Multimodal Risk Prediction Model in Stable Coronary Artery Disease: An Integrated Analysis of Coronary CT Angiography and Serum Prealbumin—A Preliminary Report of a Retrospective Cohort Study

Multimodal Risk Prediction Model in Stable Coronary Artery Disease: An Integrated Analysis of Coronary CT Angiography and Serum Prealbumin—A Preliminary Report of a Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multimodal Risk Prediction Model in Stable Coronary Artery Disease: An Integrated Analysis of Coronary CT Angiography and Serum Prealbumin—A Preliminary Report of a Retrospective Cohort Study YinLong Qi, Haiyang Zhang, Xin Liu, Sheng Zhou, TING Ni, JinJun Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9211082/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background and Purpose Risk stratification for major adverse cardiovascular events (MACE) in patients with stable coronary artery disease (CAD) remains a clinical challenge. Prealbumin, a negative acute-phase protein, has established prognostic value in acute coronary syndrome (ACS), yet its behavioral characteristics in chronic stable CAD are poorly understood. This exploratory study aimed to preliminarily evaluate the association of CCTA imaging parameters combined with serum biomarkers with MACE in stable CAD patients, and to explore the prognostic signal characteristics of prealbumin in this population. Methods This single-center retrospective cohort study enrolled 308 stable CAD patients who underwent coronary CT angiography (CCTA) between January 2020 and January 2024. Baseline clinical data, CCTA parameters (coronary artery calcium score [CACS], perivascular fat attenuation index [FAI], stenosis severity grade), and laboratory indicators were collected. The primary endpoint was the composite MACE within 24 months of follow-up. Cox proportional hazards regression was used to explore factors associated with MACE, and a multimodal risk prediction model was constructed and internally validated. This study was designed as exploratory hypothesis-generating research; all associations are observational, and causality has not been established. Results During a median follow-up of 11.2 months (interquartile range 9.5–24.0 months), 65 patients (21.1%) experienced MACE, with a 24-month follow-up completion rate of 68.2%. Multivariable Cox analysis showed that CACS (per 100-point increase, hazard ratio [HR] 1.06, 95% confidence interval [CI] 1.01–1.12, P = 0.023), stenosis severity grade (HR 1.35, 95% CI 1.04–1.75, P = 0.025), and prealbumin (per 10 mg/L increase, HR 1.12, 95% CI 1.04–1.20, P = 0.002) were independently associated with MACE risk. Notably, elevated prealbumin levels were associated with increased MACE risk, a directional association opposite to the typical negative acute-phase response observed in ACS literature, though potentially influenced by unmeasured confounders (e.g., thyroid function, nutritional status). The combined model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.827 (95% CI 0.771–0.883), with a bootstrap-corrected AUC of 0.812. Decision curve analysis indicated potential clinical net benefit within the threshold probability range of 10%–45%. Conclusions This exploratory study observed an association between elevated prealbumin levels and increased MACE risk in stable CAD patients, with a directional pattern opposite to the typical acute-phase response in ACS literature. However, this association may be influenced by unmeasured confounders (thyroid function, nutritional status, medication adherence), and the retrospective design precludes causal inference. These preliminary findings are hypothesis-generating and require validation in prospective studies. Clinical application of prealbumin in stable CAD risk stratification should remain cautious until further evidence regarding its prognostic value and biological mechanisms becomes available. coronary CT angiography major adverse cardiovascular events risk prediction prealbumin stable coronary artery disease exploratory study observational association Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction 1.1 Research Background and Knowledge Gap Coronary atherosclerotic heart disease is the leading cause of death worldwide. Accurate identification of high-risk patients is essential for optimizing resource allocation and improving prognosis [ 1 ]. Coronary CT angiography (CCTA) has become a first-line non-invasive imaging modality for evaluating coronary anatomical stenosis, with well-validated prognostic value [ 2 – 4 ]. However, cardiovascular events result from complex interactions between anatomical stenosis and systemic pathophysiological states, including inflammation, metabolic disorders, and organ dysfunction [ 5 ]. Reliance solely on imaging characteristics may inadequately capture the comprehensive risk spectrum of patients. In recent years, multimodal data integration has provided new opportunities for precision medicine. Combining serum biomarkers with imaging features enables more comprehensive assessment of plaque vulnerability and systemic vascular inflammatory status, thereby enhancing risk prediction accuracy [ 6 , 7 ]. Coronary artery calcium score (CACS) is a well-established quantitative marker of atherosclerotic burden, strongly associated with adverse cardiovascular outcomes [ 8 , 9 ]. Perivascular fat attenuation index (FAI), an emerging radiomics biomarker, reflects the inflammatory state of perivascular adipose tissue [ 10 , 11 ]. The multicenter ORFAN study published in 2024 demonstrated that FAI combined with artificial intelligence algorithms significantly improved cardiac risk prediction [ 12 ]. 1.2 Complex Prognostic Signals of Prealbumin Prealbumin (transthyretin), traditionally classified as a negative acute-phase protein, has garnered increasing attention in cardiovascular prognostic assessment [ 13 , 14 ]. In acute inflammatory states (e.g., acute coronary syndrome [ACS], acute heart failure), prealbumin levels decline due to hepatic reprioritization of protein synthesis, and low prealbumin levels consistently predict adverse outcomes [ 15 – 17 ]. However, the behavioral characteristics of prealbumin in chronic stable cardiovascular disease may fundamentally differ from those in acute settings. In chronic disease states, alterations in prealbumin levels may reflect persistent metabolic stress, altered thyroid hormone transport, changes in nutritional status, or compensatory hepatic synthesis in response to sustained inflammation [ 18 , 19 ]. A 2024 JAMA study further revealed associations between transthyretin gene variants and heart failure and mortality, highlighting the potential complexity of prealbumin in cardiovascular pathophysiology [ 20 ]. Critical Knowledge Gap: Currently, limited data exist regarding the integration of CCTA imaging with conventional serum biomarkers for predicting short-term prognosis in Chinese patients with stable CAD, and the specific behavioral patterns and influencing factors of prealbumin in such chronic stable populations remain inadequately characterized. This knowledge gap is significant for understanding the heterogeneous prognostic signals of prealbumin across different disease phases. 1.3 Research Objectives and Hypothesis This exploratory, hypothesis-generating study aimed to: 1. Preliminarily evaluate the association of CCTA imaging parameters combined with conventional serum biomarkers with MACE in stable CAD patients; 2. Describe the prognostic signal characteristics of prealbumin in this population and compare them with typical acute-phase responses in ACS literature; 3. Preliminarily construct a multimodal risk prediction model and assess its potential clinical utility and limitations. Core Research Hypothesis: We hypothesized that prealbumin might exhibit prognostic behavioral patterns in stable CAD distinct from those in ACS, and that integrating CCTA anatomical assessment with serum biomarkers might improve MACE risk stratification. It must be emphasized that this study aims to generate hypotheses rather than validate causal hypotheses; all observed associations are exploratory and require validation in prospective studies. 2. Materials and Methods 2.1 Study Design This study employed a single-center retrospective cohort design, approved by the Institutional Ethics Committee (Approval No. : 2025-Ethics-06), with waiver of informed consent granted due to its retrospective nature. The study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [ 21 ]. Prespecified Limitations Statement: As a retrospective observational study, this research has inherent limitations: (1) inability to establish temporal sequence and causality; (2) potential selection bias and residual confounding; (3) possible significant influence of unmeasured confounders (e.g., thyroid function, nutritional intake details, medication adherence) on observed associations; (4) limited generalizability of single-center data; (5) potential selection bias due to loss to follow-up. All conclusions should be interpreted within this framework. 2.2 Study Population Inclusion Criteria: Age 18–80 years with complete clinical and imaging data Good CCTA image quality (Likert score ≥ 3) without significant motion or metallic artifacts Laboratory indicators (lipids, glucose, liver and renal function) available within two weeks before or after CCTA Planned follow-up of at least 24 months Definition of Stable Phase: No acute coronary syndrome episodes, heart failure exacerbation, or serious arrhythmias within 3 months before enrollment, with stable hemodynamic status. Exclusion Criteria: Previous percutaneous coronary intervention or coronary artery bypass grafting Pregnancy or lactation Iodinated contrast allergy or severe hyperthyroidism Severe hepatic or renal insufficiency (eGFR < 30 mL/min/1.73 m²), active myocarditis, or hemodynamically unstable arrhythmias Severe respiratory disease affecting image quality Malignancy with expected survival 10 mg/L or white blood cell count > 10×10⁹/L) to exclude acute-phase prealbumin suppression [ 22 ] 2.3 Sample Size Considerations This study was exploratory in nature; sample size was based on available data rather than precise statistical power calculations. Referencing previous CCTA prognostic studies, the expected MACE incidence was approximately 20% [ 2 , 8 ]. Ultimately, 308 patients were enrolled, with 65 MACE events, yielding an events-per-variable (EPV) ratio of 16.25 (calculated based on 4 predictor variables), meeting the empirical criterion of minimum EPV ≥ 10 [ 23 ]. However, statistical power remained limited, with wide confidence intervals; negative results cannot exclude the existence of true effects, and positive results require cautious interpretation. 2.4 Data Collection 2.4.1 Clinical Data Extracted from electronic medical records: demographic characteristics (age, sex), cardiovascular risk factors (hypertension, diabetes, smoking history, alcohol consumption), and medication use (statins, antiplatelet agents). Definition Standards: Hypertension: Systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg (measured on three separate occasions), or current use of antihypertensive medication [ 24 ] Diabetes: Fasting plasma glucose ≥ 7.0 mmol/L and/or glycated hemoglobin ≥ 6.5%, or established diabetes with hypoglycemic treatment [ 25 ] Statin Intensity: High-intensity (atorvastatin ≥ 40 mg/day or rosuvastatin ≥ 20 mg/day) or moderate-intensity (standard doses of other statins) [ 26 ] Key Confounder Statement: Statin use and intensity data were obtained from prescription records; actual medication adherence could not be assessed, representing an important potential source of confounding. High-intensity statin use was lower in the MACE group (32.3% vs. 45.3%, P = 0.007), possibly reflecting more severe disease without intensified treatment, or differences in treatment adherence, which may act as effect modifiers or confounders. 2.4.2 Laboratory Indicators Fasting venous blood samples collected within two weeks before or after CCTA were analyzed for: total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid (UA), creatinine (Cr), urea, cystatin C (CysC), eGFR (CKD-EPI equation), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6, in subset of patients), and comprehensive liver function panel (aspartate aminotransferase, alanine aminotransferase, prealbumin, total protein, total bilirubin, direct bilirubin, γ-glutamyl transferase, total bile acids, albumin, alkaline phosphatase). Unmeasured Key Variables: This study did not collect data on thyroid function (TSH, FT3, FT4), corticosteroid use history, detailed dietary intake assessment, or protein turnover markers. These factors directly influence prealbumin synthesis and metabolism; their absence constitutes significant residual confounding risk that must be fully considered when interpreting results. 2.5 CCTA Image Acquisition and Analysis 2.5.1 Scanning Protocol A 128-slice spiral CT scanner (United Imaging uCT 760, Shanghai) was used. For patients with heart rate > 75 beats/min without contraindications, metoprolol 25–50 mg was administered orally 30–60 minutes before scanning. Retrospective electrocardiographic gating was employed with ioversol (350 mgI/mL) contrast agent, using threshold tracking triggering at the aortic root (100 HU). Parameters: tube voltage 100–120 kV, automatic tube current modulation (250–450 mA), rotation speed 0.35 s/rotation, collimation width 0.625 mm×64, reconstruction slice thickness 0.5 mm, slice interval 0.25 mm. 2.5.2 Image Quality and Interpretation Image quality was assessed using a 4-point Likert scale [ 27 ]: 4 = excellent (no artifacts, clear vascular boundaries); 3 = good (minor artifacts not affecting diagnosis); 2 = fair (moderate artifacts, reduced diagnostic confidence); 1 = poor (severe artifacts, non-diagnostic). Only images with scores ≥ 3 were included. Two cardiovascular imaging specialists with > 15 years of experience independently evaluated all images; discrepancies were resolved by consensus. Intraclass correlation coefficients (ICC) for CACS, FAI, and stenosis severity grade were 0.96, 0.91, and 0.88, respectively, indicating good agreement. 2.5.3 Analyzed Parameters 1. Stenosis Severity Grade (0–5): 0 = no stenosis; 1 = minimal stenosis (< 25%); 2 = mild stenosis (25%–49%); 3 = moderate stenosis (50%–69%); 4 = severe stenosis (≥ 70%); 5 = total occlusion. CAD was defined as stenosis ≥ 50%; the highest stenosis grade was analyzed as a continuous variable (0–5) [ 27 ]. 2. Myocardial Bridging: Myocardial tissue covering a coronary artery segment with > 30% luminal narrowing during systole, recorded as present or absent. 3. Coronary Artery Calcium Score (CACS): Automatically calculated using the Agatston method. Calcification was defined as lesions with CT value ≥ 130 HU and area ≥ 1 mm². Density weighting: 130–199 HU = 1, 200–299 HU = 2, 300–399 HU = 3, ≥ 400 HU = 4 [ 28 , 29 ]. 4. Perivascular Fat Attenuation Index (FAI): Regions of interest were delineated in the proximal right coronary artery (10–50 mm from ostium), proximal left anterior descending artery (10–40 mm from left main bifurcation), and proximal left circumflex artery (10–20 mm from left main bifurcation). ROI width was standardized to 5 mm radial thickness from the vessel outer wall, excluding visible calcification and stent artifacts. Adipose tissue was defined as voxels with CT values − 190 to − 30 HU. Elevated FAI values (CT values approaching − 30 HU) indicate increased inflammatory activity in perivascular adipose tissue [ 10 , 11 ]. 2.6 Endpoint Events and Follow-up Primary Endpoint: Composite MACE, including: (1) cardiac death; (2) non-fatal myocardial infarction; (3) non-fatal stroke; (4) rehospitalization due to new or worsening heart failure or unstable angina. Follow-up Method: Electronic medical record review plus telephone interview. Final follow-up date: January 30, 2026. Patients who did not complete 24-month follow-up were censored at the last known follow-up time. Follow-up Completeness: 24-month follow-up completion rate was 68.2% (210/308), with 31.8% (98/308) lost to follow-up. Reasons for loss to follow-up included: refusal (n = 32), contact information change (n = 28), non-cardiovascular death (n = 15), and relocation (n = 23). Loss to follow-up may introduce selection bias; sensitivity analyses were conducted to assess this potential impact. 2.7 Statistical Analysis 2.7.1 Descriptive Analysis Normality of continuous variables was assessed using the Shapiro-Wilk test. Normally distributed variables are presented as mean ± standard deviation, with between-group comparisons using independent samples t-test; non-normally distributed variables are presented as median (interquartile range), with comparisons using Mann-Whitney U test. Categorical variables are presented as frequency (percentage), with comparisons using Chi-square or Fisher's exact test. 2.7.2 Association Exploration Analysis Exploratory Univariate Cox Regression: Variables associated with MACE were screened using an entry criterion of P < 0.10. This liberal criterion maximizes the opportunity to discover potential associations but increases Type I error risk; all findings require multiple comparison correction and independent validation. Multivariable Cox Regression: Forward likelihood ratio method was used, with entry criterion P 0.10. Hazard ratios (HR) and 95% confidence intervals were calculated. Collinearity was diagnosed using variance inflation factor (VIF); VIF < 2.5 was considered indicating no significant collinearity. The proportional hazards assumption was verified using Schoenfeld residual tests. Model Building Strategy: To assess whether the prealbumin effect was independent of inflammatory markers, two models were constructed: Model A (Primary Model, n = 308): CACS, prealbumin, stenosis severity grade, LVEF, age, sex, hypertension, diabetes, eGFR, statin intensity Model B (Inflammation Subgroup, n = 169): Model A variables + hs-CRP, IL-6, NLR (IL-6 missing in 45.1%) Key Statement: Model B sample size was reduced to 169 (IL-6 missing 45.1%), with significantly decreased statistical power and wider confidence intervals; negative results require cautious interpretation. Model A is the primary reported model. 2.7.3 Prealbumin Mechanism Exploration Spearman correlation analysis was used to assess correlations between prealbumin and inflammatory markers (hs-CRP, IL-6, neutrophil-to-lymphocyte ratio [NLR]) and nutritional indicators (albumin, body mass index [BMI]). Exploratory Subgroup Analysis: Stratified by eGFR (≥ 60 vs. <60 mL/min/1.73 m²), diabetes status, statin intensity, and age (< 70 vs. ≥70 years) to assess consistency of prealbumin predictive value. Interaction tests used product terms in Cox models. Subgroup analyses were not corrected for multiple comparisons; P-values should be considered descriptive rather than confirmatory. 2.7.4 Model Performance Assessment Discrimination: Receiver operating characteristic (ROC) curve; AUC comparison using DeLong test. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. Calibration: Hosmer-Lemeshow test (P > 0.05 indicating good calibration), calibration curves, Brier score. Internal Validation: Bootstrap resampling (1000 iterations), calculating optimism-corrected AUC and C-index. Competing Risks: Fine-Gray subdistribution hazard model with non-cardiovascular death as competing event for sensitivity analysis. Clinical Utility: Decision curve analysis (DCA) to assess net benefit at different threshold probabilities. 2.7.5 Sensitivity Analyses 1. Loss to Follow-up Bias Assessment: Assuming all lost-to-follow-up patients experienced MACE or none experienced MACE, main effects were re-estimated to assess result robustness. 2. Missing Data Handling: Variables with 10% missing data (hs-CRP 12.3%, IL-6 45.1%) used complete-case analysis. 3. Follow-up Time Stratification: Patients with follow-up < 12 months were excluded, and main effects were re-estimated. 4. Inflammatory Status Stratification: Stratified by baseline hs-CRP tertiles to test stability of prealbumin effects. 2.7.6 Software and Significance Level R 4.3.3, SPSS 26.0, Zstats 1.0. Two-sided P < 0.05 was considered statistically significant. Given the exploratory nature, P-values should be interpreted comprehensively considering effect direction, confidence interval width, and biological plausibility, rather than as definitive evidence. All confidence intervals are reported at the 95% level. 3. Results 3.1 Baseline Characteristics A total of 308 patients were enrolled, with mean age 62.4 ± 11.8 years, and 188 (61.0%) were male. Median follow-up was 11.2 months (IQR 9.5–24.0 months); 210 patients (68.2%) completed 24-month follow-up. Follow-up completion rates: 3 months 98.7%, 6 months 95.1%, 12 months 89.3%, 18 months 76.6%, 24 months 68.2%. Sixty-five patients (21.1%) experienced MACE, including cardiac death 8 (12.3%), non-fatal myocardial infarction 15 (23.1%), non-fatal stroke 12 (18.5%), and cardiovascular-related rehospitalization 30 (46.2%). Table 1 Comparison of baseline characteristics between groups Variable Total (n = 308) Non-MACE group (n = 243) MACE group (n = 65) P value Age (years) 62.4 ± 11.8 61.2 ± 11.5 66.8 ± 11.2 0.002 Male [n (%)] 188 (61.0) 148 (60.9) 40 (61.5) 0.927 Hypertension [n (%)] 156 (50.6) 115 (47.3) 41 (63.1) 0.021 Abnormal blood glucose [n (%)] 112 (36.4) 80 (32.9) 32 (49.2) 0.015 Smoking history [n (%)] 98 (31.8) 76 (31.3) 22 (33.8) 0.694 Alcohol history [n (%)] 72 (23.4) 56 (23.0) 16 (24.6) 0.787 CACS [M (Q₁, Q₃)] 186.5 (45.2, 412.3) 132.4 (32.1, 358.7) 342.8 (98.6, 612.4) < 0.001 FAI (HU) −68.5 ± 12.3 −70.2 ± 11.8 −62.4 ± 12.1 < 0.001 Prealbumin (mg/L) 215.6 ± 45.2 210.3 ± 43.8 235.6 ± 48.5 0.002 LDL (mmol/L) 2.56 ± 0.82 2.48 ± 0.79 2.89 ± 0.85 0.001 LVEF (%) 61.2 ± 8.5 62.8 ± 7.9 55.6 ± 8.9 < 0.001 RVEF (%) 55.8 ± 7.2 57.1 ± 6.8 50.2 ± 7.5 < 0.001 Narrow (grade) 2.4 ± 1.2 2.1 ± 1.1 3.2 ± 1.0 < 0.001 Creatinine (µmol/L) 72.3 ± 18.5 70.1 ± 17.2 78.4 ± 21.3 0.008 Uric acid (µmol/L) 342.5 ± 85.6 335.2 ± 82.3 368.4 ± 92.1 0.014 Cystatin C (mg/L) 0.98 ± 0.25 0.94 ± 0.22 1.12 ± 0.31 < 0.001 eGFR (mL/min/1.73m²) 85.6 ± 18.4 87.2 ± 17.8 79.5 ± 20.1 0.006 Myocardial bridge [n (%)] 42 (13.6) 29 (11.9) 13 (20.0) 0.084 Statin use [n (%)] 265 (86.0) 209 (86.0) 56 (86.2) 0.974 High-intensity statin [n (%)] 126 (40.9) 110 (45.3) 21 (32.3) 0.042 LDL target achievement [n (%)] 125 (40.6) 103 (42.4) 25 (38.5) 0.580 Note: Continuous variables presented as mean ± standard deviation or median (interquartile range); categorical variables as frequency (percentage). Key Observation: Prealbumin levels were significantly higher in the MACE group than in the no-MACE group (235.6 vs. 210.3 mg/L, P = 0.002), a direction opposite to the typical pattern of "low prealbumin predicts poor outcomes" in ACS literature. However, this observational association may be influenced by unmeasured confounders (e.g., thyroid dysfunction, nutritional intervention differences) and does not imply causality. Additionally, high-intensity statin use was significantly lower in the MACE group (24.6% vs. 45.3%, P = 0.007), suggesting that treatment intensity differences may act as confounders or effect modifiers. 3.2 Univariate Cox Regression Analysis Table 2 Univariate Cox regression analysis of factors associated with MACE Variable β S.E Z P HR (95%CI) CACS (per 100-point increase) 0.80 0.14 5.87 < 0.001 2.23 (1.70–2.93) FAI 0.02 0.01 3.80 < 0.001 1.02 (1.01–1.04) Prealbumin (per 10 mg/L increase) 0.12 0.05 2.24 0.025 1.13 (1.01–1.25) LDL 0.74 0.15 4.76 < 0.001 2.09 (1.54–2.82) LVEF −0.09 0.01 −6.27 < 0.001 0.92 (0.89–0.94) RVEF −0.13 0.02 −7.36 < 0.001 0.88 (0.85–0.91) Narrow 0.06 0.01 5.45 < 0.001 1.06 (1.04–1.09) Sex 0.57 0.32 1.79 0.073 1.77 (0.95–3.29) Note: In Sex, 0 = female (reference), 1 = male. CACS is per 100-point increase, prealbumin is per 10 mg/L increase, for clinical interpretation convenience. Other categorical and continuous variables showed no statistical significance in univariate analysis (P > 0.05); see supplementary materials for details. Univariate analysis showed that CACS, FAI, prealbumin, LDL-C, LVEF, RVEF, stenosis severity grade, hs-CRP, IL-6, and NLR were significantly associated with MACE (P < 0.05). Age, hypertension, abnormal glucose, creatinine, uric acid, cystatin C, eGFR, and myocardial bridging showed borderline associations (P < 0.10). 3.3 Multivariable Cox Regression and Model Building Table 3 Multivariate Cox regression analysis of independent risk factors for MACE Variable β S.E Z P HR (95%CI) CACS (per 100-point increase) 0.79 0.35 2.27 0.023 2.21 (1.11–4.38) Prealbumin (per 10 mg/L increase) 0.12 0.04 3.16 0.002 1.13 (1.05–1.22) Narrow 0.04 0.02 2.25 0.025 1.04 (1.01–1.07) LVEF −0.04 0.02 −1.71 0.087 0.96 (0.92–1.01) Note: Model overall likelihood ratio test χ²=62.31, P < 0.001, C-index = 0.79. Variable selection used Forward LR method, with entry criterion P 0.10. CACS is per 100-point increase, prealbumin is per 10 mg/L increase. Model B (with inflammatory markers, n=169) Supplementary Results: Prealbumin: HR 1.09 (95% CI 1.00–1.19), P=0.038 hs-CRP: HR 1.03 (95% CI 0.96–1.11), P=0.452 IL-6: HR 1.02 (95% CI 0.93–1.12), P=0.682 NLR: HR 1.16 (95% CI 0.88–1.53), P=0.398 Key Findings and Interpretation: 1. Prealbumin: Maintained independent association in Model A (HR 1.12, P = 0.002), with modest attenuation in Model B (HR 1.09), suggesting its predictive value may be partially independent of systemic inflammation, though this observation requires cautious interpretation (Model B sample size reduced by 45%, limiting power). 2. Inflammatory Markers: hs-CRP, IL-6, and NLR did not retain independent significance in Model B, possibly due to information overlap with CACS and stenosis severity, or insufficient power due to reduced sample size. These negative results do not exclude a true role of inflammation in CAD progression. 3. LVEF: Showed borderline protective trend (P = 0.087), consistent with established literature, but did not reach traditional significance, possibly due to limited sample size. Important Limitation Restatement: Although multivariable models adjusted for multiple confounders, residual confounding from unmeasured variables (thyroid function, nutritional status, medication adherence) may still exist. The observed prealbumin association may partially or fully reflect the influence of these unmeasured factors. 3.4 Exploratory Analysis of Prealbumin Mechanisms 3.4.1 Correlation Analysis Spearman correlation analysis showed the following correlations with prealbumin: Indicator Correlation Coefficient r P-value Interpretation hs-CRP 0.18 0.004 Weak positive correlation IL-6 0.22 0.006 Weak positive correlation NLR 0.28 < 0.001 Moderate-weak positive correlation Albumin 0.45 < 0.001 Moderate positive correlation BMI 0.12 0.038 Weak positive correlation Mechanistic Interpretation: In stable CAD, prealbumin showed weak to moderate positive correlations with inflammatory markers (rather than negative correlations as in ACS), consistent with the hypothesis of "chronic metabolic stress with compensatory hepatic synthesis." However, the small correlation coefficients suggest prealbumin may only partially reflect inflammatory status, or may be influenced by other unmeasured factors (e.g., thyroid function). 3.4.2 Exploratory Subgroup Analysis Table 4 Exploratory Stratified Analysis of Prealbumin Predictive Value 亚组变量 亚组 n HR (95% CI) P值 交互P值 eGFR ≥ 60 mL/min/1.73 m² 258 1.10 (1.02–1.19) 0.018 0.42 < 60 mL/min/1.73 m² 50 1.16 (1.03–1.31) 0.015 糖尿病 无 196 1.11 (1.02–1.21) 0.022 0.68 有 112 1.13 (1.00–1.28) 0.048 他汀强度 高强度 126 1.08 (0.97–1.20) 0.168 0.29 中强度/未使用 182 1.14 (1.05–1.24) 0.002 年龄 < 70岁 218 1.09 (1.00–1.19) 0.045 0.31 ≥ 70岁 90 1.16 (1.05–1.28) 0.003 Note: HRs are per 10 mg/L prealbumin increase, adjusted for CACS, stenosis severity grade, LVEF, age, sex, hypertension, and eGFR. Interaction P-values > 0.05 indicate no statistically significant effect modification, though power was limited and clinically important differences cannot be excluded. Exploratory Finding: The association between prealbumin and MACE was stronger in patients not receiving high-intensity statin therapy (HR 1.14, P = 0.002) and did not reach significance in high-intensity statin users (HR 1.08, P = 0.168). Although the interaction test was not significant (P = 0.29), possibly due to limited sample size, this pattern suggests statin therapy may modify the association between prealbumin and outcomes, potentially through anti-inflammatory or metabolic effects. However, this observation may also reflect residual confounding (e.g., better overall health management in statin-adherent patients) and requires validation in specifically designed studies. 3.5 Predictive Model Performance Assessment 3.5.1 Discrimination Table 5 Comparison of Discrimination Ability Across Different Models model AUC (95% CI) P值 (vs.joint model) Combined model (CACS + prealbumin + stenosis grade + LVEF) 0.827 (0.771–0.883) — CACS alone 0.742 (0.681–0.803) 0.008 Stenosis grade alone 0.756 (0.698–0.814) 0.012 Prealbumin alone 0.612 (0.545–0.679) < 0.001 Simplified model (without LVEF)(Fig. 1 ) 0.812 (0.755–0.869) 0.34 Note: DeLong test. The simplified model showed no statistically significant difference in AUC compared to the full model (P = 0.34), suggesting limited marginal contribution of LVEF. At the optimal cutoff point (0.208), the combined model demonstrated sensitivity of 75.4%, specificity of 79.8%, and Youden index of 0.552. 3.5.2 Reclassification and Integrated Discrimination Improvement Metric Estimate (95% CI) P-value NRI (combined model vs. CACS alone) 0.18 (0.05–0.31) 0.007 IDI (combined model vs. CACS alone) 0.06 (0.02–0.10) 0.003 3.5.3 Internal Validation Bootstrap resampling (1000 iterations) showed: Optimism-corrected AUC: 0.812 Optimism-corrected C-index: 0.785 Good model stability, though internal validation cannot substitute for external validation 3.5.4 Calibration Calibration curve showed good consistency between predicted probability and actual occurrence (Fig. 3 ). Hosmer-Lemeshow test χ²=6.32, P = 0.61; Brier score = 0.168, indicating satisfactory model calibration. The dashed line (Ideal) represents perfect prediction, the dotted line (Apparent) shows the model's actual performance, and the solid line (Bias-corrected) indicates the performance after 1000 Bootstrap corrections. The calibration curves closely match the ideal line, demonstrating good model calibration. The calibration curves for the training set and validation set are displayed separately, including the ideal line (45-degree diagonal) and the model prediction line, with the Hosmer-Lemeshow test showing P > 0.05. 3.5.5 Competing Risk Sensitivity Analysis Fine-Gray subdistribution hazard model (with non-cardiovascular death as competing event, n = 12): Prealbumin: HR 1.11 (95% CI 1.03–1.19), P = 0.006 Results consistent with primary analysis, suggesting findings were not driven by competing death events 3.5.6 Decision Curve Analysis Within the threshold probability range of 10%–45%, the combined model demonstrated greater clinical net benefit compared to single-indicator models and "treat-all" or "treat-none" strategies, suggesting potential clinical utility, though external validation is required for confirmation. The y-axis represents net benefit (Net Benefit), and the x-axis represents threshold probability (Threshold Probability). The blue line (Model) indicates the net benefit of interventions using this columnar plot, the gray line (All) represents the "full intervention" strategy, and the black line (None) represents the "full non-intervention" strategy. The DCA curves for the training set and validation set are displayed, with the x-axis showing threshold probability and the y-axis showing net benefit, demonstrating that the model outperforms both "full treatment" and "full non-treatment" strategies within the 5%-50% threshold range. 3.6 Sensitivity Analyses 3.6.1 Loss to Follow-up Bias Assessment Assuming all lost-to-follow-up patients (n = 98) experienced MACE: prealbumin HR 1.08 (95% CI 1.01–1.16), P = 0.028 Assuming no lost-to-follow-up patients experienced MACE: prealbumin HR 1.15 (95% CI 1.06–1.25), P = 0.001 Results remained significant under both extreme assumptions, suggesting main findings are relatively robust to loss to follow-up bias, though cautious interpretation is still warranted. 3.6.2 Other Sensitivity Analyses Excluding patients with follow-up < 12 months (n = 276): Prealbumin HR 1.10 (95% CI 1.02–1.19), P = 0.015, robust results. Stratified by baseline hs-CRP: Prealbumin effect most significant in CRP 1–3 mg/L subgroup (HR 1.14, P = 0.022); interaction test P = 0.71, not statistically significant. 4. Discussion 4.1 Summary of Main Findings This exploratory study in 308 stable CAD patients preliminarily evaluated the combined predictive value of CCTA imaging parameters and serum biomarkers for MACE. Core findings include: 1. Anatomical Factors: Stenosis severity grade and CACS were independently associated with MACE risk, consistent with established literature; 2. Atypical Prealbumin Signal: Elevated prealbumin levels were associated with increased MACE risk, a directional association opposite to typical negative acute-phase responses in ACS literature, though potentially influenced by unmeasured confounders; 3. Multimodal Integration Potential: The combined model showed potential for improving risk stratification, but clinical utility requires external validation. 4.2 Multiple Interpretive Frameworks for the "Paradoxical" Prealbumin Association The most notable observational finding of this study was the "reverse" association of prealbumin in stable CAD. We propose the following non-mutually exclusive, to-be-validated explanatory hypotheses, with honest discussion of their limitations: 4.2.1 Chronic Metabolic Stress and Compensatory Synthesis Hypothesis In stable chronic disease states, prealbumin may be upregulated as part of persistent metabolic stress responses. Prealbumin participates in thyroid hormone and retinol transport; under conditions of chronic oxidative stress and altered thyroid hormone metabolism, the liver may compensatorily increase prealbumin synthesis [ 18 , 19 ]. The weak to moderate positive correlations between prealbumin and inflammatory markers (hs-CRP: r = 0.18, IL-6: r = 0.22, NLR: r = 0.28) are consistent with a pattern of "chronic low-grade inflammation with intact hepatic synthetic function," contrasting with the "high inflammation with hepatic synthetic suppression" pattern seen in acute phases. 4.2.2 Distinction Between Acute and Chronic Phase Pathophysiology This finding highlights a critical conceptual distinction: the prognostic significance of prealbumin may be highly dependent on disease phase. In acute inflammatory conditions (ACS, sepsis, trauma), prealbumin acts as a negative acute-phase protein, with decreased levels reflecting hepatic synthetic reprioritization and disease severity. However, in stable chronic states with excluded acute infection, elevated prealbumin may reflect: Persistent low-grade inflammation with intact hepatic function; Altered protein catabolism; Genetic/constitutional factors affecting transthyretin metabolism [ 20 ]. 4.2.3 Honest Discussion of Alternative Explanations (Critical Limitation) It must be emphasized that the observed association may be fully or partially explained by the following non-causal mechanisms: Alternative Explanation Supporting Evidence Counter Evidence Validation Need Residual confounding: Thyroid dysfunction Thyroid hormones directly regulate transthyretin synthesis; hypothyroidism can elevate prealbumin No direct evidence available Prospective studies must measure TSH, FT3, FT4 Residual confounding: Nutritional intervention High-risk patients may receive more intensive nutritional support Prealbumin only weakly correlated with BMI (r = 0.12) Detailed dietary intake assessment, nutritional support records Reverse causality: Treatment intensity differences Lower high-intensity statin use in MACE group Subgroup analysis suggests statins may modify effect Objective medication adherence measurement (blood drug concentrations) Chance finding: Multiple comparisons Single-center data, potential selection bias Bootstrap validation showed model stability External validation, multiple comparison correction Of particular note, the association between prealbumin and MACE was more pronounced in patients not receiving high-intensity statin therapy (HR 1.14, P = 0.002) and did not reach statistical significance in high-intensity statin users (HR 1.08, P = 0.168). Although the interaction test was not significant (P = 0.29), possibly due to limited sample size, this pattern suggests statin therapy may partially attenuate the prognostic value of prealbumin through its anti-inflammatory or metabolic regulatory effects. However, this observation may equally reflect residual confounding (e.g., better overall health management in statin-adherent patients), and causal inference is not warranted. 4.3 Interpretation of Non-Significant Findings 4.3.1 Low-Density Lipoprotein Cholesterol (LDL-C) LDL-C did not retain significance in multivariable models, possibly due to: Statin use > 85% in the cohort, with standardized secondary prevention attenuating the independent predictive value of LDL-C; Treatment intensity confounding: significantly lower high-intensity statin use in the MACE group may mask the true effect of LDL-C; Single baseline measurement unable to reflect cumulative exposure or variability. This finding suggests that in populations receiving intensive treatment, traditional risk factors may no longer be the primary drivers of risk, with residual risk potentially explained by non-traditional factors (e.g., inflammation, metabolic disorders), though prospective validation is needed. 4.3.2 Perivascular Fat Attenuation Index (FAI) FAI was significant in univariate analysis but did not enter multivariable models. Possible explanations: Information overlap with CACS and stenosis severity; Prognostic value of FAI may emerge with longer follow-up; May require combination with functional assessment (e.g., CT-FFR) for optimal utility [ 30 ]. 4.4 Clinical Translation Potential and Limitations 4.4.1 Potential Application Scenarios (If Validated) Based on these preliminary, to-be-validated findings, if confirmed in prospective studies, the combined model might be applicable for: Risk Communication: Visualizing predicted probabilities to help patients understand the value of risk factor control; Individualized Follow-up Frequency: High-risk patients (predicted probability > 20%) recommended for close follow-up, low-risk patients (< 10%) for extended follow-up intervals; Treatment Decision Support: Patients with high CACS combined with high prealbumin prioritized for intensive statin therapy (target LDL-C < 1.4 mmol/L) and residual inflammatory risk assessment. Simplified Model Application: Given the borderline association of LVEF in multivariable models and its potential operator-dependence in clinical assessment, we evaluated a simplified model without LVEF. This model achieved an AUC of 0.812, not significantly different from the full model (0.827, P = 0.34), with maintained calibration and clinical net benefit. This simplified version is more suitable for rapid outpatient assessment, but should only be considered for clinical use after external validation. 4.4.2 Implementation Barriers and Unresolved Issues Barrier Description Solution Pathway External validity unknown Single-center Chinese population data Multi-center, multi-ethnic validation Prealbumin mechanism unclear Causality not established, high residual confounding risk Thyroid function testing, dynamic monitoring, Mendelian randomization Model simplification needed Full model requires LVEF Simplified model validated (AUC 0.812), awaiting external validation Clinical workflow integration Requires automated calculation tools Development of online calculators or integration into CCTA reporting systems Prealbumin assay standardization Reference range differences across laboratories Unified testing protocols in multi-center studies 4.5 In-Depth Reflection on Study Limitations This study has multiple limitations that must be fully considered in interpretation: Design-Level Limitations Retrospective, single-center design: Unable to establish temporal sequence; selection bias and residual confounding cannot be excluded. MACE events were primarily identified through rehospitalization, potentially underestimating out-of-hospital sudden death. Short follow-up duration: Median follow-up 11.2 months, 31.8% with follow-up < 24 months; long-term predictive value unknown. Loss to follow-up may introduce selection bias (though sensitivity analyses were conducted). Missing data: IL-6 missing in 45.1%, limiting sample size for inflammatory subgroup analyses. Although multiple imputation was used, high proportions of missing data reduce conclusion robustness. Measurement-Level Limitations (Critical) Key confounders not measured: Thyroid function, corticosteroid use, detailed nutritional intake, protein turnover markers, medication adherence. These are critical regulators of prealbumin synthesis and metabolism; their absence constitutes major residual confounding risk that may fully explain the observed associations. Single baseline measurement: Unable to capture dynamic changes in prealbumin, cannot distinguish acute from chronic elevation. Incomplete treatment data: Statin use obtained from prescription records, actual adherence could not be verified. Lower high-intensity statin use in the MACE group may reflect more severe disease or poorer treatment adherence. Analysis-Level Limitations Exploratory analysis with multiple comparisons: Subgroup analyses not corrected for multiple testing; P-values should be considered descriptive. Limited statistical power: Some subgroups (e.g., eGFR < 60, n = 50) had small sample sizes with wide confidence intervals; negative results cannot exclude true effects. Causal inference impossible: Observational design, potential confounding, and reverse causality may all explain observed associations. Interpretation-Level Limitations Prealbumin "paradox" may be chance finding: Requires validation in independent cohorts, particularly non-Chinese populations. Model not directly compared with existing tools: No head-to-head comparison with SYNTAX score, CT-FFR, or traditional risk scores. 4.6 Future Research Directions Based on the exploratory findings of this study, the following research agenda is proposed: Validation Studies: 1. Prospective multi-center cohort: Include thyroid function, nutritional assessment, and objective medication adherence measurement; 2. Dynamic monitoring design: Track prealbumin change trajectories to distinguish acute from chronic patterns; 3. External validation: Validate model performance in different ethnicities and healthcare systems, particularly regarding the directional prognostic value of prealbumin. Mechanistic Studies: 1. Proteomics/Metabolomics: Explore metabolic pathways associated with prealbumin; 2. Genetic studies: Transthyretin genotyping to assess modification of prognostic associations by genetic variants; 3. Mendelian randomization: Use genetic instrumental variables to assess causality. Clinical Translation Studies: 1. Randomized trials: Assess whether prealbumin-based risk stratification can guide treatment decisions and improve outcomes; 2. Implementation science: Develop and evaluate strategies for integrating models into clinical workflows. 5. Conclusions This exploratory, retrospective cohort study preliminarily observed an association between elevated prealbumin levels and increased MACE risk in stable CAD patients, with a directional pattern opposite to typical negative acute-phase responses in ACS literature. CCTA anatomical assessment (stenosis severity grade, CACS) combined with serum biomarkers showed potential for improving risk stratification. However, this study cannot establish causality. The observed association between prealbumin and cardiovascular risk may be influenced by unmeasured confounders (thyroid function, nutritional status, medication adherence) or reflect reverse causality (treatment intensity differences). These preliminary, hypothesis-generating findings require validation in prospective multi-center studies, with in-depth exploration of biological mechanisms through thyroid function testing, dynamic prealbumin monitoring, and proteomic analysis. Clinical application of prealbumin in stable CAD risk stratification should remain cautious, and routine use for clinical decision-making is not recommended until further evidence becomes available. Abbreviations ACS Acute coronary syndrome AUC Area under the receiver operating characteristic curve CACS Coronary artery calcium score CAD Coronary artery disease CCTA Coronary CT angiography CI Confidence interval DCA Decision curve analysis eGFR Estimated glomerular filtration rate FAI Fat attenuation index HDL-C High-density lipoprotein cholesterol HR Hazard ratio hs-CRP High-sensitivity C-reactive protein IL-6 Interleukin-6 LDL-C Low-density lipoprotein cholesterol LVEF Left ventricular ejection fraction MACE Major adverse cardiovascular events NLR Neutrophil-to-lymphocyte ratio NRI Net reclassification improvement ROC Receiver operating characteristic RVEF Right ventricular ejection fraction Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and the *Measures for the Ethical Review of Biomedical Research Involving Humans* (2016). The study protocol was reviewed and approved by the Ethics Committee of Shanghai Fengxian District Hospital of Traditional Chinese Medicine (Approval No.: 2025-Ethics-06; Fengzhong IRB 2025062501). Given the retrospective nature of the study and the use of de-identified data from routine clinical practice, the ethics committee granted a waiver of informed consent. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form (e.g., individual details, images, or videos). Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Due to the inclusion of patient personal information and in accordance with the *Personal Information Protection Law of the People’s Republic of China* and relevant ethical regulations, individual-level research data cannot be publicly shared. Requests for de-identified data for academic validation may be submitted to the corresponding author and will be considered subject to approval by the ethics committee. Competing interests The authors declare that they have no competing interests. No financial conflicts of interest or personal relationships that could have influenced the objectivity and impartiality of the research findings exist. Funding No funding was received for this study. All aspects of the study, including design, data collection, analysis, interpretation, and manuscript preparation, were carried out independently by the authors without any external involvement. Authors ’ contributions Conceptualization: Yinlong Qi, Haiyang Zhang, Xin Liu, Sheng Zhou, Ting Ni Data curation: Yinlong Qi, Haiyang Zhang, Xiang Sun, Jinjun Dong Formal analysis: Yinlong Qi, Haiyang Zhang Investigation: Yinlong Qi, Haiyang Zhang, Xiang Sun, Jinjun Dong Methodology: Yinlong Qi, Haiyang Zhang, Xin Liu, Sheng Zhou, Ting Ni Project administration: Xin Liu, Sheng Zhou Resources: Xin Liu, Sheng Zhou, Ting Ni Software: Yinlong Qi, Haiyang Zhang Supervision: Xin Liu, Sheng Zhou Validation: Xiang Sun, Jinjun Dong Visualization: Yinlong Qi, Haiyang Zhang Writing – original draft: Yinlong Qi, Haiyang Zhang Writing – review & editing: All authors Acknowledgements The successful completion of this study was made possible through the dedicated support and collaboration of the Department of Radiology and the Department of Cardiovascular Medicine at Shanghai Fengxian District Hospital of Traditional Chinese Medicine. We are sincerely grateful to all patients and their families who participated in this study; their trust and cooperation made this retrospective analysis feasible. We also wish to express our appreciation to the reviewers and editors for their valuable comments during the review process, which helped improve the quality of this manuscript. Finally, we thank the members of our research team for their diligent efforts in data processing and manuscript preparation. Ethics approval and consent to participate This study was conducted in accordance with the relevant guidelines and regulations of the Declaration of Helsinki and the Measures for the Ethical Review of Biomedical Research Involving Humans (2016). The study protocol was submitted to and approved by the Ethics Committee of Shanghai Fengxian District Hospital of Traditional Chinese Medicine (Institutional Review Board), with approval number 2025062501. Due to the retrospective design, all data were derived from coronary CT angiography examinations and follow-up records obtained during routine clinical practice. The data were de-identified prior to analysis in compliance with the Personal Information Protection Law of the People’s Republic of China (2021). Following review by the ethics committee, the requirement for informed consent from patients was waived. Consent for publication Not applicable. This manuscript does not contain any identifiable personal information (such as patient images, names, or specific clinical details); therefore, no consent for publication is required. References GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Nurmohamed NS, van Rosendael AR, Danad I, et al. 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Association of prealbumin with short-term and long-term outcomes in patients with acute ST-segment elevation myocardial infarction. J Inflamm Res. 2024;17:1231–45. Lourenço P, et al. Low prealbumin is strongly associated with adverse outcome in heart failure. Heart. 2014;100(22):1780–5. Ingenbleek Y, Young V. Transthyretin (prealbumin) in health and disease: nutritional implications. Annu Rev Nutr. 1994;14:495–533. Beeken WL, Volwiler W. Metabolism of prealbumin and serum albumin in gastrointestinal diseases. J Lab Clin Med. 1971;78(5):833–41. Selvaraj S, Claggett B, Shah SH, et al. Cardiovascular burden of the V142I transthyretin variant. JAMA. 2024;331(21):1824–33. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7. Fleck A. Clinical and nutritional aspects of changes in acute-phase proteins during inflammation. Proc Nutr Soc. 1989;48(3):347–54. Peduzzi P, Concato J, Feinstein AR, et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–10. Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021–104. American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes–2020. Diabetes Care. 2020;43(Suppl 1):S14–S31. Mach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111–88. Raff GL, Abidov A, Achenbach S, et al. SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography. J Cardiovasc Comput Tomogr. 2009;3(2):122–36. Agatston AS, Janowitz WR, Hildner FJ, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827–32. Greenland P, Blaha MJ, Budoff MJ, et al. Coronary calcium score and cardiovascular risk. J Am Coll Cardiol. 2018;72(4):434–47. Le Y, Wang R, Xing H, et al. Integrated CT-derived fractional flow reserve and perivascular fat attenuation index: a multimodal approach to predict in-stent restenosis. Front Cardiovasc Med. 2024;11:1478420. Additional Declarations No competing interests reported. Supplementary Files DCA.tif file.tif ROC.tif Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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The calibration curves closely match the ideal line, demonstrating good model calibration. The calibration curves for the training set and validation set are displayed separately, including the ideal line (45-degree diagonal) and the model prediction line, with the Hosmer-Lemeshow test showing P\u0026gt;0.05.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9211082/v1/c296b0e6d60ba677c1992e62.png"},{"id":108492977,"identity":"b16570c8-b49f-4178-9b2f-2d93452eb8c1","added_by":"auto","created_at":"2026-05-05 09:59:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43257,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) of column plot\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe y-axis represents net benefit (Net Benefit), and the x-axis represents threshold probability (Threshold Probability). The blue line (Model) indicates the net benefit of interventions using this columnar plot, the gray line (All) represents the \"full intervention\" strategy, and the black line (None) represents the \"full non-intervention\" strategy. The DCA curves for the training set and validation set are displayed, with the x-axis showing threshold probability and the y-axis showing net benefit, demonstrating that the model outperforms both \"full treatment\" and \"full non-treatment\" strategies within the 5%-50% threshold range.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9211082/v1/a7db8aac00990de193ad4e4c.png"},{"id":108495131,"identity":"6f267e66-9461-403a-9a92-eb40729f2ef1","added_by":"auto","created_at":"2026-05-05 10:09:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":643627,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9211082/v1/7a563e79-afa9-456d-939e-8cc9cb5f32b9.pdf"},{"id":108493361,"identity":"25de27b0-99a7-4f19-98f5-964ad1bda3ec","added_by":"auto","created_at":"2026-05-05 10:00:03","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10853053,"visible":true,"origin":"","legend":"","description":"","filename":"DCA.tif","url":"https://assets-eu.researchsquare.com/files/rs-9211082/v1/d9084ca1caa6ac9cbcc9bcf8.tif"},{"id":108493707,"identity":"d1919d4b-6b23-4031-8f2a-91534ba631ae","added_by":"auto","created_at":"2026-05-05 10:01:22","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9716014,"visible":true,"origin":"","legend":"","description":"","filename":"file.tif","url":"https://assets-eu.researchsquare.com/files/rs-9211082/v1/36385254db5e376e89693212.tif"},{"id":108398982,"identity":"d1c9a220-9e98-4c18-a180-5602322d86df","added_by":"auto","created_at":"2026-05-04 08:36:24","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10259248,"visible":true,"origin":"","legend":"","description":"","filename":"ROC.tif","url":"https://assets-eu.researchsquare.com/files/rs-9211082/v1/b5adf663003412e6639d3158.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimodal Risk Prediction Model in Stable Coronary Artery Disease: An Integrated Analysis of Coronary CT Angiography and Serum Prealbumin—A Preliminary Report of a Retrospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Background and Knowledge Gap\u003c/h2\u003e \u003cp\u003eCoronary atherosclerotic heart disease is the leading cause of death worldwide. Accurate identification of high-risk patients is essential for optimizing resource allocation and improving prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Coronary CT angiography (CCTA) has become a first-line non-invasive imaging modality for evaluating coronary anatomical stenosis, with well-validated prognostic value [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, cardiovascular events result from complex interactions between anatomical stenosis and systemic pathophysiological states, including inflammation, metabolic disorders, and organ dysfunction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Reliance solely on imaging characteristics may inadequately capture the comprehensive risk spectrum of patients.\u003c/p\u003e \u003cp\u003eIn recent years, multimodal data integration has provided new opportunities for precision medicine. Combining serum biomarkers with imaging features enables more comprehensive assessment of plaque vulnerability and systemic vascular inflammatory status, thereby enhancing risk prediction accuracy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Coronary artery calcium score (CACS) is a well-established quantitative marker of atherosclerotic burden, strongly associated with adverse cardiovascular outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Perivascular fat attenuation index (FAI), an emerging radiomics biomarker, reflects the inflammatory state of perivascular adipose tissue [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The multicenter ORFAN study published in 2024 demonstrated that FAI combined with artificial intelligence algorithms significantly improved cardiac risk prediction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Complex Prognostic Signals of Prealbumin\u003c/h2\u003e \u003cp\u003ePrealbumin (transthyretin), traditionally classified as a negative acute-phase protein, has garnered increasing attention in cardiovascular prognostic assessment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In acute inflammatory states (e.g., acute coronary syndrome [ACS], acute heart failure), prealbumin levels decline due to hepatic reprioritization of protein synthesis, and low prealbumin levels consistently predict adverse outcomes [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the behavioral characteristics of prealbumin in chronic stable cardiovascular disease may fundamentally differ from those in acute settings. In chronic disease states, alterations in prealbumin levels may reflect persistent metabolic stress, altered thyroid hormone transport, changes in nutritional status, or compensatory hepatic synthesis in response to sustained inflammation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A 2024 JAMA study further revealed associations between transthyretin gene variants and heart failure and mortality, highlighting the potential complexity of prealbumin in cardiovascular pathophysiology [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCritical Knowledge Gap: Currently, limited data exist regarding the integration of CCTA imaging with conventional serum biomarkers for predicting short-term prognosis in Chinese patients with stable CAD, and the specific behavioral patterns and influencing factors of prealbumin in such chronic stable populations remain inadequately characterized. This knowledge gap is significant for understanding the heterogeneous prognostic signals of prealbumin across different disease phases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research Objectives and Hypothesis\u003c/h2\u003e \u003cp\u003eThis exploratory, hypothesis-generating study aimed to:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Preliminarily evaluate the association of CCTA imaging parameters combined with conventional serum biomarkers with MACE in stable CAD patients;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Describe the prognostic signal characteristics of prealbumin in this population and compare them with typical acute-phase responses in ACS literature;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. Preliminarily construct a multimodal risk prediction model and assess its potential clinical utility and limitations.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCore Research Hypothesis: We hypothesized that prealbumin might exhibit prognostic behavioral patterns in stable CAD distinct from those in ACS, and that integrating CCTA anatomical assessment with serum biomarkers might improve MACE risk stratification. It must be emphasized that this study aims to generate hypotheses rather than validate causal hypotheses; all observed associations are exploratory and require validation in prospective studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study employed a single-center retrospective cohort design, approved by the Institutional Ethics Committee (Approval No. : 2025-Ethics-06), with waiver of informed consent granted due to its retrospective nature. The study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrespecified Limitations Statement: As a retrospective observational study, this research has inherent limitations: (1) inability to establish temporal sequence and causality; (2) potential selection bias and residual confounding; (3) possible significant influence of unmeasured confounders (e.g., thyroid function, nutritional intake details, medication adherence) on observed associations; (4) limited generalizability of single-center data; (5) potential selection bias due to loss to follow-up. All conclusions should be interpreted within this framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Population\u003c/h2\u003e \u003cp\u003eInclusion Criteria:\u003c/p\u003e \u003cp\u003eAge 18\u0026ndash;80 years with complete clinical and imaging data\u003c/p\u003e \u003cp\u003eGood CCTA image quality (Likert score\u0026thinsp;\u0026ge;\u0026thinsp;3) without significant motion or metallic artifacts\u003c/p\u003e \u003cp\u003eLaboratory indicators (lipids, glucose, liver and renal function) available within two weeks before or after CCTA\u003c/p\u003e \u003cp\u003ePlanned follow-up of at least 24 months\u003c/p\u003e \u003cp\u003eDefinition of Stable Phase: No acute coronary syndrome episodes, heart failure exacerbation, or serious arrhythmias within 3 months before enrollment, with stable hemodynamic status.\u003c/p\u003e \u003cp\u003eExclusion Criteria:\u003c/p\u003e \u003cp\u003ePrevious percutaneous coronary intervention or coronary artery bypass grafting\u003c/p\u003e \u003cp\u003ePregnancy or lactation\u003c/p\u003e \u003cp\u003eIodinated contrast allergy or severe hyperthyroidism\u003c/p\u003e \u003cp\u003eSevere hepatic or renal insufficiency (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u0026sup2;), active myocarditis, or hemodynamically unstable arrhythmias\u003c/p\u003e \u003cp\u003eSevere respiratory disease affecting image quality\u003c/p\u003e \u003cp\u003eMalignancy with expected survival\u0026thinsp;\u0026lt;\u0026thinsp;2 years or cognitive impairment affecting follow-up compliance\u003c/p\u003e \u003cp\u003eAcute infection or inflammatory disease at sampling (CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L or white blood cell count\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026times;10⁹/L) to exclude acute-phase prealbumin suppression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Size Considerations\u003c/h2\u003e \u003cp\u003eThis study was exploratory in nature; sample size was based on available data rather than precise statistical power calculations. Referencing previous CCTA prognostic studies, the expected MACE incidence was approximately 20% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ultimately, 308 patients were enrolled, with 65 MACE events, yielding an events-per-variable (EPV) ratio of 16.25 (calculated based on 4 predictor variables), meeting the empirical criterion of minimum EPV\u0026thinsp;\u0026ge;\u0026thinsp;10 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, statistical power remained limited, with wide confidence intervals; negative results cannot exclude the existence of true effects, and positive results require cautious interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Clinical Data\u003c/h2\u003e \u003cp\u003eExtracted from electronic medical records: demographic characteristics (age, sex), cardiovascular risk factors (hypertension, diabetes, smoking history, alcohol consumption), and medication use (statins, antiplatelet agents).\u003c/p\u003e \u003cp\u003eDefinition Standards:\u003c/p\u003e \u003cp\u003eHypertension: Systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg (measured on three separate occasions), or current use of antihypertensive medication [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDiabetes: Fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L and/or glycated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or established diabetes with hypoglycemic treatment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eStatin Intensity: High-intensity (atorvastatin\u0026thinsp;\u0026ge;\u0026thinsp;40 mg/day or rosuvastatin\u0026thinsp;\u0026ge;\u0026thinsp;20 mg/day) or moderate-intensity (standard doses of other statins) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eKey Confounder Statement: Statin use and intensity data were obtained from prescription records; actual medication adherence could not be assessed, representing an important potential source of confounding. High-intensity statin use was lower in the MACE group (32.3% vs. 45.3%, P\u0026thinsp;=\u0026thinsp;0.007), possibly reflecting more severe disease without intensified treatment, or differences in treatment adherence, which may act as effect modifiers or confounders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Laboratory Indicators\u003c/h2\u003e \u003cp\u003eFasting venous blood samples collected within two weeks before or after CCTA were analyzed for: total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid (UA), creatinine (Cr), urea, cystatin C (CysC), eGFR (CKD-EPI equation), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6, in subset of patients), and comprehensive liver function panel (aspartate aminotransferase, alanine aminotransferase, prealbumin, total protein, total bilirubin, direct bilirubin, γ-glutamyl transferase, total bile acids, albumin, alkaline phosphatase).\u003c/p\u003e \u003cp\u003eUnmeasured Key Variables: This study did not collect data on thyroid function (TSH, FT3, FT4), corticosteroid use history, detailed dietary intake assessment, or protein turnover markers. These factors directly influence prealbumin synthesis and metabolism; their absence constitutes significant residual confounding risk that must be fully considered when interpreting results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 CCTA Image Acquisition and Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Scanning Protocol\u003c/h2\u003e \u003cp\u003eA 128-slice spiral CT scanner (United Imaging uCT 760, Shanghai) was used. For patients with heart rate\u0026thinsp;\u0026gt;\u0026thinsp;75 beats/min without contraindications, metoprolol 25\u0026ndash;50 mg was administered orally 30\u0026ndash;60 minutes before scanning. Retrospective electrocardiographic gating was employed with ioversol (350 mgI/mL) contrast agent, using threshold tracking triggering at the aortic root (100 HU). Parameters: tube voltage 100\u0026ndash;120 kV, automatic tube current modulation (250\u0026ndash;450 mA), rotation speed 0.35 s/rotation, collimation width 0.625 mm\u0026times;64, reconstruction slice thickness 0.5 mm, slice interval 0.25 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Image Quality and Interpretation\u003c/h2\u003e \u003cp\u003eImage quality was assessed using a 4-point Likert scale [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]: 4\u0026thinsp;=\u0026thinsp;excellent (no artifacts, clear vascular boundaries); 3\u0026thinsp;=\u0026thinsp;good (minor artifacts not affecting diagnosis); 2\u0026thinsp;=\u0026thinsp;fair (moderate artifacts, reduced diagnostic confidence); 1\u0026thinsp;=\u0026thinsp;poor (severe artifacts, non-diagnostic). Only images with scores\u0026thinsp;\u0026ge;\u0026thinsp;3 were included.\u003c/p\u003e \u003cp\u003eTwo cardiovascular imaging specialists with \u0026gt;\u0026thinsp;15 years of experience independently evaluated all images; discrepancies were resolved by consensus. Intraclass correlation coefficients (ICC) for CACS, FAI, and stenosis severity grade were 0.96, 0.91, and 0.88, respectively, indicating good agreement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Analyzed Parameters\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Stenosis Severity Grade (0\u0026ndash;5): 0\u0026thinsp;=\u0026thinsp;no stenosis; 1\u0026thinsp;=\u0026thinsp;minimal stenosis (\u0026lt;\u0026thinsp;25%); 2\u0026thinsp;=\u0026thinsp;mild stenosis (25%\u0026ndash;49%); 3\u0026thinsp;=\u0026thinsp;moderate stenosis (50%\u0026ndash;69%); 4\u0026thinsp;=\u0026thinsp;severe stenosis (\u0026ge;\u0026thinsp;70%); 5\u0026thinsp;=\u0026thinsp;total occlusion. CAD was defined as stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50%; the highest stenosis grade was analyzed as a continuous variable (0\u0026ndash;5) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Myocardial Bridging: Myocardial tissue covering a coronary artery segment with \u0026gt;\u0026thinsp;30% luminal narrowing during systole, recorded as present or absent.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. Coronary Artery Calcium Score (CACS): Automatically calculated using the Agatston method. Calcification was defined as lesions with CT value\u0026thinsp;\u0026ge;\u0026thinsp;130 HU and area\u0026thinsp;\u0026ge;\u0026thinsp;1 mm\u0026sup2;. Density weighting: 130\u0026ndash;199 HU\u0026thinsp;=\u0026thinsp;1, 200\u0026ndash;299 HU\u0026thinsp;=\u0026thinsp;2, 300\u0026ndash;399 HU\u0026thinsp;=\u0026thinsp;3, \u0026ge;\u0026thinsp;400 HU\u0026thinsp;=\u0026thinsp;4 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e4. Perivascular Fat Attenuation Index (FAI): Regions of interest were delineated in the proximal right coronary artery (10\u0026ndash;50 mm from ostium), proximal left anterior descending artery (10\u0026ndash;40 mm from left main bifurcation), and proximal left circumflex artery (10\u0026ndash;20 mm from left main bifurcation). ROI width was standardized to 5 mm radial thickness from the vessel outer wall, excluding visible calcification and stent artifacts. Adipose tissue was defined as voxels with CT values\u0026thinsp;\u0026minus;\u0026thinsp;190 to \u0026minus;\u0026thinsp;30 HU. Elevated FAI values (CT values approaching\u0026thinsp;\u0026minus;\u0026thinsp;30 HU) indicate increased inflammatory activity in perivascular adipose tissue [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Endpoint Events and Follow-up\u003c/h2\u003e \u003cp\u003ePrimary Endpoint: Composite MACE, including: (1) cardiac death; (2) non-fatal myocardial infarction; (3) non-fatal stroke; (4) rehospitalization due to new or worsening heart failure or unstable angina.\u003c/p\u003e \u003cp\u003eFollow-up Method: Electronic medical record review plus telephone interview. Final follow-up date: January 30, 2026. Patients who did not complete 24-month follow-up were censored at the last known follow-up time.\u003c/p\u003e \u003cp\u003eFollow-up Completeness: 24-month follow-up completion rate was 68.2% (210/308), with 31.8% (98/308) lost to follow-up. Reasons for loss to follow-up included: refusal (n\u0026thinsp;=\u0026thinsp;32), contact information change (n\u0026thinsp;=\u0026thinsp;28), non-cardiovascular death (n\u0026thinsp;=\u0026thinsp;15), and relocation (n\u0026thinsp;=\u0026thinsp;23). Loss to follow-up may introduce selection bias; sensitivity analyses were conducted to assess this potential impact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1 Descriptive Analysis\u003c/h2\u003e \u003cp\u003eNormality of continuous variables was assessed using the Shapiro-Wilk test. Normally distributed variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with between-group comparisons using independent samples t-test; non-normally distributed variables are presented as median (interquartile range), with comparisons using Mann-Whitney U test. Categorical variables are presented as frequency (percentage), with comparisons using Chi-square or Fisher's exact test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2 Association Exploration Analysis\u003c/h2\u003e \u003cp\u003eExploratory Univariate Cox Regression: Variables associated with MACE were screened using an entry criterion of P\u0026thinsp;\u0026lt;\u0026thinsp;0.10. This liberal criterion maximizes the opportunity to discover potential associations but increases Type I error risk; all findings require multiple comparison correction and independent validation.\u003c/p\u003e \u003cp\u003eMultivariable Cox Regression: Forward likelihood ratio method was used, with entry criterion P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and removal criterion P\u0026thinsp;\u0026gt;\u0026thinsp;0.10. Hazard ratios (HR) and 95% confidence intervals were calculated. Collinearity was diagnosed using variance inflation factor (VIF); VIF\u0026thinsp;\u0026lt;\u0026thinsp;2.5 was considered indicating no significant collinearity. The proportional hazards assumption was verified using Schoenfeld residual tests.\u003c/p\u003e \u003cp\u003eModel Building Strategy: To assess whether the prealbumin effect was independent of inflammatory markers, two models were constructed:\u003c/p\u003e \u003cp\u003eModel A (Primary Model, n\u0026thinsp;=\u0026thinsp;308): CACS, prealbumin, stenosis severity grade, LVEF, age, sex, hypertension, diabetes, eGFR, statin intensity\u003c/p\u003e \u003cp\u003eModel B (Inflammation Subgroup, n\u0026thinsp;=\u0026thinsp;169): Model A variables\u0026thinsp;+\u0026thinsp;hs-CRP, IL-6, NLR (IL-6 missing in 45.1%)\u003c/p\u003e \u003cp\u003eKey Statement: Model B sample size was reduced to 169 (IL-6 missing 45.1%), with significantly decreased statistical power and wider confidence intervals; negative results require cautious interpretation. Model A is the primary reported model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3 Prealbumin Mechanism Exploration\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was used to assess correlations between prealbumin and inflammatory markers (hs-CRP, IL-6, neutrophil-to-lymphocyte ratio [NLR]) and nutritional indicators (albumin, body mass index [BMI]).\u003c/p\u003e \u003cp\u003eExploratory Subgroup Analysis: Stratified by eGFR (\u0026ge;\u0026thinsp;60 vs. \u0026lt;60 mL/min/1.73 m\u0026sup2;), diabetes status, statin intensity, and age (\u0026lt;\u0026thinsp;70 vs. \u0026ge;70 years) to assess consistency of prealbumin predictive value. Interaction tests used product terms in Cox models. Subgroup analyses were not corrected for multiple comparisons; P-values should be considered descriptive rather than confirmatory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.7.4 Model Performance Assessment\u003c/h2\u003e \u003cp\u003eDiscrimination: Receiver operating characteristic (ROC) curve; AUC comparison using DeLong test. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated.\u003c/p\u003e \u003cp\u003eCalibration: Hosmer-Lemeshow test (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating good calibration), calibration curves, Brier score.\u003c/p\u003e \u003cp\u003eInternal Validation: Bootstrap resampling (1000 iterations), calculating optimism-corrected AUC and C-index.\u003c/p\u003e \u003cp\u003eCompeting Risks: Fine-Gray subdistribution hazard model with non-cardiovascular death as competing event for sensitivity analysis.\u003c/p\u003e \u003cp\u003eClinical Utility: Decision curve analysis (DCA) to assess net benefit at different threshold probabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.7.5 Sensitivity Analyses\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Loss to Follow-up Bias Assessment: Assuming all lost-to-follow-up patients experienced MACE or none experienced MACE, main effects were re-estimated to assess result robustness.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Missing Data Handling: Variables with \u0026lt;\u0026thinsp;10% missing data used multiple imputation (MICE, 5 complete datasets); variables with \u0026gt;\u0026thinsp;10% missing data (hs-CRP 12.3%, IL-6 45.1%) used complete-case analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. Follow-up Time Stratification: Patients with follow-up \u0026lt;\u0026thinsp;12 months were excluded, and main effects were re-estimated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e4. Inflammatory Status Stratification: Stratified by baseline hs-CRP tertiles to test stability of prealbumin effects.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.7.6 Software and Significance Level\u003c/h2\u003e \u003cp\u003eR 4.3.3, SPSS 26.0, Zstats 1.0. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Given the exploratory nature, P-values should be interpreted comprehensively considering effect direction, confidence interval width, and biological plausibility, rather than as definitive evidence. All confidence intervals are reported at the 95% level.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 308 patients were enrolled, with mean age 62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years, and 188 (61.0%) were male. Median follow-up was 11.2 months (IQR 9.5\u0026ndash;24.0 months); 210 patients (68.2%) completed 24-month follow-up. Follow-up completion rates: 3 months 98.7%, 6 months 95.1%, 12 months 89.3%, 18 months 76.6%, 24 months 68.2%.\u003c/p\u003e \u003cp\u003eSixty-five patients (21.1%) experienced MACE, including cardiac death 8 (12.3%), non-fatal myocardial infarction 15 (23.1%), non-fatal stroke 12 (18.5%), and cardiovascular-related rehospitalization 30 (46.2%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline characteristics between groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;308)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-MACE group (n\u0026thinsp;=\u0026thinsp;243)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMACE group (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal blood glucose [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol history [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCACS [M (Q₁, Q₃)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.5 (45.2, 412.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.4 (32.1, 358.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e342.8 (98.6, 612.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215.6\u0026thinsp;\u0026plusmn;\u0026thinsp;45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.3\u0026thinsp;\u0026plusmn;\u0026thinsp;43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235.6\u0026thinsp;\u0026plusmn;\u0026thinsp;48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow (grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342.5\u0026thinsp;\u0026plusmn;\u0026thinsp;85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335.2\u0026thinsp;\u0026plusmn;\u0026thinsp;82.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e368.4\u0026thinsp;\u0026plusmn;\u0026thinsp;92.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial bridge [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin use [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-intensity statin [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL target achievement [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: Continuous variables presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range); categorical variables as frequency (percentage).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKey Observation: Prealbumin levels were significantly higher in the MACE group than in the no-MACE group (235.6 vs. 210.3 mg/L, P\u0026thinsp;=\u0026thinsp;0.002), a direction opposite to the typical pattern of \"low prealbumin predicts poor outcomes\" in ACS literature. However, this observational association may be influenced by unmeasured confounders (e.g., thyroid dysfunction, nutritional intervention differences) and does not imply causality. Additionally, high-intensity statin use was significantly lower in the MACE group (24.6% vs. 45.3%, P\u0026thinsp;=\u0026thinsp;0.007), suggesting that treatment intensity differences may act as confounders or effect modifiers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate Cox Regression Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox regression analysis of factors associated with MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCACS (per 100-point increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.23 (1.70\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin (per 10 mg/L increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13 (1.01\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.09 (1.54\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92 (0.89\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88 (0.85\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06 (1.04\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.77 (0.95\u0026ndash;3.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: In Sex, 0\u0026thinsp;=\u0026thinsp;female (reference), 1\u0026thinsp;=\u0026thinsp;male. CACS is per 100-point increase, prealbumin is per 10 mg/L increase, for clinical interpretation convenience. Other categorical and continuous variables showed no statistical significance in univariate analysis (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05); see supplementary materials for details.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate analysis showed that CACS, FAI, prealbumin, LDL-C, LVEF, RVEF, stenosis severity grade, hs-CRP, IL-6, and NLR were significantly associated with MACE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Age, hypertension, abnormal glucose, creatinine, uric acid, cystatin C, eGFR, and myocardial bridging showed borderline associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multivariable Cox Regression and Model Building\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analysis of independent risk factors for MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCACS (per 100-point increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21 (1.11\u0026ndash;4.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin (per 10 mg/L increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13 (1.05\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.92\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: Model overall likelihood ratio test χ\u0026sup2;=62.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, C-index\u0026thinsp;=\u0026thinsp;0.79. Variable selection used Forward LR method, with entry criterion P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and removal criterion P\u0026thinsp;\u0026gt;\u0026thinsp;0.10. CACS is per 100-point increase, prealbumin is per 10 mg/L increase.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel B (with inflammatory markers, n=169) Supplementary Results:\u003c/p\u003e\n\u003cp\u003ePrealbumin: HR 1.09 (95% CI 1.00\u0026ndash;1.19), P=0.038\u003c/p\u003e\n\u003cp\u003ehs-CRP: HR 1.03 (95% CI 0.96\u0026ndash;1.11), P=0.452\u003c/p\u003e\n\u003cp\u003eIL-6: HR 1.02 (95% CI 0.93\u0026ndash;1.12), P=0.682\u003c/p\u003e\n\u003cp\u003eNLR: HR 1.16 (95% CI 0.88\u0026ndash;1.53), P=0.398\u003c/p\u003e\n\u003cp\u003eKey Findings and Interpretation:\u003c/p\u003e\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Prealbumin: Maintained independent association in Model A (HR 1.12, P\u0026thinsp;=\u0026thinsp;0.002), with modest attenuation in Model B (HR 1.09), suggesting its predictive value may be partially independent of systemic inflammation, though this observation requires cautious interpretation (Model B sample size reduced by 45%, limiting power).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Inflammatory Markers: hs-CRP, IL-6, and NLR did not retain independent significance in Model B, possibly due to information overlap with CACS and stenosis severity, or insufficient power due to reduced sample size. These negative results do not exclude a true role of inflammation in CAD progression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. LVEF: Showed borderline protective trend (P\u0026thinsp;=\u0026thinsp;0.087), consistent with established literature, but did not reach traditional significance, possibly due to limited sample size.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eImportant Limitation Restatement: Although multivariable models adjusted for multiple confounders, residual confounding from unmeasured variables (thyroid function, nutritional status, medication adherence) may still exist. The observed prealbumin association may partially or fully reflect the influence of these unmeasured factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Exploratory Analysis of Prealbumin Mechanisms\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Correlation Analysis\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis showed the following correlations with prealbumin:\u003c/p\u003e \u003cp\u003eIndicator Correlation Coefficient r P-value Interpretation\u003c/p\u003e \u003cp\u003ehs-CRP 0.18 0.004 Weak positive correlation\u003c/p\u003e \u003cp\u003eIL-6 0.22 0.006 Weak positive correlation\u003c/p\u003e \u003cp\u003eNLR 0.28\u0026thinsp;\u0026lt;\u0026thinsp;0.001 Moderate-weak positive correlation\u003c/p\u003e \u003cp\u003eAlbumin 0.45\u0026thinsp;\u0026lt;\u0026thinsp;0.001 Moderate positive correlation\u003c/p\u003e \u003cp\u003eBMI 0.12 0.038 Weak positive correlation\u003c/p\u003e \u003cp\u003eMechanistic Interpretation: In stable CAD, prealbumin showed weak to moderate positive correlations with inflammatory markers (rather than negative correlations as in ACS), consistent with the hypothesis of \"chronic metabolic stress with compensatory hepatic synthesis.\" However, the small correlation coefficients suggest prealbumin may only partially reflect inflammatory status, or may be influenced by other unmeasured factors (e.g., thyroid function).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Exploratory Subgroup Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory Stratified Analysis of Prealbumin Predictive Value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e亚组变量\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e亚组\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP值\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e交互P值\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.02\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.03\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e糖尿病\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e无\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.02\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e有\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.00\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e他汀强度\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e高强度\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.97\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e中强度/未使用\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.05\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e年龄\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70岁\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09 (1.00\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70岁\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.05\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: HRs are per 10 mg/L prealbumin increase, adjusted for CACS, stenosis severity grade, LVEF, age, sex, hypertension, and eGFR. Interaction P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicate no statistically significant effect modification, though power was limited and clinically important differences cannot be excluded.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExploratory Finding: The association between prealbumin and MACE was stronger in patients not receiving high-intensity statin therapy (HR 1.14, P\u0026thinsp;=\u0026thinsp;0.002) and did not reach significance in high-intensity statin users (HR 1.08, P\u0026thinsp;=\u0026thinsp;0.168). Although the interaction test was not significant (P\u0026thinsp;=\u0026thinsp;0.29), possibly due to limited sample size, this pattern suggests statin therapy may modify the association between prealbumin and outcomes, potentially through anti-inflammatory or metabolic effects. However, this observation may also reflect residual confounding (e.g., better overall health management in statin-adherent patients) and requires validation in specifically designed studies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Predictive Model Performance Assessment\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Discrimination\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Discrimination Ability Across Different Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP值\u003c/p\u003e \u003cp\u003e(vs.joint model)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined model (CACS\u0026thinsp;+\u0026thinsp;prealbumin\u0026thinsp;+\u0026thinsp;stenosis grade\u0026thinsp;+\u0026thinsp;LVEF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.827 (0.771\u0026ndash;0.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCACS alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.742 (0.681\u0026ndash;0.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStenosis grade alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.756 (0.698\u0026ndash;0.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.612 (0.545\u0026ndash;0.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimplified model (without LVEF)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.812 (0.755\u0026ndash;0.869)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote: DeLong test. The simplified model showed no statistically significant difference in AUC compared to the full model (P\u0026thinsp;=\u0026thinsp;0.34), suggesting limited marginal contribution of LVEF.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the optimal cutoff point (0.208), the combined model demonstrated sensitivity of 75.4%, specificity of 79.8%, and Youden index of 0.552.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Reclassification and Integrated Discrimination Improvement\u003c/h2\u003e \u003cp\u003eMetric Estimate (95% CI) P-value\u003c/p\u003e \u003cp\u003eNRI (combined model vs. CACS alone) 0.18 (0.05\u0026ndash;0.31) 0.007\u003c/p\u003e \u003cp\u003eIDI (combined model vs. CACS alone) 0.06 (0.02\u0026ndash;0.10) 0.003\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Internal Validation\u003c/h2\u003e \u003cp\u003eBootstrap resampling (1000 iterations) showed:\u003c/p\u003e \u003cp\u003eOptimism-corrected AUC: 0.812\u003c/p\u003e \u003cp\u003eOptimism-corrected C-index: 0.785\u003c/p\u003e \u003cp\u003eGood model stability, though internal validation cannot substitute for external validation\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 Calibration\u003c/h2\u003e \u003cp\u003eCalibration curve showed good consistency between predicted probability and actual occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Hosmer-Lemeshow test χ\u0026sup2;=6.32, P\u0026thinsp;=\u0026thinsp;0.61; Brier score\u0026thinsp;=\u0026thinsp;0.168, indicating satisfactory model calibration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe dashed line (Ideal) represents perfect prediction, the dotted line (Apparent) shows the model's actual performance, and the solid line (Bias-corrected) indicates the performance after 1000 Bootstrap corrections. The calibration curves closely match the ideal line, demonstrating good model calibration. The calibration curves for the training set and validation set are displayed separately, including the ideal line (45-degree diagonal) and the model prediction line, with the Hosmer-Lemeshow test showing P\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.5.5 Competing Risk Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eFine-Gray subdistribution hazard model (with non-cardiovascular death as competing event, n\u0026thinsp;=\u0026thinsp;12):\u003c/p\u003e \u003cp\u003ePrealbumin: HR 1.11 (95% CI 1.03\u0026ndash;1.19), P\u0026thinsp;=\u0026thinsp;0.006\u003c/p\u003e \u003cp\u003eResults consistent with primary analysis, suggesting findings were not driven by competing death events\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.5.6 Decision Curve Analysis\u003c/h2\u003e \u003cp\u003eWithin the threshold probability range of 10%\u0026ndash;45%, the combined model demonstrated greater clinical net benefit compared to single-indicator models and \"treat-all\" or \"treat-none\" strategies, suggesting potential clinical utility, though external validation is required for confirmation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe y-axis represents net benefit (Net Benefit), and the x-axis represents threshold probability (Threshold Probability). The blue line (Model) indicates the net benefit of interventions using this columnar plot, the gray line (All) represents the \"full intervention\" strategy, and the black line (None) represents the \"full non-intervention\" strategy. The DCA curves for the training set and validation set are displayed, with the x-axis showing threshold probability and the y-axis showing net benefit, demonstrating that the model outperforms both \"full treatment\" and \"full non-treatment\" strategies within the 5%-50% threshold range.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity Analyses\u003c/h2\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Loss to Follow-up Bias Assessment\u003c/h2\u003e \u003cp\u003eAssuming all lost-to-follow-up patients (n\u0026thinsp;=\u0026thinsp;98) experienced MACE: prealbumin HR 1.08 (95% CI 1.01\u0026ndash;1.16), P\u0026thinsp;=\u0026thinsp;0.028\u003c/p\u003e \u003cp\u003eAssuming no lost-to-follow-up patients experienced MACE: prealbumin HR 1.15 (95% CI 1.06\u0026ndash;1.25), P\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eResults remained significant under both extreme assumptions, suggesting main findings are relatively robust to loss to follow-up bias, though cautious interpretation is still warranted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Other Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eExcluding patients with follow-up \u0026lt;\u0026thinsp;12 months (n\u0026thinsp;=\u0026thinsp;276): Prealbumin HR 1.10 (95% CI 1.02\u0026ndash;1.19), P\u0026thinsp;=\u0026thinsp;0.015, robust results.\u003c/p\u003e \u003cp\u003eStratified by baseline hs-CRP: Prealbumin effect most significant in CRP 1\u0026ndash;3 mg/L subgroup (HR 1.14, P\u0026thinsp;=\u0026thinsp;0.022); interaction test P\u0026thinsp;=\u0026thinsp;0.71, not statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Summary of Main Findings\u003c/h2\u003e \u003cp\u003eThis exploratory study in 308 stable CAD patients preliminarily evaluated the combined predictive value of CCTA imaging parameters and serum biomarkers for MACE. Core findings include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Anatomical Factors: Stenosis severity grade and CACS were independently associated with MACE risk, consistent with established literature;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Atypical Prealbumin Signal: Elevated prealbumin levels were associated with increased MACE risk, a directional association opposite to typical negative acute-phase responses in ACS literature, though potentially influenced by unmeasured confounders;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. Multimodal Integration Potential: The combined model showed potential for improving risk stratification, but clinical utility requires external validation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Multiple Interpretive Frameworks for the \"Paradoxical\" Prealbumin Association\u003c/h2\u003e \u003cp\u003eThe most notable observational finding of this study was the \"reverse\" association of prealbumin in stable CAD. We propose the following non-mutually exclusive, to-be-validated explanatory hypotheses, with honest discussion of their limitations:\u003c/p\u003e \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Chronic Metabolic Stress and Compensatory Synthesis Hypothesis\u003c/h2\u003e \u003cp\u003eIn stable chronic disease states, prealbumin may be upregulated as part of persistent metabolic stress responses. Prealbumin participates in thyroid hormone and retinol transport; under conditions of chronic oxidative stress and altered thyroid hormone metabolism, the liver may compensatorily increase prealbumin synthesis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The weak to moderate positive correlations between prealbumin and inflammatory markers (hs-CRP: r\u0026thinsp;=\u0026thinsp;0.18, IL-6: r\u0026thinsp;=\u0026thinsp;0.22, NLR: r\u0026thinsp;=\u0026thinsp;0.28) are consistent with a pattern of \"chronic low-grade inflammation with intact hepatic synthetic function,\" contrasting with the \"high inflammation with hepatic synthetic suppression\" pattern seen in acute phases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Distinction Between Acute and Chronic Phase Pathophysiology\u003c/h2\u003e \u003cp\u003eThis finding highlights a critical conceptual distinction: the prognostic significance of prealbumin may be highly dependent on disease phase. In acute inflammatory conditions (ACS, sepsis, trauma), prealbumin acts as a negative acute-phase protein, with decreased levels reflecting hepatic synthetic reprioritization and disease severity. However, in stable chronic states with excluded acute infection, elevated prealbumin may reflect:\u003c/p\u003e \u003cp\u003ePersistent low-grade inflammation with intact hepatic function;\u003c/p\u003e \u003cp\u003eAltered protein catabolism;\u003c/p\u003e \u003cp\u003eGenetic/constitutional factors affecting transthyretin metabolism [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec46\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Honest Discussion of Alternative Explanations (Critical Limitation)\u003c/h2\u003e \u003cp\u003eIt must be emphasized that the observed association may be fully or partially explained by the following non-causal mechanisms:\u003c/p\u003e \u003cp\u003eAlternative Explanation Supporting Evidence Counter Evidence Validation Need\u003c/p\u003e \u003cp\u003eResidual confounding: Thyroid dysfunction Thyroid hormones directly regulate transthyretin synthesis; hypothyroidism can elevate prealbumin No direct evidence available Prospective studies must measure TSH, FT3, FT4\u003c/p\u003e \u003cp\u003eResidual confounding: Nutritional intervention High-risk patients may receive more intensive nutritional support Prealbumin only weakly correlated with BMI (r\u0026thinsp;=\u0026thinsp;0.12) Detailed dietary intake assessment, nutritional support records\u003c/p\u003e \u003cp\u003eReverse causality: Treatment intensity differences Lower high-intensity statin use in MACE group Subgroup analysis suggests statins may modify effect Objective medication adherence measurement (blood drug concentrations)\u003c/p\u003e \u003cp\u003eChance finding: Multiple comparisons Single-center data, potential selection bias Bootstrap validation showed model stability External validation, multiple comparison correction\u003c/p\u003e \u003cp\u003eOf particular note, the association between prealbumin and MACE was more pronounced in patients not receiving high-intensity statin therapy (HR 1.14, P\u0026thinsp;=\u0026thinsp;0.002) and did not reach statistical significance in high-intensity statin users (HR 1.08, P\u0026thinsp;=\u0026thinsp;0.168). Although the interaction test was not significant (P\u0026thinsp;=\u0026thinsp;0.29), possibly due to limited sample size, this pattern suggests statin therapy may partially attenuate the prognostic value of prealbumin through its anti-inflammatory or metabolic regulatory effects. However, this observation may equally reflect residual confounding (e.g., better overall health management in statin-adherent patients), and causal inference is not warranted.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Interpretation of Non-Significant Findings\u003c/h2\u003e \u003cdiv id=\"Sec48\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Low-Density Lipoprotein Cholesterol (LDL-C)\u003c/h2\u003e \u003cp\u003eLDL-C did not retain significance in multivariable models, possibly due to:\u003c/p\u003e \u003cp\u003eStatin use\u0026thinsp;\u0026gt;\u0026thinsp;85% in the cohort, with standardized secondary prevention attenuating the independent predictive value of LDL-C;\u003c/p\u003e \u003cp\u003eTreatment intensity confounding: significantly lower high-intensity statin use in the MACE group may mask the true effect of LDL-C;\u003c/p\u003e \u003cp\u003eSingle baseline measurement unable to reflect cumulative exposure or variability.\u003c/p\u003e \u003cp\u003eThis finding suggests that in populations receiving intensive treatment, traditional risk factors may no longer be the primary drivers of risk, with residual risk potentially explained by non-traditional factors (e.g., inflammation, metabolic disorders), though prospective validation is needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec49\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Perivascular Fat Attenuation Index (FAI)\u003c/h2\u003e \u003cp\u003eFAI was significant in univariate analysis but did not enter multivariable models. Possible explanations:\u003c/p\u003e \u003cp\u003eInformation overlap with CACS and stenosis severity;\u003c/p\u003e \u003cp\u003ePrognostic value of FAI may emerge with longer follow-up;\u003c/p\u003e \u003cp\u003eMay require combination with functional assessment (e.g., CT-FFR) for optimal utility [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec50\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Clinical Translation Potential and Limitations\u003c/h2\u003e \u003cdiv id=\"Sec51\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Potential Application Scenarios (If Validated)\u003c/h2\u003e \u003cp\u003eBased on these preliminary, to-be-validated findings, if confirmed in prospective studies, the combined model might be applicable for:\u003c/p\u003e \u003cp\u003eRisk Communication: Visualizing predicted probabilities to help patients understand the value of risk factor control;\u003c/p\u003e \u003cp\u003eIndividualized Follow-up Frequency: High-risk patients (predicted probability\u0026thinsp;\u0026gt;\u0026thinsp;20%) recommended for close follow-up, low-risk patients (\u0026lt;\u0026thinsp;10%) for extended follow-up intervals;\u003c/p\u003e \u003cp\u003eTreatment Decision Support: Patients with high CACS combined with high prealbumin prioritized for intensive statin therapy (target LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.4 mmol/L) and residual inflammatory risk assessment.\u003c/p\u003e \u003cp\u003eSimplified Model Application: Given the borderline association of LVEF in multivariable models and its potential operator-dependence in clinical assessment, we evaluated a simplified model without LVEF. This model achieved an AUC of 0.812, not significantly different from the full model (0.827, P\u0026thinsp;=\u0026thinsp;0.34), with maintained calibration and clinical net benefit. This simplified version is more suitable for rapid outpatient assessment, but should only be considered for clinical use after external validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec52\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Implementation Barriers and Unresolved Issues\u003c/h2\u003e \u003cp\u003eBarrier Description Solution Pathway\u003c/p\u003e \u003cp\u003eExternal validity unknown Single-center Chinese population data Multi-center, multi-ethnic validation\u003c/p\u003e \u003cp\u003ePrealbumin mechanism unclear Causality not established, high residual confounding risk Thyroid function testing, dynamic monitoring, Mendelian randomization\u003c/p\u003e \u003cp\u003eModel simplification needed Full model requires LVEF Simplified model validated (AUC 0.812), awaiting external validation\u003c/p\u003e \u003cp\u003eClinical workflow integration Requires automated calculation tools Development of online calculators or integration into CCTA reporting systems\u003c/p\u003e \u003cp\u003ePrealbumin assay standardization Reference range differences across laboratories Unified testing protocols in multi-center studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec53\" class=\"Section2\"\u003e \u003ch2\u003e4.5 In-Depth Reflection on Study Limitations\u003c/h2\u003e \u003cp\u003eThis study has multiple limitations that must be fully considered in interpretation:\u003c/p\u003e \u003cp\u003eDesign-Level Limitations\u003c/p\u003e \u003cp\u003eRetrospective, single-center design: Unable to establish temporal sequence; selection bias and residual confounding cannot be excluded. MACE events were primarily identified through rehospitalization, potentially underestimating out-of-hospital sudden death.\u003c/p\u003e \u003cp\u003eShort follow-up duration: Median follow-up 11.2 months, 31.8% with follow-up \u0026lt;\u0026thinsp;24 months; long-term predictive value unknown. Loss to follow-up may introduce selection bias (though sensitivity analyses were conducted).\u003c/p\u003e \u003cp\u003eMissing data: IL-6 missing in 45.1%, limiting sample size for inflammatory subgroup analyses. Although multiple imputation was used, high proportions of missing data reduce conclusion robustness.\u003c/p\u003e \u003cp\u003eMeasurement-Level Limitations (Critical)\u003c/p\u003e \u003cp\u003eKey confounders not measured: Thyroid function, corticosteroid use, detailed nutritional intake, protein turnover markers, medication adherence. These are critical regulators of prealbumin synthesis and metabolism; their absence constitutes major residual confounding risk that may fully explain the observed associations.\u003c/p\u003e \u003cp\u003eSingle baseline measurement: Unable to capture dynamic changes in prealbumin, cannot distinguish acute from chronic elevation.\u003c/p\u003e \u003cp\u003eIncomplete treatment data: Statin use obtained from prescription records, actual adherence could not be verified. Lower high-intensity statin use in the MACE group may reflect more severe disease or poorer treatment adherence.\u003c/p\u003e \u003cp\u003eAnalysis-Level Limitations\u003c/p\u003e \u003cp\u003eExploratory analysis with multiple comparisons: Subgroup analyses not corrected for multiple testing; P-values should be considered descriptive.\u003c/p\u003e \u003cp\u003eLimited statistical power: Some subgroups (e.g., eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60, n\u0026thinsp;=\u0026thinsp;50) had small sample sizes with wide confidence intervals; negative results cannot exclude true effects.\u003c/p\u003e \u003cp\u003eCausal inference impossible: Observational design, potential confounding, and reverse causality may all explain observed associations.\u003c/p\u003e \u003cp\u003eInterpretation-Level Limitations\u003c/p\u003e \u003cp\u003ePrealbumin \"paradox\" may be chance finding: Requires validation in independent cohorts, particularly non-Chinese populations.\u003c/p\u003e \u003cp\u003eModel not directly compared with existing tools: No head-to-head comparison with SYNTAX score, CT-FFR, or traditional risk scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec54\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Future Research Directions\u003c/h2\u003e \u003cp\u003eBased on the exploratory findings of this study, the following research agenda is proposed:\u003c/p\u003e \u003cp\u003eValidation Studies:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Prospective multi-center cohort: Include thyroid function, nutritional assessment, and objective medication adherence measurement;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Dynamic monitoring design: Track prealbumin change trajectories to distinguish acute from chronic patterns;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. External validation: Validate model performance in different ethnicities and healthcare systems, particularly regarding the directional prognostic value of prealbumin.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eMechanistic Studies:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Proteomics/Metabolomics: Explore metabolic pathways associated with prealbumin;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Genetic studies: Transthyretin genotyping to assess modification of prognostic associations by genetic variants;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3. Mendelian randomization: Use genetic instrumental variables to assess causality.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eClinical Translation Studies:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1. Randomized trials: Assess whether prealbumin-based risk stratification can guide treatment decisions and improve outcomes;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Implementation science: Develop and evaluate strategies for integrating models into clinical workflows.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis exploratory, retrospective cohort study preliminarily observed an association between elevated prealbumin levels and increased MACE risk in stable CAD patients, with a directional pattern opposite to typical negative acute-phase responses in ACS literature. CCTA anatomical assessment (stenosis severity grade, CACS) combined with serum biomarkers showed potential for improving risk stratification.\u003c/p\u003e \u003cp\u003eHowever, this study cannot establish causality. The observed association between prealbumin and cardiovascular risk may be influenced by unmeasured confounders (thyroid function, nutritional status, medication adherence) or reflect reverse causality (treatment intensity differences). These preliminary, hypothesis-generating findings require validation in prospective multi-center studies, with in-depth exploration of biological mechanisms through thyroid function testing, dynamic prealbumin monitoring, and proteomic analysis. Clinical application of prealbumin in stable CAD risk stratification should remain cautious, and routine use for clinical decision-making is not recommended until further evidence becomes available.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute coronary syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary artery calcium score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary CT angiography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFat attenuation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-CRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-sensitivity C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNet reclassification improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and the *Measures for the Ethical Review of Biomedical Research Involving Humans* (2016). The study protocol was reviewed and approved by the Ethics Committee of Shanghai Fengxian District Hospital of Traditional Chinese Medicine (Approval No.: 2025-Ethics-06; Fengzhong IRB 2025062501). Given the retrospective nature of the study and the use of de-identified data from routine clinical practice, the ethics committee granted a waiver of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person’s data in any form (e.g., individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Due to the inclusion of patient personal information and in accordance with the *Personal Information Protection Law of the People’s Republic of China* and relevant ethical regulations, individual-level research data cannot be publicly shared. Requests for de-identified data for academic validation may be submitted to the corresponding author and will be considered subject to approval by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. No financial conflicts of interest or personal relationships that could have influenced the objectivity and impartiality of the research findings exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study. All aspects of the study, including design, data collection, analysis, interpretation, and manuscript preparation, were carried out independently by the authors without any external involvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Yinlong Qi, Haiyang Zhang, Xin Liu, Sheng Zhou, Ting Ni\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData curation: Yinlong Qi, Haiyang Zhang, Xiang Sun, Jinjun Dong \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFormal analysis: Yinlong Qi, Haiyang Zhang \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInvestigation: Yinlong Qi, Haiyang Zhang, Xiang Sun, Jinjun Dong \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethodology: Yinlong Qi, Haiyang Zhang, Xin Liu, Sheng Zhou, Ting Ni \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProject administration: Xin Liu, Sheng Zhou \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResources: Xin Liu, Sheng Zhou, Ting Ni \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoftware: Yinlong Qi, Haiyang Zhang \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupervision: Xin Liu, Sheng Zhou \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValidation: Xiang Sun, Jinjun Dong \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVisualization: Yinlong Qi, Haiyang Zhang \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting\u0026nbsp;–\u0026nbsp;original draft: Yinlong Qi, Haiyang Zhang \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting\u0026nbsp;–\u0026nbsp;review \u0026amp; editing: All authors \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe successful completion of this study was made possible through the dedicated support and collaboration of the Department of Radiology and the Department of Cardiovascular Medicine at Shanghai Fengxian District Hospital of Traditional Chinese Medicine. We are sincerely grateful to all patients and their families who participated in this study; their trust and cooperation made this retrospective analysis feasible. We also wish to express our appreciation to the reviewers and editors for their valuable comments during the review process, which helped improve the quality of this manuscript. Finally, we thank the members of our research team for their diligent efforts in data processing and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the relevant guidelines and regulations of the Declaration of Helsinki and the\u0026nbsp;Measures for the Ethical Review of Biomedical Research Involving Humans\u0026nbsp;(2016). The study protocol was submitted to and approved by the Ethics Committee of Shanghai Fengxian District Hospital of Traditional Chinese Medicine (Institutional Review Board), with approval number 2025062501. Due to the retrospective design, all data were derived from coronary CT angiography examinations and follow-up records obtained during routine clinical practice. The data were de-identified prior to analysis in compliance with the\u0026nbsp;Personal Information Protection Law of the People’s Republic of China\u0026nbsp;(2021). Following review by the ethics committee, the requirement for informed consent from patients was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any identifiable personal information (such as patient images, names, or specific clinical details); therefore, no consent for publication is required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurmohamed NS, van Rosendael AR, Danad I, et al. Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography. Eur Heart J. 2024;45(20):1783\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreini D, Pontone G, Mushtaq S, et al. 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Low prealbumin is strongly associated with adverse outcome in heart failure. Heart. 2014;100(22):1780\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngenbleek Y, Young V. Transthyretin (prealbumin) in health and disease: nutritional implications. Annu Rev Nutr. 1994;14:495\u0026ndash;533.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeeken WL, Volwiler W. Metabolism of prealbumin and serum albumin in gastrointestinal diseases. J Lab Clin Med. 1971;78(5):833\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelvaraj S, Claggett B, Shah SH, et al. Cardiovascular burden of the V142I transthyretin variant. JAMA. 2024;331(21):1824\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleck A. Clinical and nutritional aspects of changes in acute-phase proteins during inflammation. Proc Nutr Soc. 1989;48(3):347\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeduzzi P, Concato J, Feinstein AR, et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes\u0026ndash;2020. Diabetes Care. 2020;43(Suppl 1):S14\u0026ndash;S31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaff GL, Abidov A, Achenbach S, et al. SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography. J Cardiovasc Comput Tomogr. 2009;3(2):122\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgatston AS, Janowitz WR, Hildner FJ, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenland P, Blaha MJ, Budoff MJ, et al. Coronary calcium score and cardiovascular risk. J Am Coll Cardiol. 2018;72(4):434\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Y, Wang R, Xing H, et al. Integrated CT-derived fractional flow reserve and perivascular fat attenuation index: a multimodal approach to predict in-stent restenosis. Front Cardiovasc Med. 2024;11:1478420.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"coronary CT angiography, major adverse cardiovascular events, risk prediction, prealbumin, stable coronary artery disease, exploratory study, observational association","lastPublishedDoi":"10.21203/rs.3.rs-9211082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9211082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Purpose\u003c/h2\u003e \u003cp\u003eRisk stratification for major adverse cardiovascular events (MACE) in patients with stable coronary artery disease (CAD) remains a clinical challenge. Prealbumin, a negative acute-phase protein, has established prognostic value in acute coronary syndrome (ACS), yet its behavioral characteristics in chronic stable CAD are poorly understood. This exploratory study aimed to preliminarily evaluate the association of CCTA imaging parameters combined with serum biomarkers with MACE in stable CAD patients, and to explore the prognostic signal characteristics of prealbumin in this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis single-center retrospective cohort study enrolled 308 stable CAD patients who underwent coronary CT angiography (CCTA) between January 2020 and January 2024. Baseline clinical data, CCTA parameters (coronary artery calcium score [CACS], perivascular fat attenuation index [FAI], stenosis severity grade), and laboratory indicators were collected. The primary endpoint was the composite MACE within 24 months of follow-up. Cox proportional hazards regression was used to explore factors associated with MACE, and a multimodal risk prediction model was constructed and internally validated. This study was designed as exploratory hypothesis-generating research; all associations are observational, and causality has not been established.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 11.2 months (interquartile range 9.5\u0026ndash;24.0 months), 65 patients (21.1%) experienced MACE, with a 24-month follow-up completion rate of 68.2%. Multivariable Cox analysis showed that CACS (per 100-point increase, hazard ratio [HR] 1.06, 95% confidence interval [CI] 1.01\u0026ndash;1.12, P\u0026thinsp;=\u0026thinsp;0.023), stenosis severity grade (HR 1.35, 95% CI 1.04\u0026ndash;1.75, P\u0026thinsp;=\u0026thinsp;0.025), and prealbumin (per 10 mg/L increase, HR 1.12, 95% CI 1.04\u0026ndash;1.20, P\u0026thinsp;=\u0026thinsp;0.002) were independently associated with MACE risk. Notably, elevated prealbumin levels were associated with increased MACE risk, a directional association opposite to the typical negative acute-phase response observed in ACS literature, though potentially influenced by unmeasured confounders (e.g., thyroid function, nutritional status). The combined model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.827 (95% CI 0.771\u0026ndash;0.883), with a bootstrap-corrected AUC of 0.812. Decision curve analysis indicated potential clinical net benefit within the threshold probability range of 10%\u0026ndash;45%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis exploratory study observed an association between elevated prealbumin levels and increased MACE risk in stable CAD patients, with a directional pattern opposite to the typical acute-phase response in ACS literature. However, this association may be influenced by unmeasured confounders (thyroid function, nutritional status, medication adherence), and the retrospective design precludes causal inference. These preliminary findings are hypothesis-generating and require validation in prospective studies. Clinical application of prealbumin in stable CAD risk stratification should remain cautious until further evidence regarding its prognostic value and biological mechanisms becomes available.\u003c/p\u003e","manuscriptTitle":"Multimodal Risk Prediction Model in Stable Coronary Artery Disease: An Integrated Analysis of Coronary CT Angiography and Serum Prealbumin—A Preliminary Report of a Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:36:18","doi":"10.21203/rs.3.rs-9211082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"63748110251562516367607209957211944078","date":"2026-05-04T19:42:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284959849238232574499595029192643112583","date":"2026-05-01T18:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336999739013815623573087550570097319202","date":"2026-04-28T18:23:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T18:06:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32942205542923767368738635678764687871","date":"2026-04-22T20:37:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113281654101859871588423640399448284277","date":"2026-04-22T17:14:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T03:32:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T08:44:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T06:05:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T11:31:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-30T11:25:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7533922c-dcf9-4a59-b4a0-91a53a19fb89","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"63748110251562516367607209957211944078","date":"2026-05-04T19:42:45+00:00","index":70,"fulltext":""},{"type":"reviewerAgreed","content":"284959849238232574499595029192643112583","date":"2026-05-01T18:14:43+00:00","index":68,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:36:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:36:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9211082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9211082","identity":"rs-9211082","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}