Inflammation and Insulin Resistance-Derived Indicator Predicts Adverse Cardiovascular Outcomes in Heart Failure Patients Undergoing Percutaneous Coronary Intervention

Inflammation and Insulin Resistance-Derived Indicator Predicts Adverse Cardiovascular Outcomes in Heart Failure Patients Undergoing Percutaneous Coronary Intervention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inflammation and Insulin Resistance-Derived Indicator Predicts Adverse Cardiovascular Outcomes in Heart Failure Patients Undergoing Percutaneous Coronary Intervention Ang Gao, Bo Peng, Yanan Gao, Zhiqiang Yang, Zhifan Li, Tingting Guo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4277196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Inflammation and insulin resistance play important roles in the initiation and progression of heart failure and coronary artery disease. However, there’s lack of indicator related to inflammation and insulin resistance to predict the prognosis of that population. This study aims to evaluate the potential value of C-reactive protein-triglyceride glucose index (CTI) in heart failure patients undergoing percutaneous coronary intervention (PCI). Methods 2797 PCI-treated patients with heart failure at Beijing Fuwai Hospital between 1st January 2016 and 31st December 2018 were retrospectively enrolled in current study. The primary endpoint was major adverse cardiac and cerebrovascular events at 12-month follow-up, defined as a composite of all-cause death, non-fatal myocardial infarction and stroke. Restricted cubic spline was applied to determine the cut-off value of CTI and examine the dose-response relationship between the CTI and the primary endpoint. Multivariate Cox proportional hazards models were used to evaluate the predictive value of CTI for the adverse cardiovascular outcomes and the results were expressed as hazard ratio with 95% confidence interval. The receiver-operating characteristics and decision curve analysis were plotted to comprehensively evaluate the predictive accuracy and clinical use of the CTI when adding it into the baseline model used to predict the prognosis of that population. Finally, subgroup analysis was conducted to evaluate the interaction between the traditional cardiovascular risk factor and CTI-related cardiovascular outcomes. The calculation method of CTI was as followed: ln[triglyceride(mg/dl) × fasting blood glucose(mg/dl)/2] + 0.412 × ln (C-reactive protein). Results Among the 2797 PCI-treated patients with heart failure, 131 experienced MACCEs. Restricted cubic spline model showed that the CTI was significantly associated with the risk of adverse cardiovascular outcomes within 12 months ( P for nonlinearity < 0.001), with a best cut-off value of 9.47. After adjusting for various confounders, the CTI remained independently associated with the incidence of endpoints (hazard ratio 1.41; 95%CI 1.13–1.77; P < 0.01) while the TyG index was not. Furthermore, Kaplan-Meier analysis demonstrated a higher incidence of endpoints (hazard ratio 1.55; 95%CI 1.11–2.16; Log rank P = 0.011) and all-cause death (hazard ratio 2.16; 95%CI 1.16–3.99; Log rank P = 0.015) in enrolled patients with high CTI (CTI ≥ 9.47). Adding the CTI into the baseline model used to predict the adverse outcomes improved the predictive ability for the endpoints (increase in C-statistic value from 0.685 to 0.694; NRI 0.217, 95% confidence interval 0.050–0.385, P = 0.011; IDI 0.003, 95% confidence interval 0.001–0.007, P = 0.049). Subgroup analysis showed that there existed an interaction between CTI and hypertension for the prediction of endpoints ( P for interaction = 0.046). Conclusions Elevated CTI is associated with an increased risk of adverse cardiovascular outcomes in heart failure patients undergoing PCI, indicating the potential use of the CTI in the risk stratification and prognosis prediction of that population. heart failure percutaneous coronary intervention C-reactive protein-triglyceride glucose index insulin resistance inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Owing to the aging population and the increasing prevalence of cardiometabolic risk factors, cardiovascular deaths have become the leading cause of mortality, accounting for more than 40% of death in China[ 1 ]. Despite the intensified pharmacological strategies, timely coronary revascularization and technical innovation in percutaneous coronary intervention (PCI) treatment, relatively high rates of adverse cardiovascular outcomes can still be foreseen in many PCI-treated patients, especially for those with multiple cardiometabolic complications, an issue commonly ascribed to the residual risk of cardiometabolic syndrome (CMS). As global rate of CMS increases there is an acceleration of the incidence and prevalence of its associated cardiovascular diseases, which presents enormous challenges in cardiovascular prevention and controlling cardiovascular mortality[ 2 ]. Insulin resistance (IR), as the central part of CMS, defined as the decrease in sensitivity and responsiveness to the metabolic action of insulin, plays detrimental role in the development of atherosclerotic cardiovascular disease (ASCVD) through impairing endothelial cell function[ 3 ]. The underlying mechanism could be that exposure of endothelial cells to the IR status promotes upregulation of proinflammatory cytokines expression via the activation of inflammatory signaling pathways, leading to diminished nitric oxide (NO) production and reactive oxygen species (ROS) formation[ 4 ]. Previous studies have substantiated that there exists a causal link between IR, inflammation and the initiation and progression of cardiovascular diseases. Recent randomized clinical trials (RCTs) targeting at IR and inflammation among individuals with cardiovascular diseases or high cardiovascular risks have delivered positive results[ 5 – 10 ]. Therefore, identifying individuals with IR and high inflammatory status is crucial to the initiation and management of targeted medication thus improving prognosis, especially for those with advanced atherosclerotic cardiovascular diseases. Triglyceride glucose (TyG) index, calculated as ln[triglyceride(mg/dl) × fasting blood glucose(mg/dl)/2], has emerged as a practical tool in clinical practice used to evaluate IR for its easy availability and convenience to use. Previous studies have fully substantiated its prognostic value of adverse outcomes in patients with various cardiovascular diseases phenotypes[ 11 – 13 ]. However, these studies only emphasize the deteriorative role of IR in the progression of the cardiovascular diseases while neglecting inflammation may contribute with similar magnitude to this process. Inflammation initiates the process of atherosclerosis through the dysregulation of endothelial dysfunction, from promoting cholesterol uptake, oxidation and accumulation to the erosion and rupture of the plaque, with the consequence of acute ischemic syndrome[ 14 ]. In current clinical practice, the best studied and most easily applied biomarker for evaluating inflammatory status in patients with cardiovascular diseases is high-sensitive C-reactive protein (CRP). Large-scale cohort studies have indicated that high-sensitive CRP could independently predict risks of incident of heart failure, myocardial infarction, stroke and sudden cardiac death even when low density lipoprotein (LDL) is low[ 15 ]. Recently, a synergetic effect was found between TyG index and high-sensitive CRP for predicting adverse outcomes in diabetic chronic coronary syndrome patients in a cohort study, implying the potential value of curbing inflammation in patients with IR[ 16 ]. However, this study fails to produce a comprehensive tool for evaluating inflammation and IR simultaneously and test its prognostic value for adverse cardiovascular outcomes. Besides, the result was only applied to diabetic patients. Recently, inflammation and IR related indicator CTI has been validated as a useful tool for predicting prognosis in patients with cancer[ 17 ], while its potential application in patients with cardiovascular diseases remains elusive. Hence, the aim of this study is to assess the potential application of a new comprehensive indicator CTI in predicting the prognosis of heart failure patients undergoing PCI treatment. Methods Study Design and Population A total of 2797 consecutive PCI-treated patients with heart failure at Beijing Fuwai Hospital between 1st January 2016 and 31st December 2018 were recruited into this single-center, retrospective observational study. This study was in accordance with the principles of the Declaration of Helsinki, and all patients signed informed consent before discharge. The exclusion criteria were as followed: 1) lack of core laboratory data, like the value of triglyceride, CRP or fasting glucose, or lost follow-up; 2) End-stage renal dysfunction or treated with renal replacement therapy, or serious liver dysfunction; 3) acute or chronic infectious diseases, malignant tumors, immune system disorders or treated with anti-inflammatory medication; 3) suspected familial hypertriglyceridemia (triglyceride ≥ 5.65 mmol/L); 4) diagnosed as cardiogenic shock at discharge; Detailed flow chart of enrolled participants was shown in Additional file S1. Clinical and Laboratory Data collection Demographic data like age, sex, height, weight, previous medical history and medication at discharge were obtained through a review of medical records, which was approved by Beijing Fuwai Hospital. Body mass index (BMI) was calculated as weight (Kg) / [Height (m)] 2 . On-admission biochemical assessment was performed in whole blood samples drawn after arrival to the emergency or general ward. Blood samples were taken after overnight fasting if participants were not indicated for emergent coronary revascularization. Laboratory indicators, including triglyceride and glucose and CRP, were all measured by standard techniques. The TyG index is quantified by the established formula: ln [triglyceride(mg/dL) × fasting glucose (mg/dL)/2]. The value of CTI is defined as TyG index + 0.412 × ln [CRP (mg/L)]. The diagnosis of diabetes mellitus is based on the previous diagnosis and treatment with glucose-lowering medication or recommendations from the American Diabetes Association[ 18 ]. Hypertension is defined by the recommendations from the European Society of Hypertension, an office systolic blood pressure value ≥ 140 mmHg or a diastolic blood pressure value ≥ 90 mmHg or the use of antihypertensive drugs in the past 2 weeks[ 19 ]. Acute myocardial infarction (AMI) is defined as increased cardiac troponin values with ischemic symptoms or ischemic changes on electrocardiogram or imaging evidence of recent loss of viable myocardium or new regional wall motion abnormalities that were not related to the procedure[ 20 ]. The definition of heart failure is clinically diagnosed on the basis of clinical symptoms, echocardiography and the value of N-terminal Pro-B-type Natriuretic Peptide (NT-proBNP) and the classification of heart function was based on the recommendations from New York Heart Association for patients with angina pectoris and Killip classification for patients with acute myocardial infarction[ 21 , 22 ]. Coronary Procedure Coronary angiography and PCI procedure was performed by at least two experienced cardiologists whose expertise are interventional cardiology. Coronary procedure was processed according to current practice guidelines. The characteristics of coronary artery lesions are defined based on the ACC/AHA guidelines for coronary lesion classification[ 23 ]. Multivessel disease is defined as a diameter stenosis of ≥ 50% occurring in 2 or more vessels. Coronary chronic total occlusion (CTO) lesions are defined on coronary angiography as coronary arteries with either absent or minimal anterograde blood flow for 12 weeks duration. Follow-up and Endpoint Events All participants were accepting guideline recommended medical therapy for heart failure and coronary artery disease at discharge, unless some contradiction or unable to endure its side effect. All enrolled participants were followed up for 12 months or until the time of a major adverse clinical event. Follow-up was performed by telephone interviewers using standardized questionnaires at 6 and 12 months after the PCI treatment. Primary endpoint of this study was the occurrence of major adverse cerebrovascular and cardiovascular events (MACCEs) after PCI during 12-month follow-up, defined as the composite of all-cause death, non-fatal acute myocardial infarction, and ischemic stroke. Ischemic stroke was defined as cerebral infarction with symptoms of neurological impairment which can be explained by imaging examinations. Statistical analysis To better understand the characteristics of enrolled participants, patients were categorized into two groups according to the CTI value. Descriptive variables are expressed as the mean ± standard deviation or median with interquartile range for normally or nonnormally distributed continuous variables. Categorical variables are presented as frequencies and percentages. Differences between the two groups were compared by Student’s t test or the Mann-Whiteney U test for normally or nonnormally distributed continuous variates. Restrict cubic spline analysis was applied to determine the cut-off value of CTI and examine the dose-response relationship between the CTI and the primary endpoint. Cumulative event rates were compared using the log-rank test, and the Kaplan-Meier method was used to depict the time-to-event curves of TyG index and CTI group. Univariable Cox proportional hazard analysis was performed to preliminarily explore the risk factors associated with MACCEs. Multivariate Cox proportional hazard models were conducted to evaluate the prognostic value of CRP, TyG index and CTI for MACCEs and the results were expressed as hazard ratio (HR) and 95% confidence interval (CI). Confounding factors that were statistically significant in the univariate analysis ( P < 0.05) were incorporated into the baseline model used to predict the occurrence of MACCEs. The receiver operating characteristic (ROC) analysis was plotted to investigate the incremental value of CTI on the discriminative performance beyond the baseline model, and decision curve analysis (DCA) was conducted to compare the predictive value of the CRP, TyG index and CTI for the endpoints. The incremental predictive value for CTI was comprehensively examined by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) when adding the CTI into the baseline model. Furthermore, subgroup and interaction analysis were performed based on the index presentation (AMI or angina pectoris), diabetes (yes or no), hypertension (yes or no), inflammatory status (CRP ≥ 3mg/L or not), cholesterol burden (LDL-C ≥ 1.8mmol/L or not) and heart function classification (I or II-IV) to identify whether the relationships between CTI and MACCEs varied based on the status of the potential covariates. Results Baseline characteristics Overall, a total of 2797 heart failure participants undergoing PCI treatment were finally enrolled into this retrospective cohort study. Of these participants, 131 experienced MACCEs during the 12-month follow-up. The baseline characteristics of the study population stratified by CTI value are summarized in Table 1 . Table 1 Baseline characteristics of enrolled participants divided by CTI value. Overall High CTI Low CTI P value (n = 2797) (n = 1374) (n = 1423) Demographic data Age (years) 62.2 ± 11.00 61.5 ± 11.32 62.8 ± 10.64 0.003 Male sex, n (%) 2191 (78.3%) 1028 (74.8%) 1163 (81.7%) < 0.001 Systolic blood pressure, mmHg 128.5 ± 17.06 128.0 ± 17.13 129.0 ± 16.99 0.962 Diastolic blood pressure, mmHg 77.8 ± 10.35 78.1 ± 10.36 77.6 ± 10.35 0.169 Heart rate, bpm 72.4 ± 12.86 74.7 ± 13.20 70.1 ± 12.10 <0.001 Body mass index (Kg/m²) 25.8 ± 3.39 26.3 ± 3.36 25.3 ± 3.35 <0.001 Comorbidities Hypertension, n (%) 1857 (66.4%) 925 (67.3%) 932 (65.5%) 0.317 Diabetes mellitus, n (%) 1107 (39.6%) 709 (51.6%) 398 (28.0%) < 0.001 Current Smokers, n (%) 1704 (60.9%) 840 (61.1%) 864 (60.7%) 0.846 Atrial fibrillation, n (%) 220 (7.9%) 105 (7.6%) 115 (8.1%) 0.674 Previous PCI, n (%) 908 (32.5%) 417 (30.3%) 491 (34.5%) 0.019 Previous CABG, n (%) 141 (5.0%) 66 (4.8%) 75 (5.3%) 0.604 Previous MI, n (%) 1207 (43.2%) 535 (38.9%) 672 (47.2%) <0.001 Previous Stroke, n (%) 339 (12.1%) 170 (12.4%) 169 (11.9%) 0.728 Peripheral artery disease, n (%) 159 (5.7%) 72 (5.2%) 87 (6.1%) 0.328 Heart class classification (NYHA or Killip) <0.001 I 668 (23.9%) 384 (28.0%) 284 (20.0%) II-IV 2129 (76.1%) 990 (72.0%) 1139 (80.0%) Laboratory measurements White blood cell count, 10 9 /L 7.2 ± 2.24 7.9 ± 2.46 6.6 ± 1.79 <0.001 Platelet, 10 12 /L 228.3 ± 68.13 237.9 ± 70.2 219.0 ± 64.78 <0.001 Hemoglobin, g/dL 14.3 ± 1.71 14.3 ± 1.77 14.2 ± 1.65 0.736 Creatinine, mmol/L 85.4 ± 19.97 86.1 ± 21.86 84.7 ± 17.94 0.069 Fasting blood glucose, mmol/L 5.9 (5.1–7.7) 7.1 (5.7–9.4) 5.4 (4.9–6.2) < 0.001 Triglyceride, mmol/L 1.6 ± 0.75 2.0 ± 0.83 1.2 ± 0.40 <0.001 Total cholesterol, mmol/L 3.9 ± 1.00 4.2 ± 1.04 3.7 ± 0.89 <0.001 High-density lipoprotein cholesterol, mmol/L 1.0 ± 0.28 1.0 ± 0.25 1.1 ± 0.29 <0.001 Low-density lipoprotein cholesterol, mmol/L 2.4 ± 0.86 2.6 ± 0.90 2.2 ± 0.76 <0.001 C-reactive protein, mg/L 3.1 (2.0-6.2) 5.4 (2.9–10.3) 2.2 (1.6–3.4) <0.001 Triglyceride glucose index 8.9 ± 0.58 9.3 ± 0.49 8.5 ± 0.36 <0.001 Echocardiography parameter Left ventricular ejection fraction, % 49.8 ± 7.29 49.2 ± 7.37 50.3 ± 7.18 <0.001 Left ventricular end diastolic diameter, mm 52.4 ± 6.53 52.4 ± 6.49 52.4 ± 6.56 0.948 Left atrial diameter, mm 38.1 ± 5.25 38.1 ± 4.99 38.1 ± 5.49 0.718 Medication at discharge Antithrombotic therapy 0.001 Aspirin + Clopidogrel, n (%) 2068 (73.9%) 973 (70.8%) 1095 (77.0%) Aspirin + Ticagrelor, n (%) 616 (22.0%) 346 (25.2%) 270 (19.0%) Others, n (%) 66 (2.4%) 33 (2.4%) 33 (2.3%) Statins, n (%) 2704 (96.7%) 1319 (96.0%) 1385 (97.3%) 0.057 β-blockers, n (%) 2477 (88.6%) 1240 (90.2%) 1237 (86.9%) 0.006 Angiotensin blockade, n (%) 1808 (64.6%) 877 (63.8%) 931 (65.4%) 0.384 Spironolactone, n (%) 971 (34.7%) 519 (37.8%) 452 (31.8%) 0.001 Diuretics, n (%) 784 (28.0%) 438 (31.9%) 346 (24.3%) < 0.001 Procedural characteristics AHA/ACC lesion: type B2/C, n (%) 2066 (73.9%) 985 (71.7%) 1081 (76.0%) 0.011 Multivessel or LMCA disease, n (%) 2324 (83.1%) 1157 (84.2%) 1167 (82.0%) 0.130 CTO lesion, n (%) 920 (32.9%) 491 (35.7%) 429 (30.1%) 0.002 Diameter, mm 3.2 ± 1.03 3.2 ± 1.05 3.2 ± 1.02 0.909 Target vessel length, mm 37.2 ± 21.39 37.7 ± 21.52 36.7 ± 21.27 0.234 MACCEs, n (%) 131 (4.7%) 84 (6.1%) 57 (4.1%) 0.012 All cause death, n (%) 46 (1.6%) 31 (2.3%) 15 (1.1%) 0.016 Acute myocardial infarction, n (%) 94 (3.4%) 53 (3.9%) 41 (2.9%) 0.172 Stroke, n (%) 26 (0.9%) 17 (1.2%) 9 (0.6%) 0.115 Data are expressed as the mean ± SD, median with interquartile range or n (%). CTI, CRP-TyG index; CRP, C reactive protein; TyG index, Triglyceride glucose index; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; MI, myocardial infarction; LMCA, left main coronary artery; CTO, chronic total lesion; MACCEs, major adverse cardiac and cerebrovascular events; The average age of the participants was 62 years, and 78.3% of this cohort were male. Compared with patients with low CTI value (CTI < 9.47), those with high CTI value have a greater rate of MACCEs (4.1% vs. 6.1%, P = 0.012) and cardiometabolic risk factors, like diabetes mellitus (28.0% vs. 51.6%, P < 0.001) and higher BMI ( P < 0.001). Patients with high CTI value also had higher heart rate, white cell count, platelet count, fasting glucose, triglyceride, total cholesterol, HDL-C, LDL-C level (all P < 0.001) and tended to have higher creatinine level ( P = 0.069). Besides, patients with higher CTI had lower left ventricular ejection fraction (LVEF) ( P < 0.001), but they had a better heart function classification (28.0% vs. 51.6%, P < 0.001) and lower rates of previous history of myocardial infarction (28.0% vs. 51.6%, P < 0.001) and PCI treatment (28.0% vs. 51.6%, P < 0.001). As with anatomical characteristics, the proportions of individuals with CTO lesions were higher in high CTI group ( P = 0.002), while the rates of type B2/C lesion are higher in low CTI group ( P = 0.011). Additionally, patients with high CTI value were more likely to use diuretics, spironolactone, β-blockers and more potent dual antiplatelet therapy (Aspirin + Ticagrelor) at discharge ( P < 0.01), but a tendency of lower rate of statin use was also observed in this population ( P = 0.057). Association between CTI and endpoints As is shown in Fig. 1 A and 1 B, univariate RCS analysis was used to explore whether there existed an association between the TyG index or CTI and the MACCEs in heart failure patients undergoing PCI. Nonlinearity relationship between the TyG index and CTI and the probability of the risk of MACCEs can be observed (P for nonlinearity < 0.05), with a best cut-off value of 8.74 and 9.47 respectively. Participants were further divided into two groups based on the CTI or the TyG index cut-off value. Figure 2 showed the cumulative incidence of MACCEs and all-cause death was significantly higher in patients with high CTI (all Log rank P < 0.05), while no significant difference of cumulative incidence of endpoints between patients with high or low TyG index. Table 2 delineates the stratified analysis result using univariate and multivariate Cox proportional hazard models to show the association between CRP, TyG index and CTI and an elevated risk of clinical endpoints. The unadjusted model indicated that continuous or categorical CTI value were all statistically significantly associated with the incidence of MACCEs [categorical variable: HR (95%CI) 1.55(1.11–2.16), P < 0.05; continuous variable: HR (95%CI) 1.37(1.11–1.70), P < 0.01]. After adjusting for heart rate, diabetes mellitus, previous PCI, atrial fibrillation and heart function classification in model 1, the CTI was independent predictor for the incidence of MACCEs no matter as a continuous [HR (95%CI) 1.35(1.07–1.71), P < 0.05] or categorical variable [HR (95%CI) 1.48 (1.04–2.11), P < 0.05]. After adjusting for model 1, HDL-C and left atrial diameter in model 2, the CTI was remained independently associated with the occurrence of MACCEs no matter as a continuous [HR (95%CI) 1.47 (1.16–1.87), P < 0.01] or categorical variable [HR (95%CI) 1.63 (1.14–2.35), P < 0.01]. Finally, the CTI was still remained independently associated with the occurrence of MACCEs continuously [HR (95%CI) 1.41 (1.13–1.77), P < 0.01] or categorically [HR (95%CI) 1.67 (1.15–2.36), P < 0.05] after adjusting for model 2, multivessel/LMCA diseases, the use of statin, β-blockers and angiotensin blockade in model 3. Furthermore, multivariate Cox proportional hazard models failed to show an association between the categorical or continuous TyG index and the incidence of MACCEs statistically (all P > 0.05). Besides, continuous CRP, but not categorical values was found to be significantly associated with the risk of MACCEs (all P < 0.001). Table 2 The predictive value of CTI, TyG index and CRP for the risks of MACCEs among heart failure patients undergoing PCI. HR (95%CI) Unadjusted Model 1 Model 2 Model 3 CTI (continuous variable) 1.37 (1.11–1.70) ** 1.35 (1.07–1.71) * 1.47 (1.16–1.87) ** 1.41 (1.13–1.77) ** CTI (categorical variable) 1.55 (1.11–2.16) * 1.48 (1.04–2.11) * 1.63 (1.14–2.35) ** 1.67 (1.15–2.36) * TyG index (continuous variable) 1.31 (0.99–1.73) 1.19 (0.88–1.61) 1.32 (0.97–1.80) 1.35 (1.00-1.83) TyG index (categorical variable) 1.21 (0.87–1.69) 1.09 (0.77–1.55) 1.18 (0.82–1.68) 1.20 (0.84–1.72) CRP (continuous variable) 1.02 (1.01–1.03) ** 1.03 (1.01–1.04) *** 1.03 (1.02–1.04) *** 1.03 (1.02–1.04) *** CRP (categorical variable) 1.29 (0.92–1.80) 1.37 (0.97–1.93) 1.43 (1.01–2.04) * 1.36 (0.96–1.93) * represented P < 0.05, ** represented P < 0.01, *** represented P < 0.001 Stratified analysis was performed using the Log-rank test and backward stepwise selection methods in a Cox proportional hazards regression model; Model 1: adjusted for heart rate, diabetes mellitus, previous PCI atrial fibrillation and heart function classification; Model 2: adjusted for Model 1 + high-density lipoprotein cholesterol, left atrial diameter; Model 3: adjusted for Model 2 + multivessel/LMCA diseases, the use of statin, β-blockers and angiotensin blockade; TyG index, triglyceride glucose index; CRP C-reactive protein; CTI, CRP-TyG index; MACCE, major adverse cerebrovascular and cardiovascular event; HR, hazard ratio; CI, confidence interval; PCI, percutaneous coronary intervention; LMCA, left main coronary artery; Predictive ability test The ROC curve and DCA were performed to comprehensively identify the predictive value of CTI or TyG index for MACCEs (Fig. 3 A and 3 B). As is shown in Fig. 3 A and Table 3 , the improvement of the area under the curve (AUC) for predicting the occurrence of MACCEs was most significant when adding the CTI into the baseline model (increase in C-statistic value from 0.685 to 0.694). In addition, DCA was conducted to assess the clinical utility of CTI, suggesting CTI has better overall net benefit and clinical impact when compared with the CRP and TyG index. Furthermore, the most significant enhancement in risk reclassification and discrimination was found after inclusion of the CTI into baseline model, with a NRI of 0.217 ( P = 0.011), and an IDI of 0.004 ( P = 0.049). Table 3 The discriminative value of TyG index and CTI for the risk of MACCEs in heart failure patients undergoing PCI treatment. ROC analysis IDI NRI AUC 95%CI Estimation 95%CI P values Estimation 95%CI P values Baseline model 0.685 0.640–0.730 Ref. Ref. Ref. Ref. +TyG index (categorical) 0.686 0.641–0.731 0.001 -0.001-0.001 0.736 0.096 -0.074-0.266 0.269 + TyG index (continuous) 0.689 0.644–0.734 0.002 -0.001-0.004 0.244 0.171 0.001–0.341 0.049 + CTI (categorical) 0.693 0.646–0.739 0.003 0.001–0.007 0.049 0.217 0.050–0.385 0.011 + CTI (continuous) 0.694 0.648–0.741 0.004 0.001–0.007 0.049 0.189 0.019–0.359 0.029 Baseline model referred to heart rate, diabetes mellitus, previous PCI, atrial fibrillation, heart function classification, high-density lipoprotein cholesterol, left atrial diameter, the use of statin, β-blockers and angiotensin blockade and multivessel/LMCA diseases. TyG index, triglyceride glucose index; CRP, C reactive protein; CTI, CRP-TyG index; MACCE, major adverse cerebrovascular and cardiovascular event; PCI, percutaneous coronary intervention; ROC, receiver operating characteristics; AUC, are under the curve; IDI, integrated discrimination improvement; NRI, net reclassification index; CI, confidence interval; Subgroup analysis Subgroup analysis was performed to confirm the association between the CTI and the risk of adverse outcomes when grouped by index presentation, diabetes, hypertension, heart function classification, CRP and LDL-C levels. An interaction was found between hypertension and CTI for the prediction of MACCEs and stroke ( P for interaction < 0.05). Discussion The main findings of this study are as follow: 1) The value of CTI was significantly higher in participants suffered from MACCEs than those free of MACCEs. 2) The CTI was associated with the incidence of MACCEs within one year in heart failure patients undergoing PCI treatment and this association remained significant after adjusting for various confounding factors. Further, this study also found that compared with those with low CTI, high CTI indicated a higher risk of MACCEs within a certain range (CTI ≥ 9.47), and enhanced the cardiovascular outcome risk by 67% over the one-year follow-up in that population. 3) the predictive value of CTI for risks of MACCEs within one year is better than single component of CTI (inflammatory biomarker: CRP, IR biomarker: TyG index). Adding the CTI into the baseline model shows the most significant incremental effect on risk discrimination for predicting the risk of MACCEs within one year. To the best of our knowledge, this is the first study focusing on the evaluating the prognostic value of CTI for the risk of major adverse cardiovascular outcomes in patients with cardiovascular diseases. The predictive ability of CTI provides an effective and convenient tool used for assessing prognosis and also indicates a detrimental role of inflammation and IR in the progression of cardiovascular diseases. CTI is mainly determined by two critical components, the one is inflammation represented by the CRP, the other one is IR represented by the TyG index. Inflammation and IR are both important components of residual cardiovascular risk factors and confer an enhanced risk of AMI and heart failure[ 24 – 27 ]. The prevalence of diabetes has increased by more than tenfold since the 1980s in China owing to the ageing populations and improving socioeconomic status[ 2 ]. Diabetic patients have twofold to eightfold higher cardiovascular event rates as compared with age-matched nondiabetic individuals and 75% of death in diabetic patients can be attributed to various cardiovascular diseases. Although hyperglycemia correlates with macro- and microvascular diseases, IR itself promotes atherosclerosis even before the onset of clinical diabetes, and available data corroborate the role of IR as independent risk factor for atherothrombosis[ 28 ]. This finding has promoted attention for increased surveillance for the status of IR in the management of cardiovascular disease patients. However, the homeostasis model assessment- IR is not very applicable for clinical practice because the serum insulin level is not a common laboratory measurement for cardiovascular disease patients, especially for those nondiabetic patients. The TyG index as a surrogate marker for IR accompanied by hypertriglyceridemia and hyperglycemia has been proven to be strongly associated with the gold standard for evaluating IR[ 29 ]. Recent studies concentrating on evaluating the effect of the TyG index on the cardiovascular events have substantiated the prognostic value of TyG index for predicting heart failure, AMI and cardiovascular death in cardiovascular patients[ 11 – 13 , 28 ]. While conflicting result that the TyG index is not an effective predictor for cardiovascular events in nondiabetic patients undergoing PCI treatment has also been reported[ 30 ], which is in accordance with the conclusion in current study. One plausible explanation could be that the prognostic role of IR for major adverse cardiovascular outcomes could be partially mediated by the inflammatory status. Recent study conducted by Li et al. demonstrated that a significant partial mediating effect of systemic inflammation on the association of IR with adverse cardiovascular events in chronic coronary syndrome diabetic patients, implying the mediated effect of inflammation on IR-related cardiovascular outcomes[ 16 ]. The underlying mechanism could be that IR could activate different inflammatory signaling cascades in cardiomyocytes which contribute to cell hypertrophy, apoptosis and dysfunction[ 31 ]. In addition, exposure of endothelial cell to IR exerts a detrimental effect on endothelial function linked to ROS formation and upregulation of inflammatory cytokine expression via activating IκB kinase, NF-κB and nucleotide-binding domain, leucine-risk-containing family, pyrin domain 3 (NLRP3) inflammasome signaling pathway[ 4 ]. The current study demonstrate that the CTI is associated with the incidence of major adverse cardiovascular outcomes in heart failure undergoing PCI treatment, implying the potential use of CTI in evaluating IR-related cardiovascular prognosis in that population. Besides, this study also provides clinical evidence for the role of inflammation and IR in the progression of cardiovascular diseases. The conclusion of this study that the comprehensive indicator of inflammation and IR determines the prognosis also provide clinical proof for selected anti-inflammatory therapies in heart failure patients undergoing PCI treatment. Recently, burgeoning evidence from large clinical trials of therapies targeting inflammation in atherosclerotic cardiovascular disease individuals is now emerging. It was not until 2017 with the publication of the CANTOS trial that proof of inflammation targets in atherosclerosis was provided. In the CANTOS trial, Canakinumab lowered cardiovascular event rates by 15–17% compared with placebo without affecting LDL-C and apolipoprotein B levels, demonstrating that inhibition of the NLRP3 to interleukin-1 to interleukin-6 pathway was a crucial treatment target for atherosclerosis[ 7 ]. In contrast, the Cardiovascular Inflammation Reduction Trial (CIRT) of 4786 stable atherosclerosis patients with diabetes or metabolic syndrome reported that low-dose methotrexate did not reduce major adverse cardiovascular events[ 32 ]. In addition, low-dose methotrexate also failed to reduce interleukin-1β and interleukin-6 level. The conflicting results of CIRT and CANTOS trial support the concept that adequate inhibition of the NLRP3 to interleukin-6 pathway of innate immunity is necessary to secure long-term cardiovascular benefits. Increased NLRP3 inflammasome expression can be observed under the condition of IR, which lead to chronic low-grade inflammation[ 33 ]. Hence, therapeutic interventions targeting at IR could be a promising strategy for patients with IR and high inflammatory burden to further reduce the incidence of cardiovascular events. Multiple randomized trials now demonstrate cardiovascular risk reduction among those with diabetes or not using novel glucose-lowering agents, in particular for glucagon-like peptide-1 receptor agonist (GLP-1RA) and for sodium-glucose cotransporter 2 (SGLT2) inhibitors, which both reduce cardiovascular events, improve IR status and alleviate inflammatory burden[ 8 – 10 , 34 , 35 ]. The potential anti-inflammatory effect of GLP-1RA could be that it could effectively lower the level of CD163, a biomarker for macrophage activation, and reversal changes in leukocyte recruitment, rolling, and adhesion/extravasation related to the inflammatory response were also observed in animal models after GLP-1RA treatment[ 36 ]. The current study demonstrated that CTI could be a favorable inflammatory and IR indicator for predicting cardiovascular prognosis, which can be viewed as a promising therapeutic target to for intervening cardiovascular disease patients with IR and high inflammatory burden and initiating novel glucose-lowering therapy in that population. Subgroup analysis indicated that there existed an interaction between the presence of hypertension and CTI-related cardiovascular outcomes and suggested a possible role of IR for mediation through hypertension. Recent study has demonstrated a mediating role of controlled hypertension on the effect of IR on cardiovascular outcomes in patients with CAD and hypertension[ 13 ]. These might indicate an intimate association of both IR and hypertension with adverse prognosis of cardiovascular disease patients. Previous study has discussed the deteriorative role of IR in hypertension via upregulating inflammatory cytokine expression and diminished NO production, leading to endothelial dysfunction[ 4 ]. While the exact underlying mechanism remain elusive, further investigations are needed to clarify the association. To the best of our knowledge, this is the first study to evaluate the potential use of CTI in heart failure patients undergoing PCI treatment. However, several limitations should be acknowledged. First, this was a single-center observational retrospective cohort study, so potential selection bias invariably existed. Second, the enrolled population in this study underwent only one-year of follow-up with a relatively low MACCE rate, which may limit the statistical analysis and make it difficult to find a connection between the CTI value with a single component of MACCEs. Third, the current study lacks serial measurements of CTI, so the effect of dynamic change of CTI after discharge on cardiovascular outcomes need to be further studied. Lastly, this study only selects TyG index and CRP as a simple surrogate marker for IR and inflammation, while the predictive value of many other IR and inflammatory indicators for MACCEs remained elusive. Conclusion The increased level of the CTI value is significantly associated with the prognosis of heart failure patients undergoing PCI treatment. As a surrogate marker for IR and inflammation, the CTI enhances the discriminative value for predicting the adverse cardiovascular events on the basis of traditional cardiovascular risk factors. Further studies are needed to investigate the potential use of CTI as a therapeutic target in randomized studies targeting at IR and inflammation. Abbreviations CTI, C-reactive protein-triglyceride glucose index; PCI, percutaneous coronary intervention; CMS, cardiometabolic syndrome; IR, insulin resistance; ASCVD, atherosclerotic cardiovascular disease; NO, nitric oxide; ROS, reactive oxygen species; RCT, randomized clinical trial; TyG index, triglyceride glucose index; CRP, C-reactive protein; LDL, low density lipoprotein; NT-proBNP, N-terminal Pro-B-type Natriuretic Peptide; BMI, body mass index; AMI, acute myocardial infarction; CTO, chronic total occlusion; MACCE, major adverse cerebrovascular and cardiovascular events; HR hazard ratio; CI confidence interval; ROC, receiver operating characteristic; DCA, decision curve analysis; NRI, net reclassification improvement; IDI integrated discrimination improvement; LVEF, left ventricular ejection fraction; GLP-1RA, glucagon-like peptide-1 receptor agonist; SGLT2, sodium-glucose cotransporter 2; Declarations Acknowledgements Not applicable Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets and materials mentioned above are available from the authors on reasonable requests Ethics and consent to participate This study has been approved by the ethics committee of Fuwai Hospital (No. 2021-1063). The informed consent from participants was waived by the Ethics Committee. Competing interests The authors declare that they have no competing interests Consent for publications Not applicable Author Contribution Ang Gao, Bo Peng, Tingting Guo and Hong Qiu contributed to conception and design of the study. Hong Qiu organized the database. Ang Gao and Bo Peng performed the statistical analysis. Ang Gao wrote the first draft of the manuscript. Zhiqiang Yang, Zhifan Li, and Tingting Guo wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version References Report on Cardiovascular Health and Diseases in China. 2021: An Updated Summary. 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A report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Subcommittee on Percutaneous Transluminal Coronary Angioplasty). Circulation 1988, 78(2):486–502. Burger PM, Koudstaal S, Mosterd A, Fiolet ATL, Teraa M, van der Meer MG, Cramer MJ, Visseren FLJ, Ridker PM, Dorresteijn JAN. C-Reactive Protein and Risk of Incident Heart Failure in Patients With Cardiovascular Disease. J Am Coll Cardiol. 2023;82(5):414–26. Matter MA, Paneni F, Libby P, Frantz S, Stähli BE, Templin C, Mengozzi A, Wang YJ, Kündig TM, Räber L, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89–103. Abel ED. Insulin signaling in the heart. Am J Physiol Endocrinol metabolism. 2021;321(1):E130–45. Xia JG, Li B, Zhang H, Li QX, Lam SM, Yin CL, Tian H, Shui G. Precise Metabolomics Defines Systemic Metabolic Dysregulation Distinct to Acute Myocardial Infarction Associated With Diabetes. Arterioscler Thromb Vasc Biol. 2023;43(4):581–96. Zhao Q, Zhang TY, Cheng YJ, Ma Y, Xu YK, Yang JQ, Zhou YJ. Triglyceride-Glucose Index as a Surrogate Marker of Insulin Resistance for Predicting Cardiovascular Outcomes in Nondiabetic Patients with Non-ST-Segment Elevation Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention. J Atheroscler Thromb. 2021;28(11):1175–94. Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007–2010 Korean National Health and Nutrition Examination Survey. PLoS ONE. 2019;14(3):e0212963. Yang J, Tang YD, Zheng Y, Li C, Zhou Q, Gao J, Meng X, Zhang K, Wang W, Shao C. The Impact of the Triglyceride-Glucose Index on Poor Prognosis in NonDiabetic Patients Undergoing Percutaneous Coronary Intervention. Front Endocrinol. 2021;12:710240. Frati G, Schirone L, Chimenti I, Yee D, Biondi-Zoccai G, Volpe M, Sciarretta S. An overview of the inflammatory signalling mechanisms in the myocardium underlying the development of diabetic cardiomyopathy. Cardiovascular Res. 2017;113(4):378–88. Ridker PM, Everett BM, Pradhan A, MacFadyen JG, Solomon DH, Zaharris E, Mam V, Hasan A, Rosenberg Y, Iturriaga E, et al. Low-Dose Methotrexate for the Prevention of Atherosclerotic Events. N Engl J Med. 2019;380(8):752–62. Rheinheimer J, de Souza BM, Cardoso NS, Bauer AC, Crispim D. Current role of the NLRP3 inflammasome on obesity and insulin resistance: A systematic review. Metab Clin Exp. 2017;74:1–9. Verma S, Bhatta M, Davies M, Deanfield JE, Garvey WT, Jensen C, Kandler K, Kushner RF, Rubino DM, Kosiborod MN. Effects of once-weekly semaglutide 2.4 mg on C-reactive protein in adults with overweight or obesity (STEP 1, 2, and 3): Exploratory analyses of three randomised, double-blind, placebo-controlled, phase 3 trials. EClinicalMedicine. 2023;55:101737. Reppo I, Jakobson M, Volke V. Effects of Semaglutide and Empagliflozin on Inflammatory Markers in Patients with Type 2 Diabetes. Int J Mol Sci 2023, 24(6). Rakipovski G, Rolin B, Nøhr J, Klewe I, Frederiksen KS, Augustin R, Hecksher-Sørensen J, Ingvorsen C, Polex-Wolf J, Knudsen LB. The GLP-1 Analogs Liraglutide and Semaglutide Reduce Atherosclerosis in ApoE(-/-) and LDLr(-/-) Mice by a Mechanism That Includes Inflammatory Pathways. JACC Basic translational Sci. 2018;3(6):844–57. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4277196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292344318,"identity":"9c5dbb6d-0ae0-4d93-9320-2e074321b4ec","order_by":0,"name":"Ang Gao","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ang","middleName":"","lastName":"Gao","suffix":""},{"id":292344319,"identity":"996f40a6-d46d-4a0e-bd00-7a925c6701be","order_by":1,"name":"Bo Peng","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Peng","suffix":""},{"id":292344320,"identity":"2acb55fe-3dae-4034-943f-983ca74ea7e5","order_by":2,"name":"Yanan Gao","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Gao","suffix":""},{"id":292344321,"identity":"48003c92-cfd2-4285-ba52-45bef4291810","order_by":3,"name":"Zhiqiang Yang","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Yang","suffix":""},{"id":292344322,"identity":"d8269766-4311-4d36-88b6-2c1b282dea5e","order_by":4,"name":"Zhifan Li","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhifan","middleName":"","lastName":"Li","suffix":""},{"id":292344323,"identity":"7ad5a154-1888-40aa-ac56-7376c567de4c","order_by":5,"name":"Tingting Guo","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Guo","suffix":""},{"id":292344324,"identity":"b6d41f21-717f-45fd-942d-f0705ca63b9d","order_by":6,"name":"Hong Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACZgY2ECVnf7yx8QGDAQlajBnOHD5sQJwWBoiWRIYbaWkSRKnnO85j9uDjjtoExp4zZpU/Cu7IM7AfProBnxbJwzzmhjPPHM9jZu8xu81j8MywgSct7QY+LQaHecykeduOFbPxnDG7DeQyNkjwmBGlJbFHIses8IfBYXtitdQkzpBIS2PgMTicSFCL5GG2MsmZbQeMDXgOH5YGakluI+QXvvOHt0l8bKuTM2BvbPz4489h2372w8fwamE4ACYPIwTY8CpHaKkjqG4UjIJRMApGMAAA3CBMfd7eDJYAAAAASUVORK5CYII=","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Qiu","suffix":""},{"id":292344325,"identity":"410c35fd-c319-407d-8bbd-536e8e1f28af","order_by":7,"name":"Runlin Gao","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Runlin","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-04-16 15:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4277196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4277196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55329408,"identity":"ddd785d7-8ad8-4af7-a4dd-5eba9d505d0b","added_by":"auto","created_at":"2024-04-25 18:59:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68821,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of CTI and TyG index values and RCS curves for the association between TyG index and CTI and MACCEs among heart failure patients undergoing PCI. Distribution and RCS curve for the CTI (1A) and TyG index (1B). MACCEs was defined as a composite of all-cause death, non-fatal acute myocardial infarction and stroke. CTI, CRP-TyG index; CRP, C reactive protein; TyG index, triglyceride glucose index; RCS, restrict cubic spine; MACCE, major adverse cerebrovascular and cardiovascular event; PCI, percutaneous coronary intervention;\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4277196/v1/c22a7e83828e3bc132814427.png"},{"id":55329409,"identity":"040eb933-9e1d-4a82-9211-fb8f4fab0411","added_by":"auto","created_at":"2024-04-25 18:59:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":376915,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves of the CTI and TyG index for the predictive value of adverse events among heart failure patients undergoing PCI. Kaplan-Meier curve of CTI for the risk of MACCEs (2A), non-fatal AMI (2B), stroke (2C) and all-cause death (2D). Kaplan-Meier curve of TyG index for the risk of MACCEs (2E), non-fatal AMI (2F), stroke (2G) and all-cause death (2H). MACCEs was defined as a composite of all-cause death, non-fatal acute myocardial infarction and stroke. CTI, CRP-TyG index; CRP, C reactive protein; TyG index, triglyceride glucose index; HR, hazard ratio; CI, confidence interval;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4277196/v1/de89b91ab6d0987f851436d8.png"},{"id":55329410,"identity":"486c16a8-5c81-41e8-936f-3243e132d1a5","added_by":"auto","created_at":"2024-04-25 18:59:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":332567,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation of the CTI, TyG index and CRP for predicting MACCEs among heart failure patients undergoing PCI. The predictive ability of various indices was performed using the ROC analysis (3A) and the DCA analysis (3B). MACCEs was defined as a composite of all-cause death, non-fatal acute myocardial infarction and stroke. Baseline model was consisted of the heart rate, diabetes mellitus, previous PCI, atrial fibrillation, heart function classification, high-density lipoprotein cholesterol, left atrial diameter, the use of statin, β-blockers and angiotensin blockade and multivessel/LMCA diseases. CTI, CRP-TyG index; CRP, C reactive protein; TyG index, triglyceride glucose index; MACCE, major adverse cerebrovascular and cardiovascular event; PCI, percutaneous coronary intervention; ROC, receiver operating characteristic; DCA decision curve analysis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4277196/v1/9efca33dbe711642d01aa696.png"},{"id":55329411,"identity":"8f039211-7c85-4542-8257-2a411c60d5da","added_by":"auto","created_at":"2024-04-25 18:59:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":300598,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the predictive value of CTI for the risks of MACCEs (4A), non-fatal MI (4B), stroke (4C) and all-cause death (4D). MACCEs was defined as a composite of all-cause death, non-fatal acute myocardial infarction and stroke. CTI, CRP-TyG index; CRP, C reactive protein; TyG index, triglyceride glucose index; MACCE, major adverse cerebrovascular and cardiovascular event; PCI, percutaneous coronary intervention; HR, hazard ratio; CI, confidence interval;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4277196/v1/ec510648d72fd8bcd1d981e7.png"},{"id":58849113,"identity":"0261ada1-f127-4fdc-8de6-93d6df7118d9","added_by":"auto","created_at":"2024-06-22 05:30:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4277196/v1/777fd8f9-14a6-4830-ad12-6cca37dde25c.pdf"},{"id":55329407,"identity":"0ccfe435-a58c-4eaa-8608-259dc0cb2e64","added_by":"auto","created_at":"2024-04-25 18:59:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51819,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4277196/v1/77e9627f78ca627edb3c8ecb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inflammation and Insulin Resistance-Derived Indicator Predicts Adverse Cardiovascular Outcomes in Heart Failure Patients Undergoing Percutaneous Coronary Intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOwing to the aging population and the increasing prevalence of cardiometabolic risk factors, cardiovascular deaths have become the leading cause of mortality, accounting for more than 40% of death in China[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the intensified pharmacological strategies, timely coronary revascularization and technical innovation in percutaneous coronary intervention (PCI) treatment, relatively high rates of adverse cardiovascular outcomes can still be foreseen in many PCI-treated patients, especially for those with multiple cardiometabolic complications, an issue commonly ascribed to the residual risk of cardiometabolic syndrome (CMS). As global rate of CMS increases there is an acceleration of the incidence and prevalence of its associated cardiovascular diseases, which presents enormous challenges in cardiovascular prevention and controlling cardiovascular mortality[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Insulin resistance (IR), as the central part of CMS, defined as the decrease in sensitivity and responsiveness to the metabolic action of insulin, plays detrimental role in the development of atherosclerotic cardiovascular disease (ASCVD) through impairing endothelial cell function[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The underlying mechanism could be that exposure of endothelial cells to the IR status promotes upregulation of proinflammatory cytokines expression via the activation of inflammatory signaling pathways, leading to diminished nitric oxide (NO) production and reactive oxygen species (ROS) formation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous studies have substantiated that there exists a causal link between IR, inflammation and the initiation and progression of cardiovascular diseases. Recent randomized clinical trials (RCTs) targeting at IR and inflammation among individuals with cardiovascular diseases or high cardiovascular risks have delivered positive results[\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, identifying individuals with IR and high inflammatory status is crucial to the initiation and management of targeted medication thus improving prognosis, especially for those with advanced atherosclerotic cardiovascular diseases. Triglyceride glucose (TyG) index, calculated as ln[triglyceride(mg/dl) \u0026times; fasting blood glucose(mg/dl)/2], has emerged as a practical tool in clinical practice used to evaluate IR for its easy availability and convenience to use. Previous studies have fully substantiated its prognostic value of adverse outcomes in patients with various cardiovascular diseases phenotypes[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, these studies only emphasize the deteriorative role of IR in the progression of the cardiovascular diseases while neglecting inflammation may contribute with similar magnitude to this process. Inflammation initiates the process of atherosclerosis through the dysregulation of endothelial dysfunction, from promoting cholesterol uptake, oxidation and accumulation to the erosion and rupture of the plaque, with the consequence of acute ischemic syndrome[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In current clinical practice, the best studied and most easily applied biomarker for evaluating inflammatory status in patients with cardiovascular diseases is high-sensitive C-reactive protein (CRP). Large-scale cohort studies have indicated that high-sensitive CRP could independently predict risks of incident of heart failure, myocardial infarction, stroke and sudden cardiac death even when low density lipoprotein (LDL) is low[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recently, a synergetic effect was found between TyG index and high-sensitive CRP for predicting adverse outcomes in diabetic chronic coronary syndrome patients in a cohort study, implying the potential value of curbing inflammation in patients with IR[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, this study fails to produce a comprehensive tool for evaluating inflammation and IR simultaneously and test its prognostic value for adverse cardiovascular outcomes. Besides, the result was only applied to diabetic patients. Recently, inflammation and IR related indicator CTI has been validated as a useful tool for predicting prognosis in patients with cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], while its potential application in patients with cardiovascular diseases remains elusive. Hence, the aim of this study is to assess the potential application of a new comprehensive indicator CTI in predicting the prognosis of heart failure patients undergoing PCI treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eA total of 2797 consecutive PCI-treated patients with heart failure at Beijing Fuwai Hospital between 1st January 2016 and 31st December 2018 were recruited into this single-center, retrospective observational study. This study was in accordance with the principles of the Declaration of Helsinki, and all patients signed informed consent before discharge. The exclusion criteria were as followed: 1) lack of core laboratory data, like the value of triglyceride, CRP or fasting glucose, or lost follow-up; 2) End-stage renal dysfunction or treated with renal replacement therapy, or serious liver dysfunction; 3) acute or chronic infectious diseases, malignant tumors, immune system disorders or treated with anti-inflammatory medication; 3) suspected familial hypertriglyceridemia (triglyceride\u0026thinsp;\u0026ge;\u0026thinsp;5.65 mmol/L); 4) diagnosed as cardiogenic shock at discharge; Detailed flow chart of enrolled participants was shown in Additional file S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Laboratory Data collection\u003c/h2\u003e \u003cp\u003eDemographic data like age, sex, height, weight, previous medical history and medication at discharge were obtained through a review of medical records, which was approved by Beijing Fuwai Hospital. Body mass index (BMI) was calculated as weight (Kg) / [Height (m)]\u003csup\u003e2\u003c/sup\u003e. On-admission biochemical assessment was performed in whole blood samples drawn after arrival to the emergency or general ward. Blood samples were taken after overnight fasting if participants were not indicated for emergent coronary revascularization. Laboratory indicators, including triglyceride and glucose and CRP, were all measured by standard techniques. The TyG index is quantified by the established formula: ln [triglyceride(mg/dL) \u0026times; fasting glucose (mg/dL)/2]. The value of CTI is defined as TyG index\u0026thinsp;+\u0026thinsp;0.412 \u0026times; ln [CRP (mg/L)]. The diagnosis of diabetes mellitus is based on the previous diagnosis and treatment with glucose-lowering medication or recommendations from the American Diabetes Association[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Hypertension is defined by the recommendations from the European Society of Hypertension, an office systolic blood pressure value\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or a diastolic blood pressure value\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg or the use of antihypertensive drugs in the past 2 weeks[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Acute myocardial infarction (AMI) is defined as increased cardiac troponin values with ischemic symptoms or ischemic changes on electrocardiogram or imaging evidence of recent loss of viable myocardium or new regional wall motion abnormalities that were not related to the procedure[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The definition of heart failure is clinically diagnosed on the basis of clinical symptoms, echocardiography and the value of N-terminal Pro-B-type Natriuretic Peptide (NT-proBNP) and the classification of heart function was based on the recommendations from New York Heart Association for patients with angina pectoris and Killip classification for patients with acute myocardial infarction[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCoronary Procedure\u003c/h2\u003e \u003cp\u003eCoronary angiography and PCI procedure was performed by at least two experienced cardiologists whose expertise are interventional cardiology. Coronary procedure was processed according to current practice guidelines. The characteristics of coronary artery lesions are defined based on the ACC/AHA guidelines for coronary lesion classification[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Multivessel disease is defined as a diameter stenosis of \u0026ge;\u0026thinsp;50% occurring in 2 or more vessels. Coronary chronic total occlusion (CTO) lesions are defined on coronary angiography as coronary arteries with either absent or minimal anterograde blood flow for 12 weeks duration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up and Endpoint Events\u003c/h2\u003e \u003cp\u003e All participants were accepting guideline recommended medical therapy for heart failure and coronary artery disease at discharge, unless some contradiction or unable to endure its side effect. All enrolled participants were followed up for 12 months or until the time of a major adverse clinical event. Follow-up was performed by telephone interviewers using standardized questionnaires at 6 and 12 months after the PCI treatment. Primary endpoint of this study was the occurrence of major adverse cerebrovascular and cardiovascular events (MACCEs) after PCI during 12-month follow-up, defined as the composite of all-cause death, non-fatal acute myocardial infarction, and ischemic stroke. Ischemic stroke was defined as cerebral infarction with symptoms of neurological impairment which can be explained by imaging examinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo better understand the characteristics of enrolled participants, patients were categorized into two groups according to the CTI value. Descriptive variables are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median with interquartile range for normally or nonnormally distributed continuous variables. Categorical variables are presented as frequencies and percentages. Differences between the two groups were compared by Student\u0026rsquo;s t test or the Mann-Whiteney U test for normally or nonnormally distributed continuous variates. Restrict cubic spline analysis was applied to determine the cut-off value of CTI and examine the dose-response relationship between the CTI and the primary endpoint. Cumulative event rates were compared using the log-rank test, and the Kaplan-Meier method was used to depict the time-to-event curves of TyG index and CTI group. Univariable Cox proportional hazard analysis was performed to preliminarily explore the risk factors associated with MACCEs. Multivariate Cox proportional hazard models were conducted to evaluate the prognostic value of CRP, TyG index and CTI for MACCEs and the results were expressed as hazard ratio (HR) and 95% confidence interval (CI). Confounding factors that were statistically significant in the univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were incorporated into the baseline model used to predict the occurrence of MACCEs. The receiver operating characteristic (ROC) analysis was plotted to investigate the incremental value of CTI on the discriminative performance beyond the baseline model, and decision curve analysis (DCA) was conducted to compare the predictive value of the CRP, TyG index and CTI for the endpoints. The incremental predictive value for CTI was comprehensively examined by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) when adding the CTI into the baseline model. Furthermore, subgroup and interaction analysis were performed based on the index presentation (AMI or angina pectoris), diabetes (yes or no), hypertension (yes or no), inflammatory status (CRP\u0026thinsp;\u0026ge;\u0026thinsp;3mg/L or not), cholesterol burden (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;1.8mmol/L or not) and heart function classification (I or II-IV) to identify whether the relationships between CTI and MACCEs varied based on the status of the potential covariates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eOverall, a total of 2797 heart failure participants undergoing PCI treatment were finally enrolled into this retrospective cohort study. Of these participants, 131 experienced MACCEs during the 12-month follow-up. The baseline characteristics of the study population stratified by CTI value are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of enrolled participants divided by CTI value.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh CTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow CTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2797)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1374)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1423)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2191 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1028 (74.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1163 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate, bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (Kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1857 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e925 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1107 (39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e709 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smokers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1704 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e840 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e864 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious PCI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e908 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e491 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious CABG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious MI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1207 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e672 (47.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious Stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e339 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart class classification (NYHA or Killip)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e668 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2129 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e990 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1139 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory measurements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, 10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.3\u0026thinsp;\u0026plusmn;\u0026thinsp;68.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237.9\u0026thinsp;\u0026plusmn;\u0026thinsp;70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219.0\u0026thinsp;\u0026plusmn;\u0026thinsp;64.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.1\u0026thinsp;\u0026plusmn;\u0026thinsp;21.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9 (5.1\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1 (5.7\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4 (4.9\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 (2.0-6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4 (2.9\u0026ndash;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 (1.6\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride glucose index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchocardiography parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular ejection fraction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular end diastolic diameter, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft atrial diameter, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication at discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntithrombotic therapy\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u0026thinsp;+\u0026thinsp;Clopidogrel, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2068 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1095 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u0026thinsp;+\u0026thinsp;Ticagrelor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e616 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2704 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1319 (96.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1385 (97.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blockers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2477 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1240 (90.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1237 (86.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiotensin blockade, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1808 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e877 (63.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e931 (65.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpironolactone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e971 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519 (37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e452 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e784 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e346 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedural characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHA/ACC lesion: type B2/C, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2066 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e985 (71.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1081 (76.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivessel or LMCA disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2324 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1157 (84.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1167 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTO lesion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e920 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget vessel length, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCEs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll cause death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median with interquartile range or n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCTI, CRP-TyG index; CRP, C reactive protein; TyG index, Triglyceride glucose index; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; MI, myocardial infarction; LMCA, left main coronary artery; CTO, chronic total lesion;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMACCEs, major adverse cardiac and cerebrovascular events;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe average age of the participants was 62 years, and 78.3% of this cohort were male. Compared with patients with low CTI value (CTI\u0026thinsp;\u0026lt;\u0026thinsp;9.47), those with high CTI value have a greater rate of MACCEs (4.1% \u003cem\u003evs.\u003c/em\u003e 6.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and cardiometabolic risk factors, like diabetes mellitus (28.0% \u003cem\u003evs.\u003c/em\u003e 51.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with high CTI value also had higher heart rate, white cell count, platelet count, fasting glucose, triglyceride, total cholesterol, HDL-C, LDL-C level (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and tended to have higher creatinine level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.069). Besides, patients with higher CTI had lower left ventricular ejection fraction (LVEF) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but they had a better heart function classification (28.0% \u003cem\u003evs.\u003c/em\u003e 51.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower rates of previous history of myocardial infarction (28.0% \u003cem\u003evs.\u003c/em\u003e 51.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PCI treatment (28.0% \u003cem\u003evs.\u003c/em\u003e 51.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As with anatomical characteristics, the proportions of individuals with CTO lesions were higher in high CTI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), while the rates of type B2/C lesion are higher in low CTI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). Additionally, patients with high CTI value were more likely to use diuretics, spironolactone, β-blockers and more potent dual antiplatelet therapy (Aspirin\u0026thinsp;+\u0026thinsp;Ticagrelor) at discharge (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but a tendency of lower rate of statin use was also observed in this population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between CTI and endpoints\u003c/h2\u003e \u003cp\u003eAs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, univariate RCS analysis was used to explore whether there existed an association between the TyG index or CTI and the MACCEs in heart failure patients undergoing PCI. Nonlinearity relationship between the TyG index and CTI and the probability of the risk of MACCEs can be observed (P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a best cut-off value of 8.74 and 9.47 respectively. Participants were further divided into two groups based on the CTI or the TyG index cut-off value. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the cumulative incidence of MACCEs and all-cause death was significantly higher in patients with high CTI (all Log rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant difference of cumulative incidence of endpoints between patients with high or low TyG index. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e delineates the stratified analysis result using univariate and multivariate Cox proportional hazard models to show the association between CRP, TyG index and CTI and an elevated risk of clinical endpoints. The unadjusted model indicated that continuous or categorical CTI value were all statistically significantly associated with the incidence of MACCEs [categorical variable: HR (95%CI) 1.55(1.11\u0026ndash;2.16), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; continuous variable: HR (95%CI) 1.37(1.11\u0026ndash;1.70), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. After adjusting for heart rate, diabetes mellitus, previous PCI, atrial fibrillation and heart function classification in model 1, the CTI was independent predictor for the incidence of MACCEs no matter as a continuous [HR (95%CI) 1.35(1.07\u0026ndash;1.71), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05] or categorical variable [HR (95%CI) 1.48 (1.04\u0026ndash;2.11), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05]. After adjusting for model 1, HDL-C and left atrial diameter in model 2, the CTI was remained independently associated with the occurrence of MACCEs no matter as a continuous [HR (95%CI) 1.47 (1.16\u0026ndash;1.87), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01] or categorical variable [HR (95%CI) 1.63 (1.14\u0026ndash;2.35), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Finally, the CTI was still remained independently associated with the occurrence of MACCEs continuously [HR (95%CI) 1.41 (1.13\u0026ndash;1.77), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01] or categorically [HR (95%CI) 1.67 (1.15\u0026ndash;2.36), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05] after adjusting for model 2, multivessel/LMCA diseases, the use of statin, β-blockers and angiotensin blockade in model 3. Furthermore, multivariate Cox proportional hazard models failed to show an association between the categorical or continuous TyG index and the incidence of MACCEs statistically (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Besides, continuous CRP, but not categorical values was found to be significantly associated with the risk of MACCEs (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe predictive value of CTI, TyG index and CRP for the risks of MACCEs among heart failure patients undergoing PCI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37 (1.11\u0026ndash;1.70) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.07\u0026ndash;1.71) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.16\u0026ndash;1.87) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.41 (1.13\u0026ndash;1.77) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI (categorical variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.11\u0026ndash;2.16) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.04\u0026ndash;2.11) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.63 (1.14\u0026ndash;2.35) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67 (1.15\u0026ndash;2.36) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.99\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.88\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32 (0.97\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.35 (1.00-1.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index (categorical variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21 (0.87\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.77\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18 (0.82\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.84\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.03) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.04) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.04) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.04) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (categorical variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (0.92\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37 (0.97\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43 (1.01\u0026ndash;2.04) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36 (0.96\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* represented \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** represented \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** represented \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eStratified analysis was performed using the Log-rank test and backward stepwise selection methods in a Cox proportional hazards regression model; Model 1: adjusted for heart rate, diabetes mellitus, previous PCI atrial fibrillation and heart function classification; Model 2: adjusted for Model 1\u0026thinsp;+\u0026thinsp;high-density lipoprotein cholesterol, left atrial diameter; Model 3: adjusted for Model 2\u0026thinsp;+\u0026thinsp;multivessel/LMCA diseases, the use of statin, β-blockers and angiotensin blockade; TyG index, triglyceride glucose index; CRP C-reactive protein; CTI, CRP-TyG index; MACCE, major adverse cerebrovascular and cardiovascular event; HR, hazard ratio; CI, confidence interval; PCI, percutaneous coronary intervention; LMCA, left main coronary artery;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictive ability test\u003c/h2\u003e \u003cp\u003eThe ROC curve and DCA were performed to comprehensively identify the predictive value of CTI or TyG index for MACCEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). As is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the improvement of the area under the curve (AUC) for predicting the occurrence of MACCEs was most significant when adding the CTI into the baseline model (increase in C-statistic value from 0.685 to 0.694). In addition, DCA was conducted to assess the clinical utility of CTI, suggesting CTI has better overall net benefit and clinical impact when compared with the CRP and TyG index. Furthermore, the most significant enhancement in risk reclassification and discrimination was found after inclusion of the CTI into baseline model, with a NRI of 0.217 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), and an IDI of 0.004 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe discriminative value of TyG index and CTI for the risk of MACCEs in heart failure patients undergoing PCI treatment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eROC analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eNRI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEstimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.640\u0026ndash;0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+TyG index (categorical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.641\u0026ndash;0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.074-0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+ TyG index (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.644\u0026ndash;0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u0026ndash;0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+ CTI (categorical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.646\u0026ndash;0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u0026ndash;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.050\u0026ndash;0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+ CTI (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.648\u0026ndash;0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u0026ndash;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.019\u0026ndash;0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eBaseline model referred to heart rate, diabetes mellitus, previous PCI, atrial fibrillation, heart function classification, high-density lipoprotein cholesterol, left atrial diameter, the use of statin, β-blockers and angiotensin blockade and multivessel/LMCA diseases. TyG index, triglyceride glucose index; CRP, C reactive protein; CTI, CRP-TyG index; MACCE, major adverse cerebrovascular and cardiovascular event; PCI, percutaneous coronary intervention; ROC, receiver operating characteristics; AUC, are under the curve; IDI, integrated discrimination improvement; NRI, net reclassification index; CI, confidence interval;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eSubgroup analysis was performed to confirm the association between the CTI and the risk of adverse outcomes when grouped by index presentation, diabetes, hypertension, heart function classification, CRP and LDL-C levels. An interaction was found between hypertension and CTI for the prediction of MACCEs and stroke (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of this study are as follow: 1) The value of CTI was significantly higher in participants suffered from MACCEs than those free of MACCEs. 2) The CTI was associated with the incidence of MACCEs within one year in heart failure patients undergoing PCI treatment and this association remained significant after adjusting for various confounding factors. Further, this study also found that compared with those with low CTI, high CTI indicated a higher risk of MACCEs within a certain range (CTI\u0026thinsp;\u0026ge;\u0026thinsp;9.47), and enhanced the cardiovascular outcome risk by 67% over the one-year follow-up in that population. 3) the predictive value of CTI for risks of MACCEs within one year is better than single component of CTI (inflammatory biomarker: CRP, IR biomarker: TyG index). Adding the CTI into the baseline model shows the most significant incremental effect on risk discrimination for predicting the risk of MACCEs within one year. To the best of our knowledge, this is the first study focusing on the evaluating the prognostic value of CTI for the risk of major adverse cardiovascular outcomes in patients with cardiovascular diseases. The predictive ability of CTI provides an effective and convenient tool used for assessing prognosis and also indicates a detrimental role of inflammation and IR in the progression of cardiovascular diseases.\u003c/p\u003e \u003cp\u003eCTI is mainly determined by two critical components, the one is inflammation represented by the CRP, the other one is IR represented by the TyG index. Inflammation and IR are both important components of residual cardiovascular risk factors and confer an enhanced risk of AMI and heart failure[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The prevalence of diabetes has increased by more than tenfold since the 1980s in China owing to the ageing populations and improving socioeconomic status[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Diabetic patients have twofold to eightfold higher cardiovascular event rates as compared with age-matched nondiabetic individuals and 75% of death in diabetic patients can be attributed to various cardiovascular diseases. Although hyperglycemia correlates with macro- and microvascular diseases, IR itself promotes atherosclerosis even before the onset of clinical diabetes, and available data corroborate the role of IR as independent risk factor for atherothrombosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding has promoted attention for increased surveillance for the status of IR in the management of cardiovascular disease patients. However, the homeostasis model assessment- IR is not very applicable for clinical practice because the serum insulin level is not a common laboratory measurement for cardiovascular disease patients, especially for those nondiabetic patients. The TyG index as a surrogate marker for IR accompanied by hypertriglyceridemia and hyperglycemia has been proven to be strongly associated with the gold standard for evaluating IR[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Recent studies concentrating on evaluating the effect of the TyG index on the cardiovascular events have substantiated the prognostic value of TyG index for predicting heart failure, AMI and cardiovascular death in cardiovascular patients[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. While conflicting result that the TyG index is not an effective predictor for cardiovascular events in nondiabetic patients undergoing PCI treatment has also been reported[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which is in accordance with the conclusion in current study. One plausible explanation could be that the prognostic role of IR for major adverse cardiovascular outcomes could be partially mediated by the inflammatory status. Recent study conducted by Li et al. demonstrated that a significant partial mediating effect of systemic inflammation on the association of IR with adverse cardiovascular events in chronic coronary syndrome diabetic patients, implying the mediated effect of inflammation on IR-related cardiovascular outcomes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The underlying mechanism could be that IR could activate different inflammatory signaling cascades in cardiomyocytes which contribute to cell hypertrophy, apoptosis and dysfunction[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, exposure of endothelial cell to IR exerts a detrimental effect on endothelial function linked to ROS formation and upregulation of inflammatory cytokine expression via activating IκB kinase, NF-κB and nucleotide-binding domain, leucine-risk-containing family, pyrin domain 3 (NLRP3) inflammasome signaling pathway[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The current study demonstrate that the CTI is associated with the incidence of major adverse cardiovascular outcomes in heart failure undergoing PCI treatment, implying the potential use of CTI in evaluating IR-related cardiovascular prognosis in that population. Besides, this study also provides clinical evidence for the role of inflammation and IR in the progression of cardiovascular diseases.\u003c/p\u003e \u003cp\u003eThe conclusion of this study that the comprehensive indicator of inflammation and IR determines the prognosis also provide clinical proof for selected anti-inflammatory therapies in heart failure patients undergoing PCI treatment. Recently, burgeoning evidence from large clinical trials of therapies targeting inflammation in atherosclerotic cardiovascular disease individuals is now emerging. It was not until 2017 with the publication of the CANTOS trial that proof of inflammation targets in atherosclerosis was provided. In the CANTOS trial, Canakinumab lowered cardiovascular event rates by 15\u0026ndash;17% compared with placebo without affecting LDL-C and apolipoprotein B levels, demonstrating that inhibition of the NLRP3 to interleukin-1 to interleukin-6 pathway was a crucial treatment target for atherosclerosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, the Cardiovascular Inflammation Reduction Trial (CIRT) of 4786 stable atherosclerosis patients with diabetes or metabolic syndrome reported that low-dose methotrexate did not reduce major adverse cardiovascular events[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, low-dose methotrexate also failed to reduce interleukin-1β and interleukin-6 level. The conflicting results of CIRT and CANTOS trial support the concept that adequate inhibition of the NLRP3 to interleukin-6 pathway of innate immunity is necessary to secure long-term cardiovascular benefits. Increased NLRP3 inflammasome expression can be observed under the condition of IR, which lead to chronic low-grade inflammation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Hence, therapeutic interventions targeting at IR could be a promising strategy for patients with IR and high inflammatory burden to further reduce the incidence of cardiovascular events. Multiple randomized trials now demonstrate cardiovascular risk reduction among those with diabetes or not using novel glucose-lowering agents, in particular for glucagon-like peptide-1 receptor agonist (GLP-1RA) and for sodium-glucose cotransporter 2 (SGLT2) inhibitors, which both reduce cardiovascular events, improve IR status and alleviate inflammatory burden[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The potential anti-inflammatory effect of GLP-1RA could be that it could effectively lower the level of CD163, a biomarker for macrophage activation, and reversal changes in leukocyte recruitment, rolling, and adhesion/extravasation related to the inflammatory response were also observed in animal models after GLP-1RA treatment[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The current study demonstrated that CTI could be a favorable inflammatory and IR indicator for predicting cardiovascular prognosis, which can be viewed as a promising therapeutic target to for intervening cardiovascular disease patients with IR and high inflammatory burden and initiating novel glucose-lowering therapy in that population.\u003c/p\u003e \u003cp\u003eSubgroup analysis indicated that there existed an interaction between the presence of hypertension and CTI-related cardiovascular outcomes and suggested a possible role of IR for mediation through hypertension. Recent study has demonstrated a mediating role of controlled hypertension on the effect of IR on cardiovascular outcomes in patients with CAD and hypertension[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These might indicate an intimate association of both IR and hypertension with adverse prognosis of cardiovascular disease patients. Previous study has discussed the deteriorative role of IR in hypertension via upregulating inflammatory cytokine expression and diminished NO production, leading to endothelial dysfunction[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While the exact underlying mechanism remain elusive, further investigations are needed to clarify the association.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to evaluate the potential use of CTI in heart failure patients undergoing PCI treatment. However, several limitations should be acknowledged. First, this was a single-center observational retrospective cohort study, so potential selection bias invariably existed. Second, the enrolled population in this study underwent only one-year of follow-up with a relatively low MACCE rate, which may limit the statistical analysis and make it difficult to find a connection between the CTI value with a single component of MACCEs. Third, the current study lacks serial measurements of CTI, so the effect of dynamic change of CTI after discharge on cardiovascular outcomes need to be further studied. Lastly, this study only selects TyG index and CRP as a simple surrogate marker for IR and inflammation, while the predictive value of many other IR and inflammatory indicators for MACCEs remained elusive.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe increased level of the CTI value is significantly associated with the prognosis of heart failure patients undergoing PCI treatment. As a surrogate marker for IR and inflammation, the CTI enhances the discriminative value for predicting the adverse cardiovascular events on the basis of traditional cardiovascular risk factors. Further studies are needed to investigate the potential use of CTI as a therapeutic target in randomized studies targeting at IR and inflammation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCTI, C-reactive protein-triglyceride glucose index;\u003c/p\u003e \u003cp\u003ePCI, percutaneous coronary intervention;\u003c/p\u003e \u003cp\u003eCMS, cardiometabolic syndrome;\u003c/p\u003e \u003cp\u003eIR, insulin resistance;\u003c/p\u003e \u003cp\u003eASCVD, atherosclerotic cardiovascular disease;\u003c/p\u003e \u003cp\u003eNO, nitric oxide;\u003c/p\u003e \u003cp\u003eROS, reactive oxygen species;\u003c/p\u003e \u003cp\u003eRCT, randomized clinical trial;\u003c/p\u003e \u003cp\u003eTyG index, triglyceride glucose index;\u003c/p\u003e \u003cp\u003eCRP, C-reactive protein;\u003c/p\u003e \u003cp\u003eLDL, low density lipoprotein;\u003c/p\u003e \u003cp\u003eNT-proBNP, N-terminal Pro-B-type Natriuretic Peptide;\u003c/p\u003e \u003cp\u003eBMI, body mass index;\u003c/p\u003e \u003cp\u003eAMI, acute myocardial infarction;\u003c/p\u003e \u003cp\u003eCTO, chronic total occlusion;\u003c/p\u003e \u003cp\u003eMACCE, major adverse cerebrovascular and cardiovascular events;\u003c/p\u003e \u003cp\u003eHR hazard ratio;\u003c/p\u003e \u003cp\u003eCI confidence interval;\u003c/p\u003e \u003cp\u003eROC, receiver operating characteristic;\u003c/p\u003e \u003cp\u003eDCA, decision curve analysis;\u003c/p\u003e \u003cp\u003eNRI, net reclassification improvement;\u003c/p\u003e \u003cp\u003eIDI integrated discrimination improvement;\u003c/p\u003e \u003cp\u003eLVEF, left ventricular ejection fraction;\u003c/p\u003e \u003cp\u003eGLP-1RA, glucagon-like peptide-1 receptor agonist;\u003c/p\u003e \u003cp\u003eSGLT2, sodium-glucose cotransporter 2;\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets and materials mentioned above are available from the authors on reasonable requests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the ethics committee of Fuwai Hospital (No. 2021-1063). The informed consent from participants was waived by the Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAng Gao, Bo Peng, Tingting Guo and Hong Qiu contributed to conception and design of the study. Hong Qiu organized the database. Ang Gao and Bo Peng performed the statistical analysis. Ang Gao wrote the first draft of the manuscript. Zhiqiang Yang, Zhifan Li, and Tingting Guo wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReport on Cardiovascular Health and Diseases in China. 2021: An Updated Summary. \u003cem\u003eBiomedical and environmental sciences: BES\u003c/em\u003e 2022, 35(7):573\u0026ndash;603.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JJ, Liu HH, Li S. Landscape of cardiometabolic risk factors in Chinese population: a narrative review. Cardiovasc Diabetol. 2022;21(1):113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, Sowers JR. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metab Clin Exp. 2021;119:154766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenzl FA, Ambrosini S, Mohammed SA, Kraler S, L\u0026uuml;scher TF, Costantino S, Paneni F. 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JACC Basic translational Sci. 2018;3(6):844\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heart failure, percutaneous coronary intervention, C-reactive protein-triglyceride glucose index, insulin resistance, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-4277196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4277196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInflammation and insulin resistance play important roles in the initiation and progression of heart failure and coronary artery disease. However, there\u0026rsquo;s lack of indicator related to inflammation and insulin resistance to predict the prognosis of that population. This study aims to evaluate the potential value of C-reactive protein-triglyceride glucose index (CTI) in heart failure patients undergoing percutaneous coronary intervention (PCI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e2797 PCI-treated patients with heart failure at Beijing Fuwai Hospital between 1st January 2016 and 31st December 2018 were retrospectively enrolled in current study. The primary endpoint was major adverse cardiac and cerebrovascular events at 12-month follow-up, defined as a composite of all-cause death, non-fatal myocardial infarction and stroke. Restricted cubic spline was applied to determine the cut-off value of CTI and examine the dose-response relationship between the CTI and the primary endpoint. Multivariate Cox proportional hazards models were used to evaluate the predictive value of CTI for the adverse cardiovascular outcomes and the results were expressed as hazard ratio with 95% confidence interval. The receiver-operating characteristics and decision curve analysis were plotted to comprehensively evaluate the predictive accuracy and clinical use of the CTI when adding it into the baseline model used to predict the prognosis of that population. Finally, subgroup analysis was conducted to evaluate the interaction between the traditional cardiovascular risk factor and CTI-related cardiovascular outcomes. The calculation method of CTI was as followed: ln[triglyceride(mg/dl) \u0026times; fasting blood glucose(mg/dl)/2]\u0026thinsp;+\u0026thinsp;0.412 \u0026times; ln (C-reactive protein).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 2797 PCI-treated patients with heart failure, 131 experienced MACCEs. Restricted cubic spline model showed that the CTI was significantly associated with the risk of adverse cardiovascular outcomes within 12 months (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a best cut-off value of 9.47. After adjusting for various confounders, the CTI remained independently associated with the incidence of endpoints (hazard ratio 1.41; 95%CI 1.13\u0026ndash;1.77; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) while the TyG index was not. Furthermore, Kaplan-Meier analysis demonstrated a higher incidence of endpoints (hazard ratio 1.55; 95%CI 1.11\u0026ndash;2.16; Log rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and all-cause death (hazard ratio 2.16; 95%CI 1.16\u0026ndash;3.99; Log rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) in enrolled patients with high CTI (CTI\u0026thinsp;\u0026ge;\u0026thinsp;9.47). Adding the CTI into the baseline model used to predict the adverse outcomes improved the predictive ability for the endpoints (increase in C-statistic value from 0.685 to 0.694; NRI 0.217, 95% confidence interval 0.050\u0026ndash;0.385, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; IDI 0.003, 95% confidence interval 0.001\u0026ndash;0.007, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Subgroup analysis showed that there existed an interaction between CTI and hypertension for the prediction of endpoints (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated CTI is associated with an increased risk of adverse cardiovascular outcomes in heart failure patients undergoing PCI, indicating the potential use of the CTI in the risk stratification and prognosis prediction of that population.\u003c/p\u003e","manuscriptTitle":"Inflammation and Insulin Resistance-Derived Indicator Predicts Adverse Cardiovascular Outcomes in Heart Failure Patients Undergoing Percutaneous Coronary Intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 18:59:02","doi":"10.21203/rs.3.rs-4277196/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"331101d8-812f-489e-bfb4-71cebed7fc7c","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-03T15:34:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 18:59:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4277196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4277196","identity":"rs-4277196","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}