":"Zia ul 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University","correspondingAuthor":false,"prefix":"","firstName":"Muad","middleName":"Ali","lastName":"Alfayea","suffix":""},{"id":360777815,"identity":"f38496c0-1380-44a8-951c-b1e4231b21c1","order_by":9,"name":"Sara Shahrani","email":"","orcid":"","institution":"Prince Faisal bin Khalid Cardiac Centre","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Shahrani","suffix":""}],"badges":[],"createdAt":"2024-09-04 05:48:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5028884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5028884/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-04362-4","type":"published","date":"2024-12-28T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67999833,"identity":"d6c34635-e5d6-423a-8387-d803e629982a","added_by":"auto","created_at":"2024-11-01 07:46:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106113,"visible":true,"origin":"","legend":"\u003cp\u003eThe Receiver operating characteristic curves for Model 1, Model 2, and Model 3.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5028884/v1/e83c411095308f2899c64753.png"},{"id":72640374,"identity":"3cb987b1-dcbf-4216-a44a-6fcf3ba5e1d1","added_by":"auto","created_at":"2024-12-30 16:05:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":776186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5028884/v1/64ca90c2-d92f-4b7a-9ed3-7f9990895515.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stress hyperglycaemia ratio is an independent predictor of in-hospital heart failure among patients with anterior ST-segment elevation myocardial infarction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe most common sequalae of acute ST-elevation myocardial infarction (STEMI) is new-onset left ventricular systolic dysfunction that poses increased risks for sudden death and heat failure (HF). Although the use of primary percutaneous coronary intervention (pPCI) results in better prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] as it can limit both the infarct size as well as preserve the left ventricular systolic function [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, previous studies have demonstrated that timely PCI in not helpful in preserving or maintaining heart function in all STEMI patients and despite successful pPCI, 4.7\u0026ndash;8.6% of STEMI patients still experience significant depression in heart function [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite the rapid advances in interventional and pharmacological treatment modalities, STEMI still is the leading cause of HF and mortality. As per an estimate the incidence of HF in patients with STEMI even after pPCI is 4.6%, 4.7%, and 5.1% at 1 month, 1 year, and at 2 years respectively[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Post-acute STEMI incidence of new-onset HF has been reported to range from 10%-45% [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previous investigations have suggested that in-hospital HF after acute myocardial infarction (MI) is a major contributor for all HF cases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous investigations studying the significant predictors of HF following STEMI have shown high heterogeneity among the study subjects in terms of type of acute MI (STEMI or non-STEMI), reperfusion modalities (thrombolysis, pPCI, and PCI after thrombolysis), exclusion of patients with cardiogenic shock, and infarct location[\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies have demonstrated higher rates of HF [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], lower left-ventricular ejection fraction [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and higher mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] in patients with anterior MI compared to patients with other infarct locations.\u003c/p\u003e \u003cp\u003eAcute hyperglycaemia in response to physiological stress because of an acute illness has been associated with significantly higher risks of morbidity and mortality in critically ill patients[\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Studies have reported this correlation between stress hyperglycaemia and morbidity and mortality irrespective of prior status of diabetes and can be more significant in non-diabetic subjects [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Recently a novel indicator of relative hyperglycaemic status, the stress hyperglycaemia ratio (SHR) has been proposed [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is defined as random glucose levels at admission divided by the estimated average glucose levels using glycosylated haemoglobin (HbA1c). It has been demonstrated that SHR is a better marker of stress hyperglycaemia and a significant predictor of critical illness compared to absolute hyperglycaemia in patients with acute illness[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have shown a significant association between SHR and poor prognosis in acute coronary syndrome (ACS) patients [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, these studies have primarily focussed on unstable angina and non-STEMI [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, the association between SHR and HF in STEMI patients treated with PCI remains unexplored, therefore necessitating an investigation.\u003c/p\u003e \u003cp\u003eSince in-hospital HF after acute MI has been demonstrated to account for majority of all HF cases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and anterior MI showing higher rates of HF [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the present study is therefore aimed at assessing the significance of SHR (calculated from HbA1c) in predicting in-hospital HF in anterior STEMI patients undergoing PCI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis retrospective study was conducted at Prince Faisal-Bin-Khalid Cardiac Centre, Abha, Kingdom of Saud Arabia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects and duration\u003c/h2\u003e \u003cp\u003eFrom 01 January 2022 to 31 January 2024, the electronic health records (EHR\u0026rsquo;s) of a total of 600 patients with anterior wall STEMI were perused. Out of 600, only 512 patients met our set inclusion / exclusion criteria, hence were finally enrolled. Amongst the enrolled 512 patients, 290 with in-hospital heart failure (HF) were included in Group I, and 222 patients who had not developed in-hospital heart failure were included in Group II. The study was approved by the ethics committee at King Khalid University (HAPO-06-B-001), Abha, Saudi Arabia vide approval number ECM# 2024\u0026thinsp;\u0026minus;\u0026thinsp;1901 and carried out in-line with the 2013 revision of the principles of Declaration of Helsinki[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Written consent was obtained from each subject enrolled.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003ePatients were only included in the study if: (1) they had been diagnosed with acute anterior wall STEMI (2) Underwent primary percutaneous intervention (pPCI) within 1.5 hours of symptom onset.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eExclusion Criteria\u003c/h2\u003e \u003cp\u003eExclusion criteria were: (1) Any history of previous myocardial infarction, congenital heart disease, cardiomyopathy, chronic heart failure, or severe valvular disease (2) Incomplete clinical data.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData and Definitions\u003c/h2\u003e \u003cp\u003eThe following clinical variables were recorded for all patients enrolled: (1) age, gender, smoking status, heart rate, systolic blood pressure, diastolic blood pressure, and past medical history (Diabetes, hypertension, past myocardial infarction (MI) (2) Laboratory variates (WBC, blood sugar, glycosylated haemoglobin (HBA1c), serum creatinine, cystatin C, low density lipoprotein (LDL), biomarkers (highest peak from repeated samples during hospitalization)- creatine kinase, creatine kinase-MB, N-Terminal pro- brain natriuretic peptide (NT-pro BNP), and cardiac troponin I (3) In-hospital complications- Atrial Fibrillation (AF), ventricular fibrillation (VF), sustained ventricular tachycardia, and nosocomial pneumonia (NP) (4) data from first transthoracic echocardiography-left ventricular ejection fraction(LVEF), left ventricular end diastolic diameter, left atrial diameter, mitral and aortic regurgitation, left ventricular diastolic dysfunction, and regional wall motion abnormality (RWMA) of left ventricular wall (5) Coronary angiographic data- pre-procedural thrombolysis in myocardial infarction (TIMI) flow grade in the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA), post-procedural TIMI flow grade\u0026thinsp;\u0026lt;\u0026thinsp;3 in infarct related artery, lesions in the proximal part of LAD, single vessel disease, three vessel diseases, and total artery occlusion.\u003c/p\u003e \u003cp\u003eThe diagnosis of acute STEMI was confirmed as per the criteria established by the European Society of Cardiology[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The infarct region was detected by electrocardiogram and confirmed by angiography. In-hospital HF was defined as documented evidence of dyspnoea upon exertion or fluid retention, and in patients with Killip class\u0026thinsp;\u0026ge;\u0026thinsp;II upon signs of HF like rales, jugular venous distention, or pulmonary edema and was only confirmed once excluding other conditions that can cause similar signs and symptoms. Patients were stratified to one of the Killip classes, as per the set criteria[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Stress hyperglycaemia ratio (SHR) was calculated as, SHR = (Fasting blood glucose (FBG) (mmol/L)) / (1.59\u0026times;HbA1c (%)\u0026mdash;2.59) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The decision of using fasting glucose (FBG) instead of admission glucose (ABG) levels as the numerator in the above equation was grounded in its comparatively better prognostic significance in patients with acute cardiovascular disease [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It has also been reported to be insensitivity to food or other sugar infusions, and limited inter-individual variability [36,37 ]. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis was performed using R[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] scripting language and the accompanying R studio [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (Version 1.2.5033, Orange Blossom). Sample size calculation was done using Cochran\u0026rsquo;s equation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Continuous variables with Gaussian distribution were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas continuous variables with non-Gaussian distribution were reported as median (interquartile interval), and categorical data in numbers and percentages (%). Continuous variables following Gaussian distribution were compared using student\u0026rsquo;s t-test, whereas continuous variables with non-Gaussian distribution were compared using Mann-Whitney U-test. Chi-Square tests were used to compare categorical variables. The odds ratio (OR) and confidence interval (CI) were obtained by multivariable logistic regression. Variates with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were considered significant and hence were included in multivariable logistic regression analysis to uncover independent predictors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The predictive value of each significant independent predictor was assessed utilizing the area under receiver operating characteristic curve (AUROC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study cohort\u003c/h2\u003e \u003cp\u003eThis study involved a total of 512 anterior-wall STEMI patients. The mean age of the study population was 62\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2. The study cohort was predominantly male (79.8%). The study subjects were stratified into two groups: one comprising of subjects who developed in-hospital HF (Group I) and the other comprising of subjects not developing in-hospital HF (Group II). The primary analysis of data extracted (demographic, clinical, echocardiographic, and angiographic characteristics of the enrolled study subjects) revealed that subjects in Group I in comparison to subjects in Group II presented with older age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increased heart rate (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher fasting blood glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher glycated hemoglobin (HbA1c) (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.\u003c/em\u003e001\u003cem\u003e)\u003c/em\u003e, higher SHR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), low blood pressure, low LVEF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), new-onset AF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ventricular tachycardia (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increased left-ventricular end-diastolic diameter, mitral regurgitation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), regional-wall motion abnormality (RWMA) in apical (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), anterior (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and rest of the left-ventricular wall (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher WBC count, higher serum creatinine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher cystatin C levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increased fibrinogen levels, higher peaks of D-dimer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and NT-pro-BNP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, 3-vessel disease, pre-procedural TIMI flow grade\u0026thinsp;\u0026le;\u0026thinsp;1 in LCX and RCA, post-procedural TIMI flow grade\u0026thinsp;\u0026ge;\u0026thinsp;3 in infarct-related artery were significantly associated with the in-hospital HF (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic and patient characteristics stratified by in-hospital HF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup I\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;290)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic and clinical variates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.32\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (bpm), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(74,101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81(72, 93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew-onset Atrial Fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustained Ventricular Tachycardia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricular Fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNosocomial Pneumonia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory variates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Blood Glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR, median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98(0.87, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.61, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8(8.6, 15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6(7.5, 10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L), (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C (mg/L), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2(1.0, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0(0.8, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Creatinine (\u0026micro;mol/L),\u003c/p\u003e \u003cp\u003emedian (IQR interval)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.0(68.6, 112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2(61.0, 86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak CK (IU/L), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1006.3(268.5, 2898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1012.7(278.0, 2383.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak CK-MB (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.2(32.2, 271.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.4(34.7, 200.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen (g/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak D-dimer (\u0026micro;g/mL), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1143(541.2, 1696.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500(287.2, 1478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak Troponin I (\u0026micro;g/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak NT-pro-BNP (pg/mL),\u003c/p\u003e \u003cp\u003emedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2168(887, 4937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e693(313, 2158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchocardiographic variates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(38, 56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(49, 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRWMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApex, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201(69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129(58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior wall, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250(86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164(74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRest of anterior wall, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154(53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003econtinued\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup I\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;290)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft-ventricular diastolic dysfunction, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft-atrial diameter (mm), median(Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(31, 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(32, 40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft-ventricular end-diastolic diameter (mm),\u003c/p\u003e \u003cp\u003eMedian(Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(41, 49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(43, 48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitral regurgitation, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary-angiographic variates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-procedural TIMI flow grade\u0026thinsp;\u0026le;\u0026thinsp;1 in\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148(51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCX, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-vessel disease, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-vessel disease, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of lesion in proximal LAD, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215(74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154(69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal artery occlusion, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139(48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-procedural TIMI flow grade\u0026thinsp;\u0026lt;\u0026thinsp;3, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; HbA1c, glycosylated haemoglobin; SHR, stress hyperglycaemia ratio; WBC, white blood cells; LDL, low density lipoprotein; CK, creatine kinase; CK-MB, creatine kinase-myocardial band; NT-pro-BNP, N-terminal pro-brain natriuretic peptide; LVEF, left-ventricular ejection fraction; RWMA, regional-wall motion abnormality; TIMI, thrombolysis in myocardial infarction; LAD, left-anterior descending artery; LCX, left-circumflex artery; RCA, right-coronary artery; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of SHR and in-hospital heart failure post anterior-wall STEMI\u003c/h2\u003e \u003cp\u003eTo find the association between SHR and in-hospital HF following anterior-wall STEMI, parameters with statistically significant differences between the two groups were further analysed with univariate and multivariate logistic regression analyses, with in-hospital HF as dependent variable. The results revealed that stress hyperglycaemia ratio (SHR) is a significant independent predictor of in-hospital HF (OR: 3.53; 95%CI: 2.02\u0026ndash;6.15; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of SHR with in-hospital HF after anterior-wall STEMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR\u0026thinsp;\u0026ge;\u0026thinsp;0.85\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR (unadjusted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60\u0026ndash;9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR (adjusted)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026ndash;6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, adjusted for age, gender, left-ventricular ejection fraction, ventricular fibrillation, nosocomial pneumonia, and N-terminal pro-brain natriuretic peptide levels; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, significant.\u003c/p\u003e \u003cp\u003eApart from SHR, the other independent predictors of in-hospital HF identified by multivariate logistic regression are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOther Independent Predictors of in-hospital HF based on Multivariate Logistic Regression NP, nosocomial pneumonia; SHR, stress hyperglycaemia ratio; LVEF, left-ventricular ejection fraction; NT-pro-BNP, N-terminal pro-brain natriuretic peptide; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, significant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Predictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;63 Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.12\u0026ndash;8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricular Fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.18\u0026ndash;10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (\u0026lt;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak NT-pro-BNP (\u0026ge;\u0026thinsp;832 pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCut-off value and predictive power of SHR\u003c/h2\u003e \u003cp\u003eUsing Youden\u0026rsquo;s index [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], the optimal cut-off value of SHR was calculated to be 0.85. Furthermore, the ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed that SHR independently (Model 2) had a moderate predictive potential for in-hospital HF in anterior wall STEMI undergoing PCI (AUC: 0.683, 95% CI 0.605\u0026ndash;0.762), which was almost comparable to the combined predictive value of other independent risk factors (Model 1) (AUC: 0.726, 95% CI 0.677\u0026ndash;0.784). Noticeably, Model 3 (a composite of Model 1 and Model 2) demonstrated a significant predictive power (AUC: 0.813, 95% CI 0.757\u0026ndash;0.881) compared to Model 1 alone (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAUROC for Model 1, Model 2, and Model 3\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUROC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 (Age\u0026thinsp;+\u0026thinsp;NP\u0026thinsp;+\u0026thinsp;VF\u0026thinsp;+\u0026thinsp;LVEF\u0026thinsp;+\u0026thinsp;NT-pro-BNP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.726 (0.677\u0026ndash;0.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 (SHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.683 (0.605\u0026ndash;0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 (Model 1\u0026thinsp;+\u0026thinsp;Model 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.813 (0.757\u0026ndash;0.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNP, nosocomial pneumonia; VF, ventricular fibrillation; LVEF, left-ventricular ejection fraction; NT-pro-BNP, N-terminal pro-brain natriuretic peptide; SHR, stress hyperglycaemia ratio; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge this is the first study to investigate the impact of SHR calculated from glycosylated haemoglobin (HbA1c) on in-hospital HF following an anterior wall STEMI. The results strongly suggest that apart from conventional risk parameters-age, VF, nosocomial pneumonia, LVEF, and NT-pro-BNP levels, SHR is an independent predictor of in-hospital HF in patients with anterior wall STEMI (OR: 3.53, 95% CI: 2.02\u0026ndash;6.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It has been suggested that the hypothalamic-pituitary-adrenal axis is responsible for stress hyperglycaemia by elevating cortisol and adrenaline secretion[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Pro-inflammatory cytokines-interleukin-1, interleukin-6, and tumor necrosis factor-α have been suggested to be responsible for impairment of insulin secretion and insulin resistance, and stress hyperglycaemia has been reported to be responsible for overexpression of these pro-inflammatory cytokines [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], thereby indicating a transitive relationship between stress hyperglycaemia and insulin resistance. Moreover, hypercoagulable state may be attributed to stress induced hyperglycaemia as it contributes to increased thrombogenic activity [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Earlier investigations have shown significant associations between increased SHR and lager thrombus burden and decreased TIMI flow grade in angiography[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The results of the present study are partially in line with various earlier studies. Stress hyperglycaemia has been reported to be a significant predictor of poor prognosis in acute coronary syndrome (ACS) patients in general and acute myocardial patients in particular [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Stress hyperglycaemia has not only been established as a significant indicator of the severity of an acute emergent condition, but it has also been demonstrated to complicate and catalyse obstruction in microvasculature[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], weaken endothelial vasodilatory mechanisms[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], hinder platelet nitric oxide response[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and augment vascular damage. However, admission blood glucose levels do not truly reflect the stress state as the actual stress hyperglycaemic state is masked by the chronic glucose levels [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Hence, as a sequel to the previous argument, Roberts et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], proposed SHR as an indicator of real stress hyperglycaemia by filtering out the chronic glycaemic state from the absolute hyperglycaemia on admission. They further proved that SHR is a robust and better biomarker of critical disease than hyperglycaemia on admission.\u003c/p\u003e \u003cp\u003eIn a retrospective study involving 905 STEMI patients, it was demonstrated that SHR was a strong predictor of no-reflow after primary PCI (pPCI) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In another study on 1553 AMI patients, SHR has been reported to be a better prognostic indicator of in-hospital mortality [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] than absolute glucose levels on admission. Yang et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], in a retrospective study involving 4362 subjects from the Catholic medical centre percutaneous coronary intervention (COACT) registry who underwent PCI, reported that the hazard ratio (HR) for upper SHR quartile (quartile 4) for long-term MACCE was 1.31 (95% CI 1.05\u0026ndash;1.64) in comparison to lower SHR quartiles (quartiles 1\u0026ndash;30). The present study also demonstrated that prior diabetic status has no influence on association of SHR and in-hospital HF following an anterior wall STEMI. This result is in line with an earlier study, wherein, a significant association was found between SHR and in-hospital death among patients following myocardial infarction (MI), irrespective of their prior diabetic status[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, in a study SHR was shown to have no substantial association on all-cause mortality and cardiovascular death among non-diabetic ACS patients[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTIMI risk score [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] is the most used prognosis stratification model for STEMI patients but despite all its strengths and significance some previous studies have reported that in majority of cases its predictive utility is limited[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This may be attributable to the fact that the TIMI risk score model takes only cardiovascular risk factors into consideration and ignores the inclusion of all important metabolic factors. Hence, a few studies have suggested inclusion of SHR to the risk scoring system may aid in early risk profiling [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The present study discovered that SHR is a significant independent risk and predictive factor for in-hospital HF post anterior wall STEMI. This assumes more importance given the ease and low cost associated with obtaining fasting blood glucose and HbA1c in clinical settings. The results of this study also demonstrate that integrating SHR with conventional risk factors augment the risk prognosis of in-hospital HF following an anterior wall STEMI. This is strongly indicative of including SHR as a significant predictive factor in constructing new prognostic models for STEMI in general.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSingle centre study, limited sample size, demographic component largely drawn from a particular geography (Aseer region, Kingdom of Saudi Arabia), might limit the generalization of the results. All these constitute the main limitations of this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSHR is an independent predictor for in-hospital HF in anterior wall STEMI patients treated with pPCI. Adding SHR to already established conventional risk factors (age, LVEF, NT-pro-BNP levels) for in-hospital HF significantly augments the discriminative ability of conventional risk factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee at King Khalid University (HAPO-06-B-001) vide approval No:\u0026nbsp;ECM# 2024-1901,\u0026nbsp;and written informed consent was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of \u0026nbsp;Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia for funding this work through Large Group Project under grant number [RPG 2/562/44]\u003c/p\u003e\n\u003cp\u003eZia-ul-Sabah, Javed Iqbal, Shahid Aziz, and Humayoun Khan Durrani majorly contributed to the conceptualization and Design. Saif Aboud M Alqahtani, Ayyub Ali Patel, and Imran Rangreze contributed towards data acquisition, analysis, and interpretation of data. Rasha Mirdad, Sara Shahrani, and Muad Ali Alfayea contributed to manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for encouragement and funding this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMehta RH, Parsons L, Rao SV, Peterson ED, National Registry of Myocardial Infarction (NRMI) Investigators. Association of bleeding and in-hospital mortality in black and white patients with st-segment-elevation myocardial infarction receiving reperfusion. 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Hellenic J Cardiol. 2023;70:36\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stress hyperglycaemia ratio, anterior wall STEMI, In-hospital heart-failure, NT-pro-BNP, Nosocomial pneumonia","lastPublishedDoi":"10.21203/rs.3.rs-5028884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5028884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStress hyperglycaemia ratio (SHR) has been reported to be independently and significantly associated with various adverse cardiovascular events as well as mortality. Moreover, in-hospital heart failure following acute myocardial infarction has been demonstrated to account for majority of all heart failure (HF) cases with anterior myocardial infarction showing higher rates of HF. However, the association between SHR and in-hospital HF following an anterior ST-elevation myocardial infarction (STEMI) has not been reported earlier. Therefore, the present study aimed at identifying the relationship between SHR and in-hospital HF post STEMI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective study electronic health records of 512 patients who presented with anterior STEMI from 01 January 2022 to 31 January 2024 were analysed. Based on the development of in-hospital HF, the enrolled patients were stratified into two groups: Group I, comprising of 290 patients who developed in-hospital HF and Group II comprising of 222 patients who did not develop in-hospital HF. ROC and Multivariable logistic regression analyses were performed to assess the relationship between SHR and in-hospital HF.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results revealed that SHR is a significant independent predictor of in-hospital HF (OR: 3.53; 95%CI: 2.02\u0026ndash;6.15; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Apart from SHR, the results also identified age, nosocomial pneumonia, ventricular fibrillation, LVEF, and NT-pro-BNP levels as other independent predictors. ROC analysis showed that SHR independently had a moderate discriminative power with AUC: 0.683, 95% CI 0.605\u0026ndash;0.762; p\u0026thinsp;=\u0026thinsp;0.04, which was almost comparable to the combined predictive value of other independent risk factors (AUC: 0.726, 95% CI 0.677\u0026ndash;0.784). Noticeably, combining SHR and other identified independent predictors demonstrated a significant predictive power (AUC: 0.813, 95% CI 0.757\u0026ndash;0.881; p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSHR is an independent predictor for in-hospital HF in anterior wall STEMI patients.\u003c/p\u003e","manuscriptTitle":"Stress hyperglycaemia ratio is an independent predictor of in-hospital heart failure among patients with anterior ST-segment elevation myocardial infarction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-01 07:46:32","doi":"10.21203/rs.3.rs-5028884/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-30T17:21:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T09:33:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-28T23:28:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144648242088501039826311266152130744626","date":"2024-09-28T21:32:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-27T12:16:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T17:39:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13200158984038217272888281636657675558","date":"2024-09-25T07:18:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T15:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322637548013877348038887870394180401664","date":"2024-09-22T09:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164310375499448830080181652744489126982","date":"2024-09-21T16:01:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7396452263699405203968189918026716477","date":"2024-09-20T10:23:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82371362794356140012190509870935538575","date":"2024-09-20T10:21:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326207166768219933460130307668016752230","date":"2024-09-19T14:42:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50379538860809534321595505070128299138","date":"2024-09-19T14:04:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-09-19T13:54:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-19T13:49:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-18T21:09:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-13T10:31:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-13T10:31:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-09-04T05:47:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cea8d020-c7c4-43f9-b25a-174e86549cc9","owner":[],"postedDate":"November 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T15:58:46+00:00","versionOfRecord":{"articleIdentity":"rs-5028884","link":"https://doi.org/10.1186/s12872-024-04362-4","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2024-12-28 15:57:00","publishedOnDateReadable":"December 28th, 2024"},"versionCreatedAt":"2024-11-01 07:46:32","video":"","vorDoi":"10.1186/s12872-024-04362-4","vorDoiUrl":"https://doi.org/10.1186/s12872-024-04362-4","workflowStages":[]},"version":"v1","identity":"rs-5028884","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5028884","identity":"rs-5028884","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}