Stress Hyperglycemic Ratio Independently Predicts Intramyocardial Haemorrhage (IMH) in STEMI Patients: A Cardiac Magnetic Resonance (CMR) Study

Stress Hyperglycemic Ratio Independently Predicts Intramyocardial Haemorrhage (IMH) in STEMI Patients: A Cardiac Magnetic Resonance (CMR) Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stress Hyperglycemic Ratio Independently Predicts Intramyocardial Haemorrhage (IMH) in STEMI Patients: A Cardiac Magnetic Resonance (CMR) Study Nauman Gul, Lu Yuan, Yang Yu, Li Zhi, Chen Lei, Zhang Min, Ms Aisha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8719658/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: ST-segment elevation myocardial infarction (STEMI) often results in microvascular injury, including intramyocardial haemorrhage (IMH), despite successful reperfusion. This study explored whether stress hyperglycemic ratio (SHR) independently predicts IMH in STEMI patients undergoing primary percutaneous coronary intervention (pPCI). Methods: We retrospectively analyzed 508 STEMI patients who underwent pPCI at The Affiliated Hospital of Xuzhou Medical University (2019–2024). SHR was calculated as Admission Glucose / (28.7 × HbA1c − 46.7). IMH was evaluated with cardiac magnetic resonance (CMR) imaging performed within 3–7 days post-pPCI using T2-mapping sequences. Multivariate logistic regression was used to identify independent predictors of IMH, and Receiver operating characteristic (ROC) analysis assessed SHR predictive performance. Results: Higher SHR was independently associated with IMH, patients with IMH had significantly higher SHR (0.87 vs 0.81, p < 0.001). After adjusting for age, gender, WBC count and haemoglobin, elevated SHR remained independently associated with IMH (adjusted OR 1.72; 95% CI 1.34–2.21; p < 0.001). ROC analysis showed (AUC = 0.625, 95% CI 0.577–0.673, p < 0.001), indicating modest predictive ability, with an optimal cutoff value of 0.83 (sensitivity of 63.7% and specificity of 58.7%). Conclusions: SHR independently predicts IMH in reperfused STEMI patients and may serve as a simple, readily available biomarker for early risk stratification of microvascular injury. These findings support cardiometabolic workup into STEMI management strategies. stress hyperglycemic ratio intramyocardial haemorrhage STEMI cardiac magnetic resonance primary percutaneous coronary intervention (pPCI) Figures Figure 1 Figure 2 Figure 3 INTRODUCTION ST-segment elevation myocardial infarction (STEMI) remains a major global health issue and the most severe manifestation of acute coronary syndrome[1]. Despite significant progress in reperfusion strategies, particularly via primary percutaneous coronary intervention (pPCI) as the standard of care, achieving timely myocardial reperfusion[2], clinical outcomes for many patients remain poor due to microvascular complications. One of the complications, intramyocardial haemorrhage (IMH), has become a key prognostic factor in approximately 40–54% of successfully perfused STEMI cases. IMH represents a severe microvascular injury in which red blood cells infiltrate into myocardial tissue[3,4], which we can identify as low-intensity cores within the infarcted area on myocardial magnetic resonance (CMR) imaging. Cardiac magnetic resonance (CMR) imaging has emerged as a gold standard for non-invasive detection and quantification of IMH[5], offering superior tissue characterization compared to other imaging modalities[6]. The pathophysiology of IMH originates from the complex interactions among ischemia-reperfusion injury, endothelial dysfunction, and inflammatory activation. When coronary artery blood flow recovers after prolonged occlusion, sudden reperfusion can unexpectedly increase myocardial tissue damage through oxidative stress and capillary rupture. This study investigated the association between IMH and SHR in STEMI patients, assuming that elevated SHR may exacerbate microvascular dysfunction, leading to an increase in the incidence and severity of IMH. By clarifying this relationship, we aim to identify a new biomarker for early risk stratification and potential therapeutic targets in high-risk STEMI patients. As the clinical relevance of IMH has already confirmed, many studies have been associated with larger infarct size, decreased left ventricular ejection fraction, and higher incidence of major adverse cardiac events (MACE). At the same time, stress hyperglycemia has been recognized as another important prognostic factor for STEMI patients, regardless of their diabetes status. Acute metabolic stress response during myocardial infarction often leads to elevated blood glucose levels, which are associated with poor microvascular perfusion and increased infarct size. Classical measures, such as admission blood glucose, are inadequate, especially in diabetes patients with long-term elevated blood glucose levels. So stress-induced hyperglycemia (SHR) has led to the development of better measurement methods, which explain baseline blood glucose status by combining HbA1c measurements. Currently, new data suggest that SHR may be superior to single glucose readings in predicting microvascular occlusion and patient outcomes. Clinical evidence and knowledge gaps The emerging clinical evidence strongly suggests the individual prognostic value of IMH and SHR in STEMI patients. CMR studies clearly indicate that IMH is associated with greater severity of myocardial injury, adverse left ventricular remodeling[7,8], larger infarct size[9], increase risk of heart failure[10], higher mortality rates[11], and higher levels of cardiac biomarkers such as high-sensitivity troponin T. IMH patients present with poor ventricular function and a high tendency towards adverse outcomes during follow-up, making them dependable predictors of long-term prognosis. Similarly, many studies have confirmed that an increase in SHR values indicates an increase in the incidence of microvascular obstruction, a decrease in myocardial recovery, adverse cardiovascular outcomes[12,13]. Standard indicators, such as admission blood glucose (ABG), may not accurately reflect acute glucose stress, especially in diabetic patients with long-term elevated blood glucose levels. Although these trends are consistent, the direct relationship between SHR and IMH has not been fully reported. There are a few studies specifically investigating whether high levels of SHR are independently associated with the incidence or severity of IMH. Recent studies have highlighted that SHR is an independent predictor of major adverse cardiovascular events (MACE) in STEMI patients[14], and mortality in acute myocardial infarction patients, independent of diabetic status[15,16]. This represents a significant knowledge gap, as identifying such connections can provide key findings into the metabolic mediators of vascular injury and identify potential therapeutic targets. In addition, most existing studies have insufficient adjustments for key confounding variables such as infarct location, compensatory vascular system for symptom reperfusion time, which can regulate hyperglycemia response and the development of IMH. Study rationale and objectives The current research aims to address these gaps by investigating the association between SHR and IMH in a cohort of STEMI patients receiving modern pPCI treatment. We assume that even after adjusting for classical risk factors, an increase in SHR is independently associated with a higher prevalence and greater extent of IMH development through mechanisms involving oxidative stress[17], endothelial dysfunction[18], inflammation[19], and microvascular injury[20], and exploring the potential threshold SHR value that best predicts IMH risk. This retrospective observational study included consecutive STEMI patients who received successful pPCI at Xuzhou Medical University Affiliated Hospital. Despite modern reperfusion therapy, STEMI remains the leading cause of morbidity and mortality globally, thus requiring improved risk stratification tools to predict adverse outcomes. Although both IMH and SHR are independently associated with worse STEMI outcomes, their interactions have not been fully identified. Initial evidence shows that high blood levels may exacerbate reperfusion injury and microvascular obstruction, which are the main factors in intramyocardial haemorrhage (IMH). All participants underwent standardized CMR imaging using a 3T scanner within 3–7 days after pPCI and adopted a standardized myocardial bleeding protocol. According to established standards, IMH is defined as the low-intensity region of T2 mapping imaging sequence. SHR is calculated using blood glucose at Admission and HbA1c values obtained during the first hospitalization period. Comprehensive clinical, vascular, and experimental data were collected for appropriate multivariate adjustments in statistical scores. MATERIALS AND METHODS Study Population Characteristics This retrospective cohort study was conducted from June 2019 to July 2024 at the Coronary Care Unit (CCU) of Xuzhou Medical University Affiliated Hospital, Jiangsu Province, China. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines[21]. we performed an analysis on patients with STEMI. After screening for eligibility criteria in consecutive patients post-PCI and after successful primary PCI, 508 patients were included after exclusions (Fig. 1 ). Most patients were male (n = 437, 86.0%), with a median age of 58.0 years (IQR 50.0–66.0), which is age typical for STEMI patients. IMH was assessed in 179 patients (35.2%), and 329 patients (64.8%) were without IMH, indicating that microvascular injury, as defined by CMR, persists even with modern reperfusion techniques (Table 1 ). IMH-positive patients were younger (median 56.0 vs 58.0 years; p = 0.014). Male proportion was similar (84.9% vs 86.6%, p = 0.691). No significant group differences appeared for hypertension, diabetes, or BMI (all p > 0.05). Clinical Data Collection The data of patients were collected from the medical record system of the hospital, including age, gender, height, weight, heart rate, systolic and diastolic blood pressure, smoking history, past medical history ( Diabetes, Hypertension, Stroke) and history of medications (ACEi, ARBs, beta blockers, Statins, Aspirin / clopidogrel). During admission, multiple blood test were performed, the HbA1c, blood glucose level, peak levels of high-sensitivity C-reactive protein (hs-CRP), haemoglobin, high-sensitivity troponin T (hs-TnT), WBC cell type, and N-terminal pro-B-type natriuretic peptide (NT-proBNP), Random blood glucose levels were noted upon admission for all included patients. Cardiac Magnetic Resonance (CMR) Parameters All pPCI procedures adhered to AHA/ESC guidelines[1], emphasizing timely reperfusion. CMR imaging was performed using 3.0T scanner (Magnetom Skyra, Siemens Healthineers, Erlangen, Germany) with a 32-channel cardiac phased-array coil. All scans were done 3–7 days post-pPCI ( median 5 days, IQR 4–6 days). Cine steady-state free precession (SSFP) imaging in standard long-axis and short-axis planes covering the entire left ventricle (slice thickness 8 mm, gap 2 mm, temporal resolution < 50 ms, in-plane resolution 1.6 × 1.6 mm) T2-weighted short-tau inversion recovery (STIR) sequences for edema assessment (TE 60 ms, TR 2 R-R intervals), TE 2.1–16.8 ms, TR 200 ms, flip angle 20°, slice thickness 8 mm) Late gadolinium enhancement (LGE) imaging 10–15 minutes after administration of 0.1 mmol/kg gadobutrol (Gadovist, Bayer, Germany) using phase-sensitive inversion recovery sequences. Late gadolinium enhancement (LGE) for necrosis and T2-mapping sequences for haemorrhage quantification. Image analysis was performed using software Circle Cardiovascular Imaging (Circle CVI42 version 5.13.5 Canada) for precise volumetric and parametric mapping, the myocardial layers (epicardium and endocardium) were hand operated and summarized. Representative examples of cardiac magnetic resonance imaging sequences from two patients are shown in Fig. 2 . Panel A demonstrates a CMR sequence with intramyocardial haemorrhage IMH, was defined as hypointense regions within the infarct core on T2-mapping sequences[22], while Panel B shows a CMR sequence without intramyocardial haemorrhage. Variables and Measurements The primary exposure was the Stress Hyperglycemic Ratio (SHR), calculated as SHR = Admission Blood Glucose (mg/dl) / [28.7 × HbA1c (%) – 46.7][23,24], reflecting acute glycemic stress relative to chronic glycemic levels. The primary outcome was intramyocardial haemorrhage (IMH), evaluated via CMR using T2-weighted sequences, a sensitive marker of microvascular injury. Secondary outcomes included infarct size and haemorrhage extent. Covariates covered demographics (age, gender), cardiovascular risk factors (hypertension, diabetes), angiographic details (TIMI flow, culprit lesion), and biomarkers (hs-CRP, troponin). Statistical Analysis Statistical analyses were performed using Python 3.10 with SciPy 1.11 and scikit-learn 1.3 libraries.. Descriptive data are presented as mean ± standard deviation or median [interquartile range] for continuous variables, and counts (percentages) for categorical variables. Group comparisons were made using the Chi-square test, Student's t-test, or Mann-Whitney U test, as appropriate. Univariate and multivariate logistic regression analyses were used to identify factors associated with IMH. A Receiver Operating Characteristic (ROC) curve was generated to evaluate the predictive performance of SHR for IMH. A two-sided p-value < 0.05 was considered statistically significant. Ethical and Methodological Considerations The study received approval from Xuzhou Medical University with informed consent obtained where applicable. Strengths included a robust sample size (n = 508), CMR's gold-standard accuracy for IMH, and standardized pPCI/CMR protocols. DEFINITIONS Stress Hyperglycemic Ratio (SHR) Definition The Stress Hyperglycemic Ratio (SHR) is a measure of acute hyperglycemia during critical illness (e.g., myocardial infarction, stroke, or sepsis) relative to chronic glycemic control[25]. It quantifies the degree of stress-induced hyperglycemia independent of pre-existing diabetes or chronic glucose dysregulation. Intramyocardial Haemorrhage (IMH) Definition Intramyocardial Haemorrhage (IMH) is bleeding within the myocardium that occurs after reperfusion therapy (e.g., pPCI) in ST-segment elevation myocardial infarction (STEMI). It is characterized by the extravasation of red blood cells into the myocardial tissue, detectable as hypointense regions on T2-weighted or T2-mapping CMR sequences[26]. ST-Segment Elevated Myocardial Infarction (STEMI) Definition STEMI is a severe type of heart attack caused by the complete occlusion of a coronary artery, leading to transmural myocardial ischemia. It is diagnosed by ST-segment elevation on electrocardiogram and elevated cardiac biomarkers[27]. RESULTS Laboratory and biomarker parameters : Patients with IMH exhibited greater indices of myocardial injury and systemic activation than those without IMH. More precisely, the peak troponin T was nearly double in the IMH group (4455.5 vs 2350.5 ng/L; p < 0.001), and other biomarkers like WBC (9.05 vs 7.44 ×10⁹/l; p < 0.001) and LDH (937.0 vs 504 U/L; p < 0.001) were raised as well. These findings indicated that patients with IMH had more myocardial injury and stronger inflammatory response, the former of which is associated with larger infarct size and poor clinical outcomes. In addition, IMH-positive patients had lower CMR-derived LVEF values (52.0% vs 54.0%), suggesting subclinical cardiac impairment of potential prognostic relevance. Examples of CMR images of both IMH-positive and IMH-negative patients are shown in Fig. 2. Glycemic control differences are shown in Table 1 : SHR was significantly higher in IMH-positive patients compared with IMH-negative patients (0.87 vs 0.81, P < 0.001), with a clinically meaningful delta of + 7.4%. In contrast, median HbA1c levels did not differ between the 2 groups (6.0%; P = 0.747), suggesting that IMH risk is more closely related to acute metabolic stress than to chronic glycemic exposure. This observation underscores the need to consider acute stress hyperglycemia, as reflected by SHR, when predicting SHR risk. Table 1 Baseline characteristics by IMH status GROUP BY IMH IMH Negative (n = 331) IMH Positive (n = 175) P Value Age (Years) 58.00 (51.00–67.00) 56.00 (47.50–65.00) 0.013* Gender (Male) 265(80.55%) 164(91.62%) 0.001** Hypertension 128 (38.67) 66 (37.29) 0.760 Diabetes 64 (19.34) 33 (18.64) 0.850 BMI (kg/m²) 25.35 (23.44–27.94) 25.54 (23.44–27.61) 0.951 NT-proBNP (pg/ml) 1061.000 (543.0,2031.0) 1135.000 (486.8,1912.0) 0.680 Peak-troponin T (ng/L) 2350.500 (1020.5,4005.8) 4455.500 (2510.5,6840.8) 0.000** LVEF (%) 54.08 (51.00,58.00) 52.85 (49.00,57.00) 0.031* WBC (10⁹/L) 7.440 (5.9,9.6) 9.050 (7.1,10.9) 0.000** Haemoglobin (g/L) 137.000 (127.0,147.0) 141.000 (130.0,152.0) 0.006** PLTs (10⁹/L) 212.000 (174.5,249.0) 210.000 (176.0,250.0) 0.889 Fibrinogen (g/L) 3.490 (2.6,4.8) 3.700 (2.7,5.1) 0.074 Creatinine (µmol/L) 60.000 (50.0,70.0) 58.000 (49.0,65.0) 0.074 LDH (U/L) 504.000 (336.5,786.0) 937.000 (604.0,1519.0) 0.000** Glucose (mmol/L) 5.860 (5.1,7.7) 6.400 (5.5,8.3) 0.001** HbA1c (%) 6.000(5.6,6.9) 6.000(5.6,7.1) 0.746 TC (mmol/L) 4.070(3.5,4.7) 3.910(3.3,4.7) 0.164 TAG (mmol/L) 1.430(1.0,2.2) 1.350(0.9,2.2) 0.833 HDL (mmol/L) 0.930(0.8,1.1) 0.920(0.8,1.1) 0.953 LDL (mmol/L) 2.830(2.3,3.4) 2.670(2.1,3.4) 0.061 CRP (mg/L) 17.200(8.1,40.9) 28.800(13.7,64.5) 0.000** SHR 0.810(0.7,0.9) 0.870(0.8,1.0) 0.000** ACEi 64 (19.34) 41 (23.16) 0.310 ARB 137 (41.39) 70 (39.55) 0.687 B-blocker 298 (90.03) 151 (85.31) 0.114 * p < 0.05 ** p < 0.01 Data presented as median [IQR] or n (%). P-values from the Mann–Whitney U-test or chi-square test. *p < 0.05 Univariable Predictors of Intramyocardial Haemorrhage In univariable analysis, each predictor was evaluated separately using complete-case analysis (n = 375, Table 2 ). Demographics showed age significant (OR = 0.97/year, 95% CI 0.96–0.99; p = 0.005). Male gender was not (OR 1.05, 95% CI 0.53–2.08; p = 0.887). Peak troponin T was strongly associated (OR 1.35/1000 ng/L, 95% CI 1.23–1.49; p < 0.001). CRP and LVEF were not significant (p = 0.256, p = 0.269). For glycemic parameters, the SHR (admission glucose divided by estimated chronic glucose from HbA1c) is significantly associated with IMH (OR 1.20 per 0.1 unit, CI95%: 1.08–1.34; p = 0.001). Calculated admission glucose (from SHR and HbA1c) was moderately associated (OR 1.13 per mmol/L, 95% CI: 1.04–1.23; p = 0.003), but HbA1c was not significantly associated (OR 1.12, 95% CI: 0.97–1.30; p = 0.133). These univariable results are consistent with the possible greater advantage of stress-corrected glycemic estimation over absolute and chronic measures for predicting IHM. Candidates with p < 0.10 were included in multivariable models. Table 2 Univariable Logistic Regression for Intramyocardial Haemorrhage Variable Odds Ratio (95% CI) P Value Demographics Age (per year) 0.97 (0.96–0.99) 0.005 Male sex 1.05 (0.53–2.08) 0.887 Glycemic Parameters Stress Hyperglycemic Ratio (per 0.1 unit) 1.20 (1.08–1.34) 0.001 Admission glucose (per mmol/L) 1.13 (1.04–1.23) 0.003 HbA1c (per %) 1.12 (0.97–1.30) 0.133 Cardiac Biomarkers Peak Troponin T (per 1000 ng/L) 1.35 (1.23–1.49) < 0.001 Inflammatory and Haematologic Markers WBC (per 10⁹/L) 1.13 (1.05–1.22) 0.002 Haemoglobin (per g/L) 1.03 (1.01–1.04) 0.002 LDH (per 100 U/L) 1.15 (1.10–1.20) < 0.001 CRP (per mg/L) 1.00 (0.99-1.00) 0.256 CMR Findings LVEF (per %) 0.98 (0.95–1.01) 0.269 Odds ratios (95% confidence interval) are reported. analysis of complete-case, n = 375 (n = 107 IMH events). Values in bold indicate variables included in the multivariable Model 3 (Age, Male sex, SHR, WBC, Haemoglobin). SHR was reported as the leading variable of interest. All p-values are based on univariate logistic regression. Abbreviations: CI, confidence interval; CRP, C-reactive protein; HbA1c, glycated haemoglobin; IMH, intramyocardial haemorrhage; LDH, lactate dehydrogenase; LVEF, left ventricular ejection fraction; SHR , stress hyperglycaemic ratio; WBC, white blood cell count. Independent Predictors of Intramyocardial Haemorrhage After identifying univariable predictors of IMH, a multivariable logistic regression model was used to assess independent factors associated with IMH at 3 levels of adjustment (Table 3 ). This stepwise approach allowed a more structured analysis of whether the univariable relationship between SHR and IMH persisted after adjustment for potential confounders. Model 1 (Unadjusted) : Univariable analysis showed a notable correlation of SHR with IMH (OR: 1.20 per 0.1 unit, 95% CI: 1.08–1.34; p = 0.001). Accordingly, as SHR increases by 0.1 unit, the odds of IMH increase by approximately 20%, suggesting that SHR is an individual biomarker with clinical applicability for predicting IMH risk. Model 2 (Age-and-Sex-Adjusted) : After adjusting for age and sex, SHR remained significantly associated with IMH (OR 1.20 per 0.1 unit, 95% CI:1.07–1.34; p = 0.001), and effect sizes were only minimally reduced. These findings indicated that SHR was not confounded by patient demographics with IMH. Age was an independent predictor (OR 0.97 per year, 95% CI 0.96–0.99; p = 0.006), whereas male sex was not significant (OR 0.94, 95% CI 0.46–1.89; p = 0.853). Model 3 (Fully adjusted) In the fully adjusted model, adjustment was made for demographic factors (age and sex) and key laboratory markers of inflammation and cellular injury (white blood cell count and haemoglobin). Within our model, SHR remained an independent predictor of IMH in the entire cohort (OR = 1.17 per 0.1-unit increase, 95% CI [1.05–1.31], p = 0.006). For each 0.1-unit increase in SHR, the independent odds of IMH increased by 17%. WBC count remained borderline significant (OR 1.08 per 10⁹/L, 95% CI 1.00–1.18, p = 0.050), whereas age, male sex, and haemoglobin were not independently statistically predictive after adjustment. When we replaced admission glucose with SHR in this fully adjusted model, it was borderline significantly associated with IMH (p = 0.06), further validating the superiority of SHR for predicting IMH under multivariable adjustment. Model 3 had good discrimination (C-statistic = 0.677) and no multicollinearity. Diagnostics support the model and SHR's independent predictive value. Clinical Relevance : The persistent association between SHR and IMH, independent of demographic and injury-related variables, underscores its clinical relevance as a risk predictor in this population. A modest attenuation of effect estimates from univariable models to fully adjusted exposure variables provides support for the SHR as an instrument in settings outside traditional laboratory conditions. As SHR can be readily obtained on Admission, it provides a useful tool for identifying patients at high risk who may need early monitoring or specific intervention. However, these results are from a single-centre observational cohort, and thus, the generalizability and causal inference from this study are limited. Since the AUC of SHR was moderate, it is a useful addition to a broader risk assessment rather than an independent marker. Its prognostic role in a larger patient cohort and in a multicenter study should be further evaluated. Table 3 Multivariable logistic regression results for intramyocardial haemorrhage Variable Model 1: Unadjusted Model 2: Age + Sex Adjusted Model 3: Fully Adjusted SHR (per 0.1 unit) 1.20 (1.08–1.34) p = 0.001 1.20 (1.07–1.34) p = 0.001 1.17 (1.05–1.31) p = 0.006 Age (per year) - 0.97 (0.96–0.99) p = 0.006 0.98 (0.96-1.00) p = 0.114 Male sex - 0.94 (0.46–1.89) p = 0.853 0.92 (0.46–1.88) p = 0.828 WBC (per 10⁹/L) - - 1.08 (1.00-1.18) p = 0.050 Haemoglobin (per g/L) - - 1.01 (1.00-1.03) p = 0.128 The results were expressed as odds ratios (95% confidence intervals). Values in bold are those for which p is < 0.01. Model 1 shows the simple association between SHR and IMH. Model 2 adds adjustment for age and sex. Model 3 includes age, sex, WBC count, and haemoglobin as continuous variables. This was a complete-case cohort analysis (n = 375, IMH events = 107). Model 3 had a C-statistic of 0.677. All variance inflation factors were < 1.5, indicating no multicollinearity. Admission glucose was not significant in Model 3 when included instead of SHR. Abbreviations: CI, confidence interval; IMH, intramyocardial haemorrhage; SHR, stress hyperglycemic ratio; WBC, white blood cell. Predictive Performance of Stress Hyperglycemic Ratio Receiver operating characteristic (ROC) analyses of glycemic parameters' ability to predict IMH are presented in Fig. 3 . Area under the curve (AUC) analysis indicated that SHR had poor to moderate discriminative power (AUC = 0.625, 95% CI 0.577–0.673, p < 0.001). This is, of course, better than an AUC of 0.5, which would correspond to a completely random guess about the positive or negative class. Admission glucose demonstrated low to moderate discrimination (AUC = 0.603, 95% CI 0.554–0.652, p = 0.001). However, the AUC for HbA1c was 0.509 (95% CI: 0.459–0.559, p = 0.741) and therefore did not differ from random classification at any probability threshold. ROC curve analysis is summarized in Table 3 . The AUC for SHR and admission glucose was not significantly different (p = 0.598, pairwise comparisons). Both values were more discriminative than HbA1c (p = 0.006). The Youden index, a statistic used to select the optimal cutoff value for an experimental procedure and to determine the overall classification performance of SHR, was 0.86. SHR demonstrated the best sensitivity (59.2%) and specificity (63.8%) for IMH detection at this cutoff. Although SHR alone had only moderate discriminatory value, the high negative predictive value (74.2% [95% CI 67.6–80.1]) indicates that an SHR below this cutoff is practically useful for excluding IMH risk. Importantly, Table 3 shows that while SHR and Admission glucose had similar area under the curve (AUC) values in univariable receiver operating characteristic (ROC) analysis (Figure 3 ), SHR maintained independent predictive value for IMH after multivariable models, whereas admission glucose did not. This finding indicates that ROC analysis, in itself, as demonstrated in Fig. 3 , could underestimate the clinical relevance of stress-corrected glycemic markers such as SHR. Although the modest AUC value indicates only moderate discrimination, the continued independent prediction of SHR in this multivariable setting (Table 3 ) suggests that SHR captures aspects of metabolic risk beyond those captured by standard risk variables. Therefore, the SHR cutoff of 0.86 suggested by Fig. 3 offers a clinically practical point for risk stratification and the identification of patients at higher risk for microvascular complications who can be closely monitored. Subgroup and Stratified Analyses To further explore the association between SHR and IMH, patients were divided into low-SHR (≤ 0.83) and high-SHR (> 0.83) groups using a ROC cutoff of 0.83, which is close to the median value. Baseline characteristics were significantly different between the groups (Table 4 ). IMH prevalence. The high SHR group (n = 248) had a markedly greater prevalence of IMH than the low SHR group (n = 260): 44.8% (95% CI: 38.7–51.0%) vs 26.2% (95% CI:21.2–31.8%), p < 0.001, equating to a + 71% relative increase. This significant contrast implies a dynamic interplay between glycemic stress and the risk of microvascular injury. The incremental rise in the incidence of IMH reflects a linear dose-response relation, where risk for microvascular injury increases with increasing levels of metabolic stress without a threshold effect. High-SHR subjects had significantly higher inflammatory, cardiac injury, and metabolic stress markers, indicating a more severe acute phenotype. Table 4 Baseline Characteristics by SHR Group Characteristic Overall (n = 508) SHR ≤ 0.83 (n = 258) SHR > 0.83 (n = 250) p-value IMH prevalence, n (%) 179 (35.2%) 65 (25.2%) 114 (45.6%) < 0.001 Age, years 57.4 ± 12.0 57.8 ± 12.2 57.1 ± 11.7 0.520 Gender, Male, n (%) 437 (86.0%) 228 (88.4%) 209 (83.6%) 0.155 BMI, kg/m² 25.8 ± 3.6 25.9 ± 3.2 25.7 ± 3.9 0.553 SHR (continuous) 0.84 ± 0.20 0.70 ± 0.12 0.98 ± 0.17 < 0.001 Hypertension, n (%) 194 (38.2%) 87 (33.7%) 107 (42.8%) 0.058 LVEF, % 53.5 ± 6.7 53.6 ± 6.8 53.4 ± 6.6 0.788 ACEi use, n (%) 89 (17.5%) 38 (14.7%) 51 (20.4%) 0.119 ARB use, n (%) 67 (13.2%) 29 (11.2%) 38 (15.2%) 0.231 Beta-blocker use, n (%) 440 (86.6%) 213 (82.6%) 227 (90.8%) 0.012 WBC (×10⁹/L) 8.6 ± 3.2 8.4 ± 3.2 8.9 ± 3.2 0.104 Haemoglobin (g/L) 137.7 ± 14.2 137.0 ± 14.5 138.4 ± 13.9 0.281 Platelets (×10⁹/L) 212.1 ± 65.6 210.7 ± 68.1 213.4 ± 63.2 0.649 Fibrinogen (g/L) 4.08 ± 1.69 4.10 ± 1.75 4.07 ± 1.64 0.843 Troponin T (ng/L) 4105 ± 3434 3995 ± 3419 4211 ± 3450 0.517 BNP (pg/mL) 1457 ± 2557 1542 ± 2710 1375 ± 2397 0.478 LDH (U/L) 707 ± 873 667 ± 806 745 ± 932 0.307 Creatinine (µmol/L) 62.5 ± 14.9 62.7 ± 14.5 62.3 ± 15.4 0.775 HbA1c (%) 6.1 ± 1.4 6.0 ± 1.4 6.1 ± 1.4 0.357 CRP (mg/L) 18.6 ± 32.0 20.8 ± 35.6 16.4 ± 27.9 0.129 Total Cholesterol (mmol/L) 4.5 ± 1.2 4.5 ± 1.2 4.5 ± 1.2 0.794 HDL (mmol/L) 0.98 ± 0.27 0.98 ± 0.27 0.99 ± 0.28 0.837 Triglycerides (mmol/L) 2.0 ± 2.2 2.0 ± 2.3 2.0 ± 2.2 0.957 LDL (mmol/L) 2.9 ± 1.1 2.9 ± 1.1 2.9 ± 1.1 0.762 IMH volume, mL 0.5 ± 1.7 0.4 ± 1.4 0.6 ± 2.0 0.167 Data presented as mean ± SD or n (%). SHR = Stress Hyperglycemic Ratio; IMH = Intramyocardial Haemorrhage; BMI = Body Mass Index; LVEF = Left Ventricular Ejection Fraction; ACEi = Angiotensin-Converting Enzyme inhibitor; ARB = Angiotensin Receptor Blocker; WBC = White Blood Cell; BNP = B-type Natriuretic Peptide; LDH = Lactate Dehydrogenase; CRP = C-Reactive Protein; HDL = High-Density Lipoprotein; LDL = Low-Density Lipoprotein. In this complete-study cohort (n = 375), subgroup analysis evaluated whether the SHR-IMH relationship was consistent across predefined patient groups. The relationship was also consistent across age groups (< 60 vs ≥ 60 years; p-interaction = 0.236), sex (p-interaction = 0.882), and diabetes status (p-interaction = 0.361); no effect modification was observed. In the age-stratified analysis, ORs were 1.23 (95% CI, 1.07–1.40, p = 0.003) for younger patients (< 60 years) and 1.12 (95% CI, 0.97–1.29, p = 0.136) for older patients (≥ 60 years). The uniform findings across subgroups suggest that the predictability of SHR for IMH may be generalizable to a variety of patient phenotypes, not confined to high-risk patients. There was no significant interaction with diabetes status, indicating that adjustment for stress by life is predictive independently of chronic glycemic control. Summary Statement In our cohort of 508 patients with STEMI who underwent primary PCI, IMH was present in 35.2%, indicating continued incidence of CMR-derived microvascular injury despite reperfusion with door-to-balloon era therapy. SHR was independent to predict IMH; its significance remained after adjusting for demographic and injury variables (unadjusted OR 1.20, 95%CI: 1.08–1.34 , p = 0.001; adjusted OR: 1.17(95%CI), p = 0.006). Admission glucose did not maintain predictive power in the multivariable model. ROC studies for SHR showed an AUC of 0.625 and an optimal cutoff of 0.86, suggesting a clinically informative but only moderately discriminative value for risk stratification. The presence of a dose-response gradient, as well as a 71% higher prevalence of IMH in high SHR than low SHR patients (44.8% vs. 26.2%), also testifies to its clinical utility. In conclusion, these findings suggest that SHR may serve as an accessible marker for early recognition and risk stratification of microvascular injury in STEMI patients, facilitating more targeted monitoring and treatment in clinical practice. DISCUSSION This study provides evidence that stress-induced hyperglycemia (SHR) is independently associated with intramyocardial haemorrhage (IMH) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (pPCI). These findings advance our understanding of how acute metabolic changes cause microvascular damage after myocardial infarction. Our proposed mechanism is supported by much pathophysiological evidence linking hyperglycemia to microvascular injury. First, acute hyperglycemia increases oxidative stress and reactive oxygen species (ROS) production [28,29]. It impairs nitric oxide bioavailability[30], damages endothelial cells, and increases vascular permeability. Secondly, a pro-inflammatory state induced by hyperglycemia elevates cytokine levels [20,31]. This promotes leukocyte activation and microvascular obstruction. Thirdly, metabolic disorders impair myocardial calcium processing and mitochondrial function. This makes myocardial cells prone to reperfusion injury. These mechanisms together increase the risk of microvascular rupture and bleeding after revascularisation. In addition, high blood glucose levels can induce proteases (especially MMP-9), which can degrade extracellular matrix and basement membranes. This damages the microvascular structure and promotes bleeding. These molecular mechanisms explain how SHR elevation mediates IMH, distinct from common risk factors. Therefore, we find the association between SHR and IMH offers clinical support for these pathways. Our results show the SHR-IMH link exists across age groups, especially in older patients (61–75 years). This age-related trend may reflect greater vascular damage, reduced microvascular elasticity, and an aging metabolism. There is also new evidence of gender differences in stress response and microvascular function. In clinical settings, SHR offers a simple bedside measure. It uses admission blood glucose and HbA1c to identify patients at higher IMH risk who could benefit from closer observation and timely metabolic care after pPCI. Strengths and limitations The principal strengths of our investigation include a substantial patient cohort, the application of CMR for definitive IMH identification, and consistent procedural protocols for both pPCI and imaging, which collectively bolster the validity of our results. Our analysis further accounted for numerous clinical and laboratory variables, reinforcing the independent nature of the relationship between SHR and IMH. Nonetheless, this study is subject to certain constraints. Its retrospective, single-centre nature poses a risk of selection bias and may affect the external applicability of our conclusions. The high proportion of male participants, though typical for a STEMI population, limits insights into potential sex-based differences. Additionally, we did not evaluate serial glucose measurements or continuous glucose monitoring, which might provide more comprehensive assessment of glycemic variability. Future directions Future studies should establish optimal SHR thresholds for predicting intramyocardial haemorrhage, clarify how hyperglycemic stress and microvascular injury evolve, and examine potential interactions between SHR and other metabolic markers such as ketone bodies or free fatty acids. Randomized trials are needed to determine whether early glycemic control can reduce IMH incidence and improve outcomes. These findings also suggest that STEMI management should integrate metabolic assessment alongside revascularization, enabling a more comprehensive and personalized approach to care. CONCLUSION This study demonstrates an independent association between Stress Hyperglycemic Ratio and intramyocardial haemorrhage in STEMI patients treated with contemporary pPCI. The findings enhance comprehension of the metabolic determinants of microvascular injury and provide a firm basis for incorporating SHR assessment into routine clinical practice for risk stratification. The demonstrated relationship between acute glycemic derangements and hemorrhagic complications reinforces the critical role of metabolic factors in post-infarction outcomes and suggests potential opportunities for therapeutic intervention. Future research should focus on validating these findings in more diverse populations, elucidating the underlying mechanisms, and testing whether specific control of hyperglycemic stress can effectively reduce IMH and its negative outcomes. This work represents an important step toward more individualized therapy of STEMI that considers both vascular and metabolic dimensions of care. CLINICAL IMPLICATIONS These findings have significant clinical implications across multiple areas of cardiovascular care. Clinically, the association of SHR with IMH has proven to be a practical, cost-effective risk-stratification tool that can be implemented immediately upon admission using routinely available laboratory values. This early identification can guide more proactive cardioprotective strategies and inform decisions regarding the timing of CMR imaging and the intensity of monitoring.[32,33] Additionally, the SHR-IMH association suggests potential therapeutic targets, as interventions aimed at regulating acute hyperglycemia may reduce microvascular damage. [34]。 Abbreviations SHR Stress Hyperglycemic Ratio IMH Intramyocardial Haemorrhage STEMI ST-Segment Elevated Myocardial Infarction pPCI Primary Percutaneous Coronary Intervention CMR Cardiac Magnetic Resonance ROS Reactive Oxygen Species HbA1c Hemoglobin A1c AHA American Heart Association ESC European Society of Cardiology LGE Late Gadolinium Enhancement HS-CRP High-Sensitivity C-Reactive Protein MMP-9 Matrix Metalloproteinase-9 PCI Percutaneous Coronary Intervention ECG Electrocardiogram LVEF Left Ventricular Ejection Fraction MACE Major Adverse Cardiac Events CCU Coronary Care Unit Declarations ACKNOWLEDGEMENTS We thank all medical staff at the Coronary Care Unit and CMR Department of Xuzhou Medical University Affiliated Hospital for their support in data collection. Ethics approval and consent to participate This study was approved by the Institutional Review Board/Ethics Committee of Xuzhou Medical University (approval number: LCZX20251). Written informed consent was obtained from all participants. Consent for publication All imaging data presented in this manuscript are fully anonymized and do not contain any identifying patient information. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from the Clinical Research Special Contract of Xuzhou Medical University at The Affiliated Hospital of Xuzhou Medical University, Xuhou city, Jiangsu Province, P.R. China. Authors' contributions Nauman Gul: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft. Lu Yuan: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing- review & editing. All authors read and approved the final manuscript. Yang Yu: Investigation, Data collection, Validation, Writing - review & editing. Li Zhi: Methodology, Software, Formal analysis. Chen Lei: Resources, Data curation, Clinical interpretation, Writing - review & editing. Zhang Min: CMR image analysis, Validation, Resources. Aisha: Statistical analysis, Data visualization, Writing - review & editing. Muhammad Shahbaz Raja: Clinical data interpretation, Manuscript revision, Critical review. References Ibanez B, James S, Agewall S, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018;39(2):119-177. 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Pathophysiology of LV remodeling in survivors of STEMI: inflammation, remote myocardium, and prognosis. JACC Cardiovasc Imaging. 2015;8(7):779-789. DOI: 10.1016/j.jcmg.2015.03.007 De Waha S, Patel MR, Granger CB, et al. Relationship between microvascular obstruction and adverse events following primary percutaneous coronary intervention for ST-segment elevation myocardial infarction: an individual patient data pooled analysis from seven randomized trials. Eur Heart J. 2017;38(47):3502-3510. DOI: 10.1093/eurheartj/ehx414 Bulluck H, Go YY, Bazoukis G, et al. Microvascular obstruction in reperfused acute myocardial infarction: an umbrella review of prognosis and imaging findings. Eur Heart J Cardiovasc Imaging. 2023;24(3):e40-e56. DOI: 10.1093/ehjci/jeac197 Cui K, Fu R, Yang J, et al. The impact of stress hyperglycemia ratio on long-term prognosis in patients with acute myocardial infarction: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):330. 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Predictive value of the stress hyperglycemia ratio in patients with acute ST-segment elevation myocardial infarction: insights from a multi-center observational study. Cardiovasc Diabetol. 2022;21(1):48. DOI: 10.1186/s12933-022-01462-y Ceriello A, Esposito K, Piconi L, et al. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes. 2008;57(5):1349-1354. DOI: 10.2337/db08-0063 Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res. 2010;107(9):1058-1070. DOI: 10.1161/CIRCRESAHA.110.223545 Esposito K, Nappo F, Marfella R, et al. Inflammatory cytokine concentrations are acutely increased by hyperglycemia in humans: role of oxidative stress. Circulation. 2002;106(16):2067-2072. DOI: 10.1161/01.cir.0000034509.14906.ae Taqueti VR, Di Carli MF. Coronary microvascular disease pathogenic mechanisms and therapeutic options: JACC state-of-the-art review. J Am Coll Cardiol. 2018;72(21):2625-2641. DOI: 10.1016/j.jacc.2018.09.042 von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457. DOI: 10.1016/S0140-6736(07)61602-X Carrick D, Haig C, Ahmed N, et al. Temporal evolution of myocardial hemorrhage and edema in patients after acute ST-segment elevation myocardial infarction: pathophysiological insights and clinical implications. J Am Heart Assoc. 2016;5(2):e002834. DOI: 10.1161/JAHA.115.002834 Nathan DM, Kuenen J, Borg R, et al. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473-1478. DOI: 10.2337/dc08-0545 Dungan KM, Braithwaite SS, Preiser JC. Stress hyperglycaemia. Lancet. 2009;373(9677):1798-1807. DOI: 10.1016/S0140-6736(09)60553-5 Marik PE, Bellomo R. Stress hyperglycemia: an essential survival response! Crit Care. 2013;17(2):305. DOI: 10.1186/cc12514 Bekkers SC, Yazdani SK, Virmani R, Waltenberger J. Microvascular obstruction: underlying pathophysiology and clinical diagnosis. J Am Coll Cardiol. 2010;55(16):1649-1660. DOI: 10.1016/j.jacc.2009.12.037 Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction (2018). Circulation. 2018;138(20):e618-e651. DOI: 10.1161/CIR.0000000000000617 Monnier L, Mas E, Ginet C, et al. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006;295(14):1681-1687. DOI: 10.1001/jama.295.14.1681 Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001;414(6865):813-820. DOI: 10.1038/414813a Williams SB, Goldfine AB, Timimi FK, et al. Acute hyperglycemia attenuates endothelium-dependent vasodilation in humans in vivo. Circulation. 1998;97(17):1695-1701. DOI: 10.1161/01.cir.97.17.1695 Devaraj S, Cheung AT, Jialal I, et al. Evidence of increased inflammation and microcirculatory abnormalities in patients with type 1 diabetes and their role in microvascular complications. Diabetes. 2007;56(11):2790-2796. DOI: 10.2337/db07-0784 Reinstadler SJ, Stiermaier T, Reindl M, et al. Intramyocardial haemorrhage and prognosis after ST-elevation myocardial infarction. Eur Heart J Cardiovasc Imaging. 2019;20(2):138-146. DOI: 10.1093/ehjci/jey101 Bulluck H, Hammond-Haley M, Weinmann S, Martinez-Macias R, Hausenloy DJ. Myocardial infarct size by CMR in clinical cardioprotection studies: insights from randomized controlled trials. JACC Cardiovasc Imaging. 2017;10(3):230-240. DOI: 10.1016/j.jcmg.2017.01.008 Goto Y, Tamura Y, Kawai H, et al. Stress hyperglycemia in acute myocardial infarction. Intern Med. 2015;54(17):2109-2114. DOI: 10.2169/internalmedicine.54.4332 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-8719658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598326259,"identity":"a6022001-9988-4168-9c99-8ac54efe8612","order_by":0,"name":"Nauman Gul","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Nauman","middleName":"","lastName":"Gul","suffix":""},{"id":598326261,"identity":"8dc4c626-2c6c-4349-9931-b2923373c3b8","order_by":1,"name":"Lu Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPghVI8fP3tj48AMxWtgg1DFjyZ7DzcYSJGhhTtxwI71NgIcoLRI5ZhI/d7Axbrj5sI1BgsFOTreBCC2SvWdkmCVvJ7Y9KGBINjY7QIwtvG1sbHy3E9sNJBgOJG4jRovk3zZmHoabB9skeIjVIs3bxiwhcIORWC08z4qtZduOGUj2JAID2YAIv/CzJ2+8+batpr6f/fjDhx8q7OQIamFg4DBA4hjgVIYM2B8QpWwUjIJRMApGMAAAJVQ8879pcckAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yuan","suffix":""},{"id":598326262,"identity":"b3988292-8487-4d07-8098-707ccfc4deb0","order_by":2,"name":"Yang Yu","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yu","suffix":""},{"id":598326266,"identity":"6859884c-2a58-4052-b9a1-60bdf5a73135","order_by":3,"name":"Li Zhi","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhi","suffix":""},{"id":598326268,"identity":"db0da955-b802-44c1-9e21-bec679f54930","order_by":4,"name":"Chen Lei","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Lei","suffix":""},{"id":598326269,"identity":"35bd081d-eb26-4e18-a5ca-4da0939ebf02","order_by":5,"name":"Zhang Min","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Min","suffix":""},{"id":598326270,"identity":"65b76fe3-3f2a-4a4a-bcf5-98a2ce304deb","order_by":6,"name":"Ms Aisha","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ms","middleName":"","lastName":"Aisha","suffix":""},{"id":598326272,"identity":"d72f88b9-8513-4f3b-a035-0caa69e7a8c4","order_by":7,"name":"Muhammad shahbaz raja","email":"","orcid":"","institution":"Lianyungang Oriental Hospital","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"shahbaz","lastName":"raja","suffix":""}],"badges":[],"createdAt":"2026-01-28 10:56:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8719658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8719658/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103840633,"identity":"6238b05c-298c-4f9d-833c-c3feda11f448","added_by":"auto","created_at":"2026-03-03 14:41:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94963,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT Patient Flow Diagram. Flow diagram of patient selection for the study. A survey of 508 consecutive STEMI patients who underwent primary PCI was conducted initially. After exclusions (n=142), 508 patients comprised the final analytic cohort. Of these, 179 patients (35.2%) had IMH evidence on CMR, and 329 patients (64.8%) did not .\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8719658/v1/fdc56cc56f2c796d73c919d2.jpg"},{"id":103840616,"identity":"b989ab12-b90e-4f9f-ab0e-f433341f1186","added_by":"auto","created_at":"2026-03-03 14:41:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104489,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of Cardiac Magnetic Resonance Images. Panel A depicts a case of IMH after STEMI. Myocardial oedema appears hyperintense in T2-weighted images. T2 mapping shows a hypointense core (arrow) in the infarcted territory with T2 values indicative of no haemorrhage. Late gadolinium enhancement reveals an infarct but no microvascular obstruction. All images were collected at 3.0 Tesla. Abbreviations: IMH, intramyocardial haemorrhage; STEMI, ST-elevation myocardial infarction; TE, echo time.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8719658/v1/f8a38aa0725c9da119595572.jpg"},{"id":103840627,"identity":"f73a0693-0ef1-4d65-b4e3-6636a1e1df30","added_by":"auto","created_at":"2026-03-03 14:41:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76741,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic Curves for IMH prediction. Discriminative ability comparing glycemic parameters for the prediction of intramyocardial haemorrhage in STEMI patients (n=508). SHR (solid orange line) only discriminates moderately (AUC=0.625, 95% CI 0.577-0.673, p\u0026lt;0.001). Similar results were observed for admission glucose (dashed blue line) (AUC = 0.603, 95% CI: 0.554-0.652, p = 0.001). HbA1c (green dashed line) had discriminatory ability that was worse than what would be expected by chance alone (AUC= 0.509, 95% CI 0.459-0.559, p=0.741). Dashed gray diagonal line indicates random (AUC=0.500). The orange dot: cutoff with the best Youden index (0.86), sensitivity 59.2%, specificity 63.8%. Results of a pairwise comparison were consistent with the overall conclusion, except for no significant difference between SHR and glucose (p=0.598), while both performed significantly better than HbA1c (p=0.006). Consistent AUCs, the continued independence of SHR as a predictor in multivariable analysis, and the lack of such support for admission glucose implied additional information. Abbreviations: AUC, area under the curve; CI, confidence interval; IMH, intramyocardial haemorrhage; SHR, stress hyperglycemic ratio; STEMI, ST-elevation myocardial infarction.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8719658/v1/b6a1838b383ae5fd1b711488.jpg"},{"id":104400717,"identity":"8918ce58-acf0-491c-82fc-d1f61912b4e5","added_by":"auto","created_at":"2026-03-11 12:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1462085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8719658/v1/6975bb24-9971-473b-99f6-0edc89aa4dc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stress Hyperglycemic Ratio Independently Predicts Intramyocardial Haemorrhage (IMH) in STEMI Patients: A Cardiac Magnetic Resonance (CMR) Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eST-segment elevation myocardial infarction (STEMI) remains a major global health issue and the most severe manifestation of acute coronary syndrome[1]. Despite significant progress in reperfusion strategies, particularly via primary percutaneous coronary intervention (pPCI) as the standard of care, achieving timely myocardial reperfusion[2], clinical outcomes for many patients remain poor due to microvascular complications. One of the complications, intramyocardial haemorrhage (IMH), has become a key prognostic factor in approximately 40\u0026ndash;54% of successfully perfused STEMI cases. IMH represents a severe microvascular injury in which red blood cells infiltrate into myocardial tissue[3,4], which we can identify as low-intensity cores within the infarcted area on myocardial magnetic resonance (CMR) imaging. Cardiac magnetic resonance (CMR) imaging has emerged as a gold standard for non-invasive detection and quantification of IMH[5], offering superior tissue characterization compared to other imaging modalities[6]. The pathophysiology of IMH originates from the complex interactions among ischemia-reperfusion injury, endothelial dysfunction, and inflammatory activation. When coronary artery blood flow recovers after prolonged occlusion, sudden reperfusion can unexpectedly increase myocardial tissue damage through oxidative stress and capillary rupture. This study investigated the association between IMH and SHR in STEMI patients, assuming that elevated SHR may exacerbate microvascular dysfunction, leading to an increase in the incidence and severity of IMH. By clarifying this relationship, we aim to identify a new biomarker for early risk stratification and potential therapeutic targets in high-risk STEMI patients. As the clinical relevance of IMH has already confirmed, many studies have been associated with larger infarct size, decreased left ventricular ejection fraction, and higher incidence of major adverse cardiac events (MACE). At the same time, stress hyperglycemia has been recognized as another important prognostic factor for STEMI patients, regardless of their diabetes status. Acute metabolic stress response during myocardial infarction often leads to elevated blood glucose levels, which are associated with poor microvascular perfusion and increased infarct size. Classical measures, such as admission blood glucose, are inadequate, especially in diabetes patients with long-term elevated blood glucose levels. So stress-induced hyperglycemia (SHR) has led to the development of better measurement methods, which explain baseline blood glucose status by combining HbA1c measurements. Currently, new data suggest that SHR may be superior to single glucose readings in predicting microvascular occlusion and patient outcomes.\u003c/p\u003e\n\u003ch3\u003eClinical evidence and knowledge gaps\u003c/h3\u003e\n\u003cp\u003eThe emerging clinical evidence strongly suggests the individual prognostic value of IMH and SHR in STEMI patients. CMR studies clearly indicate that IMH is associated with greater severity of myocardial injury, adverse left ventricular remodeling[7,8], larger infarct size[9], increase risk of heart failure[10], higher mortality rates[11], and higher levels of cardiac biomarkers such as high-sensitivity troponin T. IMH patients present with poor ventricular function and a high tendency towards adverse outcomes during follow-up, making them dependable predictors of long-term prognosis. Similarly, many studies have confirmed that an increase in SHR values indicates an increase in the incidence of microvascular obstruction, a decrease in myocardial recovery, adverse cardiovascular outcomes[12,13]. Standard indicators, such as admission blood glucose (ABG), may not accurately reflect acute glucose stress, especially in diabetic patients with long-term elevated blood glucose levels. Although these trends are consistent, the direct relationship between SHR and IMH has not been fully reported. There are a few studies specifically investigating whether high levels of SHR are independently associated with the incidence or severity of IMH. Recent studies have highlighted that SHR is an independent predictor of major adverse cardiovascular events (MACE) in STEMI patients[14], and mortality in acute myocardial infarction patients, independent of diabetic status[15,16]. This represents a significant knowledge gap, as identifying such connections can provide key findings into the metabolic mediators of vascular injury and identify potential therapeutic targets. In addition, most existing studies have insufficient adjustments for key confounding variables such as infarct location, compensatory vascular system for symptom reperfusion time, which can regulate hyperglycemia response and the development of IMH.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy rationale and objectives\u003c/h2\u003e \u003cp\u003eThe current research aims to address these gaps by investigating the association between SHR and IMH in a cohort of STEMI patients receiving modern pPCI treatment. We assume that even after adjusting for classical risk factors, an increase in SHR is independently associated with a higher prevalence and greater extent of IMH development through mechanisms involving oxidative stress[17], endothelial dysfunction[18], inflammation[19], and microvascular injury[20], and exploring the potential threshold SHR value that best predicts IMH risk. This retrospective observational study included consecutive STEMI patients who received successful pPCI at Xuzhou Medical University Affiliated Hospital. Despite modern reperfusion therapy, STEMI remains the leading cause of morbidity and mortality globally, thus requiring improved risk stratification tools to predict adverse outcomes. Although both IMH and SHR are independently associated with worse STEMI outcomes, their interactions have not been fully identified. Initial evidence shows that high blood levels may exacerbate reperfusion injury and microvascular obstruction, which are the main factors in intramyocardial haemorrhage (IMH). All participants underwent standardized CMR imaging using a 3T scanner within 3\u0026ndash;7 days after pPCI and adopted a standardized myocardial bleeding protocol. According to established standards, IMH is defined as the low-intensity region of T2 mapping imaging sequence. SHR is calculated using blood glucose at Admission and HbA1c values obtained during the first hospitalization period. Comprehensive clinical, vascular, and experimental data were collected for appropriate multivariate adjustments in statistical scores.\u003c/p\u003e \u003c/div\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population Characteristics\u003c/h2\u003e \u003cp\u003e This retrospective cohort study was conducted from June 2019 to July 2024 at the Coronary Care Unit (CCU) of Xuzhou Medical University Affiliated Hospital, Jiangsu Province, China. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines[21]. we performed an analysis on patients with STEMI. After screening for eligibility criteria in consecutive patients post-PCI and after successful primary PCI, 508 patients were included after exclusions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost patients were male (n = 437, 86.0%), with a median age of 58.0 years (IQR 50.0–66.0), which is age typical for STEMI patients. IMH was assessed in 179 patients (35.2%), and 329 patients (64.8%) were without IMH, indicating that microvascular injury, as defined by CMR, persists even with modern reperfusion techniques (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIMH-positive patients were younger (median 56.0 vs 58.0 years; p = 0.014). Male proportion was similar (84.9% vs 86.6%, p = 0.691). No significant group differences appeared for hypertension, diabetes, or BMI (all p \u0026gt; 0.05).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Data Collection\u003c/h3\u003e\n\u003cp\u003eThe data of patients were collected from the medical record system of the hospital, including age, gender, height, weight, heart rate, systolic and diastolic blood pressure, smoking history, past medical history ( Diabetes, Hypertension, Stroke) and history of medications (ACEi, ARBs, beta blockers, Statins, Aspirin / clopidogrel). During admission, multiple blood test were performed, the HbA1c, blood glucose level, peak levels of high-sensitivity C-reactive protein (hs-CRP), haemoglobin, high-sensitivity troponin T (hs-TnT), WBC cell type, and N-terminal pro-B-type natriuretic peptide (NT-proBNP), Random blood glucose levels were noted upon admission for all included patients.\u003c/p\u003e\n\u003ch3\u003eCardiac Magnetic Resonance (CMR) Parameters\u003c/h3\u003e\n\u003cp\u003e All pPCI procedures adhered to AHA/ESC guidelines[1], emphasizing timely reperfusion. CMR imaging was performed using 3.0T scanner (Magnetom Skyra, Siemens Healthineers, Erlangen, Germany) with a 32-channel cardiac phased-array coil. All scans were done 3–7 days post-pPCI ( median 5 days, IQR 4–6 days). Cine steady-state free precession (SSFP) imaging in standard long-axis and short-axis planes covering the entire left ventricle (slice thickness 8 mm, gap 2 mm, temporal resolution \u0026lt; 50 ms, in-plane resolution 1.6 × 1.6 mm) T2-weighted short-tau inversion recovery (STIR) sequences for edema assessment (TE 60 ms, TR 2 R-R intervals), TE 2.1–16.8 ms, TR 200 ms, flip angle 20°, slice thickness 8 mm) Late gadolinium enhancement (LGE) imaging 10–15 minutes after administration of 0.1 mmol/kg gadobutrol (Gadovist, Bayer, Germany) using phase-sensitive inversion recovery sequences. Late gadolinium enhancement (LGE) for necrosis and T2-mapping sequences for haemorrhage quantification. Image analysis was performed using software Circle Cardiovascular Imaging (Circle CVI42 version 5.13.5 Canada) for precise volumetric and parametric mapping, the myocardial layers (epicardium and endocardium) were hand operated and summarized.\u003c/p\u003e \u003cp\u003eRepresentative examples of cardiac magnetic resonance imaging sequences from two patients are shown in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e. Panel A demonstrates a CMR sequence with intramyocardial haemorrhage IMH, was defined as hypointense regions within the infarct core on T2-mapping sequences[22], while Panel B shows a CMR sequence without intramyocardial haemorrhage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariables and Measurements\u003c/h2\u003e \u003cp\u003eThe primary exposure was the Stress Hyperglycemic Ratio (SHR), calculated as SHR = Admission Blood Glucose (mg/dl) / [28.7 × HbA1c (%) – 46.7][23,24], reflecting acute glycemic stress relative to chronic glycemic levels. The primary outcome was intramyocardial haemorrhage (IMH), evaluated via CMR using T2-weighted sequences, a sensitive marker of microvascular injury. Secondary outcomes included infarct size and haemorrhage extent. Covariates covered demographics (age, gender), cardiovascular risk factors (hypertension, diabetes), angiographic details (TIMI flow, culprit lesion), and biomarkers (hs-CRP, troponin).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Python 3.10 with SciPy 1.11 and scikit-learn 1.3 libraries.. Descriptive data are presented as mean ± standard deviation or median [interquartile range] for continuous variables, and counts (percentages) for categorical variables. Group comparisons were made using the Chi-square test, Student's t-test, or Mann-Whitney U test, as appropriate. Univariate and multivariate logistic regression analyses were used to identify factors associated with IMH. A Receiver Operating Characteristic (ROC) curve was generated to evaluate the predictive performance of SHR for IMH. A two-sided p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical and Methodological Considerations\u003c/h3\u003e\n\u003cp\u003eThe study received approval from Xuzhou Medical University with informed consent obtained where applicable. Strengths included a robust sample size (n = 508), CMR's gold-standard accuracy for IMH, and standardized pPCI/CMR protocols.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDEFINITIONS\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eStress Hyperglycemic Ratio (SHR)\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDefinition\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe Stress Hyperglycemic Ratio (SHR) is a measure of acute hyperglycemia during critical illness (e.g., myocardial infarction, stroke, or sepsis) relative to chronic glycemic control[25]. It quantifies the degree of stress-induced hyperglycemia independent of pre-existing diabetes or chronic glucose dysregulation.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIntramyocardial Haemorrhage (IMH)\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDefinition\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eIntramyocardial Haemorrhage (IMH) is bleeding within the myocardium that occurs after reperfusion therapy (e.g., pPCI) in ST-segment elevation myocardial infarction (STEMI). It is characterized by the extravasation of red blood cells into the myocardial tissue, detectable as hypointense regions on T2-weighted or T2-mapping CMR sequences[26].\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eST-Segment Elevated Myocardial Infarction (STEMI)\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDefinition\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eSTEMI is a severe type of heart attack caused by the complete occlusion of a coronary artery, leading to transmural myocardial ischemia. It is diagnosed by ST-segment elevation on electrocardiogram and elevated cardiac biomarkers[27].\u003c/p\u003e "},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eLaboratory and biomarker parameters\u003c/b\u003e:\u003c/p\u003e\u003cp\u003ePatients with IMH exhibited greater indices of myocardial injury and systemic activation than those without IMH. More precisely, the peak troponin T was nearly double in the IMH group (4455.5 vs 2350.5 ng/L; p \u0026lt; 0.001), and other biomarkers like WBC (9.05 vs 7.44 ×10⁹/l; p \u0026lt; 0.001) and LDH (937.0 vs 504 U/L; p \u0026lt; 0.001) were raised as well. These findings indicated that patients with IMH had more myocardial injury and stronger inflammatory response, the former of which is associated with larger infarct size and poor clinical outcomes. In addition, IMH-positive patients had lower CMR-derived LVEF values (52.0% vs 54.0%), suggesting subclinical cardiac impairment of potential prognostic relevance. Examples of CMR images of both IMH-positive and IMH-negative patients are shown in Fig.\u0026nbsp;2. Glycemic control differences are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e: SHR was significantly higher in IMH-positive patients compared with IMH-negative patients (0.87 vs 0.81, P \u0026lt; 0.001), with a clinically meaningful delta of + 7.4%. In contrast, median HbA1c levels did not differ between the 2 groups (6.0%; P = 0.747), suggesting that IMH risk is more closely related to acute metabolic stress than to chronic glycemic exposure. This observation underscores the need to consider acute stress hyperglycemia, as reflected by SHR, when predicting SHR risk.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics by IMH status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eGROUP BY IMH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eIMH Negative (n = 331)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eIMH Positive (n = 175)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e58.00 (51.00–67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e56.00 (47.50–65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e265(80.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e164(91.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e128 (38.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (37.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e64 (19.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e33 (18.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBMI (kg/m²)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e25.35 (23.44–27.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e25.54 (23.44–27.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNT-proBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1061.000 (543.0,2031.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1135.000 (486.8,1912.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePeak-troponin T (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2350.500 (1020.5,4005.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4455.500 (2510.5,6840.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e54.08 (51.00,58.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e52.85 (49.00,57.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWBC (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e7.440 (5.9,9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9.050 (7.1,10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHaemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e137.000 (127.0,147.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e141.000 (130.0,152.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePLTs (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e212.000 (174.5,249.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e210.000 (176.0,250.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFibrinogen (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.490 (2.6,4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.700 (2.7,5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCreatinine (µmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e60.000 (50.0,70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e58.000 (49.0,65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e504.000 (336.5,786.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e937.000 (604.0,1519.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5.860 (5.1,7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.400 (5.5,8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.000(5.6,6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.000(5.6,7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4.070(3.5,4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.910(3.3,4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTAG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.430(1.0,2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.350(0.9,2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.930(0.8,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.920(0.8,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.830(2.3,3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.670(2.1,3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17.200(8.1,40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e28.800(13.7,64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.810(0.7,0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.870(0.8,1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eACEi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e64 (19.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e41 (23.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e137 (41.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e70 (39.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eB-blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e298 (90.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e151 (85.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* p \u0026lt; 0.05 ** p \u0026lt; 0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eData presented as median [IQR] or n (%). P-values from the Mann–Whitney U-test or chi-square test. *p \u0026lt; 0.05\u003c/p\u003e\u003cp\u003e \u003cb\u003eUnivariable Predictors of Intramyocardial Haemorrhage\u003c/b\u003e \u003c/p\u003e\u003cp\u003eIn univariable analysis, each predictor was evaluated separately using complete-case analysis (n = 375, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Demographics showed age significant (OR = 0.97/year, 95% CI 0.96–0.99; p = 0.005). Male gender was not (OR 1.05, 95% CI 0.53–2.08; p = 0.887). Peak troponin T was strongly associated (OR 1.35/1000 ng/L, 95% CI 1.23–1.49; p \u0026lt; 0.001). CRP and LVEF were not significant (p = 0.256, p = 0.269).\u003c/p\u003e\u003cp\u003eFor glycemic parameters, the SHR (admission glucose divided by estimated chronic glucose from HbA1c) is significantly associated with IMH (OR 1.20 per 0.1 unit, CI95%: 1.08–1.34; p = 0.001). Calculated admission glucose (from SHR and HbA1c) was moderately associated (OR 1.13 per mmol/L, 95% CI: 1.04–1.23; p = 0.003), but HbA1c was not significantly associated (OR 1.12, 95% CI: 0.97–1.30; p = 0.133). These univariable results are consistent with the possible greater advantage of stress-corrected glycemic estimation over absolute and chronic measures for predicting IHM. Candidates with p \u0026lt; 0.10 were included in multivariable models.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Logistic Regression for Intramyocardial Haemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.97 (0.96–0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.05 (0.53–2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eGlycemic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eStress Hyperglycemic Ratio (per 0.1 unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.20 (1.08–1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAdmission glucose (per mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.13 (1.04–1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHbA1c (per %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.12 (0.97–1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eCardiac Biomarkers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePeak Troponin T (per 1000 ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.35 (1.23–1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eInflammatory and Haematologic Markers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWBC (per 10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.13 (1.05–1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHaemoglobin (per g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.03 (1.01–1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLDH (per 100 U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.15 (1.10–1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCRP (per mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003eCMR Findings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLVEF (per %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.98 (0.95–1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eOdds ratios (95% confidence interval) are reported. analysis of complete-case, n = 375 (n = 107 IMH events). Values in bold indicate variables included in the multivariable Model 3 (Age, Male sex, SHR, WBC, Haemoglobin). SHR was reported as the leading variable of interest. All p-values are based on univariate logistic regression. Abbreviations: CI, confidence interval; CRP, C-reactive protein; HbA1c, glycated haemoglobin; IMH, intramyocardial haemorrhage; LDH, lactate dehydrogenase; LVEF, left ventricular ejection fraction; SHR , stress hyperglycaemic ratio; WBC, white blood cell count.\u003c/p\u003e\u003ch2\u003eIndependent Predictors of Intramyocardial Haemorrhage\u003c/h2\u003e\u003cp\u003eAfter identifying univariable predictors of IMH, a multivariable logistic regression model was used to assess independent factors associated with IMH at 3 levels of adjustment (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This stepwise approach allowed a more structured analysis of whether the univariable relationship between SHR and IMH persisted after adjustment for potential confounders.\u003c/p\u003e\u003cp\u003e \u003cb\u003eModel 1 (Unadjusted)\u003c/b\u003e: Univariable analysis showed a notable correlation of SHR with IMH (OR: 1.20 per 0.1 unit, 95% CI: 1.08–1.34; p = 0.001). Accordingly, as SHR increases by 0.1 unit, the odds of IMH increase by approximately 20%, suggesting that SHR is an individual biomarker with clinical applicability for predicting IMH risk.\u003c/p\u003e\u003cp\u003e \u003cb\u003eModel 2 (Age-and-Sex-Adjusted)\u003c/b\u003e: After adjusting for age and sex, SHR remained significantly associated with IMH (OR 1.20 per 0.1 unit, 95% CI:1.07–1.34; p = 0.001), and effect sizes were only minimally reduced. These findings indicated that SHR was not confounded by patient demographics with IMH. Age was an independent predictor (OR 0.97 per year, 95% CI 0.96–0.99; p = 0.006), whereas male sex was not significant (OR 0.94, 95% CI 0.46–1.89; p = 0.853).\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eModel 3 (Fully adjusted)\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eIn the fully adjusted model, adjustment was made for demographic factors (age and sex) and key laboratory markers of inflammation and cellular injury (white blood cell count and haemoglobin).\u003c/p\u003e\u003cp\u003eWithin our model, SHR remained an independent predictor of IMH in the entire cohort (OR = 1.17 per 0.1-unit increase, 95% CI [1.05–1.31], p = 0.006). For each 0.1-unit increase in SHR, the independent odds of IMH increased by 17%. WBC count remained borderline significant (OR 1.08 per 10⁹/L, 95% CI 1.00–1.18, p = 0.050), whereas age, male sex, and haemoglobin were not independently statistically predictive after adjustment. When we replaced admission glucose with SHR in this fully adjusted model, it was borderline significantly associated with IMH (p = 0.06), further validating the superiority of SHR for predicting IMH under multivariable adjustment.\u003c/p\u003e\u003cp\u003eModel 3 had good discrimination (C-statistic = 0.677) and no multicollinearity. Diagnostics support the model and SHR's independent predictive value.\u003c/p\u003e\u003cp\u003e \u003cb\u003eClinical Relevance\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe persistent association between SHR and IMH, independent of demographic and injury-related variables, underscores its clinical relevance as a risk predictor in this population. A modest attenuation of effect estimates from univariable models to fully adjusted exposure variables provides support for the SHR as an instrument in settings outside traditional laboratory conditions. As SHR can be readily obtained on Admission, it provides a useful tool for identifying patients at high risk who may need early monitoring or specific intervention. However, these results are from a single-centre observational cohort, and thus, the generalizability and causal inference from this study are limited. Since the AUC of SHR was moderate, it is a useful addition to a broader risk assessment rather than an independent marker. Its prognostic role in a larger patient cohort and in a multicenter study should be further evaluated.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression results for intramyocardial haemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eModel 1: Unadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eModel 2:\u003c/p\u003e \u003cp\u003eAge + Sex Adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eModel 3: Fully Adjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSHR (per 0.1 unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.20 (1.08–1.34) p = 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.20 (1.07–1.34) p = 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.17 (1.05–1.31) p = 0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.97 (0.96–0.99) p = 0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.98 (0.96-1.00) p = 0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.94 (0.46–1.89) p = 0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.92 (0.46–1.88) p = 0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWBC (per 10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.08 (1.00-1.18) p = 0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHaemoglobin (per g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.01 (1.00-1.03) p = 0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe results were expressed as odds ratios (95% confidence intervals). Values in bold are those for which p is \u0026lt; 0.01. Model 1 shows the simple association between SHR and IMH. Model 2 adds adjustment for age and sex. Model 3 includes age, sex, WBC count, and haemoglobin as continuous variables. This was a complete-case cohort analysis (n = 375, IMH events = 107). Model 3 had a C-statistic of 0.677. All variance inflation factors were \u0026lt; 1.5, indicating no multicollinearity. Admission glucose was not significant in Model 3 when included instead of SHR. Abbreviations: CI, confidence interval; IMH, intramyocardial haemorrhage; SHR, stress hyperglycemic ratio; WBC, white blood cell.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePredictive Performance of Stress Hyperglycemic Ratio\u003c/b\u003e \u003c/p\u003e\u003cp\u003eReceiver operating characteristic (ROC) analyses of glycemic parameters' ability to predict IMH are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Area under the curve (AUC) analysis indicated that SHR had poor to moderate discriminative power (AUC = 0.625, 95% CI 0.577–0.673, p \u0026lt; 0.001). This is, of course, better than an AUC of 0.5, which would correspond to a completely random guess about the positive or negative class. Admission glucose demonstrated low to moderate discrimination (AUC = 0.603, 95% CI 0.554–0.652, p = 0.001). However, the AUC for HbA1c was 0.509 (95% CI: 0.459–0.559, p = 0.741) and therefore did not differ from random classification at any probability threshold. ROC curve analysis is summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The AUC for SHR and admission glucose was not significantly different (p = 0.598, pairwise comparisons). Both values were more discriminative than HbA1c (p = 0.006).\u003c/p\u003e\u003cp\u003eThe Youden index, a statistic used to select the optimal cutoff value for an experimental procedure and to determine the overall classification performance of SHR, was 0.86. SHR demonstrated the best sensitivity (59.2%) and specificity (63.8%) for IMH detection at this cutoff. Although SHR alone had only moderate discriminatory value, the high negative predictive value (74.2% [95% CI 67.6–80.1]) indicates that an SHR below this cutoff is practically useful for excluding IMH risk.\u003c/p\u003e\u003cp\u003eImportantly, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that while SHR and Admission glucose had similar area under the curve (AUC) values in univariable receiver operating characteristic (ROC) analysis (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), SHR maintained independent predictive value for IMH after multivariable models, whereas admission glucose did not. This finding indicates that ROC analysis, in itself, as demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, could underestimate the clinical relevance of stress-corrected glycemic markers such as SHR. Although the modest AUC value indicates only moderate discrimination, the continued independent prediction of SHR in this multivariable setting (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) suggests that SHR captures aspects of metabolic risk beyond those captured by standard risk variables. Therefore, the SHR cutoff of 0.86 suggested by Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e offers a clinically practical point for risk stratification and the identification of patients at higher risk for microvascular complications who can be closely monitored.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Stratified Analyses\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTo further explore the association between SHR and IMH, patients were divided into low-SHR (≤ 0.83) and high-SHR (\u0026gt; 0.83) groups using a ROC cutoff of 0.83, which is close to the median value. Baseline characteristics were significantly different between the groups (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). IMH prevalence. The high SHR group (n = 248) had a markedly greater prevalence of IMH than the low SHR group (n = 260): 44.8% (95% CI: 38.7–51.0%) vs 26.2% (95% CI:21.2–31.8%), p \u0026lt; 0.001, equating to a + 71% relative increase. This significant contrast implies a dynamic interplay between glycemic stress and the risk of microvascular injury.\u003c/p\u003e\u003cp\u003eThe incremental rise in the incidence of IMH reflects a linear dose-response relation, where risk for microvascular injury increases with increasing levels of metabolic stress without a threshold effect. High-SHR subjects had significantly higher inflammatory, cardiac injury, and metabolic stress markers, indicating a more severe acute phenotype.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics by SHR Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOverall (n = 508)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSHR ≤ 0.83 (n = 258)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSHR \u0026gt; 0.83 (n = 250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIMH prevalence, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e179 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e65 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e114 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e57.4 ± 12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e57.8 ± 12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e57.1 ± 11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGender, Male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e437 (86.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e228 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e209 (83.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBMI, kg/m²\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.8 ± 3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.9 ± 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.7 ± 3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSHR (continuous)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.84 ± 0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.70 ± 0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.98 ± 0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e194 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e87 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e107 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLVEF, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.5 ± 6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.6 ± 6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.4 ± 6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eACEi use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e89 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e38 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e51 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eARB use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e67 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e38 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBeta-blocker use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e440 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e213 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e227 (90.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWBC (×10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.6 ± 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.4 ± 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.9 ± 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHaemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e137.7 ± 14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e137.0 ± 14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e138.4 ± 13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePlatelets (×10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e212.1 ± 65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e210.7 ± 68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e213.4 ± 63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFibrinogen (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.08 ± 1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.10 ± 1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.07 ± 1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTroponin T (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4105 ± 3434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3995 ± 3419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4211 ± 3450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBNP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1457 ± 2557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1542 ± 2710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1375 ± 2397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e707 ± 873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e667 ± 806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e745 ± 932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCreatinine (µmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e62.5 ± 14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e62.7 ± 14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e62.3 ± 15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.1 ± 1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.0 ± 1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.1 ± 1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.6 ± 32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.8 ± 35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16.4 ± 27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTotal Cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.5 ± 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.5 ± 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.5 ± 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.98 ± 0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.98 ± 0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.99 ± 0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.0 ± 2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.0 ± 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.0 ± 2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.9 ± 1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.9 ± 1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.9 ± 1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIMH volume, mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.5 ± 1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.4 ± 1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.6 ± 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eData presented as mean ± SD or n (%). SHR = Stress Hyperglycemic Ratio; IMH = Intramyocardial Haemorrhage; BMI = Body Mass Index; LVEF = Left Ventricular Ejection Fraction; ACEi = Angiotensin-Converting Enzyme inhibitor; ARB = Angiotensin Receptor Blocker; WBC = White Blood Cell; BNP = B-type Natriuretic Peptide; LDH = Lactate Dehydrogenase; CRP = C-Reactive Protein; HDL = High-Density Lipoprotein; LDL = Low-Density Lipoprotein.\u003c/p\u003e\u003cp\u003eIn this complete-study cohort (n = 375), subgroup analysis evaluated whether the SHR-IMH relationship was consistent across predefined patient groups. The relationship was also consistent across age groups (\u0026lt; 60 vs ≥ 60 years; p-interaction = 0.236), sex (p-interaction = 0.882), and diabetes status (p-interaction = 0.361); no effect modification was observed. In the age-stratified analysis, ORs were 1.23 (95% CI, 1.07–1.40, p = 0.003) for younger patients (\u0026lt; 60 years) and 1.12 (95% CI, 0.97–1.29, p = 0.136) for older patients (≥ 60 years). The uniform findings across subgroups suggest that the predictability of SHR for IMH may be generalizable to a variety of patient phenotypes, not confined to high-risk patients. There was no significant interaction with diabetes status, indicating that adjustment for stress by life is predictive independently of chronic glycemic control.\u003c/p\u003e\u003ch2\u003eSummary Statement\u003c/h2\u003e\u003cp\u003eIn our cohort of 508 patients with STEMI who underwent primary PCI, IMH was present in 35.2%, indicating continued incidence of CMR-derived microvascular injury despite reperfusion with door-to-balloon era therapy. SHR was independent to predict IMH; its significance remained after adjusting for demographic and injury variables (unadjusted OR 1.20, 95%CI: 1.08–1.34 , p = 0.001; adjusted OR: 1.17(95%CI), p = 0.006). Admission glucose did not maintain predictive power in the multivariable model. ROC studies for SHR showed an AUC of 0.625 and an optimal cutoff of 0.86, suggesting a clinically informative but only moderately discriminative value for risk stratification. The presence of a dose-response gradient, as well as a 71% higher prevalence of IMH in high SHR than low SHR patients (44.8% vs. 26.2%), also testifies to its clinical utility. In conclusion, these findings suggest that SHR may serve as an accessible marker for early recognition and risk stratification of microvascular injury in STEMI patients, facilitating more targeted monitoring and treatment in clinical practice.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides evidence that stress-induced hyperglycemia (SHR) is independently associated with intramyocardial haemorrhage (IMH) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (pPCI). These findings advance our understanding of how acute metabolic changes cause microvascular damage after myocardial infarction. Our proposed mechanism is supported by much pathophysiological evidence linking hyperglycemia to microvascular injury. First, acute hyperglycemia increases oxidative stress and reactive oxygen species (ROS) production [28,29]. It impairs nitric oxide bioavailability[30], damages endothelial cells, and increases vascular permeability. Secondly, a pro-inflammatory state induced by hyperglycemia elevates cytokine levels [20,31]. This promotes leukocyte activation and microvascular obstruction. Thirdly, metabolic disorders impair myocardial calcium processing and mitochondrial function. This makes myocardial cells prone to reperfusion injury. These mechanisms together increase the risk of microvascular rupture and bleeding after revascularisation. In addition, high blood glucose levels can induce proteases (especially MMP-9), which can degrade extracellular matrix and basement membranes. This damages the microvascular structure and promotes bleeding. These molecular mechanisms explain how SHR elevation mediates IMH, distinct from common risk factors. Therefore, we find the association between SHR and IMH offers clinical support for these pathways.\u003c/p\u003e \u003cp\u003eOur results show the SHR-IMH link exists across age groups, especially in older patients (61\u0026ndash;75 years). This age-related trend may reflect greater vascular damage, reduced microvascular elasticity, and an aging metabolism. There is also new evidence of gender differences in stress response and microvascular function. In clinical settings, SHR offers a simple bedside measure. It uses admission blood glucose and HbA1c to identify patients at higher IMH risk who could benefit from closer observation and timely metabolic care after pPCI.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe principal strengths of our investigation include a substantial patient cohort, the application of CMR for definitive IMH identification, and consistent procedural protocols for both pPCI and imaging, which collectively bolster the validity of our results. Our analysis further accounted for numerous clinical and laboratory variables, reinforcing the independent nature of the relationship between SHR and IMH. Nonetheless, this study is subject to certain constraints. Its retrospective, single-centre nature poses a risk of selection bias and may affect the external applicability of our conclusions. The high proportion of male participants, though typical for a STEMI population, limits insights into potential sex-based differences. Additionally, we did not evaluate serial glucose measurements or continuous glucose monitoring, which might provide more comprehensive assessment of glycemic variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eFuture studies should establish optimal SHR thresholds for predicting intramyocardial haemorrhage, clarify how hyperglycemic stress and microvascular injury evolve, and examine potential interactions between SHR and other metabolic markers such as ketone bodies or free fatty acids. Randomized trials are needed to determine whether early glycemic control can reduce IMH incidence and improve outcomes. These findings also suggest that STEMI management should integrate metabolic assessment alongside revascularization, enabling a more comprehensive and personalized approach to care.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates an independent association between Stress Hyperglycemic Ratio and intramyocardial haemorrhage in STEMI patients treated with contemporary pPCI. The findings enhance comprehension of the metabolic determinants of microvascular injury and provide a firm basis for incorporating SHR assessment into routine clinical practice for risk stratification. The demonstrated relationship between acute glycemic derangements and hemorrhagic complications reinforces the critical role of metabolic factors in post-infarction outcomes and suggests potential opportunities for therapeutic intervention. Future research should focus on validating these findings in more diverse populations, elucidating the underlying mechanisms, and testing whether specific control of hyperglycemic stress can effectively reduce IMH and its negative outcomes. This work represents an important step toward more individualized therapy of STEMI that considers both vascular and metabolic dimensions of care.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCLINICAL IMPLICATIONS\u003c/h2\u003e \u003cp\u003eThese findings have significant clinical implications across multiple areas of cardiovascular care. Clinically, the association of SHR with IMH has proven to be a practical, cost-effective risk-stratification tool that can be implemented immediately upon admission using routinely available laboratory values. This early identification can guide more proactive cardioprotective strategies and inform decisions regarding the timing of CMR imaging and the intensity of monitoring.[32,33] Additionally, the SHR-IMH association suggests potential therapeutic targets, as interventions aimed at regulating acute hyperglycemia may reduce microvascular damage. [34]。\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStress Hyperglycemic Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntramyocardial Haemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTEMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eST-Segment Elevated Myocardial Infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epPCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary Percutaneous Coronary Intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiac Magnetic Resonance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Oxygen Species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin A1c\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Society of Cardiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLate Gadolinium Enhancement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHS-CRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Sensitivity C-Reactive Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMP-9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMatrix Metalloproteinase-9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePercutaneous Coronary Intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectrocardiogram\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft Ventricular Ejection Fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor Adverse Cardiac Events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all \u0026nbsp;medical staff at the Coronary Care Unit and CMR Department of Xuzhou Medical University Affiliated Hospital for their support in data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study was approved by the Institutional Review Board/Ethics Committee of Xuzhou Medical University (approval number: LCZX20251). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll imaging data presented in this manuscript are fully anonymized and do not contain any identifying patient information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Clinical Research Special Contract of Xuzhou Medical University at The Affiliated Hospital of Xuzhou Medical University, Xuhou city, Jiangsu Province, P.R. China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNauman Gul: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Lu Yuan: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing- review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eYang Yu:\u0026nbsp;Investigation, Data collection, Validation, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eLi Zhi:\u0026nbsp;Methodology, Software, Formal analysis.\u003c/p\u003e\n\u003cp\u003eChen Lei:\u0026nbsp;Resources, Data curation, Clinical interpretation, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eZhang Min:\u0026nbsp;CMR image analysis, Validation, Resources.\u003c/p\u003e\n\u003cp\u003eAisha:\u0026nbsp;Statistical analysis, Data visualization, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eMuhammad Shahbaz Raja: Clinical data interpretation, Manuscript revision, Critical review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIbanez B, James S, Agewall S, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). 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DOI: 10.2169/internalmedicine.54.4332\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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 hyperglycemic ratio, intramyocardial haemorrhage, STEMI, cardiac magnetic resonance, primary percutaneous coronary intervention (pPCI)","lastPublishedDoi":"10.21203/rs.3.rs-8719658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8719658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eST-segment elevation myocardial infarction (STEMI) often results in microvascular injury, including intramyocardial haemorrhage (IMH), despite successful reperfusion. This study explored whether stress hyperglycemic ratio (SHR) independently predicts IMH in STEMI patients undergoing primary percutaneous coronary intervention (pPCI).\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 508 STEMI patients who underwent pPCI at The Affiliated Hospital of Xuzhou Medical University (2019\u0026ndash;2024). SHR was calculated as Admission Glucose / (28.7 \u0026times; HbA1c\u0026thinsp;\u0026minus;\u0026thinsp;46.7). IMH was evaluated with cardiac magnetic resonance (CMR) imaging performed within 3\u0026ndash;7 days post-pPCI using T2-mapping sequences. Multivariate logistic regression was used to identify independent predictors of IMH, and Receiver operating characteristic (ROC) analysis assessed SHR predictive performance.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eHigher SHR was independently associated with IMH, patients with IMH had significantly higher SHR (0.87 vs 0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age, gender, WBC count and haemoglobin, elevated SHR remained independently associated with IMH (adjusted OR 1.72; 95% CI 1.34\u0026ndash;2.21; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis showed (AUC\u0026thinsp;=\u0026thinsp;0.625, 95% CI 0.577\u0026ndash;0.673, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating modest predictive ability, with an optimal cutoff value of 0.83 (sensitivity of 63.7% and specificity of 58.7%).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eSHR independently predicts IMH in reperfused STEMI patients and may serve as a simple, readily available biomarker for early risk stratification of microvascular injury. These findings support cardiometabolic workup into STEMI management strategies.\u003c/p\u003e","manuscriptTitle":"Stress Hyperglycemic Ratio Independently Predicts Intramyocardial Haemorrhage (IMH) in STEMI Patients: A Cardiac Magnetic Resonance (CMR) Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 14:40:21","doi":"10.21203/rs.3.rs-8719658/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-17T11:00:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-14T18:07:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T19:02:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330143315735952711110360882470846359915","date":"2026-03-05T18:04:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T10:23:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295510599309628325299574112558556212534","date":"2026-02-27T18:43:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205267485926670684743795797099005232460","date":"2026-02-27T02:31:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5915082568055955087706993326174645844","date":"2026-02-25T02:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T22:54:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T17:17:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T10:37:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T10:32:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-28T09:20:31+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":"46e1e7a1-693d-4612-b364-7ab71747a6b2","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T06:56:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 14:40:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8719658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8719658","identity":"rs-8719658","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}