Interpretation between CAD-RADS & CAC-DRS CT angiography reporting systems to evaluate coronary artery disease severity

The Coronary Artery Disease–Reporting and Data System (CAD-RADS): Prognostic and Clinical Implications Associated With Standardized Coronary Computed Tomography Angiography Reporting

To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA). Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA. Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

Background and ObjectivesThe coronary artery disease reporting and data system (CAD-RADS) is intended to standardize the reporting of CCTA and the subsequent management guidelines of CAD. The present study was conducted to investigate the validation of CAD-RADS and the application of coronary calcium grading in CAD management.Patients and MethodsThe current study is a single-center prospective study that involved 177 participants with chest pain who were submitted to coronary CT angiography (CCTA). Two reviewers independently assessed CCTA results and gave each patient a CAD-RADS category. The reference standard for determining the clinical utility of CAD-RADS was invasive coronary angiography (ICA). The inter-reviewer agreement (IRA) was tested using the intra-class correlation (ICC).ResultsThe study enrolled 111 cases with non-significant CAD and 66 cases with significant CAD based on ICA findings. According to the reviewer, the CAD-RADS had a sensitivity, specificity, and accuracy of 90.9 to 100%, 89.2 to 94.6%, and 93.16 to 93.2%, respectively, for predicting severe CAD. The IRA for CAD-RADS categories was excellent (ICC = 0.960). The best cut-off value for predicting severe CAD was CAD-RADS > 3. Significant relation between Ca and severe CAD (p<0.001) was detected.ConclusionThe current study provides a good understanding of CAD-RADS as a standard tool with high diagnostic accuracy.

Rationale, design, and baseline characteristics of Chinese registry in early detection and risk stratification of coronary plaques (C-STRAT) study.

Introduction: Coronary CT angiography (CCTA) is a valuable clinical tool for evaluation of coronary artery disease. However, the standardized framework, Coronary Artery Disease Reporting and Data System (CAD-RADS), is not consistently applied to classify CCTA reports due to variability in terminology and narrative format. We address the gap by developing an artificial intelligence-based system leveraging large language models (LLM) to automate the interpretation of CCTA reports and support guideline-directed clinical decision-making. Methods: CCTA reports from native coronary artery evaluation protocols were selected. The training dataset (940 reports) was established from CCTA reports selected from a clinical database at our institution between January 2020 and July 2024. The dataset exhibits a natural class imbalance based on clinical indications of CCTA. Four LLMs previously validated for medical applications were evaluated. BioBERT demonstrated the highest performance and was therefore selected for model deployment. External validation was further conducted using an independent test set (500 reports) comprising CCTA reports between August 2024 and April 2025 at our institution. Concordance between radiologist-interpreted and AI-generated CAD-RADS classfication was assessed. All data handling adhered to HIPAA regulations. Results: The distribution of CAD-RADS classifications in the training dataset was as follows: CAD-RADS 0 (35.9%), CAD-RADS 1 (14.9%), CAD-RADS 2 (14.2%), CAD-RADS 3 (7.9%), CAD-RADS 4 (7.7%), CAD-RADS 5(0.7%), and CAD-RADS N (18.7%). The overall accuracy of AI-interpreted CAD-RADS classification was 87.6% (438/ 500). The distribution of CAD-RADS classifications in the testing dataset was as follows: CAD-RADS 0 (35.2%), CAD-RADS 1 (19.2%), CAD-RADS 2 (13.6%), CAD-RADS 3 (8.6%), CAD-RADS 4 (8.2%), CAD-RADS 5 (2.0%), and CAD-RADS N (13.2%). The class-wise accuracy of the AI model on the testing dataset for CAD-RADS classification was as follows: CAD-RADS 0 (96.0%), CAD-RADS 1 (96.9%), CAD-RADS 2 (98.5%), CAD-RADS 3 (97.7%), CAD-RADS 4 (85.3%), CAD-RADS 5(10.0%), and CAD-RADS N (48.9%). Conclusion: We propose an artificial intelligence tool powered by the BioBERT LLM to enhance the interpretation of CCTA reports. This approach aims to address the existing discrepancies and heterogeneity in CCTA reporting systems. Further effort is needed to optimize the tool in supporting clinical decision-making among internal medicine and cardiology providers.

BackgroundThe coronary artery disease reporting and data system (CAD-RADS) is designed for a uniform standardization of coronary computed tomography angiography (CCTA) reporting and further management recommendations of coronary artery disease (CAD). This study aimed to assess clinical validity, applicability, and reproducibility of CAD-RADS in the management of patients with CAD.Methods and resultsA single-center prospective study included 287 patients with clinically suspected or operated CAD who underwent CCTA. Four reviewers evaluated the CCTA images independently and assigned a CAD-RADS category to each patient. The invasive coronary angiography (ICA) was used as the reference standard for calculating diagnostic performance of CAD-RADS for categorizing the degree of coronary artery stenosis. The intra-class correlation (ICC) was used to test the inter-reviewer agreement (IRA). Reporting was provided to referring consultants according to the CAD-RADS. Based on ICA results, we have 156 patients with non-significant CAD and 131 patients with significant CAD. On a patient-based analysis, regarding those patients classified as CAD-RADS 4 and CAD-RADS 5 for predicting significant CAD, the CAD-RADS had a sensitivity, specificity, and an accuracy of 100%, 96.8 to 98.7%, and 98.3 to 99.3%, respectively, depending on the reviewer. There was an excellent IRA for CAD-RADS categories (ICC = 0.9862). The best cutoff value for predicting significant CAD was > CAD-RADS 3. Eighty-seven percentage of referring consultants considered CAD-RADS reporting system to be “quite helpful” or “completely helpful” for clinical decision-making in CAD.ConclusionCAD-RADS is valuable for improving CCTA structural reports and facilitating decision-making with high diagnostic accuracy and high reproducibility.

Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an on-premise AI-based coronary artery calcium scoring (CACS) and CCTA analysis software against expert interpretation based on Coronary Artery Disease Reporting and Data System (CAD-RADS) 2.0. Materials and Methods This retrospective study included consecutive patients undergoing CCTA for coronary analysis at a tertiary academic center between January 2017 and October 2021 across four scanners from three vendors. Patients with stents, bypass grafts, anomalies, or nondiagnostic studies were excluded. On-premise AI output included CACS, CAD-RADS category, and segment involvement score (SIS) within less than 5 minutes. Original CCTA reports were used as the reference, and discrepancies between AI and reports were further adjudicated by two blinded level-III readers with 8 and 5 years of CCTA experience. Agreement among CACS risk categories, CAD-RADS categories, and plaque burden scores was measured with the weighted κ. The area under the receiver operating characteristic curve, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. Bootstrapping was used to estimate 95% CIs. Results A total of 1032 patients (median age, 62 [IQR, 54-69] years; 581 female) with 1041 CCTA images were included: 361 of the 1041 images (35%) were classified as CAD-RADS 0, 274 (26%) as CAD-RADS 1, 186 (18%) as CAD-RADS 2, 101 (10%) as CAD-RADS 3, 95 (9%) as CAD-RADS 4A, 11 (1%) as CAD-RADS 4B, and 13 (1%) as CAD-RADS 5. There was substantial agreement between AI and expert CAD-RADS stenosis severity categories (weighted κ = 0.73). AI demonstrated high performance (per-scan area under the receiver operating characteristic curve, 0.90; 95% CI: 0.87, 0.92) for CAD-RADS greater than or equal to 3 or greater than or equal to 4A and high negative predictive value (98%; 95% CI: 97, 99) but low positive predictive value (39%; 95% CI: 32, 45) for CAD-RADS greater than or equal to 4A. AI-based plaque burden scores derived from CACS reached near-perfect agreement with experts (weighted κ = 0.97), whereas those derived from SIS showed substantial agreement (weighted κ = 0.79). Conclusion On-premise AI accurately ruled out obstructive coronary artery disease at CCTA and achieved substantial to near-perfect agreement with human experts for CAD-RADS 2.0 stenosis severity and plaque burden. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by van Assen and De Cecco in this issue.

ObjectiveWe surveyed UK practice and compliance with the National Institute for Health and Care Excellence (NICE) ‘recent-onset chest pain’ guidance (Clinical Guideline 95, 2016) as a service quality initiative. We...

ObjectivesTo assess the prognostic implications of standardized reporting systems for coronary computed tomography angiography (CCTA) and coronary artery calcium scores (CACS) in patients with stable chest pain. BackgroundThe Coronary Artery Disease Reporting And Data System (CAD-RADS) and Coronary Artery Calcium – Data and Reporting System (CAC-DRS) aim to improve communication of CACS and CCTA results, but its influence on prognostication is unknown. MethodsImages from 1769 patients who underwent CCTA as part of the Scottish Computed Tomography of the HEART (SCOT-HEART) multi-center randomized controlled trial were assessed. CACS were classified as CAC-DRS 0 to 3 based on Agatston scores. CCTA were classified as CAD-RADS 0 to 5 based on the most clinically relevant finding per patient. The primary outcome was the five-year events of fatal and non-fatal myocardial infarction. ResultsPatients had a mean age of 58 ± 10 years and 56% were male. CAC-DRS 0, 1, 2 and 3 occurred in 642 (36%), 510 (29%), 239 (14%) and 379 (21%) patients respectively. CAD-RADS 0, 1, 2, 3, 4A, 4B and 5 occurred in 622 (35%), 327 (18%), 211 (12%), 165 (9%), 221 (12%), 42 (2%) and 181 (10%) patients respectively. Patients classified as CAC-DRS 3 were at an increased risk of fatal or non-fatal myocardial infarction compared to CAC-DRS 0 patients (hazard ratio (HR) 9.41; 95% confidence interval (CI) 3.24, 27.31; p < 0.001). Patients with higher CAD-RADS categories were at an increased risk of fatal or non-fatal myocardial infarction, with patients classified as CAD-RADS 4B at the highest risk compared to CAD-RADS 0 patients (HR 19.14; 95% CI 4.28, 85.53; p < 0.001). ConclusionPatients with higher CAC-DRS and CAD-RADS scores were at increased risk of subsequent fatal and non-fatal myocardial infarction. This confirms that the classification provides additional prognostic discrimination for future coronary heart disease events.

Coronary artery disease (CAD) is a leading cause of mortality and disability worldwide. CT coronary angiography is a fast and non-invasive method for diagnosing coronary artery pathology. To standardize the assessment of CT coronary angiography results, the CAD-RADS (Coronary Artery Disease – Reporting and Data System) was developed, which is based on determining the degree of coronary artery stenosis. According to recent studies, in addition to CT coronary angiography, an important tool for cardiovascular risk stratification in asymptomatic patients is the assessment of coronary calcium score using the Agatston scale (CAC Score). Aim. To assess the relationship and degree of correlation between the CAC Score and the presence of significant coronary artery stenosis when combining CAC Score and CT coronary angiography. Materials and Methods. The data of 464 patients from the National Institute of Cardiovascular Surgery were analyzed. These patients presented with typical or atypical angina symptoms and had a low or moderate risk of coronary artery disease between September 1, 2024, and January 15, 2025. All patients underwent clinical examination, risk factor assessment, CAC Score evaluation, and CT coronary angiography. Patients with atherosclerosis were divided into two groups: those with non-significant stenosis and those with significant stenosis (≥50% in the left main coronary artery or ≥70% in the major epicardial coronary arteries). Results. The study included 464 patients, predominantly male (55.6%), with a mean age of 59 ± 10.22 years. Atherosclerotic coronary artery disease was detected in 273 patients (58.8%). Based on the CAD-RADS, 24.54% of patients had CAD-RADS 1, 31.14% had CAD-RADS 2, 16.12% had CAD-RADS 3, 20.88% had CAD-RADS 4A, 5.49% had CAD-RADS 4B, and 1.83% had CAD-RADS 5. In the first group (non-significant stenosis), there were 196 patients (71.8%), while the second group (significant stenosis) included 77 patients (28.2%). The median CAC Score in the first group was 24.5 (1–103.25), while in the second group, it was 271.5 (88–666.5), p&lt;0.001. A moderately strong positive correlation was found between the CAC Score and the presence of significant coronary artery stenosis (ρ=0.635, p&lt;0.001). ROC curve analysis shows that the optimal cutoff value of the CAC Score for detecting significant stenosis was 282, with a sensitivity of 48.7%, specificity of 88.8%, and an AUC of 0.819. Conclusions. The study identified a statistically significant correlation between the level of coronary calcification and the presence of significant stenosis. The CAC Score is a reliable prognostic marker for significant coronary artery stenosis in patients with suspected coronary artery disease.

Coronary Artery Disease-Reporting and Data System (CAD-RADS) standardises Computed Tomography Coronary Angiography (CTCA) reporting. Coronary calcification can overestimate stenosis. We hypothesized where CADRADS category is assigned due to predominantly calcified maximal stenosis (Ca+), the CTCA-derived Fractional Flow Reserve (FFRCT) would be lower compared to predominantly non-calcified maximal stenoses (Ca-) of the same CAD-RADS category. Consecutive patients undergoing routine clinical CTCA (September 2018 to May 2020) with ≥1 stenosis ≥25% with FFRCT correlation were included. CTCA’s were subdivided into Ca+ and Ca-. FFRCT was measured in the left anterior descending (LAD), left circumflex (LCx) and right coronary artery (RCA). Potentially flow-limiting classified as FFRCT≤0.8. A subset had Invasive Coronary Angiography (ICA). 561 patients screened, 320 included (60% men, 69±10 years). Ca+ in 51%, 69% and 50% of CAD-RADS 2, 3 and 4 respectively. There was no difference in the prevalence of FFRCT≤0.8 between Ca+ and Ca- stenoses for each CAD-RADS categories. No difference was demonstrated in the median maximal stenoses FFRCT or end-vessel FFRCT within CAD-RADS 2 and 4. CAD-RADS 3 Ca+ had a lower FFRCT (maximal stenosis p= .02, end-vessel p= .005) vs Ca-. No difference in the prevalence of obstructive disease at ICA between predominantly Ca+ and Ca- for any CAD-RADS category. There was no difference in median FFRCT values or rate of obstructive disease at ICA between Ca+ and Castenosis in both CAD-RADS 2 and 4. Ca+ CAD-RADS 3 was suggestive of an underestimation based on FFRCT but not corroborated at ICA.

Background Coronary Artery Disease Reporting and Data System (CAD-RADS) was developed to standardize and optimize disease management in patients after coronary CT angiography (CCTA), but the impact of CAD-RADS management recommendations on clinical outcomes remains unclear. Purpose To retrospectively assess the association between the appropriateness of post-CCTA management according to CAD-RADS version 2.0 and clinical outcomes. Materials and Methods From January 2016 to January 2018, consecutive participants with stable chest pain referred for CCTA were prospectively included in a Chinese registry and followed for 4 years. Retrospectively, CAD-RADS 2.0 classification and the appropriateness of post-CCTA management were determined. Propensity score matching (PSM) was used to adjust for confounding variables. Hazard ratios (HRs) for a major adverse cardiovascular event (MACE), relative risks for invasive coronary angiography (ICA), and the corresponding number needed to treat were estimated. Results Of the 14 232 included participants (mean age, 61 years ± 13 [SD]; 8852 male), 2330, 2756, and 2614 were retrospectively categorized in CAD-RADS 1, 2, and 3, respectively. Only 26% of participants with CAD-RADS 1-2 disease and 20% with CAD-RADS 3 received appropriate post-CCTA management. After PSM, appropriate post-CCTA management was associated with lower risk of MACEs (HR, 0.34; 95% CI: 0.22, 0.51; P < .001), corresponding to a number needed to treat of 21 in CAD-RADS 1-2 but not CAD-RADS 3 (HR, 0.86; 95% CI: 0.49, 1.85; P = .42). Appropriate post-CCTA management was associated with decreased use of ICA in CAD-RADS 1-2 (relative risk, 0.40; 95% CI: 0.29, 0.55; P < .001) and 3 (relative risk, 0.33; 95% CI: 0.28, 0.39; P < .001), resulting in a number needed to treat of 14 and 2, respectively. Conclusion In this retrospective secondary analysis, appropriate disease management after CCTA according to CAD-RADS 2.0 was associated with lower risk of MACEs and more prudent use of ICA. ClinicalTrials.gov registration no. NCT04691037 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Leipsic and Tzimas in this issue.

Background Coronary computed tomography angiography (CCTA) plays an important role in the modern assessment of coronary artery disease (CAD). With the increasing number of CCTA examinations, technologies such as Artificial Intelligence (AI) that reduce reader workload and improve inter-reader agreement are of great interest. The Coronary Artery Disease Reporting and Data System (CAD-RADS) is a structured reporting system to categorise the severity of CAD and guide patient management. Patients with low CAD-RADS categories (1-2) usually need preventive medical therapies but no downstream testing. However, patients in more severe categories (CAD-RADS 3-5) may require further testing, such as stress tests or invasive coronary angiography. The aim of this study was to assess the potential of an on-site AI to differentiate between non-obstructive and potentially obstructive CAD. Methods In this study, 395 patients (65.3% male, 64.9 ± 10.0 years) who underwent a clinically indicated CCTA examination were retrospectively included. Cases with significant artefacts and coronary artery segments &amp;lt;2 mm in diameter were not included into the analysis. The severity of CAD was graded as non-obstructive (CAD-RADS ≤2) or potentially obstructive (CAD-RADS ≥3) by consensus of two highly experienced readers. The final study population consisted of 50.6% non-obstructive and 49.4% obstructive CAD patients. The CCTA reconstruction with the best image quality was selected, transferred to the on-site AI prototype and fully automatically processed to yield the CAD-RADS category. Cohen's kappa and intraclass correlation were used to compare the results of the readers and the AI prototype. Results In the subgroup with non-obstructive CAD (n=200, 58.0% male), there was high agreement between the AI and the readers, with 96.0% of patients being correctly classified by the AI. Accordingly, 92.8% of patients with obstructive CAD (n=195, 72.8% male) were correctly classified by the AI. The results showed very good agreement between human and AI results in the overall population, with a kappa of 0.89 (95% CI 0.84-0.94) and an overall ICC of 0.89 (95% CI 0.84-0.93). Conclusions AI can reliably differentiate between non-obstructive and obstructive CAD in cases with good image quality. This could help readers to identify low-risk patients and expedite CCTA analysis. However, AI still has some limitations, such as the identification of artefacts in cases with impaired image quality or the detection of totally occluded coronary arteries. Further development is needed to improve its diagnostic accuracy.

Aims This study evaluated the clinical prospects of Coronary Artery Disease—Reporting and Data System (CAD-RADS) scoring in coronary computed tomography angiography (CTA). The aim of the study was to determine the guidance value of CAD-RADS scoring in patient follow-up after CTA.Methods and Materials Reports of cases reported between 2010 and 2013 were reevaluated with CAD-RADS scoring. Clinical risk analysis was performed with initial forms of anamnesis. Clinical follow-up was performed on 7 to 10 years (mean: 8 years, 4 months) hospital records. Univariate and multivariate Cox modeling was performed with Kaplan–Meier method to define the relationship between clinical (age, gender, diabetes mellitus, hypertension, smoking, family history) and CAD-RADS variables, and for risk analysis based on these causes. Cox proportional-hazards analysis results were presented as a hazard ratio with a 95% confidence interval. CAD-RADS scores were evaluated as meaningful determinants of univariate and multivariate Cox proportional survival analysis.Results Totally, 359 cases were evaluated in the study. Severe coronary pathology development rate was observed as CAD-RADS 0to 1%, CAD-RADS 1 to 3%, CAD-RADS 2 to 4%, CAD-RADS 3 to 9%, CAD-RADS 4A to 21%, 4B to 25%, CAD-RADS 5 to 50%. There were no coronary artery deaths in CAD-RADS 1,2,3 cases in 10 years of follow-up. Two cases with CAD-RADS 4 A score, three cases with 4 B score, and four patients with CAD-RADS 5 had a history of death as a result of coronary disease.Conclusions The cases with a high risk of side effects with CAD-RADS scores were clearly shown. CAD-RADS score accurately identifies risks in postimaging follow-up and is a reliable reporting system in the required treatment planning.

Background: Coronary artery disease (CAD) is a leading global cause of mortality, with dyslipidemia and inflammation playing central roles in its pathogenesis. While traditional lipid markers are widely used, non-traditional lipid indices may offer superior predictive value for CAD severity. Additionally, the monocyte/HDL-cholesterol ratio (MHR) has emerged as a novel marker combining lipid metabolism and inflammation, offering potential as a predictor of cardiovascular risk. However, existing studies often overlook gender-specific differences in these indices for predicting CAD severity and the need for invasive coronary angiography (ICA). Objectives: This study investigates the predictive value of MHR and other lipid indices in assessing CAD severity and ICA necessity in male and female patients undergoing coronary computed tomography angiography (CCTA). Patients and Methods: This retrospective study included 419 patients (207 males, 212 females) who underwent CCTA at a tertiary hospital between January 1 and August 1, 2024, after applying exclusion criteria (patients with missing atherogenic index data, prior coronary interventions, unclear coronary assessments, known CAD, severe valvular disease, or aortic aneurysm). Patients were classified into two grouping systems based on CAD-RADS (CAD - Reporting and Data System) scores: Grouping 1 (six CAD-RADS categories) and Grouping 2 (CAD-RADS 0 - 2 vs. 3 - 5). The MHR, plasma atherogenic index (PAI), Atherogenic Coefficient (AC), Castelli Risk Index-I and II (CRI-I, and CRI-II) were analyzed, with P ≤ 0.05 considered statistically significant. Results: When classified into six CAD-RADS groups, the CAD-RADS-0 group was significantly younger than groups 1 - 4 (P = 0.003, &lt; 0.001, &lt; 0.001, and &lt; 0.001, respectively). A significant gender difference was also found among the groups (P &lt; 0.001), but no significant differences in MHR, PAI, AC, CRI-I, or CRI-II were observed. However, when grouped as CAD-RADS 0 - 2 vs. 3 - 5, gender distribution differed significantly (P &lt; 0.001), and MHR, AC, CRI-I, and CRI-II were significantly higher in the CAD-RADS 3 - 5 group (P = 0.029, 0.017, 0.017, 0.008, respectively), with no significant difference in PAI (P = 0.250). Further gender-based analysis within the CAD-RADS 0 - 2 and CAD-RADS 3 - 5 groups revealed no significant differences in lipid indices between subgroups. Conclusion: While MHR, AC, CRI-I, and CRI-II differed between CAD-RADS 0 - 2 and 3 - 5 groups, these associations disappeared in further gender-based analysis. Gender-based lipid reference value differences may confound the predictive value of these variables, necessitating cautious interpretation.