A Novel Glyco-Inflammatory Healing Index for Risk Prediction After Isolated Coronary Artery Bypass Grafting: A Multicenter Retrospective Cohort Study in Patients with Type 2 Diabetes

A Novel Glyco-Inflammatory Healing Index for Risk Prediction After Isolated Coronary Artery Bypass Grafting: A Multicenter Retrospective Cohort Study in Patients with Type 2 Diabetes | 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 A Novel Glyco-Inflammatory Healing Index for Risk Prediction After Isolated Coronary Artery Bypass Grafting: A Multicenter Retrospective Cohort Study in Patients with Type 2 Diabetes Haitham Abu Khadija, Mohammad Alnees, Enrique Z. Fisman, Kareem Ibraheem, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9457970/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background The Glyco-Inflammatory Healing Index (GIHI), calculated as HbA1c × C-reactive protein/albumin, integrates glycemic control, systemic inflammation, and nutritional status. Its prognostic value after isolated coronary artery bypass grafting (CABG) remains unclear, particularly according to diabetes status. Methods We conducted a retrospective, international, multicenter cohort study of 6,262 adults who underwent isolated CABG between 2010 and 2025. Patients were stratified by diabetes status. GIHI was analyzed as a standardized continuous variable and descriptively categorized as high or low based on subgroup medians. The primary outcome was 30-day major adverse cardiovascular events (MACE), defined as all-cause mortality, myocardial infarction, stroke, or repeat revascularization. The secondary outcome included long-term all-cause mortality. Associations were assessed using Cox regression, Kaplan-Meier analysis, restricted cubic splines, subgroup analyses, and receiver operating characteristic curves. Results Among 6,262 patients, 2,783 (44.5%) had diabetes. In diabetic patients, higher GIHI was independently associated with 30-day MACE (adjusted hazard ratio [HR] 1.45, 95% confidence interval [CI] 1.28–1.65), 30-day myocardial infarction (HR 1.27, 95% CI 1.00-1.60), 30-day mortality (HR 2.56, 95% CI 2.06–3.18), and long-term mortality (HR 2.57, 95% CI 2.33–2.83). In non-diabetic patients, GIHI was not associated with 30-day MACE but remained associated with 30-day mortality and long-term mortality. Adding GIHI improved discrimination for 30-day MACE in diabetic patients (AUC 0.65 to 0.69; p < 0.001). Conclusions GIHI independently predicts adverse postoperative outcomes after isolated CABG, with the strongest prognostic relevance in patients with diabetes. Coronary artery bypass grafting. Diabetes mellitus. Glyco-Inflammatory Healing Index. Major adverse cardiovascular events Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Research Insights What is currently known about this topic ? Diabetes worsens early and late outcomes after CABG. HbA1c, CRP, and albumin each have prognostic value. Integrated biologic risk markers may improve risk stratification. What is the key research question? GIHI may improve risk stratification after isolated CABG, particularly in patients with diabetes. What is new? GIHI independently predicted 30-day MACE in diabetic patients, showed stronger prognostic value in diabetic than non-diabetic patients, and improved discrimination for 30-day MACE in diabetes. How might this study influence clinical practice? GIHI may help identify diabetic CABG patients who need closer perioperative surveillance and optimization. Background Coronary artery disease remains a major global health problem, and ischemic heart disease continues to be the leading cardiovascular cause of death and disability worldwide. Contemporary burden estimates show that cardiovascular disease accounted for 19.2 million deaths in 2023, with ischemic heart disease remaining the leading contributor to cardiovascular disability-adjusted life years [ 1 ]. In this context, coronary artery bypass grafting (CABG) remains a cornerstone of revascularization for patients with complex coronary anatomy, including left main and multivessel disease. Current guidelines also emphasize that, in patients with diabetes and multivessel coronary artery disease, surgical revascularization is often the preferred strategy [ 2 ]. Patients with diabetes represent a particularly high-risk subgroup after CABG. Beyond having a greater burden of diffuse and accelerated atherosclerosis, they frequently present with coexisting renal dysfunction, endothelial injury, impaired microvascular function, and disordered wound healing, all of which may adversely influence postoperative recovery [ 3 ]. Evidence from randomized and observational studies has shown that although CABG is generally superior to percutaneous coronary intervention for diabetic patients with multivessel disease, diabetic patients still experience worse early and late outcomes after surgery than non-diabetic patients, including higher operative mortality and poorer long-term survival. These observations suggest that traditional clinical risk markers do not fully capture the biologic vulnerability of this population [ 4 ], [ 5 ]. Inflammation may be a key mechanism underlying adverse outcomes after CABG. Cardiac surgery, especially with cardiopulmonary bypass, provokes a systemic inflammatory response driven by surgical injury, blood–surface contact, ischemia–reperfusion, and cytokine release. This inflammatory activation has been associated with complications such as atrial fibrillation, stroke, and mortality, and higher CRP levels after CABG further support its role in postoperative risk rather than serving as a simple bystander phenomenon [ 6 ], [ 7 ],[ 8 ]. From a biological standpoint, a biomarker that integrates glycemic control, inflammation, and nutritional reserve may therefore be especially informative in diabetic CABG patients. HbA1c reflects chronic glycemic exposure and has been associated with adverse events and reduced long-term survival after CABG [ 9 ], [ 10 ]. CRP reflects systemic inflammatory activation, while albumin is both a marker of nutritional/physiologic reserve and a negative acute-phase reactant that falls in inflammatory states; low albumin has independently predicted poor long-term survival after CABG. Prior CABG studies have also shown that the CRP-to-albumin ratio carries prognostic information, including an association with mortality after off-pump CABG [ 11 ], [ 12 ]. Taken together, these observations suggest that combining HbA1c, CRP, and albumin into a single index may better represent the integrated metabolic-inflammatory burden that underlies postoperative risk than any component alone. However, these pathways have mostly been studied separately. Whether GIHI (HbA1c × CRP / albumin) improves risk stratification after isolated CABG, particularly by diabetes status, remains unknown. We therefore examined the association between GIHI and postoperative outcomes, hypothesizing that higher GIHI would predict a greater risk, especially in patients with diabetes. Methods Study Design and Population This study was designed as a retrospective, international, multicenter cohort study including adult patients who underwent isolated CABG between January 2010 and December 2025. Consecutive patients were identified from institutional databases across four participating centers. Two centers were located in Israel, while the remaining two centers were based in the West Bank, as detailed in Supplementary Appendix 1 (eAppendix 1). As shown in Fig. 1 , 8,965 patients undergoing isolated CABG were identified. After excluding 2,082 patients based on predefined criteria and an additional 623 patients with missing HbA1c data, 6,262 patients remained and were stratified into diabetic and non-diabetic groups. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational studies [ 13 ]. Exposure Definition (GIHI) The Glyco-Inflammatory Healing Index (GIHI) was calculated as the product of glycated hemoglobin (HbA1c) and C-reactive protein (CRP), divided by serum albumin [ 9 ], [ 10 ], [ 11 ], [ 12 ], [ 14 ]: GIHI = (HbA1c × CRP) / albumin This composite index was designed to integrate glycemic control, systemic inflammation, and nutritional status into a single biomarker that reflects the metabolic-inflammatory burden. GIHI was analyzed as a continuous variable and standardized as z-scores (mean = 0, standard deviation = 1). For descriptive and visualization purposes, GIHI was additionally categorized into high and low groups based on the median value within each diabetes subgroup. This composite index was developed to capture the combined effects of metabolic dysregulation, systemic inflammation, and nutritional status on postoperative outcomes. Outcomes Definition The primary outcome was 30-day major adverse cardiovascular events (MACE), defined as a composite of all-cause mortality, myocardial infarction, stroke, or repeat revascularization occurring within 30 days following CABG. Secondary outcomes included long-term all-cause mortality. Time-to-event variables were defined from the date of surgery to the occurrence of the first event or censoring at the last follow-up [ 2 ]. Statistical analysis Continuous variables are presented as mean ± standard deviation, while categorical variables are expressed as counts and percentages. Between-group differences were assessed using standardized mean differences (SMD), with values < 0.1 indicating negligible imbalance. The Glyco-Inflammatory Healing Index (GIHI) was analyzed as a continuous variable and standardized as z-scores (mean = 0, standard deviation = 1). Hazard ratios (HRs) were therefore reported per 1-standard deviation (SD) increase in GIHI. For descriptive and visualization purposes, GIHI was additionally dichotomized at the median within each diabetes subgroup to define low- and high-GIHI categories. Time-to-event outcomes were analyzed using Cox proportional hazards regression models, with results reported as HRs and 95% confidence intervals (CIs). Analyses were stratified by diabetes status, and separate models were fitted for diabetic and non-diabetic patients. Multivariable models were constructed using clinically relevant covariates selected a priori based on established literature. Kaplan–Meier survival curves were generated to estimate event-free survival, and differences between groups were compared using the log-rank test. To evaluate potential non-linear associations between GIHI and outcomes, restricted cubic spline analyses were performed within the Cox regression framework using three knots at the 10th, 50th, and 90th percentiles. The median GIHI value was used as the reference, and non-linearity was assessed using Wald tests for spline terms. Model discrimination was assessed using receiver operating characteristic (ROC) curve analysis. Predicted probabilities were derived from multivariable logistic regression models with and without inclusion of standardized GIHI. The area under the curve (AUC) was compared using the DeLong test, and incremental predictive value was assessed based on changes in AUC. These analyses were restricted to diabetic patients. Pre-specified subgroup analyses were conducted to assess effect modification across clinically relevant strata, including surgical urgency (elective vs emergency), age (< 65 vs ≥ 65 years), and sex. Interaction was formally tested using multiplicative interaction terms within Cox models. All analyses were performed using Stata version 17 (StataCorp, College Station, TX, USA), and a two-sided p-value < 0.05 was considered statistically significant. Results A total of 6,262 patients were included in the analysis, of whom 2,783 (44.5%) had diabetes and 3,479 (55.1%) were non-diabetic. Baseline characteristics stratified by diabetes status and 30-day major adverse cardiovascular events (MACE) are presented in Table 1 . Among diabetic patients, those who developed 30-day MACE were older and had a higher operative risk profile, as reflected by increased Euroscore II values (SMD = 0.36). Echocardiographic parameters showed clinically meaningful differences, particularly an increase in septal thickness (SMD = 0.25) among patients with MACE. In non-diabetic patients, similar trends were observed, although the magnitude of differences was generally smaller. Notably, inflammatory markers such as white blood cell count were only elevated (SMD = 0.04). Additionally, the Glyco-Inflammatory Healing Index (GIHI) demonstrated a marked imbalance among diabetic patients, with higher values observed in those who developed 30-day MACE (SMD = 0.55), indicating a strong association with adverse postoperative outcomes. In contrast, this difference was minimal among non-diabetic patients (SMD = 0.07). Table 1 Baseline Characteristics Stratified by Diabetes Status and 30-day Major Adverse Cardiovascular Events (MACE) Variable Diabetic(n = 2783) Non-diabetic (n = 3479) Category No MACE n = 2569 (%) MACE n = 214 (%) SMD No MACE n = 3239 (%) MACE n = 240 (%) SMD Demographic data Age, y Mean ± SD 65.3 ± 9.0 67.0 ± 9.0 0.19 64.2 ± 9.7 65.3 ± 10.0 0.11 Gender n, % Female 769 (29.9%) 86 (40.2%) 0.22 907 (28.0%) 72 (30.0%) 0.04 BMI, kg/m 2 Mean ± SD 28.4 ± 3.9 28.4 ± 4.4 0.00 28.2 ± 4.1 28.1 ± 4.5 0.03 Euroscore II Mean ± SD 3.36 ± 2.03 4.36 ± 3.32 0.36 2.31 ± 2.10 3.00 ± 2.71 0.28 Medical History Hypertension Yes 2026 (78.9%) 175 (81.8%) 0.07 1490 (46.0%) 123 (51.3%) 0.11 Smoker Yes 939 (36.6%) 82 (38.3%) 0.04 884 (27.3%) 69 (28.8%) 0.03 COPD Yes 184 (7.2%) 16 (7.5%) 0.01 204 (6.3%) 30 (12.5%) 0.21 CKD Yes 311 (12.1%) 28 (13.1%) 0.03 231 (7.1%) 23 (9.6%) 0.09 Peripheral Vascular Disease (PVD) Yes 367 (14.3%) 29 (13.6%) 0.02 224 (6.9%) 23 (9.6%) 0.10 Medication Insulin Yes 496 (19.3%) 52 (24.2%) 0.12 — — — Oral diabetic Yes 2044 (79.5%) 179 (83.6%) 0.11 — — — Laboratory White blood cells (K/uL) Mean ± SD 8.48 ± 3.12 9.09 ± 3.43 0.18 8.54 ± 2.97 8.66 ± 3.00 0.04 Platelets (K/uL) Mean ± SD 224.8 ± 68.0 231.7 ± 73.8 0.10 223.3 ± 65.7 221.1 ± 63.2 0.03 Total cholesterol (mg/dl) Mean ± SD 155.0 ± 50.3 159.2 ± 44.2 0.09 162.5 ± 46.3 159.2 ± 42.6 0.07 Total protein (g/dl) Mean ± SD 6.76 ± 0.73 6.53 ± 0.87 0.28 6.75 ± 0.74 6.61 ± 0.80 0.18 Creatinine (mg/dl) Mean ± SD 1.12 ± 0.76 1.32 ± 1.05 0.22 1.04 ± 0.57 1.29 ± 1.22 0.26 Glyco-Inflammatory Healing Index (GIHI) GIHI Mean ± SD 11.68 ± 4.03 14.28 ± 5.26 0.55 11.07 ± 5.34 11.47 ± 5.75 0.07 GIHI (high vs low) High 1228 (47.8%) 163 (76.2%) 0.61 1599 (49.4%) 140 (58.3%) 0.18 Procedural Parameters Surgical urgency, n (%) Elective 1561 (60.76%) 132 (61.68%) 0.02 1977 (61.04%) 136 (56.67%) 0.09 Emergency 1008 (39.24%) 82 (38.32%) 1262 (38.96%) 104 (43.33%) Number of grafts ≥ 3, n (%) Yes 1342 (52.2%) 100 (46.7%) 0.11 1660 (51.3%) 105 (43.8%) 0.15 LIMA use, n (%) Yes 2487 (96.8%) 208 (97.2%) 0.02 3121 (96.4%) 232 (96.7%) 0.02 RIMA use, n (%) Yes 714 (27.8%) 61 (28.5%) 0.007 856 (26.4%) 71 (29.6%) 0.07 SVG use, n (%) Yes 1881 (73.2%) 166 (77.6%) 0.10 2349 (72.5%) 161 (67.1%) 0.12 Radial artery use, n (%) Yes 318 (12.4%) 16 (7.5%) 0.16 232 (7.2%) 19 (7.9%) 0.03 Cross-clamp time, min Mean ± SD 66.9 ± 27.4 68.5 ± 32.1 0.06 65.0 ± 25.4 66.8 ± 25.8 0.07 Cardiopulmonary bypass time, min Mean ± SD 97.4 ± 35.7 100.9 ± 37.9 0.09 97.2 ± 36.8 104.0 ± 38.8 0.18 Echocardiography Septum thickness (mm) Mean ± SD 10.30 ± 2.62 10.95 ± 2.46 0.25 10.19 ± 2.58 10.70 ± 2.41 0.21 LVEF% Mean ± SD 49.9 ± 9.5 48.0 ± 11.1 0.18 51.5 ± 12.4 50.1 ± 10.2 0.12 Continuous variables are presented as mean ± standard deviation, while categorical variables are expressed as counts and percentages. Standardized mean differences (SMD) were used to assess between-group differences, with values 0.2 clinically meaningful imbalance. Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; PVD, peripheral vascular disease; WBC, white blood cells; LVEF, left ventricular ejection fraction; SVG, saphenous vein graft; MACE, major adverse cardiovascular events. GIHI was calculated as (HbA1c × CRP) / albumin, representing a composite biomarker integrating glycemic control, systemic inflammation, and nutritional status. In univariable Cox regression analysis, several predictors were associated with 30-day MACE in both diabetic and non-diabetic patients (Table 2 ). Among diabetic patients, increasing age (HR 1.02, 95% CI 1.01–1.04, p = 0.006), female sex (HR 1.55, 95% CI 1.18–2.03, p = 0.002), and higher EuroSCORE II (HR 1.16, 95% CI 1.11–1.21, p < 0.001) were significantly associated with increased risk. Additionally, insulin therapy (HR 1.60, p = 0.014) and use of oral hypoglycemic agents (HR 1.50, p = 0.012) were associated with higher MACE incidence. Laboratory markers including elevated WBC (HR 1.05, p = 0.005) and creatinine (HR 1.20, p < 0.001) increased risk, while higher total protein was protective (HR 0.69, p < 0.001). In non-diabetic patients, EuroSCORE II remained a strong predictor (HR 1.12, p < 0.001), along with COPD (HR 2.05, p < 0.001) and creatinine (HR 1.31, p < 0.001). Bypass time (HR 1.004, p = 0.019) and septal thickness (HR 1.08, p = 0.003), were also significantly associated with increased MACE risk, while higher total protein was protective (HR 0.79, p = 0.005). Table 2 Univariable Cox Regression Analysis of Predictors of 30-Day MACE Stratified by Diabetes Status Diabetic(n = 2783) Non-diabetic (n = 3479) MACE Event = 214 MACE Event = 240 Variable Category HR (95% CI) P value HR(%95CI) P value Demographic data Age, y Per year 1.02 (1.01–1.04) 0.006 1.01 (1.00–1.03) 0.087 Gender n, % Female vs male 1.55 (1.18–2.03) 0.002 1.10 (0.83–1.44) 0.516 BMI, kg/m 2 Per unit 1.00 (0.96–1.03) 0.929 0.99 (0.96–1.03) 0.694 Euroscore II Per unit 1.16 (1.11–1.21) < 0.001 1.12 (1.07–1.17) < 0.001 Medical History Hypertension Yes, vs no 1.19 (0.84–1.69) 0.320 1.22 (0.95–1.58) 0.118 Smoker Yes, vs no 1.01 (0.67–1.17) 0.394 1.08 (0.82–1.43) 0.582 COPD Yes, vs no 1.04 (0.62–1.73) 0.887 2.05 (1.40–3.01) < 0.001 CKD Yes, vs no 1.09 (0.73–1.62) 0.676 1.35 (0.88–2.08) 0.169 Peripheral Vascular Disease (PVD) Yes, vs no 0.94 (0.63–1.39) 0.747 1.41 (0.92–2.17) 0.115 Medication Insulin Yes, vs no 1.60 (1.10–2.33) 0.014 — — Oral diabetic Yes, vs no 1.50 (1.09–2.05) 0.012 — — Laboratory White blood cells (K/uL) Per unit 1.05 (1.01–1.08) 0.005 1.01 (0.97–1.06) 0.529 Platelets (K/uL) Per unit 1.00 (1.00–1.00) 0.151 1.00 (1.00–1.00) 0.648 Total cholesterol (mg/dl) Per unit 1.00 (1.00–1.00) 0.249 1.00 (1.00–1.00) 0.301 Total protein (g/dl) Per unit 0.69 (0.59–0.81) < 0.001 0.79 (0.67–0.93) 0.005 Creatinine (mg/dl) Per unit 1.20 (1.09–1.32) < 0.001 1.31 (1.19–1.44) < 0.001 Procedural Parameters Number of grafts ≥ 3, n (%) Yes, vs no 0.81 (0.62–1.05) 0.116 0.75 (0.58–0.97) 0.026 LIMA use, n (%) Yes, vs no 1.14 (0.51–2.56) 0.755 1.09 (0.54–2.21) 0.808 RIMA use, n (%) Yes, vs no 1.10 (0.65–1.20) 0.409 1.16 (0.88–1.53) 0.287 SVG use, n (%) Yes, vs no 1.25 (0.90–1.72) 0.177 0.78 (0.60–1.02) 0.077 SVG count Per graft 1.08 (0.91–1.27) 0.373 0.92 (0.78–1.08) 0.310 Radial artery use, n (%) Yes, vs no 0.58 (0.35–0.97) 0.036 1.11 (0.70–1.78) 0.657 Cross-clamp time, min Per minute 1.00 (1.00–1.01) 0.479 1.00 (1.00–1.01) 0.416 Cardiopulmonary bypass time, min Per minute 1.00 (1.00–1.01) 0.210 1.00 (1.00–1.01) 0.019 Echocardiography Septum thickness (mm) Per mm 1.09 (1.04–1.15) < 0.001 1.08 (1.03–1.13) 0.003 LVEF% Per % 0.98 (0.97–0.99) 0.005 0.99 (0.97–1.00) 0.038 Data are presented as hazard ratios (HR) with 95% confidence intervals (CI) derived from univariable Cox proportional hazards regression analyses. Continuous variables are expressed per unit increase as indicated, while categorical variables are presented as comparisons (yes vs no or reference category). Analyses were stratified by diabetes status. A two-sided p value < 0.05 was considered statistically significant. Kaplan–Meier analysis demonstrated a clear divergence in 30-day MACE risk according to GIHI status, with a pronounced separation of event curves observed in diabetic patients (Fig. 2A). Patients with higher GIHI exhibited a substantially greater cumulative incidence of MACE early after CABG, and this difference persisted throughout the 30-day follow-up period (log-rank p < 0.001). In contrast, among non-diabetic patients, the Kaplan–Meier curves showed considerable overlap, with no significant difference in event rates between GIHI categories (Fig. 2B; log-rank p = 0.58). Among diabetic patients, higher standardized GIHI values were strongly associated with adverse clinical outcomes (Table 3 ). In unadjusted analyses, GIHI demonstrated a significant association with 30-day MACE (HR 1.62, 95% CI 1.45–1.81, p < 0.001), as well as with short- and long-term mortality. These associations remained robust after adjustment for demographic and clinical variables in Model 2 and persisted in the fully adjusted Model 3, where GIHI remained independently associated with 30-day MACE (HR 1.45, 95% CI 1.28–1.65, p < 0.001), 30-day mortality (HR 2.56, 95% CI 2.06–3.18, p < 0.001), and long-term mortality (HR 2.57, 95% CI 2.33–2.83, p < 0.001). In contrast, no significant associations were observed for stroke or revascularization after adjustment. The full Model 3 results, including all covariates, are presented in Supplementary Table 1. Table 3 Association Between Standardized GIHI and Clinical Outcomes in Diabetic Patients (n = 2,783) Model 1 Model 2 Model 3 n events HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value 30-day MACE 214 1.62 (1.45–1.81) < 0.001 1.49 (1.32–1.69) < 0.001 1.45 (1.28–1.65) < 0.001 30-day Stroke 60 0.95 (0.70–1.30) 0.763 0.90 (0.65–1.24) 0.530 0.91 (0.66–1.27) 0.592 30-day MI 89 1.26 (1.02–1.57) 0.034 1.32 (1.04–1.67) 0.021 1.27 (1.00–1.60) 0.049 30-day Revascularization 17 1.25 (0.79–2.00) 0.341 1.10 (0.67–1.79) 0.709 0.97 (0.59–1.59) 0.905 30-day Mortality 58 2.93 (2.43–3.53) < 0.001 2.36 (1.91–2.91) < 0.001 2.56 (2.06–3.18) < 0.001 Long-term Mortality 264 3.32 (3.05–3.62) < 0.001 2.75 (2.50–3.02) < 0.001 2.57 (2.33–2.83) < 0.001 Model 1: Unadjusted (crude model); Model 2: Adjusted for age, gender, BMI, and Euroscore II; Model 3: Additionally adjusted for age, gender, Euroscore II, white blood cell count, total protein, creatinine, radial artery use, septal thickness, cardiopulmonary bypass time, and LVEF. In non-diabetic patients, standardized GIHI showed no significant association with most short-term outcomes, including 30-day MACE, stroke, MI, and revascularization across all models (Table 4 ). However, GIHI was significantly associated with mortality endpoints. In the fully adjusted Model 3, GIHI remained independently associated with 30-day mortality (HR 1.37, 95% CI 1.06–1.77, p = 0.016) and long-term mortality (HR 2.00, 95% CI 1.85–2.16, p < 0.001). Notably, the magnitude of association was consistently weaker compared to diabetic patients, suggesting a differential impact of GIHI according to diabetes status. Detailed Model 3 estimates, including all adjustment variables, are provided in Supplementary Table 2. Table 4 Association Between Standardized GIHI and Clinical Outcomes in Non-Diabetic Patients (n = 3,479) Outcomes Model 1 Model 2 Model 3 n events HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value 30-day MACE 240 1.07 (0.95–1.19) 0.267 1.07 (0.96–1.20) 0.234 1.03 (0.92–1.16) 0.596 30-day Stroke 63 1.04 (0.83–1.29) 0.735 0.99 (0.79–1.25) 0.957 0.98 (0.78–1.24) 0.887 30-day MI 111 1.03 (0.86–1.22) 0.761 0.99 (0.82–1.19) 0.891 0.99 (0.83–1.19) 0.957 30-day Revascularization 44 0.88 (0.65–1.17) 0.372 0.93 (0.70–1.24) 0.615 0.88 (0.66–1.18) 0.402 30-day Mortality 34 1.59 (1.23–2.05) < 0.001 1.37 (1.06–1.78) 0.018 1.37 (1.06–1.77) 0.016 Long-term Mortality 313 2.31 (2.12–2.50) < 0.001 2.17 (2.01–2.34) < 0.001 2.00 (1.85–2.16) < 0.001 Model 1: Unadjusted (crude model); Model 2: Adjusted for age, gender, BMI, and Euroscore II; Model 3: Additionally adjusted for age, Euroscore II, COPD, total protein, creatinine, number of grafts ≥ 3, cardiopulmonary bypass time, septal thickness, and LVEF. Restricted cubic spline analysis demonstrated a strong, non-linear association between standardized GIHI and 30-day MACE in diabetic patients (Fig. 3 ). The risk of MACE increased progressively with higher GIHI values, with a marked acceleration in hazard beyond the median reference value. There was clear evidence of non-linearity (P for non-linearity < 0.001), supporting a non-linear dose–response relationship between GIHI and adverse clinical outcomes. In contrast, among non-diabetic patients, restricted cubic spline analysis demonstrated no meaningful association between standardized GIHI and 30-day MACE, with hazard ratios remaining close to unity across the entire range of GIHI values and no evidence of non-linearity. The addition of standardized GIHI to the multivariable model significantly improved discrimination for 30-day MACE in diabetic patients (Fig. 4 ). The AUC increased from 0.65 for the baseline model to 0.69 after inclusion of GIHI (ΔAUC = 0.04; P < 0.001), demonstrating meaningful incremental prognostic value beyond established clinical predictors. Subgroup analysis demonstrated a statistically significant interaction between the Glyco-Inflammatory Healing Index (GIHI) and surgical urgency (P for interaction = 0.029), indicating that the prognostic impact of GIHI differed according to operative context (Fig. 5 ). Specifically, while higher GIHI values were associated with an increased risk of 30-day major adverse cardiovascular events (MACE) across all subgroups, the association was more pronounced among patients undergoing emergency CABG (HR 1.75, 95% CI 1.53–2.00) compared with elective procedures (HR 1.39, 95% CI 1.13–1.71). In contrast, the association between GIHI and 30-day MACE remained consistent across age and sex subgroups, with no evidence of significant effect modification. Discussion In this multicenter cohort of patients undergoing isolated CABG, we found that GIHI was strongly and independently associated with adverse postoperative outcomes, particularly among patients with diabetes. Higher GIHI values were associated with a significantly increased risk of 30-day MACE, 30-day mortality, and long-term mortality in diabetic patients, even after multivariable adjustment, whereas in non-diabetic patients, the association was largely limited to mortality outcomes. We also observed a non-linear relationship between GIHI and 30-day MACE in diabetics, with risk increasing more sharply at higher values, and the addition of GIHI significantly improved model discrimination for postoperative MACE. The variables included in the study are themselves well-established determinants of adverse outcomes after CABG and support the biological credibility of the adjusted association observed for GIHI. Older age has consistently been associated with worse post-CABG outcomes, likely reflecting greater frailty, more diffuse atherosclerosis, impaired endothelial repair, and lower physiological reserve under surgical stress [ 15 ], [ 16 ]. Female sex has also been linked to higher short-term mortality and postoperative stroke after CABG in meta-analytic data, potentially owing to smaller coronary and graft vessel caliber, older age at presentation, and a heavier comorbidity burden [ 17 ]. EuroSCORE II remains a robust marker of operative risk because it condenses multiple adverse baseline characteristics into a single estimate of perioperative vulnerability, and contemporary registry data continue to support its value in isolated CABG [ 15 ]. Elevated white blood cell count is another plausible covariate, as preoperative leukocytosis has been associated with adverse CABG outcomes and may reflect heightened systemic inflammation, endothelial activation, and intensified reperfusion-related tissue injury [ 18 ], [ 19 ]. Higher creatinine similarly identifies patients with renal dysfunction, a well-known predictor of mortality and complications after CABG, likely through reduced cardiorenal reserve, greater vascular disease burden, impaired drug handling, and susceptibility to perioperative acute kidney injury [ 20 ], [ 21 ]. Lower total protein may reflect reduced protein reserve and a worse inflammatory-nutritional state, although CABG-specific evidence is less robust than for albumin [ 22 ]. Radial artery use is relevant because it is associated with better long-term outcomes than saphenous vein grafting, likely due to superior patency. [ 23 ]. Greater septal thickness may reflect left ventricular hypertrophy, which has been associated with worse early and late outcomes after CABG and may act through increased myocardial oxygen demand, impaired diastolic filling, microvascular dysfunction, and reduced ischemic tolerance [ 24 ]. Longer cardiopulmonary bypass time is likewise a recognized adverse marker, probably because it represents more complex surgery and greater exposure to hemodilution, inflammatory activation, ischemia-reperfusion injury, and end-organ hypoperfusion [ 25 ]. Lower LVEF is another established predictor of poorer CABG outcomes, reflecting limited myocardial reserve and a lower capacity to tolerate perioperative ischemia, low-output states, and arrhythmic or hemodynamic complications [ 26 ]. Taken together, adjustment for these variables is clinically important because it tests GIHI against a broad set of demographic, operative, inflammatory, renal, nutritional, conduit-related, and cardiac structural/function markers rather than against a surgical score alone. In that context, the persistence of GIHI after full adjustment suggests that it captures a distinct metabolic-inflammatory dimension of risk not fully represented by EuroSCORE II or by conventional clinical covariates. Our findings are consistent with prior CABG literature showing prognostic value for the individual components of GIHI. Halkos et al. reported that each 1% increase in preoperative HbA1c was associated with higher in-hospital mortality after CABG (OR 1.40, P = 0.019), and that HbA1c > 8.6% was associated with a 4-fold increase in mortality; in a subsequent long-term analysis, the same group showed that each 1% increase in HbA1c was associated with reduced long-term survival (HR 1.15, P 7% was independently associated with long-term all-cause mortality (HR 2.67, P = 0.001) [ 10 ]. For inflammation, van Straten et al. showed that preoperative CRP > 10 mg/L independently predicted early mortality, whereas CRP > 5 mg/L predicted late mortality after CABG [ 27 ]. For nutritional reserve, de la Cruz et al. reported in a propensity-matched CABG cohort that preoperative albumin < 3.5 g/dL was associated with worse 8-year survival (65% ± 7% vs 86% ± 3%; HR 2.2, 95% CI 1.4–3.6; P 1.326 predicted 1-year mortality with an adjusted HR of 5.01 (95% CI 2.01–12.50; P < 0.001), while it was not significantly associated with 1-year major cardiovascular and cerebrovascular events (HR 1.11, 95% CI 0.69–1.78; P = 0.66) [ 12 ]. Taken together, these studies suggest that dysglycemia, inflammation, and nutritional status are each prognostically important after CABG; our findings extend this evidence by showing that a composite marker integrating all three may better capture postoperative risk, especially in patients with diabetes. Cardiac surgery, especially when performed with cardiopulmonary bypass, triggers a complex systemic inflammatory response driven by operative trauma, contact of blood with non-endothelial extracorporeal surfaces, ischemia-reperfusion injury, complement activation, leukocyte and platelet stimulation, and the subsequent release of pro-inflammatory cytokines. When this response is excessive, it may amplify endothelial injury, capillary leak, tissue edema, microcirculatory dysfunction, and organ hypoperfusion, thereby contributing to adverse postoperative outcomes [ 12 ], [ 27 ]. In parallel, poor chronic glycemic control, as reflected by elevated HbA1c, may further increase perioperative susceptibility through several interconnected pathways, including oxidative stress, endothelial dysfunction, impaired nitric oxide bioavailability, microvascular injury, a prothrombotic milieu, and defective immune and reparative responses. These abnormalities may reduce tolerance to perioperative ischemia, impair wound healing, and promote infectious and cardiovascular complications after CABG [ 28 ]. Within this context, CRP and albumin represent complementary biological domains. CRP reflects the magnitude of the pre-existing and perioperative inflammatory burden; elevated preoperative CRP has been associated with both early and late mortality after coronary bypass surgery, suggesting that patients entering surgery with heightened inflammation may have less physiological reserve and a greater tendency toward maladaptive postoperative responses [ 12 ], [ 27 ]. Albumin, by contrast, is not only a marker of nutritional state but also an important negative acute-phase reactant with antioxidant, anti-inflammatory, endothelial-stabilizing, and microvascular protective properties. Reduced albumin levels may therefore indicate a state of impaired host defense and diminished capacity to buffer oxidative and inflammatory stress, while also favoring interstitial fluid shift, poorer tissue perfusion, and delayed recovery [ 11 ], [ 29 ]. Taken together, a high GIHI may identify a subgroup in whom chronic metabolic dysregulation and perioperative inflammatory vulnerability coexist and potentially reinforce one another. In such patients, longstanding glycemic injury may prime the vasculature and tissues for damage, while an exaggerated inflammatory state may magnify the clinical consequences of that vulnerability during and after surgery. This convergence provides a biologically plausible explanation for why the prognostic signal of GIHI appears strongest in diabetic patients and for mortality-related endpoints, where the combined effects of inflammation, oxidative stress, endothelial dysfunction, impaired repair, and reduced physiologic reserve are most likely to translate into clinically meaningful events [ 11 ], [ 12 ], [ 27 ], [ 28 ], [ 29 ]. In addition, the subgroup analysis (Fig. 5 ) showed that the association between higher GIHI and 30-day MACE was broadly consistent across sex and age strata, supporting the overall robustness of the finding. The stronger association observed in emergency compared with elective CABG, together with the significant interaction, may indicate that the adverse metabolic-inflammatory profile reflected by GIHI becomes particularly relevant in more acute operative settings, where baseline perioperative risk is already amplified [ 30 ]. However, as subgroup findings should be interpreted cautiously and primarily on the basis of interaction testing, this observation should be considered hypothesis-generating and warrants confirmation in future studies [ 31 ]. Clinically, GIHI may serve as a simple preoperative risk-enrichment tool rather than a replacement for established surgical risk models. Because its components are routinely available, it may help identify CABG patients, especially those with diabetes, who need closer perioperative surveillance and better metabolic, inflammatory, and nutritional optimization. This is consistent with prior CABG evidence and guidelines recognizing diabetes as a high-risk subgroup. Future studies should determine whether adding GIHI to existing models improves decision-making and whether optimizing these pathways can reduce the excess risk associated with high GIHI. Strengths This study has several strengths. First, it included a large, international, multicenter cohort of patients undergoing isolated CABG, which enhances the robustness and generalizability of the findings. Second, the analysis was performed separately in diabetic and non-diabetic patients, allowing a clearer assessment of effect modification by diabetes status. Third, GIHI was evaluated using multiple complementary approaches, including continuous standardized modeling, Kaplan–Meier analysis, restricted cubic splines, subgroup analyses, and discrimination testing, providing consistent evidence across different statistical frameworks. Fourth, the study examined both short-term and long-term outcomes, allowing a more comprehensive assessment of the prognostic relevance of GIHI. Finally, because GIHI is derived from routinely available laboratory parameters, the findings have direct clinical applicability and support the potential use of this biomarker in real-world perioperative risk stratification. Limitations This study has several limitations. Its retrospective observational design precludes causal inference and allows residual confounding despite multivariable adjustment. Exclusion of patients with missing data or unavailable HbA1c may have introduced selection bias and limited representativeness. GIHI was based on a single preoperative measurement, so temporal changes in glycemic, inflammatory, and nutritional status were not captured. Multicenter data collection over a long period may have introduced heterogeneity in assays, perioperative practice, surgical technique, medical therapy, and follow-up. Although adjusted for several clinically relevant variables, other factors, including frailty, infection status, diabetes duration, antidiabetic treatment intensity, and postoperative management, were not fully captured. Outcomes were identified from registry and institutional data rather than centralized adjudication. The high-versus-low GIHI categorization was based on an internal median split and is descriptive rather than a clinically validated threshold. Finally, because the findings were derived from isolated CABG patients in 4 centers without external validation, generalizability to other populations, health systems, combined procedures, or broader cardiac surgery cohorts remains uncertain. Future studies should externally validate GIHI, assess its value across broader cardiac surgery settings, and determine whether serial measurements and integration into existing risk models improve prognostic performance and clinical decision-making. Interventional studies are also needed to test whether optimizing glycemic, inflammatory, and nutritional status can reduce the excess risk associated with high GIHI, particularly in patients with diabetes. Conclusion In patients undergoing isolated CABG, GIHI was independently associated with adverse postoperative outcomes, with the prognostic value observed among patients with diabetes. Higher GIHI levels were linked to increased risk of 30-day MACE, short-term mortality, and long-term mortality, while also improving risk discrimination beyond conventional clinical variables in diabetic patients. These findings suggest that GIHI may serve as a simple and clinically applicable marker of metabolic-inflammatory risk, with potential utility for perioperative risk stratification after CABG. Abbreviations AUC area under the curve BMI body mass index CABG coronary artery bypass grafting CI confidence interval CKD chronic kidney disease COPD chronic obstructive pulmonary disease CRP C-reactive protein GIHI Glyco-Inflammatory Healing Index HbA1c glycated hemoglobin HR hazard ratio LVEF left ventricular ejection fraction MACE major adverse cardiovascular events ROC receiver operating characteristic SMD standardized mean difference Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Ethics Committee of Kaplan Medical Center (approval no. 0143-22-KMC). All study procedures were performed in accordance with the Declaration of Helsinki and its subsequent amendments. Because of the retrospective study design and the use of anonymized registry data, the requirement for written informed consent was waived by the Institutional Ethics Committee. Consent for publication Not applicable. Competing interests E.Z. Fisman served as Editor-in-Chief of Cardiovascular Diabetology Authors' information Not applicable. Funding No funding or sponsorship was received for this study or the publication of this article. Author Contribution HAK and LS conceived and designed the study. HAK collected the data. HAK and MA performed the statistical analysis. HAK, MA, KI, DN, TZM, MM, AMS, NAH, YZF, and AD drafted the manuscript. AK, ER, NAH, KI, and LS critically revised the manuscript for important intellectual content. All authors interpreted the data, read and approved the final manuscript, and agree to be accountable for all aspects of the work. Acknowledgement The authors sincerely thank all study participants for their time and cooperation. They also gratefully acknowledge the hospital staff for their professionalism and support throughout the study. Special appreciation is extended to the Palestinian Clinical Research Center for its valuable support, coordination, and contribution to the successful completion of this research. Availability of data and materials De-identified participant data generated and analyzed in this study are not publicly available because of patient confidentiality and institutional ethical restrictions. However, the data will be available from the corresponding author upon reasonable request from the time of publication. Access may be granted to researchers who submit a methodologically sound proposal and agree to the terms of a data access agreement. References Stark BA, et al. Global, Regional, and National Burden of Cardiovascular Diseases and Risk Factors in 204 Countries and Territories, 1990–2023. JACC. Dec. 2025;86(22):2167–243. 10.1016/j.jacc.2025.08.015 . 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Supplementary Files Supplment1GIHI1f22.docx DMCABGGA.png Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 18 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9457970","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633530594,"identity":"d15ef697-0ce1-4c7e-899c-cea6c0550020","order_by":0,"name":"Haitham Abu Khadija","email":"","orcid":"","institution":"Kaplan Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Haitham","middleName":"Abu","lastName":"Khadija","suffix":""},{"id":633530595,"identity":"b4b36668-2c8e-4358-96f3-b84514790b91","order_by":1,"name":"Mohammad Alnees","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Alnees","suffix":""},{"id":633530596,"identity":"6929bad2-d676-47a9-b418-a80f122e0803","order_by":2,"name":"Enrique Z. 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Fraitekh","email":"","orcid":"","institution":"Palestinian Clinical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"Z.","lastName":"Fraitekh","suffix":""},{"id":633530606,"identity":"2a8b4f4f-8360-4859-a92f-4ced454b4c96","order_by":12,"name":"Alexander Kogan","email":"data:image/png;base64,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","orcid":"","institution":"Sheba Medical Centre at Tel Hashomer","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Kogan","suffix":""},{"id":633530607,"identity":"0a3cb512-9051-4220-8535-6741dec3637e","order_by":13,"name":"Leonid Sternik","email":"","orcid":"","institution":"Sheba Medical Centre at Tel Hashomer","correspondingAuthor":false,"prefix":"","firstName":"Leonid","middleName":"","lastName":"Sternik","suffix":""}],"badges":[],"createdAt":"2026-04-18 20:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9457970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9457970/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108411919,"identity":"d3594ad3-1118-4c8d-92a3-8b09b351ed26","added_by":"auto","created_at":"2026-05-04 10:24:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart and cohort selection process.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/d95b33fd1954f91a5d3625d4.jpeg"},{"id":108411957,"identity":"1a8fcc36-e045-45e3-8a5b-6399301b2854","added_by":"auto","created_at":"2026-05-04 10:24:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for 30-day major adverse cardiovascular events (MACE) according to GIHI categories, stratified by diabetes status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Diabetic patients. Higher GIHI was associated with a markedly increased cumulative incidence of MACE within 30 days following CABG (log-rank p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Non-diabetic patients. No significant difference in event rates was observed between GIHI categories (log-rank p = 0.58).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/44924f2557fb33afbd6f15ef.png"},{"id":108411912,"identity":"664fb2fd-0ca5-46a5-b485-88a708eff888","added_by":"auto","created_at":"2026-05-04 10:24:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted Cubic Spline Analysis of the Association Between Standardized GIHI and 30-Day MACE in Diabetic Patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/faa38f9f279767e343f47c5e.png"},{"id":108411914,"identity":"96c54372-0ecb-4fff-b3d6-c6cb1a447b40","added_by":"auto","created_at":"2026-05-04 10:24:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncremental Predictive Value of Standardized GIHI for 30-Day MACE in Diabetic Patients: Receiver Operating Characteristic (ROC) Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe baseline clinical model included age, sex, Euroscore II, white blood cell count, total protein, creatinine, radial artery use, septal thickness, cardiopulmonary bypass time, and left ventricular ejection fraction.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/1c6fb67843100ed44d8d1b28.png"},{"id":108411920,"identity":"b9507806-8b62-47a7-be03-92e73f51a14c","added_by":"auto","created_at":"2026-05-04 10:24:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation Between Standardized Glyco-Inflammatory Healing Index (GIHI) and 30-Day Major Adverse Cardiovascular Events Across Clinical Subgroups in Diabetic Patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/143dc6db4864937d86a5b17f.png"},{"id":108412471,"identity":"80f8611c-255e-4020-9e08-8212e0d63fcd","added_by":"auto","created_at":"2026-05-04 10:26:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1051199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/6016b9fe-d1ca-4111-ace5-ed7b590b7b10.pdf"},{"id":108412129,"identity":"80bdc5fc-6560-466a-abbd-feb7d634b958","added_by":"auto","created_at":"2026-05-04 10:25:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27069,"visible":true,"origin":"","legend":"","description":"","filename":"Supplment1GIHI1f22.docx","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/c9b1ae292d81d606ed7611ba.docx"},{"id":108411908,"identity":"06ba04bf-5eea-4cce-89f4-616e8f9c743c","added_by":"auto","created_at":"2026-05-04 10:24:48","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7554028,"visible":true,"origin":"","legend":"","description":"","filename":"DMCABGGA.png","url":"https://assets-eu.researchsquare.com/files/rs-9457970/v1/01f8052978b277080c55390c.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Glyco-Inflammatory Healing Index for Risk Prediction After Isolated Coronary Artery Bypass Grafting: A Multicenter Retrospective Cohort Study in Patients with Type 2 Diabetes","fulltext":[{"header":"Research Insights ","content":"\u003cp\u003e\u003cb\u003eWhat is currently known about this topic\u003c/b\u003e? Diabetes worsens early and late outcomes after CABG. HbA1c, CRP, and albumin each have prognostic value. Integrated biologic risk markers may improve risk stratification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhat is the key research question?\u003c/b\u003e GIHI may improve risk stratification after isolated CABG, particularly in patients with diabetes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhat is new?\u003c/b\u003e GIHI independently predicted 30-day MACE in diabetic patients, showed stronger prognostic value in diabetic than non-diabetic patients, and improved discrimination for 30-day MACE in diabetes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHow might this study influence clinical practice?\u003c/b\u003e GIHI may help identify diabetic CABG patients who need closer perioperative surveillance and optimization.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eCoronary artery disease remains a major global health problem, and ischemic heart disease continues to be the leading cardiovascular cause of death and disability worldwide. Contemporary burden estimates show that cardiovascular disease accounted for 19.2\u0026nbsp;million deaths in 2023, with ischemic heart disease remaining the leading contributor to cardiovascular disability-adjusted life years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In this context, coronary artery bypass grafting (CABG) remains a cornerstone of revascularization for patients with complex coronary anatomy, including left main and multivessel disease. Current guidelines also emphasize that, in patients with diabetes and multivessel coronary artery disease, surgical revascularization is often the preferred strategy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with diabetes represent a particularly high-risk subgroup after CABG. Beyond having a greater burden of diffuse and accelerated atherosclerosis, they frequently present with coexisting renal dysfunction, endothelial injury, impaired microvascular function, and disordered wound healing, all of which may adversely influence postoperative recovery [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Evidence from randomized and observational studies has shown that although CABG is generally superior to percutaneous coronary intervention for diabetic patients with multivessel disease, diabetic patients still experience worse early and late outcomes after surgery than non-diabetic patients, including higher operative mortality and poorer long-term survival. These observations suggest that traditional clinical risk markers do not fully capture the biologic vulnerability of this population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInflammation may be a key mechanism underlying adverse outcomes after CABG. Cardiac surgery, especially with cardiopulmonary bypass, provokes a systemic inflammatory response driven by surgical injury, blood\u0026ndash;surface contact, ischemia\u0026ndash;reperfusion, and cytokine release. This inflammatory activation has been associated with complications such as atrial fibrillation, stroke, and mortality, and higher CRP levels after CABG further support its role in postoperative risk rather than serving as a simple bystander phenomenon [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a biological standpoint, a biomarker that integrates glycemic control, inflammation, and nutritional reserve may therefore be especially informative in diabetic CABG patients. HbA1c reflects chronic glycemic exposure and has been associated with adverse events and reduced long-term survival after CABG [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CRP reflects systemic inflammatory activation, while albumin is both a marker of nutritional/physiologic reserve and a negative acute-phase reactant that falls in inflammatory states; low albumin has independently predicted poor long-term survival after CABG. Prior CABG studies have also shown that the CRP-to-albumin ratio carries prognostic information, including an association with mortality after off-pump CABG [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Taken together, these observations suggest that combining HbA1c, CRP, and albumin into a single index may better represent the integrated metabolic-inflammatory burden that underlies postoperative risk than any component alone. However, these pathways have mostly been studied separately. Whether GIHI (HbA1c \u0026times; CRP / albumin) improves risk stratification after isolated CABG, particularly by diabetes status, remains unknown. We therefore examined the association between GIHI and postoperative outcomes, hypothesizing that higher GIHI would predict a greater risk, especially in patients with diabetes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis study was designed as a retrospective, international, multicenter cohort study including adult patients who underwent isolated CABG between January 2010 and December 2025. Consecutive patients were identified from institutional databases across four participating centers. Two centers were located in Israel, while the remaining two centers were based in the West Bank, as detailed in Supplementary Appendix 1 (eAppendix 1).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 8,965 patients undergoing isolated CABG were identified. After excluding 2,082 patients based on predefined criteria and an additional 623 patients with missing HbA1c data, 6,262 patients remained and were stratified into diabetic and non-diabetic groups.\u003c/p\u003e \u003cp\u003eThis study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure Definition (GIHI)\u003c/h3\u003e\n\u003cp\u003eThe Glyco-Inflammatory Healing Index (GIHI) was calculated as the product of glycated hemoglobin (HbA1c) and C-reactive protein (CRP), divided by serum albumin [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eGIHI = (HbA1c \u0026times; CRP) / albumin\u003c/p\u003e \u003cp\u003eThis composite index was designed to integrate glycemic control, systemic inflammation, and nutritional status into a single biomarker that reflects the metabolic-inflammatory burden.\u003c/p\u003e \u003cp\u003eGIHI was analyzed as a continuous variable and standardized as z-scores (mean\u0026thinsp;=\u0026thinsp;0, standard deviation\u0026thinsp;=\u0026thinsp;1). For descriptive and visualization purposes, GIHI was additionally categorized into high and low groups based on the median value within each diabetes subgroup. This composite index was developed to capture the combined effects of metabolic dysregulation, systemic inflammation, and nutritional status on postoperative outcomes.\u003c/p\u003e\n\u003ch3\u003eOutcomes Definition\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was 30-day major adverse cardiovascular events (MACE), defined as a composite of all-cause mortality, myocardial infarction, stroke, or repeat revascularization occurring within 30 days following CABG. Secondary outcomes included long-term all-cause mortality. Time-to-event variables were defined from the date of surgery to the occurrence of the first event or censoring at the last follow-up [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables are expressed as counts and percentages. Between-group differences were assessed using standardized mean differences (SMD), with values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicating negligible imbalance.\u003c/p\u003e \u003cp\u003eThe Glyco-Inflammatory Healing Index (GIHI) was analyzed as a continuous variable and standardized as z-scores (mean\u0026thinsp;=\u0026thinsp;0, standard deviation\u0026thinsp;=\u0026thinsp;1). Hazard ratios (HRs) were therefore reported per 1-standard deviation (SD) increase in GIHI. For descriptive and visualization purposes, GIHI was additionally dichotomized at the median within each diabetes subgroup to define low- and high-GIHI categories.\u003c/p\u003e \u003cp\u003eTime-to-event outcomes were analyzed using Cox proportional hazards regression models, with results reported as HRs and 95% confidence intervals (CIs). Analyses were stratified by diabetes status, and separate models were fitted for diabetic and non-diabetic patients. Multivariable models were constructed using clinically relevant covariates selected a priori based on established literature.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival curves were generated to estimate event-free survival, and differences between groups were compared using the log-rank test.\u003c/p\u003e \u003cp\u003eTo evaluate potential non-linear associations between GIHI and outcomes, restricted cubic spline analyses were performed within the Cox regression framework using three knots at the 10th, 50th, and 90th percentiles. The median GIHI value was used as the reference, and non-linearity was assessed using Wald tests for spline terms.\u003c/p\u003e \u003cp\u003eModel discrimination was assessed using receiver operating characteristic (ROC) curve analysis. Predicted probabilities were derived from multivariable logistic regression models with and without inclusion of standardized GIHI. The area under the curve (AUC) was compared using the DeLong test, and incremental predictive value was assessed based on changes in AUC. These analyses were restricted to diabetic patients.\u003c/p\u003e \u003cp\u003ePre-specified subgroup analyses were conducted to assess effect modification across clinically relevant strata, including surgical urgency (elective vs emergency), age (\u0026lt;\u0026thinsp;65 vs\u0026thinsp;\u0026ge;\u0026thinsp;65 years), and sex. Interaction was formally tested using multiplicative interaction terms within Cox models. All analyses were performed using Stata version 17 (StataCorp, College Station, TX, USA), and a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 6,262 patients were included in the analysis, of whom 2,783 (44.5%) had diabetes and 3,479 (55.1%) were non-diabetic. Baseline characteristics stratified by diabetes status and 30-day major adverse cardiovascular events (MACE) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong diabetic patients, those who developed 30-day MACE were older and had a higher operative risk profile, as reflected by increased Euroscore II values (SMD\u0026thinsp;=\u0026thinsp;0.36). Echocardiographic parameters showed clinically meaningful differences, particularly an increase in septal thickness (SMD\u0026thinsp;=\u0026thinsp;0.25) among patients with MACE.\u003c/p\u003e \u003cp\u003eIn non-diabetic patients, similar trends were observed, although the magnitude of differences was generally smaller. Notably, inflammatory markers such as white blood cell count were only elevated (SMD\u0026thinsp;=\u0026thinsp;0.04). Additionally, the Glyco-Inflammatory Healing Index (GIHI) demonstrated a marked imbalance among diabetic patients, with higher values observed in those who developed 30-day MACE (SMD\u0026thinsp;=\u0026thinsp;0.55), indicating a strong association with adverse postoperative outcomes. In contrast, this difference was minimal among non-diabetic patients (SMD\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics Stratified by Diabetes Status and 30-day Major Adverse Cardiovascular Events (MACE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eDiabetic(n\u0026thinsp;=\u0026thinsp;2783)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNon-diabetic (n\u0026thinsp;=\u0026thinsp;3479)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo MACE\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2569 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;214 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo MACE\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3239 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;240 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender n, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e769 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e907 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuroscore II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical History\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2026 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1490 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e939 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e884 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e311 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e231 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Vascular Disease (PVD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e367 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e224 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2044 (79.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (83.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells (K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224.8\u0026thinsp;\u0026plusmn;\u0026thinsp;68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231.7\u0026thinsp;\u0026plusmn;\u0026thinsp;73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e223.3\u0026thinsp;\u0026plusmn;\u0026thinsp;65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e221.1\u0026thinsp;\u0026plusmn;\u0026thinsp;63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.0\u0026thinsp;\u0026plusmn;\u0026thinsp;50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159.2\u0026thinsp;\u0026plusmn;\u0026thinsp;44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162.5\u0026thinsp;\u0026plusmn;\u0026thinsp;46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e159.2\u0026thinsp;\u0026plusmn;\u0026thinsp;42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlyco-Inflammatory Healing Index (GIHI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.28\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIHI (high vs low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1228 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1599 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e140 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSurgical urgency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eElective\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1561 (60.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (61.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1977 (61.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136 (56.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEmergency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1008 (39.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (38.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1262 (38.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104 (43.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of grafts\u0026thinsp;\u0026ge;\u0026thinsp;3, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1342 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1660 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIMA use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2487 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208 (97.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3121 (96.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e232 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIMA use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e714 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e856 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVG use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1881 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (77.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2349 (72.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e161 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadial artery use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e232 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-clamp time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.8\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary bypass time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.9\u0026thinsp;\u0026plusmn;\u0026thinsp;37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104.0\u0026thinsp;\u0026plusmn;\u0026thinsp;38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptum thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables are expressed as counts and percentages. Standardized mean differences (SMD) were used to assess between-group differences, with values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicating negligible imbalance, 0.1\u0026ndash;0.2 small imbalance, and \u0026gt;\u0026thinsp;0.2 clinically meaningful imbalance. Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; PVD, peripheral vascular disease; WBC, white blood cells; LVEF, left ventricular ejection fraction; SVG, saphenous vein graft; MACE, major adverse cardiovascular events. GIHI was calculated as (HbA1c \u0026times; CRP) / albumin, representing a composite biomarker integrating glycemic control, systemic inflammation, and nutritional status.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn univariable Cox regression analysis, several predictors were associated with 30-day MACE in both diabetic and non-diabetic patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among diabetic patients, increasing age (HR 1.02, 95% CI 1.01\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.006), female sex (HR 1.55, 95% CI 1.18\u0026ndash;2.03, p\u0026thinsp;=\u0026thinsp;0.002), and higher EuroSCORE II (HR 1.16, 95% CI 1.11\u0026ndash;1.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with increased risk. Additionally, insulin therapy (HR 1.60, p\u0026thinsp;=\u0026thinsp;0.014) and use of oral hypoglycemic agents (HR 1.50, p\u0026thinsp;=\u0026thinsp;0.012) were associated with higher MACE incidence. Laboratory markers including elevated WBC (HR 1.05, p\u0026thinsp;=\u0026thinsp;0.005) and creatinine (HR 1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased risk, while higher total protein was protective (HR 0.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn non-diabetic patients, EuroSCORE II remained a strong predictor (HR 1.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with COPD (HR 2.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and creatinine (HR 1.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Bypass time (HR 1.004, p\u0026thinsp;=\u0026thinsp;0.019) and septal thickness (HR 1.08, p\u0026thinsp;=\u0026thinsp;0.003), were also significantly associated with increased MACE risk, while higher total protein was protective (HR 0.79, p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Cox Regression Analysis of Predictors of 30-Day MACE Stratified by Diabetes Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDiabetic(n\u0026thinsp;=\u0026thinsp;2783)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNon-diabetic (n\u0026thinsp;=\u0026thinsp;3479)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003cp\u003eEvent\u0026thinsp;=\u0026thinsp;214\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003cp\u003eEvent\u0026thinsp;=\u0026thinsp;240\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR(%95CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (1.00\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender n, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale vs male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 (1.18\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.83\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuroscore II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.11\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (1.07\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical History\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.84\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22 (0.95\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.67\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.82\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.62\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05 (1.40\u0026ndash;3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.73\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35 (0.88\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Vascular Disease (PVD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.63\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41 (0.92\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60 (1.10\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.09\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells (K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (1.01\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.59\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.67\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.09\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31 (1.19\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of grafts\u0026thinsp;\u0026ge;\u0026thinsp;3, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.62\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75 (0.58\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIMA use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.51\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.54\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIMA use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.65\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.88\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVG use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.90\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 (0.60\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVG count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer graft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.91\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.78\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadial artery use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, vs no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.35\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.70\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-clamp time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary bypass time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptum thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (1.04\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData are presented as hazard ratios (HR) with 95% confidence intervals (CI) derived from univariable Cox proportional hazards regression analyses. Continuous variables are expressed per unit increase as indicated, while categorical variables are presented as comparisons (yes vs no or reference category). Analyses were stratified by diabetes status. A two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier analysis demonstrated a clear divergence in 30-day MACE risk according to GIHI status, with a pronounced separation of event curves observed in diabetic patients (Fig.\u0026nbsp;2A). Patients with higher GIHI exhibited a substantially greater cumulative incidence of MACE early after CABG, and this difference persisted throughout the 30-day follow-up period (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, among non-diabetic patients, the Kaplan\u0026ndash;Meier curves showed considerable overlap, with no significant difference in event rates between GIHI categories (Fig.\u0026nbsp;2B; log-rank p\u0026thinsp;=\u0026thinsp;0.58).\u003c/p\u003e\u003cp\u003eAmong diabetic patients, higher standardized GIHI values were strongly associated with adverse clinical outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In unadjusted analyses, GIHI demonstrated a significant association with 30-day MACE (HR 1.62, 95% CI 1.45\u0026ndash;1.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as with short- and long-term mortality. These associations remained robust after adjustment for demographic and clinical variables in Model 2 and persisted in the fully adjusted Model 3, where GIHI remained independently associated with 30-day MACE (HR 1.45, 95% CI 1.28\u0026ndash;1.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 30-day mortality (HR 2.56, 95% CI 2.06\u0026ndash;3.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and long-term mortality (HR 2.57, 95% CI 2.33\u0026ndash;2.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, no significant associations were observed for stroke or revascularization after adjustment. The full Model 3 results, including all covariates, are presented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Standardized GIHI and Clinical Outcomes in Diabetic Patients (n\u0026thinsp;=\u0026thinsp;2,783)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en events\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day MACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.62 (1.45\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.49 (1.32\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.45 (1.28\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day Stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.70\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.65\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91 (0.66\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.02\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32 (1.04\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.27 (1.00\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day Revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.79\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.67\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.59\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.93 (2.43\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.36 (1.91\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.56 (2.06\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.32 (3.05\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.75 (2.50\u0026ndash;3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.57 (2.33\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 1: Unadjusted (crude model); Model 2: Adjusted for age, gender, BMI, and Euroscore II; Model 3: Additionally adjusted for age, gender, Euroscore II, white blood cell count, total protein, creatinine, radial artery use, septal thickness, cardiopulmonary bypass time, and LVEF.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn non-diabetic patients, standardized GIHI showed no significant association with most short-term outcomes, including 30-day MACE, stroke, MI, and revascularization across all models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, GIHI was significantly associated with mortality endpoints. In the fully adjusted Model 3, GIHI remained independently associated with 30-day mortality (HR 1.37, 95% CI 1.06\u0026ndash;1.77, p\u0026thinsp;=\u0026thinsp;0.016) and long-term mortality (HR 2.00, 95% CI 1.85\u0026ndash;2.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the magnitude of association was consistently weaker compared to diabetic patients, suggesting a differential impact of GIHI according to diabetes status. Detailed Model 3 estimates, including all adjustment variables, are provided in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Standardized GIHI and Clinical Outcomes in Non-Diabetic Patients (n\u0026thinsp;=\u0026thinsp;3,479)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en events\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day MACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.95\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07 (0.96\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03 (0.92\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day Stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.83\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.79\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98 (0.78\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.86\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.82\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99 (0.83\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day Revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.65\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.70\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.66\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59 (1.23\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.37 (1.06\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37 (1.06\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.31 (2.12\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.17 (2.01\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00 (1.85\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 1: Unadjusted (crude model); Model 2: Adjusted for age, gender, BMI, and Euroscore II; Model 3: Additionally adjusted for age, Euroscore II, COPD, total protein, creatinine, number of grafts\u0026thinsp;\u0026ge;\u0026thinsp;3, cardiopulmonary bypass time, septal thickness, and LVEF.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRestricted cubic spline analysis demonstrated a strong, non-linear association between standardized GIHI and 30-day MACE in diabetic patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The risk of MACE increased progressively with higher GIHI values, with a marked acceleration in hazard beyond the median reference value. There was clear evidence of non-linearity (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting a non-linear dose\u0026ndash;response relationship between GIHI and adverse clinical outcomes.\u003c/p\u003e \u003cp\u003eIn contrast, among non-diabetic patients, restricted cubic spline analysis demonstrated no meaningful association between standardized GIHI and 30-day MACE, with hazard ratios remaining close to unity across the entire range of GIHI values and no evidence of non-linearity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe addition of standardized GIHI to the multivariable model significantly improved discrimination for 30-day MACE in diabetic patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The AUC increased from 0.65 for the baseline model to 0.69 after inclusion of GIHI (ΔAUC\u0026thinsp;=\u0026thinsp;0.04; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating meaningful incremental prognostic value beyond established clinical predictors.\u003c/p\u003e \u003cp\u003eSubgroup analysis demonstrated a statistically significant interaction between the Glyco-Inflammatory Healing Index (GIHI) and surgical urgency (P for interaction\u0026thinsp;=\u0026thinsp;0.029), indicating that the prognostic impact of GIHI differed according to operative context (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Specifically, while higher GIHI values were associated with an increased risk of 30-day major adverse cardiovascular events (MACE) across all subgroups, the association was more pronounced among patients undergoing emergency CABG (HR 1.75, 95% CI 1.53\u0026ndash;2.00) compared with elective procedures (HR 1.39, 95% CI 1.13\u0026ndash;1.71). In contrast, the association between GIHI and 30-day MACE remained consistent across age and sex subgroups, with no evidence of significant effect modification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this multicenter cohort of patients undergoing isolated CABG, we found that GIHI was strongly and independently associated with adverse postoperative outcomes, particularly among patients with diabetes. Higher GIHI values were associated with a significantly increased risk of 30-day MACE, 30-day mortality, and long-term mortality in diabetic patients, even after multivariable adjustment, whereas in non-diabetic patients, the association was largely limited to mortality outcomes. We also observed a non-linear relationship between GIHI and 30-day MACE in diabetics, with risk increasing more sharply at higher values, and the addition of GIHI significantly improved model discrimination for postoperative MACE.\u003c/p\u003e \u003cp\u003eThe variables included in the study are themselves well-established determinants of adverse outcomes after CABG and support the biological credibility of the adjusted association observed for GIHI. Older age has consistently been associated with worse post-CABG outcomes, likely reflecting greater frailty, more diffuse atherosclerosis, impaired endothelial repair, and lower physiological reserve under surgical stress [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Female sex has also been linked to higher short-term mortality and postoperative stroke after CABG in meta-analytic data, potentially owing to smaller coronary and graft vessel caliber, older age at presentation, and a heavier comorbidity burden [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. EuroSCORE II remains a robust marker of operative risk because it condenses multiple adverse baseline characteristics into a single estimate of perioperative vulnerability, and contemporary registry data continue to support its value in isolated CABG [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElevated white blood cell count is another plausible covariate, as preoperative leukocytosis has been associated with adverse CABG outcomes and may reflect heightened systemic inflammation, endothelial activation, and intensified reperfusion-related tissue injury [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Higher creatinine similarly identifies patients with renal dysfunction, a well-known predictor of mortality and complications after CABG, likely through reduced cardiorenal reserve, greater vascular disease burden, impaired drug handling, and susceptibility to perioperative acute kidney injury [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Lower total protein may reflect reduced protein reserve and a worse inflammatory-nutritional state, although CABG-specific evidence is less robust than for albumin [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRadial artery use is relevant because it is associated with better long-term outcomes than saphenous vein grafting, likely due to superior patency. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Greater septal thickness may reflect left ventricular hypertrophy, which has been associated with worse early and late outcomes after CABG and may act through increased myocardial oxygen demand, impaired diastolic filling, microvascular dysfunction, and reduced ischemic tolerance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Longer cardiopulmonary bypass time is likewise a recognized adverse marker, probably because it represents more complex surgery and greater exposure to hemodilution, inflammatory activation, ischemia-reperfusion injury, and end-organ hypoperfusion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Lower LVEF is another established predictor of poorer CABG outcomes, reflecting limited myocardial reserve and a lower capacity to tolerate perioperative ischemia, low-output states, and arrhythmic or hemodynamic complications [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Taken together, adjustment for these variables is clinically important because it tests GIHI against a broad set of demographic, operative, inflammatory, renal, nutritional, conduit-related, and cardiac structural/function markers rather than against a surgical score alone. In that context, the persistence of GIHI after full adjustment suggests that it captures a distinct metabolic-inflammatory dimension of risk not fully represented by EuroSCORE II or by conventional clinical covariates.\u003c/p\u003e \u003cp\u003eOur findings are consistent with prior CABG literature showing prognostic value for the individual components of GIHI. Halkos et al. reported that each 1% increase in preoperative HbA1c was associated with higher in-hospital mortality after CABG (OR 1.40, P\u0026thinsp;=\u0026thinsp;0.019), and that HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;8.6% was associated with a 4-fold increase in mortality; in a subsequent long-term analysis, the same group showed that each 1% increase in HbA1c was associated with reduced long-term survival (HR 1.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Abu Tailakh et al. likewise found that, among diabetic patients undergoing CABG, HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7% was independently associated with long-term all-cause mortality (HR 2.67, P\u0026thinsp;=\u0026thinsp;0.001) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For inflammation, van Straten et al. showed that preoperative CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L independently predicted early mortality, whereas CRP\u0026thinsp;\u0026gt;\u0026thinsp;5 mg/L predicted late mortality after CABG [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For nutritional reserve, de la Cruz et al. reported in a propensity-matched CABG cohort that preoperative albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL was associated with worse 8-year survival (65% \u0026plusmn; 7% vs 86% \u0026plusmn; 3%; HR 2.2, 95% CI 1.4\u0026ndash;3.6; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore recently, Oh et al. found that in OPCAB, a CRP/albumin ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.326 predicted 1-year mortality with an adjusted HR of 5.01 (95% CI 2.01\u0026ndash;12.50; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while it was not significantly associated with 1-year major cardiovascular and cerebrovascular events (HR 1.11, 95% CI 0.69\u0026ndash;1.78; P\u0026thinsp;=\u0026thinsp;0.66) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Taken together, these studies suggest that dysglycemia, inflammation, and nutritional status are each prognostically important after CABG; our findings extend this evidence by showing that a composite marker integrating all three may better capture postoperative risk, especially in patients with diabetes.\u003c/p\u003e \u003cp\u003eCardiac surgery, especially when performed with cardiopulmonary bypass, triggers a complex systemic inflammatory response driven by operative trauma, contact of blood with non-endothelial extracorporeal surfaces, ischemia-reperfusion injury, complement activation, leukocyte and platelet stimulation, and the subsequent release of pro-inflammatory cytokines. When this response is excessive, it may amplify endothelial injury, capillary leak, tissue edema, microcirculatory dysfunction, and organ hypoperfusion, thereby contributing to adverse postoperative outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In parallel, poor chronic glycemic control, as reflected by elevated HbA1c, may further increase perioperative susceptibility through several interconnected pathways, including oxidative stress, endothelial dysfunction, impaired nitric oxide bioavailability, microvascular injury, a prothrombotic milieu, and defective immune and reparative responses. These abnormalities may reduce tolerance to perioperative ischemia, impair wound healing, and promote infectious and cardiovascular complications after CABG [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin this context, CRP and albumin represent complementary biological domains. CRP reflects the magnitude of the pre-existing and perioperative inflammatory burden; elevated preoperative CRP has been associated with both early and late mortality after coronary bypass surgery, suggesting that patients entering surgery with heightened inflammation may have less physiological reserve and a greater tendency toward maladaptive postoperative responses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Albumin, by contrast, is not only a marker of nutritional state but also an important negative acute-phase reactant with antioxidant, anti-inflammatory, endothelial-stabilizing, and microvascular protective properties. Reduced albumin levels may therefore indicate a state of impaired host defense and diminished capacity to buffer oxidative and inflammatory stress, while also favoring interstitial fluid shift, poorer tissue perfusion, and delayed recovery [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, a high GIHI may identify a subgroup in whom chronic metabolic dysregulation and perioperative inflammatory vulnerability coexist and potentially reinforce one another. In such patients, longstanding glycemic injury may prime the vasculature and tissues for damage, while an exaggerated inflammatory state may magnify the clinical consequences of that vulnerability during and after surgery. This convergence provides a biologically plausible explanation for why the prognostic signal of GIHI appears strongest in diabetic patients and for mortality-related endpoints, where the combined effects of inflammation, oxidative stress, endothelial dysfunction, impaired repair, and reduced physiologic reserve are most likely to translate into clinically meaningful events [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, the subgroup analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that the association between higher GIHI and 30-day MACE was broadly consistent across sex and age strata, supporting the overall robustness of the finding. The stronger association observed in emergency compared with elective CABG, together with the significant interaction, may indicate that the adverse metabolic-inflammatory profile reflected by GIHI becomes particularly relevant in more acute operative settings, where baseline perioperative risk is already amplified [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, as subgroup findings should be interpreted cautiously and primarily on the basis of interaction testing, this observation should be considered hypothesis-generating and warrants confirmation in future studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinically, GIHI may serve as a simple preoperative risk-enrichment tool rather than a replacement for established surgical risk models. Because its components are routinely available, it may help identify CABG patients, especially those with diabetes, who need closer perioperative surveillance and better metabolic, inflammatory, and nutritional optimization. This is consistent with prior CABG evidence and guidelines recognizing diabetes as a high-risk subgroup. Future studies should determine whether adding GIHI to existing models improves decision-making and whether optimizing these pathways can reduce the excess risk associated with high GIHI.\u003c/p\u003e\n\u003ch3\u003eStrengths\u003c/h3\u003e\n\u003cp\u003eThis study has several strengths. First, it included a large, international, multicenter cohort of patients undergoing isolated CABG, which enhances the robustness and generalizability of the findings. Second, the analysis was performed separately in diabetic and non-diabetic patients, allowing a clearer assessment of effect modification by diabetes status. Third, GIHI was evaluated using multiple complementary approaches, including continuous standardized modeling, Kaplan\u0026ndash;Meier analysis, restricted cubic splines, subgroup analyses, and discrimination testing, providing consistent evidence across different statistical frameworks. Fourth, the study examined both short-term and long-term outcomes, allowing a more comprehensive assessment of the prognostic relevance of GIHI. Finally, because GIHI is derived from routinely available laboratory parameters, the findings have direct clinical applicability and support the potential use of this biomarker in real-world perioperative risk stratification.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. Its retrospective observational design precludes causal inference and allows residual confounding despite multivariable adjustment. Exclusion of patients with missing data or unavailable HbA1c may have introduced selection bias and limited representativeness. GIHI was based on a single preoperative measurement, so temporal changes in glycemic, inflammatory, and nutritional status were not captured. Multicenter data collection over a long period may have introduced heterogeneity in assays, perioperative practice, surgical technique, medical therapy, and follow-up. Although adjusted for several clinically relevant variables, other factors, including frailty, infection status, diabetes duration, antidiabetic treatment intensity, and postoperative management, were not fully captured. Outcomes were identified from registry and institutional data rather than centralized adjudication. The high-versus-low GIHI categorization was based on an internal median split and is descriptive rather than a clinically validated threshold. Finally, because the findings were derived from isolated CABG patients in 4 centers without external validation, generalizability to other populations, health systems, combined procedures, or broader cardiac surgery cohorts remains uncertain.\u003c/p\u003e \u003cp\u003eFuture studies should externally validate GIHI, assess its value across broader cardiac surgery settings, and determine whether serial measurements and integration into existing risk models improve prognostic performance and clinical decision-making. Interventional studies are also needed to test whether optimizing glycemic, inflammatory, and nutritional status can reduce the excess risk associated with high GIHI, particularly in patients with diabetes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients undergoing isolated CABG, GIHI was independently associated with adverse postoperative outcomes, with the prognostic value observed among patients with diabetes. Higher GIHI levels were linked to increased risk of 30-day MACE, short-term mortality, and long-term mortality, while also improving risk discrimination beyond conventional clinical variables in diabetic patients. These findings suggest that GIHI may serve as a simple and clinically applicable marker of metabolic-inflammatory risk, with potential utility for perioperative risk stratification after CABG.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCABG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronary artery bypass grafting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGIHI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlyco-Inflammatory Healing Index\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\u003eglycated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\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 cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandardized mean difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study protocol was approved by the Institutional Ethics Committee of Kaplan Medical Center (approval no. 0143-22-KMC). All study procedures were performed in accordance with the Declaration of Helsinki and its subsequent amendments. Because of the retrospective study design and the use of anonymized registry data, the requirement for written informed consent was waived by the Institutional Ethics Committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eE.Z. Fisman served as Editor-in-Chief of Cardiovascular Diabetology\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors' information\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding or sponsorship was received for this study or the publication of this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHAK and LS conceived and designed the study. HAK collected the data. HAK and MA performed the statistical analysis. HAK, MA, KI, DN, TZM, MM, AMS, NAH, YZF, and AD drafted the manuscript. AK, ER, NAH, KI, and LS critically revised the manuscript for important intellectual content. All authors interpreted the data, read and approved the final manuscript, and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors sincerely thank all study participants for their time and cooperation. They also gratefully acknowledge the hospital staff for their professionalism and support throughout the study. Special appreciation is extended to the Palestinian Clinical Research Center for its valuable support, coordination, and contribution to the successful completion of this research.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eDe-identified participant data generated and analyzed in this study are not publicly available because of patient confidentiality and institutional ethical restrictions. However, the data will be available from the corresponding author upon reasonable request from the time of publication. Access may be granted to researchers who submit a methodologically sound proposal and agree to the terms of a data access agreement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStark BA, et al. 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Updating criteria to evaluate the credibility of subgroup analyses, \u003cem\u003eBMJ\u003c/em\u003e, vol. 340, no. 7751, pp. 850\u0026ndash;854, Mar. 2010, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/BMJ.C117\u003c/span\u003e\u003cspan address=\"10.1136/BMJ.C117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary artery bypass grafting. Diabetes mellitus. Glyco-Inflammatory Healing Index. Major adverse cardiovascular events","lastPublishedDoi":"10.21203/rs.3.rs-9457970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9457970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Glyco-Inflammatory Healing Index (GIHI), calculated as HbA1c \u0026times; C-reactive protein/albumin, integrates glycemic control, systemic inflammation, and nutritional status. Its prognostic value after isolated coronary artery bypass grafting (CABG) remains unclear, particularly according to diabetes status.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a retrospective, international, multicenter cohort study of 6,262 adults who underwent isolated CABG between 2010 and 2025. Patients were stratified by diabetes status. GIHI was analyzed as a standardized continuous variable and descriptively categorized as high or low based on subgroup medians. The primary outcome was 30-day major adverse cardiovascular events (MACE), defined as all-cause mortality, myocardial infarction, stroke, or repeat revascularization. The secondary outcome included long-term all-cause mortality. Associations were assessed using Cox regression, Kaplan-Meier analysis, restricted cubic splines, subgroup analyses, and receiver operating characteristic curves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong 6,262 patients, 2,783 (44.5%) had diabetes. In diabetic patients, higher GIHI was independently associated with 30-day MACE (adjusted hazard ratio [HR] 1.45, 95% confidence interval [CI] 1.28\u0026ndash;1.65), 30-day myocardial infarction (HR 1.27, 95% CI 1.00-1.60), 30-day mortality (HR 2.56, 95% CI 2.06\u0026ndash;3.18), and long-term mortality (HR 2.57, 95% CI 2.33\u0026ndash;2.83). In non-diabetic patients, GIHI was not associated with 30-day MACE but remained associated with 30-day mortality and long-term mortality. Adding GIHI improved discrimination for 30-day MACE in diabetic patients (AUC 0.65 to 0.69; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGIHI independently predicts adverse postoperative outcomes after isolated CABG, with the strongest prognostic relevance in patients with diabetes.\u003c/p\u003e","manuscriptTitle":"A Novel Glyco-Inflammatory Healing Index for Risk Prediction After Isolated Coronary Artery Bypass Grafting: A Multicenter Retrospective Cohort Study in Patients with Type 2 Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:23:48","doi":"10.21203/rs.3.rs-9457970/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T11:34:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T10:54:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169523870891611671060726206160950066411","date":"2026-04-25T09:45:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44879648330662328500454472049573555190","date":"2026-04-23T11:48:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100149103069764633278191389957622113649","date":"2026-04-23T07:15:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T06:28:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T13:17:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-22T12:42:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2026-04-18T20:29:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41077408-8bbd-44bd-ad26-eb3fdcf2ca7d","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:23:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9457970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9457970","identity":"rs-9457970","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}