Development and Internal Validation of a Risk-Prediction Nomogram for Calcific Aortic Valve Stenosis: A Single-Center Real-World Study

Development and Internal Validation of a Risk-Prediction Nomogram for Calcific Aortic Valve Stenosis: A Single-Center Real-World 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 Development and Internal Validation of a Risk-Prediction Nomogram for Calcific Aortic Valve Stenosis: A Single-Center Real-World Study Zhiwei Wu, Yubin Huang, Jinzao Chen, Guoyan Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8915746/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Calcific aortic valve stenosis (CAVS) is a common valvular disease, and early identification of high-risk populations can help optimize screening and follow-up strategies. However, there is still a relative lack of risk prediction models for CAVS based on routine clinical indicators that have undergone internal validation. This study aims to develop and validate a clinically applicable CAVS prediction model, assessing its discrimination, calibration, and clinical net benefit. Methods This was a single-center retrospective study including patients diagnosed with CAVS at our hospital between 2019 and 2024 and individuals undergoing health examinations during the same period. Participants were classified according to echocardiographic aortic valve area (AVA): AVA < 3 cm² as the CAVS group and AVA ≥ 3 cm² as the normal group. A total of 580 participants were enrolled (450 controls and 130 CAVS cases) and randomly split at a 7:3 ratio into a training set (n = 406; 315 controls and 91 CAVS cases) and a validation set (n = 174; 135 controls and 39 CAVS cases). In the training set, the least absolute shrinkage and selection operator (LASSO) was used for variable selection, followed by multivariable logistic regression to construct the prediction model and nomogram. Model calibration, discrimination, and clinical net benefit were evaluated in both datasets using calibration curves/Hosmer–Lemeshow (H–L) test, receiver operating characteristic (ROC) curve with area under the curve (AUC), and decision curve analysis (DCA), respectively. Results Based on LASSO selection and multivariate logistic regression, 11 independent predictors were identified: ApoB/ApoA1 ratio, age, SIRI(Systemic Inflammation Response Index), sex, fasting glucose, history of CKD, triglycerides, history of hypertension, CRP, history of diabetes, and uric acid. The model demonstrated good calibration: Hosmer–Lemeshow test χ²=6.43, P = 0.600 for the training set; χ²=6.86, P = 0.552 for the validation set, with calibration curves closely approaching the ideal line. The model exhibited high discrimination: AUC = 0.932 (95% CI 0.905–0.959) for the training set and AUC = 0.907 (95% CI 0.855–0.958) for the validation set. At a cutoff probability of 0.269, the sensitivity and specificity were 81.3% and 88.3% for the training set, and 82.1% and 82.2% for the validation set, respectively. DCA indicated that the nomogram provided higher net benefits across a wide range of threshold probabilities (0.01–0.94 for the training set; 0.01–0.89 and 0.96–0.99 for the validation set). Conclusion Using single-center retrospective data, we developed and internally validated a nomogram for predicting CAVS risk. This tool may facilitate individualized risk assessment and inform screening decisions for CAVS. Calcific Aortic Valve Stenosis(CAVS) Risk Prediction Model Nomogram Real-World Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Calcific aortic valve stenosis (CAVS) is one of the most common heart valve diseases in developed countries and is the most significant acquired valvular disease in the elderly.[ 1 ] The pathological process is characterized by leaflet fibrosis, thickening, and progressive calcification, leading to gradual narrowing of the valve opening, obstruction of left ventricular outflow, and a series of hemodynamic changes and structural remodeling. [ 2 ]Epidemiological studies show that the prevalence of CAVS significantly increases with age, being particularly common in older populations, with rates approaching about 10% in those over 75 years old. [ 3 ] As the population ages, the overall disease burden continues to rise.[ 4 ]The clinical hazards of CAVS are reflected not only in its high prevalence but also in the poor outcomes once patients enter the symptomatic phase. [ 5 ]Early CAVS often lacks specific clinical manifestations, with many patients only being identified when they present with symptoms such as exertional dyspnea, angina, syncope, or heart failure, which typically indicate a decline in cardiac functional reserve and progression to a high-risk stage.[ 6 ] Previous studies have shown that patients with symptomatic severe aortic stenosis who do not receive effective interventions, such as valve replacement, have a very poor natural prognosis, with survival rates significantly declining in the short term. [ 7 ]It is often cited that a considerable proportion of these patients may experience death within a few years, with some studies suggesting the risk of death may approach 50% within two years. [ 8 ]Therefore, identifying high-risk individuals before or during the early stages of the disease and initiating imaging assessments and dynamic follow-ups is crucial for reducing adverse outcomes.[ 9 ] Mechanistically, CAVS is no longer viewed as merely an age-related “passive calcification”. [ 10 ] Increasing evidence suggests that the development of CAVS is closely related to hemodynamic stress-induced valve injury, endothelial dysfunction, lipid deposition, chronic inflammation and immune responses, oxidative stress, and the osteoblastic differentiation of valve interstitial cells.[ 11 ] Furthermore, CAVS often coexists with various cardiovascular and metabolic diseases, indicating that its risk may be driven by multiple factors.[ 12 ] Consequently, constructing a risk assessment tool based on readily available clinical information (demographic characteristics and medical history) and routine examination indicators could provide a more actionable basis for early screening and stratified management in health examination and outpatient populations.[ 13 ] However, current studies on clinical prediction models for CAVS remain relatively limited, and differences in population structure, health examination demographics, and clinical practice scenarios across regions may affect the applicability and generalizability of the models. [ 14 ] Therefore, this study aims to evaluate candidate factors associated with CAVS using real-world data, constructing and validating a prediction model to identify high-risk individuals for CAVS, thereby supporting early screening and risk stratification management.[ 15 ] Methods Patients The data for this study were sourced from the electronic medical record system of Putian First Hospital. To protect patient privacy, all data were anonymized after extraction. This study adhered to the Declaration of Helsinki (2013 revision) and was approved by the Ethics Committee of Putian First Hospital. [ 16 ] As this study is retrospective, informed consent from participants was waived.We consecutively included individuals who visited the cardiology department or underwent health examinations from 2019 to 2024. Complete clinical data, laboratory tests, and echocardiographic results were retrieved from the electronic medical record system. Based on echocardiographic findings indicating aortic valve stenosis, participants were categorized into CAVS and control groups. Inclusion and Exclusion Criteria Inclusion Criteria (I) Case group: Patients diagnosed with aortic stenosis according to the 2020 ACC/AHA guidelines for the management of patients with valvular heart disease;[ 17 ] (II) Control group: Individuals without aortic stenosis as determined by echocardiography;(III) Complete clinical data. Exclusion Criteria (I) Secondary aortic valve lesions: rheumatic heart disease, congenital valve malformations;(II)Conditions affecting calcium metabolism: hyper/hypoparathyroidism (parathyroid hormone > 88 pg/ml or < 15 pg/ml), thyrotoxicosis (thyroid-stimulating hormone < 0.1 mIU/L and elevated serum free thyroid hormones), chronic kidney disease stage 4–5 (estimated glomerular filtration rate < 30 ml/min/1.73 m²); (III) Other confounding valvular factors: active infective endocarditis, connective tissue disease-related valvular involvement (e.g., Marfan syndrome, systemic lupus erythematosus, collagen disease, syphilis, etc.). Diagnostic Criteria for Aortic Stenosis According to the 2020 ACC/AHA guidelines, the normal adult aortic valve area is approximately 3.0–4.0 cm².[ 18 ] When the mean pressure gradient across the aortic valve reaches or exceeds 40 mmHg, it typically indicates that stenosis has caused significant hemodynamic burden. The severity of aortic stenosis is primarily graded based on Doppler echocardiographic parameters: mild stenosis may present as a peak transvalvular flow velocity (Vmax) of 2.0–2.9 m/s and/or a mean transvalvular pressure gradient < 20 mmHg; moderate stenosis corresponds to Vmax of 3.0–3.9 m/s and/or a mean transvalvular pressure gradient of 20–39 mmHg. Severe stenosis is often accompanied by degenerative changes in the aortic valve structure (such as leaflet thickening, calcification, and restricted mobility) and meets the criteria of Vmax ≥ 4.0 m/s along with an aortic valve area (AVA) ≤ 1.0 cm².[ 19 ] Statistical Analysis To select candidate variables associated with CAVS and reduce the impact of multicollinearity, LASSO regression was employed for variable selection. [ 20 ]Subsequently, selected variables were included in multivariate logistic regression to construct the prediction model and create a nomogram. [ 21 ] The discrimination of the model was assessed using ROC curves and AUC; [ 22 ]calibration was evaluated through calibration curves and the Hosmer–Lemeshow test;[ 23 ] and the clinical utility was assessed via decision curve analysis (DCA) for net benefit. [ 24 ] Statistical analysis software included SPSS (version 26.0; IBM, Armonk, NY, USA) and R software (version 4.0.5; R Foundation for Statistical Computing, Vienna, Austria). R packages ( https //www.R-project.org ) included "glmnet," "RMS," "Foreign," "caret," and "nricens." All tests were two-tailed, and P < 0.05 was considered statistically significant. Results Study Population and Data Set Division According to the inclusion and exclusion criteria, a total of 580 subjects were included in this study, with 450 in the control group and 130 in the CAVS group. For model construction and internal validation, samples were divided into training and validation sets as planned: 406 cases in the training set (315 controls and 91 CAVS) and 174 cases in the validation set (135 controls and 39 CAVS). Candidate Variables and Feature Selection Results A total of 38 candidate variables were collected in the training set, covering demographic characteristics, medical history, and laboratory indicators. Considering the relatively high number of variables compared to sample size, and potential correlations between variables, we used LASSO regression for dimensionality reduction. In the training set, the selection process ultimately retained 26 variables closely associated with the outcome(Fig. 1A,Fig. 1B). The retained variables included: sex, age, history of hypertension, history of diabetes mellitus, history of chronic kidney disease, body mass index, smoking status, systemic inflammation response index, C-reactive protein, albumin-to-globulin ratio, cystatin C, blood urea nitrogen-to-creatinine ratio, alkaline phosphatase, uric acid, phosphorus, chloride, potassium, calcium, apolipoprotein B to apolipoprotein A1 ratio, lipoprotein(a), triglycerides, total cholesterol, systolic blood pressure, glucose, fibrinogen, and international normalized ratio. Risk Factor Analysis and Model Construction The 26 variables selected by LASSO were further analyzed univariately, with 21 showing significant associations with CAVS (P < 0.05,Table 1 ). After including statistically significant and clinically reasonable variables in multivariate binary logistic regression, 11 variables remained independently associated: ApoB/ApoA1 ratio, age, SIRI, sex, fasting glucose, history of CKD, triglycerides, history of hypertension, CRP, history of diabetes, and uric acid (P < 0.05,Table 1 ). Based on these independent variables, we established a nomogram to predict the risk of CAVS (Fig. 2) . Model Performance: Calibration, Discrimination, and Clinical Benefit Calibration curves indicated that the predicted probabilities from the model were consistent with actual occurrence probabilities, with curves closely approaching the ideal 45° reference line. The Hosmer–Lemeshow test further indicated good model fit: HL χ²=6.43, P = 0.600 for the training set; HL χ²=6.86, P = 0.552 for the validation set(Fig. 3). ROC analysis demonstrated good discrimination capacity, with AUC of 0.932 (95% CI: 0.905–0.959) for the training set and 0.907 (95% CI: 0.855–0.958) for the validation set(Fig. 4). In terms of clinical utility, decision curve analysis showed that the nomogram provided higher net benefits across a wide range of threshold probabilities, indicating its potential clinical application: the net benefit threshold range was approximately 0.01–0.94 for the training set and 0.01–0.89 and 0.96–0.99 for the validation set(Fig. 5,Table 2 ). Discussion Calcific aortic valve stenosis (CAVS) continues to rise in prevalence against the backdrop of an aging population, becoming one of the important chronic degenerative diseases in the cardiovascular field. Contrary to the traditional view of “passive degeneration,” increasing research indicates that the occurrence and development of CAVS is an actively regulated process driven by multiple pathways, involving endothelial injury, lipid deposition, chronic inflammatory responses, oxidative stress, and osteoblastic differentiation of valve interstitial cells. [ 25 ]Currently, there are no effective drugs to prevent or delay the progression of valve calcification, and the main clinical interventions remain surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR), both of which carry risks of perioperative complications, long-term anticoagulation, and valve durability issues. [ 26 ] Therefore, establishing risk assessment tools based on routinely available information in health examination and outpatient populations to identify high-risk individuals and optimize ultrasound screening and follow-up strategies has clear clinical value and practical significance. This study utilized single-center real-world data, applying LASSO regression for variable selection and combining multivariate logistic regression to construct a CAVS risk prediction model and nomogram. Ultimately, 11 independent predictors were identified: ApoB/ApoA1, age, SIRI, sex, fasting glucose, history of CKD, triglycerides, history of hypertension, CRP, history of diabetes, and uric acid. The model demonstrated high discrimination (AUCs of 0.932 and 0.907) and good calibration (P = 0.600 for the training set and P = 0.552 for the validation set), while decision curve analysis indicated that it could provide net benefits across a wide range of threshold probabilities, supporting its potential application in early risk stratification and screening decisions. From a pathophysiological perspective, the variables retained in our multifactorial model point to three key pathways: “inflammation—metabolism—renal function/uric acid load,” which align closely with current mechanistic research on CAVS. [ 27 ]In terms of inflammation, both CRP and SIRI were significantly associated with CAVS in univariate analysis and maintained independent associations in multivariate correction (CRP: OR = 1.628, 95% CI 1.066–2.487, P < 0.001; SIRI: OR = 2.188, 95% CI 1.434–3.338, P < 0.001). Previous studies have confirmed that early endothelial injury can promote monocyte/macrophage infiltration and the release of inflammatory mediators, with the inflammatory response and oxidative stress amplifying each other, driving valve interstitial cells toward an osteoblastic phenotype and promoting calcific nodule formation. [ 28 ]CRP, as a commonly used clinical inflammatory marker, reflects systemic inflammatory load; while SIRI reflects the dynamic balance of neutrophils, monocytes, and lymphocytes, providing a closer approximation to the “immune-inflammatory phenotype.” The inclusion of both variables in the multivariate model suggests that the inflammatory signals associated with CAVS in this cohort cannot be fully captured by a single dimension. Structural changes in immune cell lineages and elevated levels of inflammatory proteins may provide complementary information, further supporting the central role of chronic inflammation in the pathogenesis of CAVS.[ 29 ] Metabolic abnormalities also exhibited stable independent contributions in this study. Both history of diabetes and fasting glucose were independent predictors (history of diabetes: OR = 2.91, 95% CI 1.086–7.8, P = 0.03; Glucose: OR = 1.816, 95% CI 1.191–2.768, P < 0.001), suggesting that both short-term metabolic status and long-term metabolic exposure are associated with increased CAVS risk. [ 30 ] Hyperglycemia may promote the activation of calcification-related signals through multiple mechanisms, including the deposition of advanced glycation end-products, enhanced oxidative stress, endothelial dysfunction, and amplified inflammation. Additionally, triglycerides and the ApoB/ApoA1 ratio remained significant in the multivariate model (OR = 1.725, 95% CI 1.127–2.638, P = 0.011; ApoB/ApoA1: OR = 2.660, 95% CI 1.799–3.931, P < 0.001), suggesting that the risk structure at the lipoprotein particle level may better reflect the lipid deposition background associated with valve calcification compared to traditional lipid fraction measurements. [ 31 ] Notably, total cholesterol was associated with CAVS in univariate analysis (P = 0.035) but showed a weakened association in the multivariate model (P = 0.088); lipoprotein(a) was also significant in univariate analysis (P < 0.001) but not in multivariate analysis (P = 0.193). This phenomenon suggests that, once ApoB/ApoA1, triglycerides, and variables related to inflammation and glucose metabolism were included, the information carried by total cholesterol and lipoprotein(a) may have been partially absorbed by more representative lipoprotein profile indicators, or their effects may be more indirectly reflected through metabolic-inflammation pathways. Combined with previous clinical studies reporting limited effects of certain lipid-lowering interventions on the progression of valve stenosis, these findings further support that, at the early risk identification level, the structure of lipoprotein profiles remains of significant value, but their influence on CAVS may be more indicative of a composite phenotype acting in concert with inflammation and metabolic abnormalities, rather than a linear effect of a single lipid indicator.[ 32 ] The renal function and uric acid axis also stood out in this study. History of CKD maintained significant and strong effects in the multivariate model (OR = 4.626, 95% CI 1.529–13.992, P = 0.006), and uric acid was also an independent predictor (OR = 1.513, 95% CI 1.025–2.233, P = 0.037). Previous studies have indicated that CKD-related mineral metabolism disorders, chronic inflammatory states, oxidative stress, and endothelial dysfunction significantly increase susceptibility to vascular and valve calcification; elevated uric acid is closely associated with oxidative stress, inflammatory activation, and endothelial dysfunction, potentially participating in the calcification process by promoting lipid oxidation and amplifying inflammation. [ 33 ]In this study, cystatin C was significant in univariate analysis (P < 0.001) but only marginally significant in multivariate analysis (P = 0.061), and phosphorus also showed a similar pattern (P = 0.061), suggesting that signals related to renal function and mineral metabolism may be represented to some extent by the more “integrated” clinical variable of CKD history. This also indicates that future studies with larger sample sizes, incorporating more refined stages of renal function and calcium-phosphorus metabolism-related indicators, may further enhance the characterization of the “renal-mineral metabolism-calcification” pathway.[ 34 ] In terms of blood pressure-related factors, history of hypertension maintained an independent association in the multivariate model (OR = 2.631, 95% CI 1.141–6.067, P = 0.02), while systolic blood pressure was significant in univariate analysis (P < 0.001) but not in multivariate analysis (P = 0.182). This suggests that long-term blood pressure exposure (reflected by history) may better characterize the cumulative mechanical stress on the valve and its impact on endothelial injury than a single measurement of blood pressure. This result aligns with the view that the initial stages of CAVS are related to changes in shear stress and endothelial injury, providing a basis for emphasizing the importance of medical history in risk assessment.[ 35 ] Additionally, this study observed that several variables were associated with CAVS in univariate analysis but were no longer significant after multivariate correction, such as BMI, alkaline phosphatase, fibrinogen, and INR. This may reflect that these indicators are more downstream manifestations or accompanying phenotypes of inflammation, metabolism, and renal function pathways, with their independent contributions diminished after including more core variables. This phenomenon also suggests that when constructing prediction models for clinical application, selecting a combination of variables that can represent key pathological pathways and remain relatively stable may be preferable to simply aggregating a large number of related indicators.[ 36 ] This study does have certain limitations. First, as a single-center retrospective design, it may be subject to selection bias and information bias, making causal inferences difficult. [ 37 ] Second, although internal validation was completed, there is still a lack of external independent cohort validation, and the model's generalizability needs further testing in different regions and populations.[ 38 ] Third, some potentially key variables were not included, such as medication use (lipid-lowering, glucose-lowering, uric acid-lowering, etc.), lifestyle factors, and imaging quantitative indicators of valve calcification burden, which may lead to residual confounding or limit further enhancement of the model. Finally, the outcome grouping in this study was based on AVA thresholds, which, while operational, often combines parameters like Vmax and mean transvalvular pressure gradient in clinical grading. Future evaluations of model performance under more standardized grading systems could improve clinical consistency.[ 39 ] Conclusion There are currently no effective pharmacological therapies to reverse CAVS, and clinical management still relies primarily on interventional or surgical aortic valve replacement.[ 40 ] We developed and internally validated a nomogram based on 11 routinely available clinical variables, which can be used for early risk stratification in health examination or outpatient settings, thereby providing quantitative support for optimizing echocardiographic screening priorities and follow-up strategies. Abbreviations ApoB/ApoA1 Apolipoprotein B to Apolipoprotein A1 ratio AVA Aortic Valve Area BMI Body Mass Index CAVS Calcific Aortic Valve Stenosis CKD Chronic Kidney Disease CRP C-reactive Protein SAVR Surgical Aortic Valve Replacement SIRI Systemic Inflammation Response Index TAVR Transcatheter Aortic Valve Replacement. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of The First Hospital of Putian City. Due to the retrospective nature of the study and the use of anonymized patient data, the requirement for informed consent was waived by the Ethics Committee. Consent for publication • Not applicable. This manuscript does not contain any individual person's data in any form. Competing Interests • No, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and discussion reported in this paper. Funding This study was funded by the Putian University Research Project (No. 2023100). Author Contribution Z.W. and Y.H. conceived and designed the study. Z.W., Y.H., J.C., and G.P. performed the experiments and/or data collection. Z.W. and Y.H. analyzed the data and drafted the manuscript. J.C. and G.P. contributed to interpretation of results and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements • The authors would like to thank all the medical and nursing staff of the Department of Cardiology, The First Hospital of Putian City, for their invaluable assistance and support during this study. Data Availability The datasets generated and analyzed during the current study are not publicly available due to hospital privacy policy but are available from the corresponding author on reasonable request. References Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet. 2006;368:1005–11. Lindman BR, Clavel MA, Mathieu P, Iung B, Lancellotti P, Otto CM, et al. Calcific aortic stenosis. Nat Rev Dis Primers. 2016;2:16006. 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Sample size considerations for the external validation of a multivariable prognostic model. BMC Med Res Methodol. 2016;16:1–14. van Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KGM. Clinical prediction models: diagnosis vs prognosis. J Clin Epidemiol. 2021;132:142–52. Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. Baumgartner H, Falk V, Bax JJ, De Bonis M, Hamm C, Holm PJ, et al. 2017 ESC/EACTS Guidelines for the management of valvular heart disease. Eur Heart J. 2017;38:2739–91. Nishimura RA, Otto CM, Bonow RO, Carabello BA, Erwin JP 3rd, Fleisher LA, et al. 2014 AHA/ACC guideline for the management of patients with valvular heart disease. J Am Coll Cardiol. 2014;63:e57–185. Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models. BMJ. 2016;352:i6. Tables Table1 Univariate and multivariate logistic regression analysis in the training set . Characteristics Univariate analysis Multivariate analysis OR 95% CI P OR 95% CI P Upper limit Lower limit Upper limit Lower limit Sex 1.875 3.079 1.16 0.011 3.67 8.79 1.53 0.003 Age 2.66 3.631 1.995 <0.001 3.04 5.008 1.847 <0.001 Hypertension 2.676 4.416 1.65 <0.001 2.631 1.141 6.067 0.02 Diabetes mellitus 2.807 4.736 1.654 <0.001 2.91 7.8 1.086 0.03 CKD 3.375 5.969 1.898 <0.001 4.626 13.992 1.529 0.006 BMI 1.297 1.644 1.027 0.029 1.367 2.077 0.899 0.143 Smoking 1.350 2.252 0.794 0.257 1.821 4.598 0.721 0.204 SIRI 2.632 3.589 1.989 <0.001 2.188 3.338 1.434 <0.001 CRP 1.600 2.088 1.267 <0.001 1.628 2.487 1.066 <0.001 A/G ratio 0.600 0.771 0.461 <0.001 0.791 1.163 0.538 0.234 Cystatin C 1.580 1.998 1.256 <0.001 1.494 2.277 0.980 0.061 BUN/CREA ratio 1.141 1.427 0.906 0.252 1.105 1.638 0.745 0.617 ALP 1.290 1.614 1.031 0.025 1.139 1.717 0.756 0.531 UA 1.325 1.662 1.056 0.015 1.513 2.233 1.025 0.037 P 1.473 1.885 1.160 0.002 1.506 2.289 0.991 0.054 Cl 1.105 1.397 0.875 0.401 1.157 1.746 0.767 0.485 K 1.140 1.441 0.903 0.268 1.093 1.606 0.744 0.649 Ca 0.816 1.030 0.646 0.089 0.702 1.061 0.464 0.093 ApoB/ApoA1 2.367 3.105 1.835 <0.001 2.660 3.931 1.799 <0.001 Lp(a) 1.441 1.799 1.167 <0.001 1.251 1.753 0.892 0.193 TG 1.578 2.002 1.260 <0.001 1.725 2.638 1.127 0.011 TC 1.285 1.627 1.018 0.035 1.423 2.137 0.947 0.088 SBP 1.680 2.157 1.321 <0.001 1.316 1.973 0.878 0.182 Glu 1.623 2.062 1.285 <0.001 1.816 2.768 1.191 <0.001 Fib 1.495 1.898 1.188 <0.001 1.161 1.670 0.806 0.421 INR 1.401 1.775 1.111 0.005 1.301 1.925 0.879 0.187 OR, odds ratio; CI, confidence interval; A/G ratio, Albumin/Globulin ratio; Lp(a), lipoprotein(a); BUN/CREA ratio, blood urea nitrogen/creatinine ratio;SIRI,s ystemic i nflammation r esponse i ndex ; Table 2 The net benefit interval of the DCA curves . Subgroup Net benefit interval Training set 0.01–0.94 Validation set 0.01–0.89, 0.96–0.99 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Editor invited by journal 25 Feb, 2026 Submission checks completed at journal 24 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8915746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599132738,"identity":"40a2737a-467d-42e4-8f15-0bf78472f44b","order_by":0,"name":"Zhiwei Wu","email":"","orcid":"","institution":"The First Hospital of Putian City","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Wu","suffix":""},{"id":599132739,"identity":"19964729-3ad9-4c5d-accc-b88863f52faf","order_by":1,"name":"Yubin Huang","email":"","orcid":"","institution":"The First Hospital of Putian City","correspondingAuthor":false,"prefix":"","firstName":"Yubin","middleName":"","lastName":"Huang","suffix":""},{"id":599132740,"identity":"e443732c-1b8d-4b5e-912b-934020440d15","order_by":2,"name":"Jinzao Chen","email":"","orcid":"","institution":"The First Hospital of Putian City","correspondingAuthor":false,"prefix":"","firstName":"Jinzao","middleName":"","lastName":"Chen","suffix":""},{"id":599132741,"identity":"222715bd-5bb8-447e-9e7c-692533b6e250","order_by":3,"name":"Guoyan Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIie3PoQoCQRDG8VkW9sqAdcXgK5wIYjjwQSxjuaQgWAXXfg+gGHyGe4ORAZMPsIJN0HrxkihYbLc2wf3n+cF8ALHYD9bq3lnq+rHaJy6QtN10IqpgVRYcSFKmvoBh5TyFfsZMMseLVttr6WGZjRuFWq9ZNsObSTr5YgjHfOaaiNZAjKhR7aYDq5w0E2Nec9BoC+dTIEGEVNBICh4DibVIh22RU694baGQLSOfSFXVGXUTKX21zJrJZ6mlb87f5FsRi8Vi/9ETfXZDeaeofZAAAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of Putian City","correspondingAuthor":true,"prefix":"","firstName":"Guoyan","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2026-02-19 09:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8915746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8915746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104404346,"identity":"1b9b0ee6-8ba9-4f5f-b77a-a1ddb3052b6e","added_by":"auto","created_at":"2026-03-11 12:20:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110995,"visible":true,"origin":"","legend":"\u003cp\u003eClinical data were selected using the LASSO binary logistic regression model. (A) The coefficient profile of LASSO regression of 48 clinical indicators. Generated coefficient profile based on the log (λ) sequence. When λ had one standard error, 26 predictors had non- zero coefficients. (B) The selection of parameter log λ in the LASSO model was adjusted, and the minimum criterion of 10-time cross- validation was adopted. The AUC curve was plotted against log λ. The vertical line was drawn at the optimal value using one standard error of the minimum standard and the minimum standard. LASSO, Least Absolute Shrinkage and Selector Operation; AUC, area under the receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8915746/v1/1f0b092a7ab38d18313c0c89.png"},{"id":104177853,"identity":"87d93d0f-7830-4959-a858-79c6051a6abd","added_by":"auto","created_at":"2026-03-08 16:50:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":492595,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram was constructed in the training set to predict disease risk.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8915746/v1/f06bf0b609ce9d98110cffcf.png"},{"id":104177850,"identity":"7d6cd737-0af7-4487-a09a-c107659e921e","added_by":"auto","created_at":"2026-03-08 16:50:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":506047,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the nomogram in the (A) training and (B) validation sets. ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8915746/v1/8cd3142b469a0a88f634f474.png"},{"id":104177848,"identity":"8199a270-e86b-4b2e-b583-7f0dd1c2f12b","added_by":"auto","created_at":"2026-03-08 16:50:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":449647,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the nomogram predicting CAVS in the (A) training and (B) validation sets. ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8915746/v1/3e5474868bb2f30932f3c2b0.png"},{"id":104177849,"identity":"01ad96a1-29ad-4c1d-96e0-fc7a225752b2","added_by":"auto","created_at":"2026-03-08 16:50:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":276851,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves of the nomogram in the (A) training and (B) validation sets. DCA, decision curve analysis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8915746/v1/94dceae490de049ff5c37499.png"},{"id":104408878,"identity":"0ae83c64-f67a-4318-b88e-d01577b7c665","added_by":"auto","created_at":"2026-03-11 12:43:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2977624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8915746/v1/132495cf-a22a-4ea4-b8e2-b374d99fa758.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Internal Validation of a Risk-Prediction Nomogram for Calcific Aortic Valve Stenosis: A Single-Center Real-World Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCalcific aortic valve stenosis (CAVS) is one of the most common heart valve diseases in developed countries and is the most significant acquired valvular disease in the elderly.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The pathological process is characterized by leaflet fibrosis, thickening, and progressive calcification, leading to gradual narrowing of the valve opening, obstruction of left ventricular outflow, and a series of hemodynamic changes and structural remodeling. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]Epidemiological studies show that the prevalence of CAVS significantly increases with age, being particularly common in older populations, with rates approaching about 10% in those over 75 years old. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] As the population ages, the overall disease burden continues to rise.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]The clinical hazards of CAVS are reflected not only in its high prevalence but also in the poor outcomes once patients enter the symptomatic phase. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]Early CAVS often lacks specific clinical manifestations, with many patients only being identified when they present with symptoms such as exertional dyspnea, angina, syncope, or heart failure, which typically indicate a decline in cardiac functional reserve and progression to a high-risk stage.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Previous studies have shown that patients with symptomatic severe aortic stenosis who do not receive effective interventions, such as valve replacement, have a very poor natural prognosis, with survival rates significantly declining in the short term. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]It is often cited that a considerable proportion of these patients may experience death within a few years, with some studies suggesting the risk of death may approach 50% within two years. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]Therefore, identifying high-risk individuals before or during the early stages of the disease and initiating imaging assessments and dynamic follow-ups is crucial for reducing adverse outcomes.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMechanistically, CAVS is no longer viewed as merely an age-related \u0026ldquo;passive calcification\u0026rdquo;. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Increasing evidence suggests that the development of CAVS is closely related to hemodynamic stress-induced valve injury, endothelial dysfunction, lipid deposition, chronic inflammation and immune responses, oxidative stress, and the osteoblastic differentiation of valve interstitial cells.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Furthermore, CAVS often coexists with various cardiovascular and metabolic diseases, indicating that its risk may be driven by multiple factors.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Consequently, constructing a risk assessment tool based on readily available clinical information (demographic characteristics and medical history) and routine examination indicators could provide a more actionable basis for early screening and stratified management in health examination and outpatient populations.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHowever, current studies on clinical prediction models for CAVS remain relatively limited, and differences in population structure, health examination demographics, and clinical practice scenarios across regions may affect the applicability and generalizability of the models. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Therefore, this study aims to evaluate candidate factors associated with CAVS using real-world data, constructing and validating a prediction model to identify high-risk individuals for CAVS, thereby supporting early screening and risk stratification management.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThe data for this study were sourced from the electronic medical record system of Putian First Hospital. To protect patient privacy, all data were anonymized after extraction. This study adhered to the Declaration of Helsinki (2013 revision) and was approved by the Ethics Committee of Putian First Hospital. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] As this study is retrospective, informed consent from participants was waived.We consecutively included individuals who visited the cardiology department or underwent health examinations from 2019 to 2024. Complete clinical data, laboratory tests, and echocardiographic results were retrieved from the electronic medical record system. Based on echocardiographic findings indicating aortic valve stenosis, participants were categorized into CAVS and control groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003e(I) Case group: Patients diagnosed with aortic stenosis according to the 2020 ACC/AHA guidelines for the management of patients with valvular heart disease;[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e(II) Control group: Individuals without aortic stenosis as determined by echocardiography;(III) Complete clinical data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003e(I) Secondary aortic valve lesions: rheumatic heart disease, congenital valve malformations;(II)Conditions affecting calcium metabolism: hyper/hypoparathyroidism (parathyroid hormone\u0026thinsp;\u0026gt;\u0026thinsp;88 pg/ml or \u0026lt;\u0026thinsp;15 pg/ml), thyrotoxicosis (thyroid-stimulating hormone\u0026thinsp;\u0026lt;\u0026thinsp;0.1 mIU/L and elevated serum free thyroid hormones), chronic kidney disease stage 4\u0026ndash;5 (estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;30 ml/min/1.73 m\u0026sup2;);\u003cb\u003e(III)\u003c/b\u003eOther confounding valvular factors: active infective endocarditis, connective tissue disease-related valvular involvement (e.g., Marfan syndrome, systemic lupus erythematosus, collagen disease, syphilis, etc.).\u003c/p\u003e\n\u003ch3\u003eDiagnostic Criteria for Aortic Stenosis\u003c/h3\u003e\n\u003cp\u003e According to the 2020 ACC/AHA guidelines, the normal adult aortic valve area is approximately 3.0\u0026ndash;4.0 cm\u0026sup2;.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] When the mean pressure gradient across the aortic valve reaches or exceeds 40 mmHg, it typically indicates that stenosis has caused significant hemodynamic burden. The severity of aortic stenosis is primarily graded based on Doppler echocardiographic parameters: mild stenosis may present as a peak transvalvular flow velocity (Vmax) of 2.0\u0026ndash;2.9 m/s and/or a mean transvalvular pressure gradient\u0026thinsp;\u0026lt;\u0026thinsp;20 mmHg; moderate stenosis corresponds to Vmax of 3.0\u0026ndash;3.9 m/s and/or a mean transvalvular pressure gradient of 20\u0026ndash;39 mmHg. Severe stenosis is often accompanied by degenerative changes in the aortic valve structure (such as leaflet thickening, calcification, and restricted mobility) and meets the criteria of Vmax\u0026thinsp;\u0026ge;\u0026thinsp;4.0 m/s along with an aortic valve area (AVA)\u0026thinsp;\u0026le;\u0026thinsp;1.0 cm\u0026sup2;.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eTo select candidate variables associated with CAVS and reduce the impact of multicollinearity, LASSO regression was employed for variable selection. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]Subsequently, selected variables were included in multivariate logistic regression to construct the prediction model and create a nomogram. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The discrimination of the model was assessed using ROC curves and AUC; [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]calibration was evaluated through calibration curves and the Hosmer\u0026ndash;Lemeshow test;[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the clinical utility was assessed via decision curve analysis (DCA) for net benefit. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Statistical analysis software included SPSS (version 26.0; IBM, Armonk, NY, USA) and R software (version 4.0.5; R Foundation for Statistical Computing, Vienna, Austria). R packages (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps\u003c/span\u003e\u003cspan address=\"https\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003e//www.R-project.org\u003c/span\u003e \u003cspan address=\"http:////www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e) included \"glmnet,\" \"RMS,\" \"Foreign,\" \"caret,\" and \"nricens.\" All tests were two-tailed, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and Data Set Division\u003c/h2\u003e \u003cp\u003eAccording to the inclusion and exclusion criteria, a total of 580 subjects were included in this study, with 450 in the control group and 130 in the CAVS group. For model construction and internal validation, samples were divided into training and validation sets as planned: 406 cases in the training set (315 controls and 91 CAVS) and 174 cases in the validation set (135 controls and 39 CAVS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCandidate Variables and Feature Selection Results\u003c/h2\u003e \u003cp\u003eA total of 38 candidate variables were collected in the training set, covering demographic characteristics, medical history, and laboratory indicators. Considering the relatively high number of variables compared to sample size, and potential correlations between variables, we used LASSO regression for dimensionality reduction. In the training set, the selection process ultimately retained 26 variables closely associated with the outcome(Fig.\u0026nbsp;1A,Fig.\u0026nbsp;1B). The retained variables included: sex, age, history of hypertension, history of diabetes mellitus, history of chronic kidney disease, body mass index, smoking status, systemic inflammation response index, C-reactive protein, albumin-to-globulin ratio, cystatin C, blood urea nitrogen-to-creatinine ratio, alkaline phosphatase, uric acid, phosphorus, chloride, potassium, calcium, apolipoprotein B to apolipoprotein A1 ratio, lipoprotein(a), triglycerides, total cholesterol, systolic blood pressure, glucose, fibrinogen, and international normalized ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factor Analysis and Model Construction\u003c/h2\u003e \u003cp\u003eThe 26 variables selected by LASSO were further analyzed univariately, with 21 showing significant associations with CAVS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05,Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After including statistically significant and clinically reasonable variables in multivariate binary logistic regression, 11 variables remained independently associated: ApoB/ApoA1 ratio, age, SIRI, sex, fasting glucose, history of CKD, triglycerides, history of hypertension, CRP, history of diabetes, and uric acid (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05,Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on these independent variables, we established a nomogram to predict the risk of CAVS (Fig.\u0026nbsp;2) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance: Calibration, Discrimination, and Clinical Benefit\u003c/h2\u003e \u003cp\u003eCalibration curves indicated that the predicted probabilities from the model were consistent with actual occurrence probabilities, with curves closely approaching the ideal 45\u0026deg; reference line. The Hosmer\u0026ndash;Lemeshow test further indicated good model fit: HL χ\u0026sup2;=6.43, P\u0026thinsp;=\u0026thinsp;0.600 for the training set; HL χ\u0026sup2;=6.86, P\u0026thinsp;=\u0026thinsp;0.552 for the validation set(Fig.\u0026nbsp;3). ROC analysis demonstrated good discrimination capacity, with AUC of 0.932 (95% CI: 0.905\u0026ndash;0.959) for the training set and 0.907 (95% CI: 0.855\u0026ndash;0.958) for the validation set(Fig.\u0026nbsp;4). In terms of clinical utility, decision curve analysis showed that the nomogram provided higher net benefits across a wide range of threshold probabilities, indicating its potential clinical application: the net benefit threshold range was approximately 0.01\u0026ndash;0.94 for the training set and 0.01\u0026ndash;0.89 and 0.96\u0026ndash;0.99 for the validation set(Fig.\u0026nbsp;5,Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCalcific aortic valve stenosis (CAVS) continues to rise in prevalence against the backdrop of an aging population, becoming one of the important chronic degenerative diseases in the cardiovascular field. Contrary to the traditional view of \u0026ldquo;passive degeneration,\u0026rdquo; increasing research indicates that the occurrence and development of CAVS is an actively regulated process driven by multiple pathways, involving endothelial injury, lipid deposition, chronic inflammatory responses, oxidative stress, and osteoblastic differentiation of valve interstitial cells. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]Currently, there are no effective drugs to prevent or delay the progression of valve calcification, and the main clinical interventions remain surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR), both of which carry risks of perioperative complications, long-term anticoagulation, and valve durability issues. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Therefore, establishing risk assessment tools based on routinely available information in health examination and outpatient populations to identify high-risk individuals and optimize ultrasound screening and follow-up strategies has clear clinical value and practical significance.\u003c/p\u003e \u003cp\u003eThis study utilized single-center real-world data, applying LASSO regression for variable selection and combining multivariate logistic regression to construct a CAVS risk prediction model and nomogram. Ultimately, 11 independent predictors were identified: ApoB/ApoA1, age, SIRI, sex, fasting glucose, history of CKD, triglycerides, history of hypertension, CRP, history of diabetes, and uric acid. The model demonstrated high discrimination (AUCs of 0.932 and 0.907) and good calibration (P\u0026thinsp;=\u0026thinsp;0.600 for the training set and P\u0026thinsp;=\u0026thinsp;0.552 for the validation set), while decision curve analysis indicated that it could provide net benefits across a wide range of threshold probabilities, supporting its potential application in early risk stratification and screening decisions.\u003c/p\u003e \u003cp\u003eFrom a pathophysiological perspective, the variables retained in our multifactorial model point to three key pathways: \u0026ldquo;inflammation\u0026mdash;metabolism\u0026mdash;renal function/uric acid load,\u0026rdquo; which align closely with current mechanistic research on CAVS. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]In terms of inflammation, both CRP and SIRI were significantly associated with CAVS in univariate analysis and maintained independent associations in multivariate correction (CRP: OR\u0026thinsp;=\u0026thinsp;1.628, 95% CI 1.066\u0026ndash;2.487, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SIRI: OR\u0026thinsp;=\u0026thinsp;2.188, 95% CI 1.434\u0026ndash;3.338, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Previous studies have confirmed that early endothelial injury can promote monocyte/macrophage infiltration and the release of inflammatory mediators, with the inflammatory response and oxidative stress amplifying each other, driving valve interstitial cells toward an osteoblastic phenotype and promoting calcific nodule formation. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]CRP, as a commonly used clinical inflammatory marker, reflects systemic inflammatory load; while SIRI reflects the dynamic balance of neutrophils, monocytes, and lymphocytes, providing a closer approximation to the \u0026ldquo;immune-inflammatory phenotype.\u0026rdquo; The inclusion of both variables in the multivariate model suggests that the inflammatory signals associated with CAVS in this cohort cannot be fully captured by a single dimension. Structural changes in immune cell lineages and elevated levels of inflammatory proteins may provide complementary information, further supporting the central role of chronic inflammation in the pathogenesis of CAVS.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMetabolic abnormalities also exhibited stable independent contributions in this study. Both history of diabetes and fasting glucose were independent predictors (history of diabetes: OR\u0026thinsp;=\u0026thinsp;2.91, 95% CI 1.086\u0026ndash;7.8, P\u0026thinsp;=\u0026thinsp;0.03; Glucose: OR\u0026thinsp;=\u0026thinsp;1.816, 95% CI 1.191\u0026ndash;2.768, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that both short-term metabolic status and long-term metabolic exposure are associated with increased CAVS risk. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Hyperglycemia may promote the activation of calcification-related signals through multiple mechanisms, including the deposition of advanced glycation end-products, enhanced oxidative stress, endothelial dysfunction, and amplified inflammation. Additionally, triglycerides and the ApoB/ApoA1 ratio remained significant in the multivariate model (OR\u0026thinsp;=\u0026thinsp;1.725, 95% CI 1.127\u0026ndash;2.638, P\u0026thinsp;=\u0026thinsp;0.011; ApoB/ApoA1: OR\u0026thinsp;=\u0026thinsp;2.660, 95% CI 1.799\u0026ndash;3.931, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the risk structure at the lipoprotein particle level may better reflect the lipid deposition background associated with valve calcification compared to traditional lipid fraction measurements. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] Notably, total cholesterol was associated with CAVS in univariate analysis (P\u0026thinsp;=\u0026thinsp;0.035) but showed a weakened association in the multivariate model (P\u0026thinsp;=\u0026thinsp;0.088); lipoprotein(a) was also significant in univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in multivariate analysis (P\u0026thinsp;=\u0026thinsp;0.193). This phenomenon suggests that, once ApoB/ApoA1, triglycerides, and variables related to inflammation and glucose metabolism were included, the information carried by total cholesterol and lipoprotein(a) may have been partially absorbed by more representative lipoprotein profile indicators, or their effects may be more indirectly reflected through metabolic-inflammation pathways. Combined with previous clinical studies reporting limited effects of certain lipid-lowering interventions on the progression of valve stenosis, these findings further support that, at the early risk identification level, the structure of lipoprotein profiles remains of significant value, but their influence on CAVS may be more indicative of a composite phenotype acting in concert with inflammation and metabolic abnormalities, rather than a linear effect of a single lipid indicator.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe renal function and uric acid axis also stood out in this study. History of CKD maintained significant and strong effects in the multivariate model (OR\u0026thinsp;=\u0026thinsp;4.626, 95% CI 1.529\u0026ndash;13.992, P\u0026thinsp;=\u0026thinsp;0.006), and uric acid was also an independent predictor (OR\u0026thinsp;=\u0026thinsp;1.513, 95% CI 1.025\u0026ndash;2.233, P\u0026thinsp;=\u0026thinsp;0.037). Previous studies have indicated that CKD-related mineral metabolism disorders, chronic inflammatory states, oxidative stress, and endothelial dysfunction significantly increase susceptibility to vascular and valve calcification; elevated uric acid is closely associated with oxidative stress, inflammatory activation, and endothelial dysfunction, potentially participating in the calcification process by promoting lipid oxidation and amplifying inflammation. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]In this study, cystatin C was significant in univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but only marginally significant in multivariate analysis (P\u0026thinsp;=\u0026thinsp;0.061), and phosphorus also showed a similar pattern (P\u0026thinsp;=\u0026thinsp;0.061), suggesting that signals related to renal function and mineral metabolism may be represented to some extent by the more \u0026ldquo;integrated\u0026rdquo; clinical variable of CKD history. This also indicates that future studies with larger sample sizes, incorporating more refined stages of renal function and calcium-phosphorus metabolism-related indicators, may further enhance the characterization of the \u0026ldquo;renal-mineral metabolism-calcification\u0026rdquo; pathway.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn terms of blood pressure-related factors, history of hypertension maintained an independent association in the multivariate model (OR\u0026thinsp;=\u0026thinsp;2.631, 95% CI 1.141\u0026ndash;6.067, P\u0026thinsp;=\u0026thinsp;0.02), while systolic blood pressure was significant in univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in multivariate analysis (P\u0026thinsp;=\u0026thinsp;0.182). This suggests that long-term blood pressure exposure (reflected by history) may better characterize the cumulative mechanical stress on the valve and its impact on endothelial injury than a single measurement of blood pressure. This result aligns with the view that the initial stages of CAVS are related to changes in shear stress and endothelial injury, providing a basis for emphasizing the importance of medical history in risk assessment.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAdditionally, this study observed that several variables were associated with CAVS in univariate analysis but were no longer significant after multivariate correction, such as BMI, alkaline phosphatase, fibrinogen, and INR. This may reflect that these indicators are more downstream manifestations or accompanying phenotypes of inflammation, metabolism, and renal function pathways, with their independent contributions diminished after including more core variables. This phenomenon also suggests that when constructing prediction models for clinical application, selecting a combination of variables that can represent key pathological pathways and remain relatively stable may be preferable to simply aggregating a large number of related indicators.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study does have certain limitations. First, as a single-center retrospective design, it may be subject to selection bias and information bias, making causal inferences difficult. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Second, although internal validation was completed, there is still a lack of external independent cohort validation, and the model's generalizability needs further testing in different regions and populations.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] Third, some potentially key variables were not included, such as medication use (lipid-lowering, glucose-lowering, uric acid-lowering, etc.), lifestyle factors, and imaging quantitative indicators of valve calcification burden, which may lead to residual confounding or limit further enhancement of the model. Finally, the outcome grouping in this study was based on AVA thresholds, which, while operational, often combines parameters like Vmax and mean transvalvular pressure gradient in clinical grading. Future evaluations of model performance under more standardized grading systems could improve clinical consistency.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere are currently no effective pharmacological therapies to reverse CAVS, and clinical management still relies primarily on interventional or surgical aortic valve replacement.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] We developed and internally validated a nomogram based on 11 routinely available clinical variables, which can be used for early risk stratification in health examination or outpatient settings, thereby providing quantitative support for optimizing echocardiographic screening priorities and follow-up strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eApoB/ApoA1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApolipoprotein B to Apolipoprotein A1 ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAortic Valve Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAVS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalcific Aortic Valve Stenosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurgical Aortic Valve Replacement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystemic Inflammation Response Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscatheter Aortic Valve Replacement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of The First Hospital of Putian City. Due to the retrospective nature of the study and the use of anonymized patient data, the requirement for informed consent was waived by the Ethics Committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e\u0026bull; Not applicable. This manuscript does not contain any individual person's data in any form.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003e\u0026bull; No, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and discussion reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the Putian University Research Project (No. 2023100).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.W. and Y.H. conceived and designed the study. Z.W., Y.H., J.C., and G.P. performed the experiments and/or data collection. Z.W. and Y.H. analyzed the data and drafted the manuscript. J.C. and G.P. contributed to interpretation of results and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003e\u0026bull; The authors would like to thank all the medical and nursing staff of the Department of Cardiology, The First Hospital of Putian City, for their invaluable assistance and support during this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to hospital privacy policy but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. 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Sample size considerations for the external validation of a multivariable prognostic model. BMC Med Res Methodol. 2016;16:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KGM. Clinical prediction models: diagnosis vs prognosis. J Clin Epidemiol. 2021;132:142\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaumgartner H, Falk V, Bax JJ, De Bonis M, Hamm C, Holm PJ, et al. 2017 ESC/EACTS Guidelines for the management of valvular heart disease. Eur Heart J. 2017;38:2739\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimura RA, Otto CM, Bonow RO, Carabello BA, Erwin JP 3rd, Fleisher LA, et al. 2014 AHA/ACC guideline for the management of patients with valvular heart disease. J Am Coll Cardiol. 2014;63:e57\u0026ndash;185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models. BMJ. 2016;352:i6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eUnivariate and multivariate logistic regression analysis in the training set\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1011\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 417px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 417px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e3.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e3.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e5.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e4.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e6.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e4.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e5.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e13.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.350\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e4.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e3.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.628\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eA/G ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.580\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.494\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.980\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eBUN/CREA ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.290\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.139\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.140\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.093\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.061\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eApoB/ApoA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.660\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e3.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eLp(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.680\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eGlu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eFib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.670\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; CI, confidence interval; A/G ratio, Albumin/Globulin ratio; Lp(a), lipoprotein(a); BUN/CREA ratio, blood urea nitrogen/creatinine ratio;SIRI,s\u003cstrong\u003eystemic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003enflammation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003er\u003c/strong\u003e\u003cstrong\u003eesponse\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003endex\u003c/strong\u003e\u003cstrong\u003e;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe net benefit interval of the DCA curves\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 559px;\"\u003e\n \u003cp\u003eSubgroup \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Net benefit interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 559px;\"\u003e\n \u003cp\u003eTraining set \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.01\u0026ndash;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 559px;\"\u003e\n \u003cp\u003eValidation set \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.01\u0026ndash;0.89, 0.96\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Calcific Aortic Valve Stenosis(CAVS), Risk Prediction Model, Nomogram, Real-World Study","lastPublishedDoi":"10.21203/rs.3.rs-8915746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8915746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCalcific aortic valve stenosis (CAVS) is a common valvular disease, and early identification of high-risk populations can help optimize screening and follow-up strategies. However, there is still a relative lack of risk prediction models for CAVS based on routine clinical indicators that have undergone internal validation. This study aims to develop and validate a clinically applicable CAVS prediction model, assessing its discrimination, calibration, and clinical net benefit.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective study including patients diagnosed with CAVS at our hospital between 2019 and 2024 and individuals undergoing health examinations during the same period. Participants were classified according to echocardiographic aortic valve area (AVA): AVA\u0026thinsp;\u0026lt;\u0026thinsp;3 cm\u0026sup2; as the CAVS group and AVA\u0026thinsp;\u0026ge;\u0026thinsp;3 cm\u0026sup2; as the normal group. A total of 580 participants were enrolled (450 controls and 130 CAVS cases) and randomly split at a 7:3 ratio into a training set (n\u0026thinsp;=\u0026thinsp;406; 315 controls and 91 CAVS cases) and a validation set (n\u0026thinsp;=\u0026thinsp;174; 135 controls and 39 CAVS cases). In the training set, the least absolute shrinkage and selection operator (LASSO) was used for variable selection, followed by multivariable logistic regression to construct the prediction model and nomogram. Model calibration, discrimination, and clinical net benefit were evaluated in both datasets using calibration curves/Hosmer\u0026ndash;Lemeshow (H\u0026ndash;L) test, receiver operating characteristic (ROC) curve with area under the curve (AUC), and decision curve analysis (DCA), respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on LASSO selection and multivariate logistic regression, 11 independent predictors were identified: ApoB/ApoA1 ratio, age, SIRI(Systemic Inflammation Response Index), sex, fasting glucose, history of CKD, triglycerides, history of hypertension, CRP, history of diabetes, and uric acid. The model demonstrated good calibration: Hosmer\u0026ndash;Lemeshow test χ\u0026sup2;=6.43, P\u0026thinsp;=\u0026thinsp;0.600 for the training set; χ\u0026sup2;=6.86, P\u0026thinsp;=\u0026thinsp;0.552 for the validation set, with calibration curves closely approaching the ideal line. The model exhibited high discrimination: AUC\u0026thinsp;=\u0026thinsp;0.932 (95% CI 0.905\u0026ndash;0.959) for the training set and AUC\u0026thinsp;=\u0026thinsp;0.907 (95% CI 0.855\u0026ndash;0.958) for the validation set. At a cutoff probability of 0.269, the sensitivity and specificity were 81.3% and 88.3% for the training set, and 82.1% and 82.2% for the validation set, respectively. DCA indicated that the nomogram provided higher net benefits across a wide range of threshold probabilities (0.01\u0026ndash;0.94 for the training set; 0.01\u0026ndash;0.89 and 0.96\u0026ndash;0.99 for the validation set).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eUsing single-center retrospective data, we developed and internally validated a nomogram for predicting CAVS risk. This tool may facilitate individualized risk assessment and inform screening decisions for CAVS.\u003c/p\u003e","manuscriptTitle":"Development and Internal Validation of a Risk-Prediction Nomogram for Calcific Aortic Valve Stenosis: A Single-Center Real-World Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 16:50:11","doi":"10.21203/rs.3.rs-8915746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-02T08:33:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T08:32:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-26T04:00:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T04:32:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-02-25T04:20:42+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":"a440c646-edd8-482e-9c5a-0c03fa0c8003","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T16:50:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 16:50:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8915746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8915746","identity":"rs-8915746","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}