A Nomogram Predicting 1-Year Co-Progression of Carotid and Coronary Plaque in Patients After Coronary Stenting

A Nomogram Predicting 1-Year Co-Progression of Carotid and Coronary Plaque in Patients After Coronary Stenting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Nomogram Predicting 1-Year Co-Progression of Carotid and Coronary Plaque in Patients After Coronary Stenting Chunyan Zhang, Min Li, Chi Zhang, Yanyi Sun, Zhenyue Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4964687/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 Backgroud : The concordance of carotid and coronary plaque progression warrants investigation. Objectives : We aimed to analyze the correlation between them and to explore a nomogram for predicting multisite plaque co-progression in patients implanted with coronary drug-eluting stents (DES). Methods : 909 patients were enrolled and randomized in a 7:3 ratio to the training and validation groups. LASSO (Least absolute shrinkage and selection operator) and logistic regressions determined risk factors. The nomogram visualized the prediction model. Concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve validated the discrimination and calibration of the model. Decision curve analysis (DCA) and clinical impact curve assessed the clinical utility value of the nomogram. Results : In our study, there was no statistical correlation between the concordance assessed by McNemar's test. Five variables were selected to establish the nomogram which displayed a robust discriminative ability with C-index of 0.837 [95% confidence interval (CI)=0.779-0.895) and 0.802 (95% CI=0.762-0.842), and area under the receiver operating characteristic curve (AUC) of 0.823 (95% CI=0.769-0.887) and 0.803 (95% CI=0.762-0.842) for the training and validation cohorts, respectively. The calibration results indicated favorable agreement between the predicted and actual probability. Furthermore, DCA and clinical impact curve showed the benefit of the nomogram in the clinical decision. Conclusion : The progression of carotid and coronary plaque was not highly concordant in patients with coronary DES at one-year follow up. The atherosclerotic plaque risk factor (ASPRF) nomogram showed effectively predictive value and clinical utility for the co-progression of carotid and coronary plaque after coronary DES implantation. Health sciences/Cardiology Health sciences/Diseases Health sciences/Health care Atherosclerosis Plaque co-progression Gensini score Crouse score Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights Co-progression of carotid and coronary plaques lacked statistical concordance at one-year in post-DES patients. The nomogram effectively predicts carotid-coronary plaque co-progression, incorporating vessel count, BMI, dyslipidemia, diabetes, and uric acid. The nomogram displays robust clinical utility, with strong discrimination and calibration. 1. Introduction Both carotid and coronary plaques are manifestations of atherosclerosis, and although the lesions are in different locations, the predisposing risk factors are similar [ 1 ] . A recent RCT study found that almost 1/3 of patients requiring carotid endarterectomy were complicated by coronary artery lesions [ 2 ] . Coronary artery calcification and stenosis are significantly associated with carotid intima-media thickness and the presence of carotid plaque [ 3 ] . The coexistence of carotid stenosis in patients with coronary atherosclerotic disease (CAD) is a sign of extensive atherosclerosis, and such patients have a higher risk of atherothrombotic lesions [ 4 ] . However, whether the progression of carotid and coronary atherosclerosis is associated or consistent remains to be explored. Based on epidemiological evidence, carotid plaque is a poor prognostic marker for subclinical coronary atherosclerosis in asymptomatic individuals [ 5 ] , whereas femoral plaque is a better surrogate predictor for coronary events [ 6 ] . It is of interest to explore the correlation between coexisting carotid and coronary artery disease and artery-specific risk factors. Although the progression of carotid and coronary plaque is influenced by similar risk factors, the degree of their progression and clinical complications may vary [ 7 ] . Sparse evidence illustrates the progression of carotid and coronary plaque is concordant in patients undergoing coronary DES implantation. It is of great clinical importance to clarify the consistency of carotid and coronary artery progression after coronary DES implantation. In addition, exploring the underlying systemic risk factors that promote plaque co-progression provides supportive guidance for secondary prevention. 2. Patients and methods 2.1 Patients The primary cohort study comprised an evaluation of medical records from the institutional database from January 2015 to October 2020. Patients with confirmed myocardial infarction (MI) and implanted with coronary DES were consecutively included and reviewed by coronary arteriography (CAG) and carotid ultrasound at one-year follow-up. Patients were then randomly divided into a training and validation cohort in a 7:3 ratio. Based on the changes in Crouse score and Gensini score from baseline levels, carotid and coronary plaques were assessed for progression at the 1-year follow-up, respectively, and then patients were assigned to the progressive and non-progressive groups. Exclusion criteria included pulmonary heart disease, ongoing systemic inflammatory diseases, systemic autoimmune disease, renal or hepatic dysfunction, malignancy, and stress disorders such as acute abdominal disease. The ethical approval for this retrospective analysis was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and the requirement for obtaining informed consent from the participants was waived by the same committee. All methods were performed in accordance with relevant guidelines/regulations. 2.2 Acquisition and interpretation of coronary angiograms and carotid ultrasound Coronary arteries were angiographed and evaluated by the chief or associate chief physician of the Department of Cardiology at Ruijin Hospital. Quantitative angiographic scoring of coronary atherosclerosis was quantified by the Gensini scoring system, which assesses the severity of coronary atherosclerosis by calculating the score for each lesion ≥ 25% and adjusting for total occlusions or 99% obstructive lesions receiving collaterals. A multiplier for specific coronary tree locations was used to weigh each lesion score for prognostic significance. The final Gensini score equaled the sum of the scores for all lesions [ 8 – 10 ] . Carotid intima-media thickness (CIMT) and plaque Crouse score are used to assess carotid plaque burden. The Crouse score, assessed by Color Doppler ultrasound system for carotid plaques, is described as follows: the maximum thickness of isolated atherosclerotic plaques in the ipsilateral carotid artery is summed, regardless of the length of each plaque. The sum of the bilateral carotid plaque scores is the total plaque score [ 11 – 13 ] . Subjects were categorized as ‘progressors’ and ‘non-progressors’ based on a Gensini rate change of > 1 or ≤ 0.5 points per year [ 14 ] . Similarly, according to the Crouse score, ‘progressors’ and ‘non-progressors’ were defined as changes > 1mm and ≤ 1mm per year [ 15 ] . 2.3 Clinical data collection Cardiovascular risk factors including age, gender, BMI, smoking, physical parameters, laboratory examinations, and medical histories, were collected from the medical records. Dyslipidemia was defined as known but untreated dyslipidemia or current intervention with lipid-lowering medications [ 16 ] . Diabetes was defined as confirmation of diagnosis and/or use of insulin or oral hypoglycemic agents. 2.4 Statistical analysis Statistical analyses were conducted with R software (version 4.2.1). We take our responsibility to conduct clinical research with the utmost care and respect for human subjects seriously and declare that our data were analyzed in full compliance with Good Clinical Practice (GCP) Guidelines. Categorical variables were presented as proportions (%) and continuous variables as means (SD), and differences between groups were determined by the χ 2 test and analyzed by one-way ANOVA. When the data were normally distributed with uniform variance, the t-test was used to compare parametric variables between groups, otherwise the Wilcoxon rank-sum test was used. The McNemar's test was used to determine whether carotid plaque and coronary plaque progressed consistently. The LASSO coefficient profiles compromised 57 variables, and the optimal lambda was selected by five-fold cross-validation via 1se criteria, resulting in five predictive variables. These five identified parameters were anatomized by multivariable logistic regression analysis and used to build the ASPRF nomogram. The probability that an individual will experience both carotid and coronary plaque progression was calculated based on the sum of the number of risk points for each weighted covariate in the nomogram. Calibration plots were generated to compare the actual co-progression estimates and predicted probabilities in the training and validation datasets. ROC curve and C-index with their respective confident intervals (CIs) were used to measure the predictive discrimination of the nomograms. Five-fold cross-validation was adopted to address the problem of model overfitting. DCA and clinical impact curve were applied to quantify the clinical utility of the nomogram model at different threshold probabilities. A two-sided P < 0.05 was considered significant. LASSO regression was performed with “glmnet” package. ROC, calibration plots, nomograms and internal validation were carried out with the “rms” package. Decision curve analysis was performed with the “rmda” package, and C-index calculation was conducted with the “Hmisc” package. 3. Results 3.1 Clinical characteristics Of the 909 patients enrolled, 559 were Gensini ‘progressors’ (61.50%) and 283 were Crouse ‘progressors’ (31.13%). The age of the patients ranged from 26 to 90 years, with a mean age of 64.51 years. We divided the cohorts into four groups based on the Crouse and Gensini scores ( Figure 1 ). 240 patients (26.40%) with no progression in both sites were defined as the C-G- group, 386 patients (42.46%) undergoing coronary plaque progression but no carotid plaque progression were assigned to the C-G+ group, 110 subjects (12.10%) with carotid plaque progression alone belonged to the C+G- group, and the remaining 173 patients (19.03%) with progression of both sites were classified as the C+G+ group. 909 patients were randomly assigned to the training (n=636) and validation cohort (n=273) in a 7:3 ratio. Mean baseline levels of low-density lipoprotein cholesterol (LDL-C) were 2.45±0.93 and 2.39±0.85 mmol/L for the training and validation cohort, respectively, and LDL-C levels at 1-year follow-up were 1.87±0.67 and 1.86±0.6 mmol/L, respectively. Optimal LDL-C levels were achieved in 122 (16.58%) and 30 (17.34%) patients in the training and validation groups, as recommended by the 2019 ACC/AHA guidelines,which state that very high-risk patients have a ≥50% reduction in LDL-C from baseline and an LDL-C target of <1.4 mmol/L (<55mg/dL) [17] . The baseline characteristics of patients in the training and validation groups are presented in Table 1 . There were no significant differences between the two datasets for each parameter. 3.2 Relationship between carotid and coronary atherosclerosis progression Among the 909 participants enrolled, 173 patients had progression at both sites. The McNemar’s test was conducted to determine whether carotid plaque progression was associated with coronary artery lesions. Chi-squared statistic was 152.47, P-value was 5.00×10 −35 and the Kappa value was equal to -0.004 (<0.75). Additionally, based on the observed probability differences, we estimated that a sample size of approximately 36.4 would be required to achieve a target power of 0.80. The significantly smaller estimated sample size (36.4) compared to the actual sample size (909) suggests that plaque progression was not consistent at both sites. Table1 Characteristics of training and validation dataset Characteristic Training(n=636) Validation(n=273) P value Male (n (%)) 506 (79.56) 203 (74.36) 0.0827 Age(mean±SD) 63.64 (8.95) 64.51 (9.19) 0.1817 BMI (mean±SD) 25.06 (3.16) 24.8 (3.37) 0.2674 Smoking (n (%)) 177 (27.83) 73 (26.74) 0.7358 Physical patameters SBP (mean±SD) 137.39 (19.57) 136.68 (19.67) 0.6148 DBP (mean±SD) 76.02 (11.57) 75.18 (11.03) 0.3084 HR (mean±SD) 76.98 (11.05) 76.38 (11.45) 0.458 Medical history Hypertension (n (%)) 443 (69.65) 194 (71.06) 0.6708 Hyperlipidemia (n (%)) 202(31.76) 91(33.33) 0.642 Diabetes (n (%)) 222 (34.91) 100 (36.63) 0.6183 Cerebrovascular disease (n (%)) 77 (12.11) 30 (10.99) 0.6316 Arrhythmia (n (%)) 65 (10.22) 36 (13.19) 0.192 Coronary heart disease (n (%)) 47 (7.39) 13 (4.76) 0.1435 Kidney disease (n (%)) 39 (6.13) 19 (6.96) 0.6398 Carotid ultrasound Plaque thickness of right common carotid artery (mean±SD) 1.68 (1.06) 1.75 (1.12) 0.3576 Plaque thickness of right internal carotid (mean±SD) 0.52 (0.99) 0.55 (1.03) 0.6872 Plaque thickness of left common carotid artery (mean±SD) 1.9 (1.03) 1.93 (0.95) 0.6586 Plaque thickness of left internal carotid (mean±SD) 0.53 (1.01) 0.55 (0.96) 0.7332 Crouse (mean±SD) 4.63 (2.71) 4.79 (2.77) 0.4234 Delta of Crouse (mean±SD) 0.36 (1.85) 0.46 (1.81) 0.464 Carotid plaque progressors(n (%)) 191 (30.03) 92 (33.7) 0.2736 Coronary angiography Branches of coronary artery disease(mean±SD) 2.3 (0.82) 2.22 (0.86) 0.1834 Gensini (mean±SD) 7.34 (9.65) 6.44 (7.87) 0.1756 Delta of Gensini (mean±SD) 6.69 (13.14) 5.84 (12.84) 0.3729 Coronary plaque progressors (n (%)) 402 (63.21) 157 (57.51) 0.1056 Laboratory examinations Hb (mean±SD) 138.28 (13.57) 138.03 (14.8) 0.7976 RBC (mean±SD) 4.42 (0.45) 4.54 (2.06) 0.1509 WBC (mean±SD) 8.02 (34.82) 6.41 (2.14) 0.4445 PLT (mean±SD) 188.42 (71.37) 179.96 (49.59) 0.0753 Lymphocyte (mean±SD) 28.41 (8.44) 29.18 (8.7) 0.2085 Neotrophil (mean±SD) 59.95 (9.33) 58.73 (9.24) 0.0691 Monocyte (mean±SD) 8.2 (1.91) 8.46 (2.08) 0.0676 Basophilic granulocyte (mean±SD) 0.58 (0.32) 0.58 (0.31) 0.7822 Eosinophilic granulocyte (mean±SD) 2.83 (2.16) 3.05 (2.2) 0.1627 ALT (mean±SD) 27.75 (27.53) 25.48 (18.64) 0.2124 AST (mean±SD) 33.14 (58.67) 30.48 (56.61) 0.526 TBIL (mean±SD) 13.2 (5.35) 13.33 (6.03) 0.7507 DBIL (mean±SD) 2.42 (1.04) 2.48 (1.01) 0.4555 Cr (mean±SD) 82.67 (29.89) 82.71 (55.36) 0.9894 UN (mean±SD) 5.82 (1.7) 5.86 (1.82) 0.746 eGFR (mean±SD) 84.47 (17.98) 85.6 (17.69) 0.3841 UA (mean±SD) 329.38 (86.25) 328.9 (86.42) 0.052 TC (mean±SD) 4.11 (1.14) 4.04 (1.08) 0.4125 TG (mean±SD) 1.66 (1.06) 1.58 (0.97) 0.3077 HDLC (mean±SD) 1.09 (0.27) 1.10 (0.28) 0.0608 LDLC -baseline(mean±SD) 2.45 (0.93) 2.41 (0.9) 0.5463 LDLC-follow-up 1.87(0.67) 1.86(0.6) 0.9034 LDLC-1450（n(%)） 122(16.58%) 30(17.34%) 0.8083 Non-HDLC (mean±SD) 3.03 (1.11) 2.93 (1.06) 0.2382 RC (mean±SD) 0.57 (0.43) 0.52 (0.35) 0.0693 ApoAI (mean±SD) 1.2 (0.2) 1.23 (0.22) 0.0517 ApoB (mean±SD) 0.82 (0.39) 0.8 (0.26) 0.441 Lp(a) (mean±SD) 0.25 (0.24) 0.24 (0.22) 0.7196 ApoE (mean±SD) 3.63 (1.06) 3.61 (0.96) 0.8637 Fibrinogen (mean±SD) 2.9 (0.71) 2.89 (0.63) 0.8909 INR (mean±SD) 0.96 (0.11) 0.96 (0.08) 0.3962 DDI (mean±SD) 0.42 (0.66) 0.36 (0.55) 0.2007 PT (mean±SD) 11.44 (1.88) 11.34 (1) 0.3845 TT (mean±SD) 18.29 (2.48) 18.16 (1.54) 0.426 APTT (mean±SD) 30.16 (5.83) 30.42 (7.86) 0.5676 CK (mean±SD) 225.36 (827.89) 163.75 (511.36) 0.2547 LDH (mean±SD) 185.74 (143.19) 167 (113.31) 0.0552 MYO (mean±SD) 55.38 (238.22) 38.4 (98.5) 0.2559 TNI (mean±SD) 3.21 (17.86) 2.47 (17.64) 0.5656 CKMB (mean±SD) 9.6 (42.76) 6.78 (31.07) 0.3253 CRP (mean±SD) 5.38 (14.96) 4.28 (12.41) 0.289 INS (mean±SD) 13.45 (26.86) 13.62 (28.21) 0.9345 HbAlc (mean±SD) 6.38 (1.18) 6.46 (1.31) 0.3724 ProBNP (mean±SD) 409.52 (1555.69) 326.96 (976.64) 0.4177 BMI, body mass index; SBP , systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; Hb, hemoglobin; RBC, red blood cell; WBC, white blood cell; PLT, platelet; ALT, alanine transaminase; AST, aspartic transaminase; TBIL, total bilirubin; DBIL, direct bilirubin; Cr, creatinine; UN, urea nitrogen; eGFR, estimated glomerular filtration rate; UA, uric acid; TC, total cholesterol; TG, triglyceride; HDLC, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LDLC-1450, an LDL-C reduction of ≥50% from baseline and LDL-C goal of <1.4 mmol/L(<55mg/dL); Non-HDLC, non-high-density lipoprotein cholesterol; RC, remnant cholesterol; ApoA1 , apolipoprotein A-I; ApoB , apolipoprotein B; Lp(a), lipoprotein a; ApoE , apolipoprotein E; INR, international; DDI, d-dimer ; TT, thrombin time ; APTT, activated partial thromboplastin time; CKMB, creatine kinase isoenzyme; LDH, lactate dehydrogenase; MYO, myoglobin; CRP, C-reactive protein; INS, insulin;HbA1c , hemoglobin A1c; ProBNP , pro–brain natriuretic peptide; ISR, in-stent restenosis. Compared with Training cohort: ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. 3.3 Defining outcomes and identifying covariates for nomograms Although the progression of carotid and coronary plaques was not consistent during the 1-year follow-up, 173 subjects experienced concurrent plaque progression at both sites. Hence, we wanted to determine the distinguishing characteristics of these 173 co-progressors in C+G+ group. The goal of our nomogram was to predict the probability of carotid and coronary plaque co-progression. Therefore, C+G+ group was set as case (co-progressors) and the other patients as control (non-progressors). LASSO regression, which can adjust variable selection and regularization and avoid overfitting when fitting the generalized linear model, was used to screen for risk factors (Figure 2) . The 57 variables including age, gender, body mass index (BMI), smoking, heart rate (HR), hypertension, dyslipidemia, diabetes mellitus, cerebrovascular disease, arrhythmia, kidney disease, lipid profiles, and other laboratory parameters listed in the Table 1 were taken into LASSO regression and the optimal lambda with 1se criteria resulted in five variables containing the number of involved vessels, BMI, dyslipidemia, diabetes mellitus, and uric acid ( Figure 3 ). Multivariable odds ratios indicated that the number of involved vessels, BMI, dyslipidemia, diabetes mellitus, and uric acid were risk factors for co-progression of carotid and coronary plaque in both training and validation cohorts ( Table 2 ). 3.4 Development and explanation of an ASPRF nomogram for predicting the probability of carotid and coronary plaque co-progression Based on the regression results, a nomogram incorporating 5 significant risk factors was created ( Figure 3 ). The value of each variable corresponds to a score on the point scale axis. The total score was calculated by adding each individual score, and the probability of carotid and coronary plaque co-progression was estimated by projecting the total score to the lower total point scale. For example, the patient was confirmed to have two lesioned vessels (score=10), a BMI of 32.1 (score=67.5), had dyslipidemia (score=22.5) and diabetes mellitus (score=17.5), and had a uric acid level of 520μmol/L (score=45). Summing these individual scores gave a total point of 162.5, implying an 80% probability of co-progression of carotid and coronary plaque. 3.5 Validation of the ASPRF Nomogram The C-index of the nomogram for prediction of carotid and coronary plaque co-progression were 0.837 (95% CI= 0.779-0.895) and 0.802 (95% CI=0.762-0.842) in the training and validation cohorts, respectively. The AUCs were 0.823 (95% CI= 0.769-0.887) and 0.803 (95% CI= 0.762-0.842) respectively, indicating satisfactory discrimination of the ASPRF nomogram ( Figure 4 A, B ). The calibration results showed favorable consistencies between predicted and observed plaque co-progression probability. Brier scores (range = 0-1), a measurement of the difference between the algorithm’s predicted probabilities and actual outcomes, with lower Brier scores representing more accurate predictions of actual events, were 0.106 and 0.124 for the training and validation cohorts, respectively ( Figure 4 C, D ). 3.6 Decision curve analysis (DCA) of the ASPRF Nomogram DCA is always used to evaluate whether a model is beneficial for clinical decision making, that is, who should receive treatment or therapeutic interventions [18] . The decision curves for the ASPRF nomogram in the training and validation cohorts are presented in Figure 5. We are concerned with the net benefit (NB) in the DCA, namely income minus expenditure. In our study, the “income” represents true positives - cases of carotid and coronary plaque co-progression; the “expenditure” represents false positives - unnecessary invasive operations, such as CAG rechecking. In the DCA plot, the Y-axis represents the NB, and the X-axis is the threshold probability. As shown in Figure 5 , the green and blue lines correspond to predictive models in the training and validation cohorts, respectively, exhibiting high NB over a wide range of risk thresholds from 0 to 0.8. It is conducive to understand DCA to know that the benefit is good, and the referred risk threshold varies depending on the specific clinical scenario. The clinical impact curve visualized the estimated number of people considered high risk and true positives in the range of 0 to 0.6 by using our prediction model. For example, if 1000 participants were screened using a risk threshold of 0.2, 350 patients would be classified as high-risk for co-progression of carotid and coronary plaques, whereas 150 participants would be true positives. Clinical prediction of coronary plaque progression in an additional 650 participants through our nomogram model may prevent them from undergoing unnecessary invasive CAG manipulations. Table 2 LASSO and Multivariable logistic regression analysis of progression of coronary and Carotid plaque in the training (n=636) and validation(n=273) dataset Training Validation Variable Coefficient OR 95% CI p value Coefficient OR 95% CI p value Number of vessels disease 0.022 1.841 1.352-2.559 0.003** 0.021 2.065 1.558-4.478 0.006** Body mass index 0.020 1.250 1.161-1.349 <0.001*** 0.017 1.258 1.558-4.478 <0.001*** dyslipidemia 0.081 2.771 1.769-4.355 <0.001*** 0.067 2.826 1.354-5.911 <0.006** Diabetes 0.077 2.544 1.815-4.488 <0.001*** 0.070 1.637 0.787-3.406 0.185 Uric acid 0.000 1.004 1.002-1.007 0.001** 0.000 1.004 1.002-1.008 0.043* Coefficient in LASSO regression and odd ratio (OR) in multivariable logistic regression of the five risk factors were summarized. LASSO, least absolute shrinkage and selection operator; CI, confident interval. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. 4. Discussion The “classic” risk factors for cardiovascular disease may not affect the carotid and coronary vascular systems in the same way [19] . In our study, the incidence of carotid and coronary plaque co-progression in patients after coronary stenting is approximately 19.03% at 1 year follow-up, which is lower than the incidence of progression at a single vascular lesion site (31.13% for carotid plaque and 61.50% for coronary plaque). The McNemar’s test [20] is used to pair nominal data to determine whether the marginal frequencies of rows and columns are equal. The McNemar’s test shows that the co-progression of these two events was not statistically significant. Thus, it may not be sensible or reliable to predict whether coronary atherosclerosis affects downstream myocardial perfusion [21,22] , through ultrasound assessment of peripheral carotid plaques, although this is a noninvasive and low-cost procedure, in patients after coronary DES implantation. Nomograms have been frequently used in cancer prognosis [23–25] , and are getting increasingly popular in the cardiovascular field [24,26,27] . To date, there is no nomogram that can predict atherosclerotic plaque progression. The nomogram of our study may be a valuable tool for clinical practice, because the included factors are readily available and routinely collected in clinical interviews. A large body of epidemiological evidence has demonstrated a link between some proven risk factors and predisposing risk markers and CAD. Three-vessel disease predicted a worse prognosis than one- or two-vessel lesion among CAD patients [28,29] . Dyslipidemia is a major classic and culprit risk factor for atherosclerosis and is associated with the severity and progression of atherosclerotic plaques [30] . Elevated blood glucose and obesity are proven risk markers for cardiovascular diseases [31,32] . Patients with diabetes showed a higher prevalence and severity of coronary plaque progression of non-stented segments after stenting [33] . The 2013 AHA obesity guideline defines overweight as adults with a BMI between 25.0 and 29.9 kg/m 2 and obesity≥30 kg/m 2 . Obesity recapitulates many features of the inflammatory process in atherosclerosis, which is considered to be a subacute inflammatory state of the vasculature [34] . The obese population always possesses atherogenic dyslipidemia, characterized by high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C), which strongly increases the risk of atherosclerosis [35] . To more robustly predict prognostic risk, we did not transform the continuous variable BMI into a categorical variable, but simply classified patients as overweight/obese or not obese based on cut-off points of 30 and 25 kg/m 2 . Knowledge of obesity-related multi-vessel plaque progression could guide clinical practice in risk stratification and therapeutic interventions for obese patients with coronary stenting. In the Chinese population, the overall prevalence of hyperuricemia is 13.3% and has become a common metabolic disease after diabetes [36] . Although hyperuricemia is not an independent risk factor for atherosclerosis, it is observed in the Framingham Heart Study that hyperuricemia is a covariable of other known cardiovascular risk factors that contribute to cardiac death and CAD [37] . Hence, lowering uric acid may be an effective treatment for atherosclerosis. Atherogenic lipids are known to play a key role in the atherosclerotic process, but it is noteworthy that lipid parameters were not addressed in our ASPRF nomogram. Further analysis of univariable logistic regression of risk factors for carotid and coronary plaque progression showed that lipids were strongly associated with coronary plaque progression, but not with carotid plaque progression ( Supplemental Table 1 ). Taken together, the predictive value of lipids for plaque co-progression was diluted by the poor performance of carotid plaque. Nonetheless, the nomogram model somewhat compromises the general concept of dyslipidemia as a consistent atherogenic factor. By assessing the risk of carotid and coronary plaque co-progression, some intervention or adjustment of therapy will be recommended for individuals whose net benefit is higher than that of a full intervention or no intervention strategy. Similarly, when the predicted risk is low and the net benefit is small, health care costs and potential contraindications such as allergy to iodine contrast and CIN, should be considered. This simple scoring system, in combination with DCA and clinical impact curve, can help physicians and patients evaluate the individualized probability of carotid and coronary plaque co-progression within one year after PCI, thus facilitating personalized treatment adjustments and higher net benefits decisions, which is in line with the trend of personalized medicine. 5. Limitations The limitations are that although the nomogram has a high risk predictive value and is well discriminated and calibrated, it is a retrospective and single-center cohort study. A multicenter clinical validation would have been better, and external validation may provide more convincing evidence than internal cross-validation. Besides, our ASPRF nomogram may be more applicable to Asian cohorts who had their first MI and received coronary DES implantation and had corresponding data for the five variables in the model. Therefore, extrapolation of the ASPRF nomogram model requires additional consideration and caution. Moreover, while all carotid ultrasound examinations were conducted within the same ultrasound department, the operator was not always the same person, which introduces potential variability. 6. Conclusion Our study demonstrates that the progression of carotid plaques and coronary plaques is inconsistent in patients at 1-year follow-up after coronary DES implantation. The ASPRF nomogram includes five easily accessible clinical parameters that can be conveniently used as an evaluation tool for predicting the probability of the aforementioned co-progression. Declarations Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions. Access to the data will be granted upon request and after review of a data access agreement to ensure compliance with relevant regulations and ethical standards. Conflicts of interest The authors declare they have no conflict of interest. Financial support This work was supported by the National Key Research and Development Program of China (grant no. 2021YFC2500602), and the National Natural Science Foundation of China to Z. Chen (81370331) and to Y. Sun (82200311). Author contributions Chunyan Zhang: Data analysis and interpretation, Writing- Original draft preparation. Min Li: Original data analysis and draft preparation. Chi Zhang: Collection and curation of data. Yanyi Sun: Data interpretation, Writing- Reviewing and Editing. Zhenyue Chen: Conceptualization and design. Clinical Perspectives Our investigation underscores the absence of substantial concordance in plaque progression between the carotid and coronary arteries in patients undergoing coronary drug-eluting stent (DES) implantation. Clinicians should be cognizant of the necessity for distinct management strategies at these anatomical sites, even within a single patient. The development of the atherosclerotic plaque risk factor (ASPRF) nomogram constitutes a valuable instrument for risk assessment. Clinicians can leverage this nomogram to discern patients at heightened risk of multisite plaque co-progression, thereby facilitating individualized patient care strategies. Translational Outlook While this study elucidates the aspects of concordance and predictive modeling of plaque progression, subsequent research endeavors should undertake validation of the ASPRF nomogram across heterogeneous patient cohorts to ascertain its universal applicability across varying demographic strata. Moreover, delving into the underlying pathophysiological mechanisms responsible for the observed discordance may unveil novel therapeutic targets and strategies germane to plaque management. References V. Aboyans, J.-B. Ricco, M.-L.E.L. Bartelink, M. Björck, M. Brodmann, T. Cohnert, J.-P. Collet, M. Czerny, M. De Carlo, S. Debus, C. Espinola-Klein, T. Kahan, S. Kownator, L. Mazzolai, A.R. Naylor, M. Roffi, J. Röther, M. Sprynger, M. Tendera, G. Tepe, M. Venermo, C. Vlachopoulos, I. 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Lanctôt, Randomized Placebo-Controlled Trial of Nabilone for Agitation in Alzheimer’s Disease, The American Journal of Geriatric Psychiatry. 27 (2019) 1161–1173. https://doi.org/10.1016/j.jagp.2019.05.002. R.S. Driessen, W.J. Stuijfzand, P.G. Raijmakers, I. Danad, J.K. Min, J.A. Leipsic, A. Ahmadi, J. Narula, P.M. van de Ven, M.C. Huisman, A.A. Lammertsma, A.C. van Rossum, N. van Royen, P. Knaapen, Effect of Plaque Burden and Morphology on Myocardial Blood Flow and Fractional Flow Reserve, Journal of the American College of Cardiology. 71 (2018) 499–509. https://doi.org/10.1016/j.jacc.2017.11.054. R. Blankstein, L.J. Shaw, M.F. Di Carli, Coronary Plaque Volume and Stenosis, Journal of the American College of Cardiology. 71 (2018) 510–512. https://doi.org/10.1016/j.jacc.2017.12.018. Postoperative Nomogram Predicting Risk of Recurrence After Radical Cystectomy for Bladder Cancer, Journal of Clinical Oncology. (2016). https://doi.org/10.1200/JCO.2005.05.3884. J. Wu, H. Zhang, L. Li, M. Hu, L. Chen, B. Xu, Q. Song, A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis, Cancer Communications. 40 (2020) 301–312. https://doi.org/10.1002/cac2.12067. C. Jin, J. Cao, Y. Cai, L. Wang, K. Liu, W. Shen, J. Hu, A nomogram for predicting the risk of invasive pulmonary adenocarcinoma for patients with solitary peripheral subsolid nodules, J Thorac Cardiovasc Surg. 153 (2017) 462-469.e1. https://doi.org/10.1016/j.jtcvs.2016.10.019. Lauer, M. S., Pothier, C. E., Magid, D. J., Smith, S. S., & Kattan, M. W. (2007). An externally validated model for predicting long-term survival after exercise treadmill testing in patients with suspected coronary artery disease and a normal electrocardiogram. Annals of internal medicine, 147(12), 821–828. https://doi.org/10.7326/0003-4819-147-12-200712180-00001. S. Huang, X. Xie, Y. Sun, T. Zhang, Y. Cai, X. Xu, H. Li, S. Wu, Development of a nomogram that predicts the risk for coronary atherosclerotic heart disease, Aging. 12 (2020) 9427–9439. https://doi.org/10.18632/aging.103216. A.C. Pereira, N.H.M. Lopes, P.R. Soares, J.E. Krieger, S.A. de Oliveira, L.A.M. Cesar, J.A.F. Ramires, W. Hueb, Clinical Judgment and Treatment Options in Stable Multivessel Coronary Artery Disease, Journal of the American College of Cardiology. 48 (2006) 948–953. https://doi.org/10.1016/j.jacc.2005.11.094. N.H. Lopes, F. da S. Paulitsch, A.F. Gois, A.C. Pereira, N.A. Stolf, L.O. Dallan, J.A.F. Ramires, W.A. Hueb, Impact of number of vessels disease on outcome of patients with stable coronary artery disease: 5-year follow-up of the Medical, Angioplasty, and bypass Surgery Study (MASS), European Journal of Cardio-Thoracic Surgery. 33 (2008) 349–354. https://doi.org/10.1016/j.ejcts.2007.11.025. L. Kopin, C. J. Lowenstein, Dyslipidemia, Annals of Internal Medicine. (2017). https://doi.org/10.7326/AITC201712050 S. Yusuf, S. Reddy, S. Ôunpuu, S. Anand, Global Burden of Cardiovascular Diseases, Circulation. 104 (2001) 2746–2753. https://doi.org/10.1161/hc4601.099487. S.N. Bhupathiraju, F.B. Hu, Epidemiology of Obesity and Diabetes and Their Cardiovascular Complications, Circ Res. 118 (2016) 1723–1735. https://doi.org/10.1161/CIRCRESAHA.115.306825. R. Shi, K. Shi, Z. Yang, Y. Guo, K. Diao, Y. Gao, Y. Zhang, S. Huang, Serial coronary computed tomography angiography-verified coronary plaque progression: comparison of stented patients with or without diabetes, Cardiovascular Diabetology. 18 (2019) 123. https://doi.org/10.1186/s12933-019-0924-z. S.N. Bhupathiraju, F.B. Hu, Epidemiology of Obesity and Diabetes and Their Cardiovascular Complications, Circulation Research. 118 (2016) 1723–1735. https://doi.org/10.1161/CIRCRESAHA.115.306825. V. Bamba, D.J. Rader, Obesity and Atherogenic Dyslipidemia, Gastroenterology. 132 (2007) 2181–2190. https://doi.org/10.1053/j.gastro.2007.03.056. J. Rao, P. Ye, J. Lu, B. Chen, N. Li, H. Zhang, H. Bo, X. Chen, H. Liu, C. Zhang, H. Wei, Q. Wu, Y. Yan, C. Li, J. Mi, Prevalence and related factors of hyperuricaemia in Chinese children and adolescents: a pooled analysis of 11 population-based studies, Annals of Medicine. 54 (2022) 1608–1615. https://doi.org/10.1080/07853890.2022.2083670. Culleton, B. F., Larson, M. G., Kannel, W. B., & Levy, D. (1999). Serum uric acid and risk for cardiovascular disease and death: the Framingham Heart Study. Annals of internal medicine, 131(1), 7–13. https://doi.org/10.7326/0003-4819-131-1-199907060-00003 Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4964687","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":358624376,"identity":"b349f1db-c3e9-47dc-aa7c-42854eef3327","order_by":0,"name":"Chunyan Zhang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Zhang","suffix":""},{"id":358624377,"identity":"e44258d1-f6eb-4e8a-84b1-9b34b1360fa1","order_by":1,"name":"Min Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACNoaDjY9/VNTIMTYzHyBOCx/j4cPGDGeOGTO3tyUQp0WO+ViaMGMbc2J7zxkDIh3GdsaMubCNjbF3Rs7HG28Y7OR0Gwhp4Tlj9njGORlmyRm5my3nMCQbmx0gpEXijLkBTxkbm+GM3G3SPAwHErcR1CL/xkyCh42Zx/5GzjMitTAcS5PmaWOWYOw5w0aslsOHDWecOWbA2N5mbDnHgAi/yDccbHzwoaKmvrGZ+eGNNxV2cgS1oAAJHiKjBlkLqTpGwSgYBaNgRAAAfHtEItKBQ0QAAAAASUVORK5CYII=","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":358624378,"identity":"aab37ce6-96ac-4aa3-9edc-61873b225639","order_by":2,"name":"Chi Zhang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Zhang","suffix":""},{"id":358624379,"identity":"285eb142-bd02-4b60-a305-e1e965728a70","order_by":3,"name":"Yanyi Sun","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanyi","middleName":"","lastName":"Sun","suffix":""},{"id":358624380,"identity":"0d62bb28-e519-48d7-a0a7-c1a14ffa334d","order_by":4,"name":"Zhenyue Chen","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhenyue","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-08-23 13:47:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4964687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4964687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67174424,"identity":"fb533eae-a476-47e3-a7d7-dbedd97ce455","added_by":"auto","created_at":"2024-10-22 04:41:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35519,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of carotid plaque and coronary plaque progression. A. C+G-, Carotid plaque progressors but coronary plaque non-progressor. B. C+G+, Both carotid and coronary plaque progressor. C. C-G-, Neither carotid nor coronary plaque progressor. D. C-G+, Coronary plaque progressor but carotid plaque non-progressor.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/824a90f0f7d9664cf782e889.png"},{"id":67174427,"identity":"6557f7ec-af25-4bb8-ac82-fdfc2b571a65","added_by":"auto","created_at":"2024-10-22 04:41:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174475,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic and clinical variables selection using the LASSO regression. A. Optimal parameter (lambda) selection in the LASSO model was cross-validated fivefold via minimum criteria. The binomial deviance curve was plotted vs. Log (lambda). Dotted vertical lines were drawn at the optimal values using the minimum criteria and the 1se of the minimum criteria (the 1-SE criteria). B. LASSO coefficient profiles for 57 variables. A coefficient profile plot was produced against the log (lambda) sequence. LASSO, least absolute shrinkage and selection operator; SE, standard error.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/cf68192e37986720c8563525.png"},{"id":67174426,"identity":"3ca1089a-49fc-404c-982f-62c9e011a7c3","added_by":"auto","created_at":"2024-10-22 04:41:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249195,"visible":true,"origin":"","legend":"\u003cp\u003eAn atherosclerotic plaque risk factor (ASPRF) nomogram was developed in our cohort containing 5 key risk factors: Number of involved vessels, Body mass index, Dyslipidemia, Diabetes, and Uric acid. Probability of CG, the probability of a patient simultaneously developing carotid and coronary plaque progression.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/b442965197e9ded88149639c.png"},{"id":67174429,"identity":"a2660489-a537-4f49-bb97-328d809c8ead","added_by":"auto","created_at":"2024-10-22 04:41:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":946828,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver-operating characteristic (ROC) curves of predictive model compromising atherosclerotic plaque risk factors (ASPRF, Number of involved vessels, Body mass index, Dyslipidemia, Diabetes, and Uric acid.) in the training (A) and validation datasets (B). Calibration curves of the ASPRF model in the training (C) and validation datasets (D). AUC, area under the receiver-operating characteristic curve.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/7c31a2f4948acac1c3edd311.png"},{"id":67174428,"identity":"e705d6ef-2f53-45a4-bbe3-f1ad7f5c5b16","added_by":"auto","created_at":"2024-10-22 04:41:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216774,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Decision curves for the ASPRF nomogram models for carotid and coronary plaque co-progression. The Y-axis displays the net benefit. The X-axis shows the risk threshold. (B) Clinical impact curves for the nomogram model. The red solid line shows the total number of people considered to be at high risk at each risk threshold, out of 1000 patients. The blue dashed line shows how many of them are true positives (co-progression).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/50d90c11eaba0c5279c8f506.png"},{"id":72779292,"identity":"405c1c8b-be50-4ede-8095-350c056d29de","added_by":"auto","created_at":"2025-01-02 05:38:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2528535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/2be4bf21-0ac7-43c0-94c0-c6ef858a77d5.pdf"},{"id":67174423,"identity":"8e959721-850e-4617-bdb2-c265ca310fbc","added_by":"auto","created_at":"2024-10-22 04:41:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19078,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4964687/v1/ebd6b31152737a6b02c333a4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram Predicting 1-Year Co-Progression of Carotid and Coronary Plaque in Patients After Coronary Stenting","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eCo-progression of carotid and coronary plaques lacked statistical concordance at one-year in post-DES patients.\u003c/li\u003e\n \u003cli\u003eThe nomogram effectively predicts carotid-coronary plaque co-progression, incorporating vessel count, BMI, dyslipidemia, diabetes, and uric acid.\u003c/li\u003e\n \u003cli\u003eThe nomogram displays robust clinical utility, with strong discrimination and calibration.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eBoth carotid and coronary plaques are manifestations of atherosclerosis, and although the lesions are in different locations, the predisposing risk factors are similar \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. A recent RCT study found that almost 1/3 of patients requiring carotid endarterectomy were complicated by coronary artery lesions \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Coronary artery calcification and stenosis are significantly associated with carotid intima-media thickness and the presence of carotid plaque \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The coexistence of carotid stenosis in patients with coronary atherosclerotic disease (CAD) is a sign of extensive atherosclerosis, and such patients have a higher risk of atherothrombotic lesions \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, whether the progression of carotid and coronary atherosclerosis is associated or consistent remains to be explored. Based on epidemiological evidence, carotid plaque is a poor prognostic marker for subclinical coronary atherosclerosis in asymptomatic individuals \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, whereas femoral plaque is a better surrogate predictor for coronary events \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. It is of interest to explore the correlation between coexisting carotid and coronary artery disease and artery-specific risk factors.\u003c/p\u003e \u003cp\u003eAlthough the progression of carotid and coronary plaque is influenced by similar risk factors, the degree of their progression and clinical complications may vary \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Sparse evidence illustrates the progression of carotid and coronary plaque is concordant in patients undergoing coronary DES implantation. It is of great clinical importance to clarify the consistency of carotid and coronary artery progression after coronary DES implantation. In addition, exploring the underlying systemic risk factors that promote plaque co-progression provides supportive guidance for secondary prevention.\u003c/p\u003e"},{"header":"2. Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eThe primary cohort study comprised an evaluation of medical records from the institutional database from January 2015 to October 2020. Patients with confirmed myocardial infarction (MI) and implanted with coronary DES were consecutively included and reviewed by coronary arteriography (CAG) and carotid ultrasound at one-year follow-up. Patients were then randomly divided into a training and validation cohort in a 7:3 ratio. Based on the changes in Crouse score and Gensini score from baseline levels, carotid and coronary plaques were assessed for progression at the 1-year follow-up, respectively, and then patients were assigned to the progressive and non-progressive groups. Exclusion criteria included pulmonary heart disease, ongoing systemic inflammatory diseases, systemic autoimmune disease, renal or hepatic dysfunction, malignancy, and stress disorders such as acute abdominal disease.\u003c/p\u003e \u003cp\u003e The ethical approval for this retrospective analysis was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and the requirement for obtaining informed consent from the participants was waived by the same committee. All methods were performed in accordance with relevant guidelines/regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Acquisition and interpretation of coronary angiograms and carotid ultrasound\u003c/h2\u003e \u003cp\u003eCoronary arteries were angiographed and evaluated by the chief or associate chief physician of the Department of Cardiology at Ruijin Hospital. Quantitative angiographic scoring of coronary atherosclerosis was quantified by the Gensini scoring system, which assesses the severity of coronary atherosclerosis by calculating the score for each lesion\u0026thinsp;\u0026ge;\u0026thinsp;25% and adjusting for total occlusions or 99% obstructive lesions receiving collaterals. A multiplier for specific coronary tree locations was used to weigh each lesion score for prognostic significance. The final Gensini score equaled the sum of the scores for all lesions \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Carotid intima-media thickness (CIMT) and plaque Crouse score are used to assess carotid plaque burden. The Crouse score, assessed by Color Doppler ultrasound system for carotid plaques, is described as follows: the maximum thickness of isolated atherosclerotic plaques in the ipsilateral carotid artery is summed, regardless of the length of each plaque. The sum of the bilateral carotid plaque scores is the total plaque score \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Subjects were categorized as \u0026lsquo;progressors\u0026rsquo; and \u0026lsquo;non-progressors\u0026rsquo; based on a Gensini rate change of \u0026gt;\u0026thinsp;1 or \u0026le;\u0026thinsp;0.5 points per year \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Similarly, according to the Crouse score, \u0026lsquo;progressors\u0026rsquo; and \u0026lsquo;non-progressors\u0026rsquo; were defined as changes\u0026thinsp;\u0026gt;\u0026thinsp;1mm and \u0026le;\u0026thinsp;1mm per year \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Clinical data collection\u003c/h2\u003e \u003cp\u003eCardiovascular risk factors including age, gender, BMI, smoking, physical parameters, laboratory examinations, and medical histories, were collected from the medical records. Dyslipidemia was defined as known but untreated dyslipidemia or current intervention with lipid-lowering medications \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Diabetes was defined as confirmation of diagnosis and/or use of insulin or oral hypoglycemic agents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted with R software (version 4.2.1). We take our responsibility to conduct clinical research with the utmost care and respect for human subjects seriously and declare that our data were analyzed in full compliance with Good Clinical Practice (GCP) Guidelines. Categorical variables were presented as proportions (%) and continuous variables as means (SD), and differences between groups were determined by the χ 2 test and analyzed by one-way ANOVA. When the data were normally distributed with uniform variance, the t-test was used to compare parametric variables between groups, otherwise the Wilcoxon rank-sum test was used. The McNemar's test was used to determine whether carotid plaque and coronary plaque progressed consistently.\u003c/p\u003e \u003cp\u003eThe LASSO coefficient profiles compromised 57 variables, and the optimal lambda was selected by five-fold cross-validation via 1se criteria, resulting in five predictive variables. These five identified parameters were anatomized by multivariable logistic regression analysis and used to build the ASPRF nomogram. The probability that an individual will experience both carotid and coronary plaque progression was calculated based on the sum of the number of risk points for each weighted covariate in the nomogram. Calibration plots were generated to compare the actual co-progression estimates and predicted probabilities in the training and validation datasets. ROC curve and C-index with their respective confident intervals (CIs) were used to measure the predictive discrimination of the nomograms. Five-fold cross-validation was adopted to address the problem of model overfitting. DCA and clinical impact curve were applied to quantify the clinical utility of the nomogram model at different threshold probabilities. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. LASSO regression was performed with \u0026ldquo;glmnet\u0026rdquo; package. ROC, calibration plots, nomograms and internal validation were carried out with the \u0026ldquo;rms\u0026rdquo; package. Decision curve analysis was performed with the \u0026ldquo;rmda\u0026rdquo; package, and C-index calculation was conducted with the \u0026ldquo;Hmisc\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 909 patients enrolled, 559 were Gensini \u0026lsquo;progressors\u0026rsquo; (61.50%) and 283 were Crouse \u0026lsquo;progressors\u0026rsquo; (31.13%). The age of the patients ranged from 26 to 90 years, with a mean age of 64.51 years. We divided the cohorts into four groups based on the Crouse and Gensini scores (\u003cstrong\u003eFigure 1\u003c/strong\u003e). 240 patients (26.40%) with no progression in both sites were defined as the C-G- group, 386 patients (42.46%) undergoing coronary plaque progression but no carotid plaque progression were assigned to the C-G+ group, 110 subjects (12.10%) with carotid plaque progression alone belonged to the C+G- group, and the remaining 173 patients (19.03%) with progression of both sites were classified as the C+G+ group. 909 patients were randomly assigned to the training (n=636) and validation cohort (n=273) in a 7:3 ratio. Mean baseline levels of low-density lipoprotein cholesterol (LDL-C) were 2.45\u0026plusmn;0.93 and 2.39\u0026plusmn;0.85 mmol/L for the training and validation cohort, respectively, and LDL-C levels at 1-year follow-up were 1.87\u0026plusmn;0.67 and 1.86\u0026plusmn;0.6 mmol/L, respectively. Optimal LDL-C levels were achieved in 122 (16.58%) and 30 (17.34%) patients in the training and validation groups, as recommended by the 2019 ACC/AHA guidelines,which state that very high-risk patients have a \u0026ge;50% reduction in LDL-C from baseline and an LDL-C target of \u0026lt;1.4 mmol/L (\u0026lt;55mg/dL) \u003csup\u003e[17]\u003c/sup\u003e. The baseline characteristics of patients in the training and validation groups are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e. There were no significant differences between the two datasets for each parameter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Relationship between carotid and coronary atherosclerosis progression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 909 participants enrolled, 173 patients had progression at both sites. The McNemar\u0026rsquo;s test was conducted to determine whether carotid plaque progression was associated with coronary artery lesions. Chi-squared statistic was 152.47, P-value was 5.00\u0026times;10\u003csup\u003e\u0026minus;35\u003c/sup\u003e and the Kappa value was equal to -0.004 (\u0026lt;0.75). Additionally, based on the observed probability differences, we estimated that a sample size of approximately 36.4 would be required to achieve a target power of 0.80. The significantly smaller estimated sample size (36.4) compared to the actual sample size (909) suggests that plaque progression was not consistent at both sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1 Characteristics of training and validation dataset\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining(n=636)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation(n=273)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eMale (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e506 (79.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e203 (74.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eAge(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e63.64 (8.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e64.51 (9.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eBMI (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e25.06 (3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e24.8 (3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eSmoking (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e177 (27.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e73 (26.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.7358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical patameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eSBP (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e137.39 (19.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e136.68 (19.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eDBP (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e76.02 (11.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e75.18 (11.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eHR (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e76.98 (11.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e76.38 (11.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eHypertension (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e443 (69.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e194 (71.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eHyperlipidemia (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e202(31.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e91(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eDiabetes (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e222 (34.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e100 (36.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCerebrovascular disease (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e77 (12.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e30 (10.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eArrhythmia (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e65 (10.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e36 (13.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCoronary heart disease (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e47 (7.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e13 (4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eKidney disease (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e39 (6.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e19 (6.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarotid ultrasound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003ePlaque thickness of right common carotid artery (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.68 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.75 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003ePlaque thickness of right internal carotid (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.52 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.55 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003ePlaque thickness of left common carotid artery (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.9 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.93 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.6586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003ePlaque thickness of left internal carotid (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.53 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.55 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.7332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCrouse (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.63 (2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.79 (2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.4234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eDelta of Crouse (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.36 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.46 (1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCarotid plaque progressors(n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e191 (30.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e92 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary angiography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eBranches of coronary artery disease(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.3 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.22 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eGensini (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e7.34 (9.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e6.44 (7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eDelta of Gensini (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e6.69 (13.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e5.84 (12.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCoronary plaque progressors (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e402 (63.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e157 (57.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory examinations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eHb (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e138.28 (13.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e138.03 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.7976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eRBC (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.42 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.54 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eWBC (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e8.02 (34.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e6.41 (2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.4445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003ePLT (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e188.42 (71.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e179.96 (49.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eLymphocyte (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e28.41 (8.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e29.18 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eNeotrophil (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e59.95 (9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e58.73 (9.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eMonocyte (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e8.2 (1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e8.46 (2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eBasophilic granulocyte (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.58 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.58 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.7822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eEosinophilic granulocyte (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.83 (2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e3.05 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.1627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eALT (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e27.75 (27.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e25.48 (18.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eAST (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e33.14 (58.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e30.48 (56.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eTBIL (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e13.2 (5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e13.33 (6.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.7507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eDBIL (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.42 (1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.48 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.4555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCr (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e82.67 (29.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e82.71 (55.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.9894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eUN (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e5.82 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e5.86 (1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eeGFR (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e84.47 (17.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e85.6 (17.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eUA (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e329.38 (86.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e328.9 (86.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eTC (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.11 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.04 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.4125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eTG (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.66 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.58 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eHDLC (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.09 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.10 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eLDLC -baseline(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.45 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.41 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.5463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eLDLC-follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.87(0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.86(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.9034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eLDLC-1450（n(%)）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e122(16.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e30(17.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.8083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eNon-HDLC (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e3.03 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.93 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eRC (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.57 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.52 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eApoAI (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.2 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e1.23 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eApoB (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.82 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.8 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eLp(a) (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.25 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.24 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.7196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eApoE (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e3.63 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e3.61 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.8637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eFibrinogen (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.9 (0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.89 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.8909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eINR (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.96 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.96 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eDDI (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.42 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e0.36 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003ePT (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e11.44 (1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e11.34 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eTT (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e18.29 (2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e18.16 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eAPTT (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e30.16 (5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e30.42 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.5676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCK (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e225.36 (827.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e163.75 (511.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eLDH (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e185.74 (143.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e167 (113.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.0552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eMYO (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e55.38 (238.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e38.4 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.2559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eTNI (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e3.21 (17.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e2.47 (17.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.5656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCKMB (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e9.6 (42.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e6.78 (31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eCRP (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e5.38 (14.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e4.28 (12.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eINS (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e13.45 (26.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e13.62 (28.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.9345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eHbAlc (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e6.38 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e6.46 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.3724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.1379%;\"\u003e\n \u003cp\u003eProBNP (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e409.52 (1555.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.931%;\"\u003e\n \u003cp\u003e326.96 (976.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.4177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI, body mass index; SBP , systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; Hb, hemoglobin; RBC, red blood cell; WBC, white blood cell; PLT, platelet; ALT, alanine transaminase; AST, aspartic transaminase; TBIL, total bilirubin; DBIL, direct bilirubin; Cr, creatinine; UN, urea nitrogen; eGFR, estimated glomerular filtration rate; UA, uric acid; TC, total cholesterol; TG, triglyceride; HDLC, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LDLC-1450, an LDL-C reduction of \u0026ge;50% from baseline and LDL-C goal of \u0026lt;1.4 mmol/L(\u0026lt;55mg/dL); Non-HDLC, non-high-density lipoprotein cholesterol; RC, remnant cholesterol; ApoA1 , apolipoprotein A-I; ApoB , apolipoprotein B; Lp(a), lipoprotein a; ApoE , apolipoprotein E; INR, international; DDI, d-dimer ; TT, thrombin time ; APTT, activated partial thromboplastin time; CKMB, creatine kinase isoenzyme; LDH, lactate dehydrogenase; MYO, myoglobin; CRP, C-reactive protein; INS, insulin;HbA1c , hemoglobin A1c; ProBNP , pro\u0026ndash;brain natriuretic peptide; ISR, in-stent restenosis. Compared with Training cohort:\u0026nbsp;\u0026lowast;P \u0026lt; 0.05;\u0026nbsp;\u0026lowast;\u0026lowast;P \u0026lt; 0.01;\u0026nbsp;\u0026lowast;\u0026lowast;\u0026lowast;P \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Defining outcomes and identifying covariates for nomograms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the progression of carotid and coronary plaques was not consistent during the 1-year follow-up, 173 subjects experienced concurrent plaque progression at both sites. Hence, we wanted to determine the distinguishing characteristics of these 173 co-progressors in C+G+ group. The goal of our nomogram was to predict the probability of carotid and coronary plaque co-progression. Therefore, C+G+ group was set as case (co-progressors) and the other patients as control (non-progressors). LASSO regression, which can adjust variable selection and regularization and avoid overfitting when fitting the generalized linear model, was used to screen for risk factors \u003cstrong\u003e(Figure 2)\u003c/strong\u003e. The 57 variables including age, gender, body mass index (BMI), smoking, heart rate (HR), hypertension, dyslipidemia, diabetes mellitus, cerebrovascular disease, arrhythmia, kidney disease, lipid profiles, and other laboratory parameters listed in the \u003cstrong\u003eTable 1\u003c/strong\u003e were taken into LASSO regression and the optimal lambda with 1se criteria resulted in five variables containing the number of involved vessels, BMI, dyslipidemia, diabetes mellitus, and uric acid (\u003cstrong\u003eFigure 3\u003c/strong\u003e). Multivariable odds ratios indicated that the number of involved vessels, BMI, dyslipidemia, diabetes mellitus, and uric acid were risk factors for co-progression of carotid and coronary plaque in both training and validation cohorts (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Development and explanation of an ASPRF nomogram for predicting the probability of carotid and coronary plaque co-progression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the regression results, a nomogram incorporating 5 significant risk factors was created\u0026nbsp;(\u003cstrong\u003eFigure 3\u003c/strong\u003e). The value of each variable corresponds to a score on the point scale axis. The total score was calculated by adding each individual score, and the probability of carotid and coronary plaque co-progression was estimated by projecting the total score to the lower total point scale. For example, the patient was confirmed to have two lesioned vessels (score=10), a BMI of 32.1 (score=67.5), had dyslipidemia (score=22.5) and diabetes mellitus (score=17.5), and had a uric acid level of 520\u0026mu;mol/L (score=45). Summing these individual scores gave a total point of 162.5, implying an 80% probability of co-progression of carotid and coronary plaque.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Validation of the ASPRF Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe C-index of the nomogram for prediction of carotid and coronary plaque co-progression were 0.837 (95% CI= 0.779-0.895) and 0.802 (95% CI=0.762-0.842) in the training and validation cohorts, respectively.\u0026nbsp;The AUCs were 0.823 (95% CI= 0.769-0.887) and 0.803 (95% CI= 0.762-0.842) respectively, indicating satisfactory discrimination of the ASPRF nomogram (\u003cstrong\u003eFigure 4 A, B\u003c/strong\u003e). The calibration results showed favorable consistencies between predicted and observed plaque co-progression probability. Brier scores (range = 0-1), a measurement of the difference between the algorithm\u0026rsquo;s predicted probabilities and actual outcomes, with lower Brier scores representing more accurate predictions of actual events, were 0.106 and 0.124 for the training and validation cohorts, respectively (\u003cstrong\u003eFigure 4 C, D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Decision curve analysis (DCA) of the ASPRF Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDCA is always used to evaluate whether a model is beneficial for clinical decision making, that is, who should receive treatment or therapeutic interventions\u003csup\u003e[18]\u003c/sup\u003e. The decision curves for the ASPRF nomogram in the training and validation cohorts are presented in \u003cstrong\u003eFigure 5.\u003c/strong\u003e We are concerned with the net benefit (NB) in the DCA, namely income minus expenditure. In our study, the \u0026ldquo;income\u0026rdquo; represents true positives - cases of carotid and coronary plaque co-progression; the \u0026ldquo;expenditure\u0026rdquo; represents false positives - unnecessary invasive operations, such as CAG rechecking. In the DCA plot, the Y-axis represents the NB, and the X-axis is the threshold probability. As shown in \u003cstrong\u003eFigure 5\u003c/strong\u003e, the green and blue lines correspond to predictive models in the training and validation cohorts, respectively, exhibiting high NB over a wide range of risk thresholds from 0 to 0.8. It is conducive to understand DCA to know that the benefit is good, and the referred risk threshold varies depending on the specific clinical scenario.\u003c/p\u003e\n\u003cp\u003eThe clinical impact curve visualized the estimated number of people considered high risk and true positives in the range of 0 to 0.6 by using our prediction model. For example, if 1000 participants were screened using a risk threshold of 0.2, 350 patients would be classified as high-risk for co-progression of carotid and coronary plaques, whereas 150 participants would be true positives. Clinical prediction of coronary plaque progression in an additional 650 participants through our nomogram model may prevent them from undergoing unnecessary invasive CAG manipulations.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2 LASSO and Multivariable logistic regression analysis of progression of coronary and Carotid plaque in the training (n=636) and validation(n=273) dataset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNumber of vessels disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e1.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.352-2.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e2.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.558-4.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.161-1.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.558-4.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003edyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e2.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.769-4.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e2.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.354-5.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e2.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.815-4.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.787-3.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003eUric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.002-1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.002-1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.043*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCoefficient in LASSO regression and odd ratio (OR) in multivariable logistic regression of the five risk factors were summarized. LASSO, least absolute shrinkage and selection operator; CI, confident interval.\u0026nbsp;\u0026lowast;P \u0026lt; 0.05;\u0026nbsp;\u0026lowast;\u0026lowast;P \u0026lt; 0.01;\u0026nbsp;\u0026lowast;\u0026lowast;\u0026lowast;P \u0026lt; 0.001.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe \u0026ldquo;classic\u0026rdquo; risk factors for cardiovascular disease may not affect the carotid and coronary vascular systems in the same way\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[19]\u003c/sup\u003e. In our study, the incidence of carotid and coronary plaque co-progression in patients after coronary stenting is approximately 19.03% at 1 year follow-up, which is lower than the incidence of progression at a single vascular lesion site (31.13% for carotid plaque and 61.50% for coronary plaque). The McNemar\u0026rsquo;s test\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[20]\u003c/sup\u003e is used to pair nominal data to determine whether the marginal frequencies of rows and columns are equal. The McNemar\u0026rsquo;s test shows that the co-progression of these two events was not statistically significant. Thus, it may not be sensible or reliable to predict whether coronary atherosclerosis affects downstream myocardial perfusion \u003csup\u003e[21,22]\u003c/sup\u003e, through ultrasound assessment of peripheral carotid plaques, although this is a noninvasive and low-cost procedure, in patients after coronary DES implantation. Nomograms have been frequently used in cancer prognosis \u003csup\u003e[23\u0026ndash;25]\u003c/sup\u003e, and are getting increasingly popular in the cardiovascular field \u003csup\u003e[24,26,27]\u003c/sup\u003e. To date, there is no nomogram that can predict atherosclerotic plaque progression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe nomogram of our study may be a valuable tool for clinical practice, because the included factors are readily available and routinely collected in clinical interviews. A large body of epidemiological evidence has demonstrated a link between some proven risk factors and predisposing risk markers and CAD. Three-vessel disease predicted a worse prognosis than one- or two-vessel lesion among CAD patients \u003csup\u003e[28,29]\u003c/sup\u003e. Dyslipidemia is a major classic and culprit risk factor for atherosclerosis and is associated with the severity and progression of atherosclerotic plaques \u003csup\u003e[30]\u003c/sup\u003e. Elevated blood glucose and obesity are proven risk markers for cardiovascular diseases \u003csup\u003e[31,32]\u003c/sup\u003e. Patients with diabetes showed a higher prevalence and severity of coronary plaque progression of non-stented segments after stenting \u003csup\u003e[33]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 2013 AHA obesity guideline defines overweight as adults with a BMI between 25.0 and 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e and obesity\u0026ge;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Obesity recapitulates many features of the inflammatory process in atherosclerosis, which is considered to be a subacute inflammatory state of the vasculature \u003csup\u003e[34]\u003c/sup\u003e. The obese population always possesses atherogenic dyslipidemia, characterized by high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C), which strongly increases the risk of atherosclerosis\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[35]\u003c/sup\u003e. To more robustly predict prognostic risk, we did not transform the continuous variable BMI into a categorical variable, but simply classified patients as overweight/obese or not obese based on cut-off points of 30 and 25 kg/m\u003csup\u003e2\u003c/sup\u003e. Knowledge of obesity-related multi-vessel plaque progression could guide clinical practice in risk stratification and therapeutic interventions for obese patients with coronary stenting.\u003c/p\u003e\n\u003cp\u003eIn the Chinese population, the overall prevalence of hyperuricemia is 13.3% and has become a common metabolic disease after diabetes \u003csup\u003e[36]\u003c/sup\u003e. Although hyperuricemia is not an independent risk factor for atherosclerosis, it is observed in the Framingham Heart Study that hyperuricemia is a covariable of other known cardiovascular risk factors that contribute to cardiac death and CAD \u003csup\u003e[37]\u003c/sup\u003e. Hence, lowering uric acid may be an effective treatment for atherosclerosis.\u003c/p\u003e\n\u003cp\u003eAtherogenic lipids are known to play a key role in the atherosclerotic process, but it is noteworthy that lipid parameters were not addressed in our ASPRF nomogram. Further analysis of univariable logistic regression of risk factors for carotid and coronary plaque progression showed that lipids were strongly associated with coronary plaque progression, but not with carotid plaque progression (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e). Taken together, the predictive value of lipids for plaque co-progression was diluted by the poor performance of carotid plaque. Nonetheless, the nomogram model somewhat compromises the general concept of dyslipidemia as a consistent atherogenic factor.\u003c/p\u003e\n\u003cp\u003eBy assessing the risk of carotid and coronary plaque co-progression, some intervention or adjustment of therapy will be recommended for individuals whose net benefit is higher than that of a full intervention or no intervention strategy. Similarly, when the predicted risk is low and the net benefit is small, health care costs and potential contraindications such as allergy to iodine contrast and CIN, should be considered. This simple scoring system, in combination with DCA and clinical impact curve, can help physicians and patients evaluate the individualized probability of carotid and coronary plaque co-progression within one year after PCI, thus facilitating personalized treatment adjustments and higher net benefits decisions, which is in line with the trend of personalized medicine.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThe limitations are that although the nomogram has a high risk predictive value and is well discriminated and calibrated, it is a retrospective and single-center cohort study. A multicenter clinical validation would have been better, and external validation may provide more convincing evidence than internal cross-validation. Besides, our ASPRF nomogram may be more applicable to Asian cohorts who had their first MI and received coronary DES implantation and had corresponding data for the five variables in the model. Therefore, extrapolation of the ASPRF nomogram model requires additional consideration and caution. Moreover, while all carotid ultrasound examinations were conducted within the same ultrasound department, the operator was not always the same person, which introduces potential variability.\u0026nbsp;\u003c/p\u003e"},{"header":"6. Conclusion","content":" \u003cp\u003eOur study demonstrates that the progression of carotid plaques and coronary plaques is inconsistent in patients at 1-year follow-up after coronary DES implantation. The ASPRF nomogram includes five easily accessible clinical parameters that can be conveniently used as an evaluation tool for predicting the probability of the aforementioned co-progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions. Access to the data will be granted upon request and after review of a data access agreement to ensure compliance with relevant regulations and ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (grant no. 2021YFC2500602), and the National Natural Science Foundation of China to Z. Chen (81370331) and to Y. Sun (82200311).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChunyan Zhang: Data analysis and interpretation, Writing- Original draft preparation.\u003c/p\u003e\n\u003cp\u003eMin Li: Original data analysis and draft preparation.\u003c/p\u003e\n\u003cp\u003eChi Zhang: Collection and curation of data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYanyi Sun: Data interpretation, Writing- Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003eZhenyue Chen: Conceptualization and design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Perspectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur investigation underscores the absence of substantial concordance in plaque progression between the carotid and coronary arteries in patients undergoing coronary drug-eluting stent (DES) implantation. Clinicians should be cognizant of the necessity for distinct management strategies at these anatomical sites, even within a single patient.\u003c/p\u003e\n\u003cp\u003eThe development of the atherosclerotic plaque risk factor (ASPRF) nomogram constitutes a valuable instrument for risk assessment. Clinicians can leverage this nomogram to discern patients at heightened risk of multisite plaque co-progression, thereby facilitating individualized patient care strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranslational Outlook\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile this study elucidates the aspects of concordance and predictive modeling of plaque progression, subsequent research endeavors should undertake validation of the ASPRF nomogram across heterogeneous patient cohorts to ascertain its universal applicability across varying demographic strata. Moreover, delving into the underlying pathophysiological mechanisms responsible for the observed discordance may unveil novel therapeutic targets and strategies germane to plaque management.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eV. Aboyans, J.-B. Ricco, M.-L.E.L. Bartelink, M. Bj\u0026ouml;rck, M. Brodmann, T. Cohnert, J.-P. Collet, M. Czerny, M. De Carlo, S. Debus, C. Espinola-Klein, T. Kahan, S. Kownator, L. Mazzolai, A.R. Naylor, M. Roffi, J. R\u0026ouml;ther, M. Sprynger, M. Tendera, G. Tepe, M. Venermo, C. Vlachopoulos, I. 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Annals of internal medicine, 131(1), 7\u0026ndash;13. https://doi.org/10.7326/0003-4819-131-1-199907060-00003\u003c/li\u003e\n\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":"Atherosclerosis, Plaque co-progression, Gensini score, Crouse score, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4964687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4964687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackgroud\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: \u003c/em\u003eThe concordance of carotid and coronary plaque progression warrants investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e We aimed to analyze the correlation between them and to explore a nomogram for predicting multisite plaque co-progression in patients implanted with coronary drug-eluting stents (DES).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e 909 patients were enrolled and randomized in a 7:3 ratio to the training and validation groups. LASSO (Least absolute shrinkage and selection operator) and logistic regressions determined risk factors. The nomogram visualized the prediction model. Concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve validated the discrimination and calibration of the model. Decision curve analysis (DCA) and clinical impact curve assessed the clinical utility value of the nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e In our study, there was no statistical correlation between the concordance assessed by McNemar's test. Five variables were selected to establish the nomogram which displayed a robust discriminative ability with C-index of 0.837 [95% confidence interval (CI)=0.779-0.895) and 0.802 (95% CI=0.762-0.842), and area under the receiver operating characteristic curve (AUC) of 0.823 (95% CI=0.769-0.887) and 0.803 (95% CI=0.762-0.842) for the training and validation cohorts, respectively. The calibration results indicated favorable agreement between the predicted and actual probability. Furthermore, DCA and clinical impact curve showed the benefit of the nomogram in the clinical decision.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e The progression of carotid and coronary plaque was not highly concordant in patients with coronary DES at one-year follow up. The atherosclerotic plaque risk factor (ASPRF) nomogram showed effectively predictive value and clinical utility for the co-progression of carotid and coronary plaque after coronary DES implantation.\u003c/p\u003e","manuscriptTitle":"A Nomogram Predicting 1-Year Co-Progression of Carotid and Coronary Plaque in Patients After Coronary Stenting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 04:41:42","doi":"10.21203/rs.3.rs-4964687/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":"fde1b750-bdb8-4cae-a87c-4c0d4075bf08","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38156933,"name":"Health sciences/Cardiology"},{"id":38156934,"name":"Health sciences/Diseases"},{"id":38156935,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-01-02T05:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 04:41:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4964687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4964687","identity":"rs-4964687","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}