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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39278952
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10.1038/s41598-024-72544-3
Article
The potential of a nomogram risk assessment model for the diagnosis of abdominal aortic aneurysm: a multicenter retrospective study
Huo Guijun 1
Shen Han 2
Zheng Jin 1
Zeng Yuqi
Yao Zhichao 1
Cao Junjie 1
Tang Yao 1
Huang Jian 1
Liu Zhanao 1
Zhou Dayong zhoudy@njmu.edu.cn

1
1 grid.440227.7 0000 0004 1758 3572 The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Suzhou, Jiangsu China
2 https://ror.org/051jg5p78 grid.429222.d 0000 0004 1798 0228 Department of Cardiovascular Surgery, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu China
15 9 2024
15 9 2024
2024
14 2153624 5 2024
9 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The incidence of abdominal aortic aneurysm (AAA) is very high, but there is no risk assessment model for early identification of AAA in clinic. The aim of this study was to develop a nomogram risk assessment model for predicting AAA. The data of 280 patients diagnosed as AAA and 385 controls in The Affiliated Suzhou Hospital of Nanjing Medical University were retrospectively reviewed. The LASSO regression method was applied to filter variables, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The calibration capability of the model is evaluated by using bootstrap (resampling = 1000) internal validation and Hosmer–Lemeshow test. The clinical utility and clinical application value were evaluated by decision curve analysis (DCA) and clinical impact curve (CIC). In addition, a retrospective review of 133 AAA patients and 262 controls from The First Affiliated Hospital of Soochow University was performed as an external validation cohort. Eight variables are selected to construct the nomogram of AAA risk assessment model. The nomogram predicted AAA with AUC values of 0.928 (95%CI, 0.907–0.950) in the training cohort, and 0.902 (95%CI, 0.865–0.940) in the external validation cohort, the risk prediction model has excellent discriminative ability. The calibration curve and Hosmer–Lemeshow test proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, the DCA curve and CIC curve showed that patients could benefit. This finding was also confirmed in the external validation cohort. In this study, a nomogram was constructed that incorporated eight demographic and clinical characteristics of AAA patients, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.

Keywords

Abdominal aortic aneurysms
Nomogram
Diagnosis
Risk factors
Subject terms

Experimental models of disease
Cardiovascular diseases
Risk factors
Suzhou“Science and Education Revitalize Health” Youth Science and Technology Projectissue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Abdominal aortic aneurysm (AAA) is defined as localized dilatation of abdominal aorta ≥ 50% of normal artery diameter1,2. The pathophysiological characteristics of AAA include extracellular matrix degradation, immune response, inflammatory and oxidative stress response, cell apoptosis, vascular remodeling and so on3,4. At present, there is no effective drug that can inhibit the growth of AAA or prevent the rupture of AAA. Surgery is the only effective way to treat AAA, which mainly includes endovascular isolation with coated stent or prosthesis graft replacement. However, the surgical technique is complicated, the risk is high, and the cost is high, and many small medical centers cannot complete the surgical treatment of AAA. So the patients missed the best time for rescue5,6.

The incidence of AAA is about 0.7%-5.1% in the population, and it is about 8.8% in the population over 65 years old7. The onset of most AAA is insidious, usually without obvious clinical symptoms, and clinicians often neglect the prevention and treatment of AAA. When patients have severe abdominal pain, it is often indicated that AAA is ruptured, which is one of the most serious complications of AAA patients. The fatality rate is as high as 30% to 50% even if the patient receives emergency treatment in the emergency department. Therefore, early identification of high-risk groups of AAA is crucial8,9. In clinical practice, ultrasound is often used to screen AAA. Computed tomography angiography (CTA) has always been considered as the most reliable method to identify and diagnose AAA, but asymptomatic AAA are often ignored without further examination, so missed diagnosis or delayed diagnosis of AAA is still very common10,11. Therefore, how to identify the potential population of AAA early, carry out individual risk prediction and risk stratification for patients with high-risk AAA, and accurately carry out prevention and treatment for high-risk groups, it is of great clinical significance to reduce the occurrence and development of AAA. However, as far as we know, there are few risk assessment models that can be used to predict AAA.

In this study, we established a model to predict the risk factors of AAA based on the demographic and clinical characteristics of patients. This study can help clinicians diagnose AAA early, has far-reaching significance for the prevention and treatment of AAA, and provides a new strategy for the management of AAA.

Material and methods

Patient data

This retrospective study complied with the declaration of Helsinki and was approved by The Affiliated Suzhou Hospital of Nanjing Medical University Ethical Committee and The First Affiliated Hospital of Soochow University Ethical Committee. Human participants’ names have been removed from all sections of the manuscript. The Affiliated Suzhou Hospital of Nanjing Medical University Ethical Committee and The First Affiliated Hospital of Soochow University Ethical Committee waived the need for informed consent. The ethical approval number is KL901463. we confirm that all methods were performed in accordance with the relevant guidelines and regulations. The clinical data of patients hospitalized in the Vascular Surgery Center of Suzhou Hospital of Nanjing Medical University from January 2010 to January 2024 were obtained as training Cohort. The diagnosis was made based on the results of a total aortic CTA by two experienced radiologists and one specialist in vascular surgery. The inclusion criteria for this study included the following: (1) The patient was hospitalized in the vascular surgery center. (2) All patients underwent complete aortic CTA examination with high quality images. (3) Agree to participate in the research. The exclusion criteria for this study included the following: (1) Previously diagnosed AAA patients. (2) Patients with active inflammation, hematologic diseases. (3) Traumatic aortic aneurysm, syphilitic aortic aneurysm, pseudo aortic aneurysm, aortic dissection. (4) Patients with autoimmune diseases, connective tissue diseases, rheumatic immune diseases, or malignant tumors. (5) Patients with incomplete baseline or laboratory data. (6) Long-term use of corticosteroids drug was excluded. All patients underwent routine physical examination, CTA examination, electrocardiography, and laboratory examination for a comprehensive evaluation.

In addition, the clinical data of patients hospitalized in the Vascular Surgery Center of the First Affiliated Hospital of Soochow University from January 2016 to January 2024 were obtained as external test cohort.

Data collection, definition of investigation factors and laboratory examination

The following clinical data were obtained: sex, age, diabetes mellitus (DM), body mass index (BMI), smoking, hypertension, statins, antiplatelet, metformin, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), neutrophil to lymphocyte ratio (NLR), triglyceride-glucose (TyG) index, uric acid to high-density lipoprotein cholesterol ratio (UHR). All the blood test results are the first test results in this interview. All participants wore light clothes and were asked to take off their shoes and measure their height and weight according to standard methods. BMI was then calculated as weight in kilograms divided by height in meters squared.

Hypertension: All patients were measured in the supine position at the brachial artery of the upper arm in a quiet state, and the average value was taken after three measurements, with SBP ≥ 140mmhg or DBP ≥ 90 mmHg; Or a patient who has been previously diagnosed with hypertension and is taking antihypertensive drugs. Diabetes: FBG ≥ 7.0 mmol/L, postprandial blood glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, typical diabetic symptoms and random blood glucose ≥ 11.1 mmol/L, and one of the above indicators can be diagnosed. Smoking: Smoking more than 1 cigarette a day for 6 consecutive months or a total of 6 months; Smoking more than 4 times per week, but less than 1 cigarette per day on average.

All the blood test results are the first test results in this interview. Fasting blood samples were taken from all participants and biochemical measurements were analyzed. Serum biochemical parameters, including TC, LDL-C, TG, FBG, HDL-C, and uric acid, were determined with a biochemical autoanalyzer (Automatic Analyzer 7600 Series, HITACHI, Japan). NLR refers to the ratio of neutrophils to lymphocytes. The TyG index was determined using the formula: ln [TG (mg/dL) × FBG (mg/dL)/2]. UHR refers to the ratio of uric acid to high density lipoprotein cholesterol.

Statistical analysis

Values are presented as frequencies or percentages for categorical factors and were calculated using the chi-square test. The mean ± standard deviation (SD) for continuous variables was analyzed using Student’s t-test. The LASSO regression technique was used for predictor selection. Multivariate Logistic regression was used to construct a nomogram of AAA. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The bootstrapping method (resampling = 1000) was employed for internal validation. The calibration of the model was evaluated by using the Hosmer–Lemeshow test. The clinical utility and clinical application value were evaluated by DCA and CIC. In addition, the data from the First Affiliated Hospital of Soochow University served as an external validation cohort. All statistical analysis was conducted using R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient characteristics

All patients’ demographic characteristics are summarized in Table 1. Of the14 variables collected from patients, the related indexes were preliminarily screened by univariate analysis, the results showed that sex, age, BMI, smoking, hypertension, antiplatelet, metformin, LDL-C, NLR, TyG and UHR were significantly different between the two groups (p < 0.05).Table 1 Baseline demographic and clinical characteristics of the study populationc.

Variables	The Affiliated Suzhou Hospital of Nanjing Medical University	The First Affiliated Hospital of Soochow University	
Training cohort	External test cohort	
AAA (n = 280)	Controls (n = 385)	P	AAA (n = 133)	Controls (n = 262)	P	
Sex			 < 0.001			 < 0.001	
 Male	213 (76.1%)	179 (46.5%)		99 (74.4%)	117 (44.7%)		
 Female	67 (23.9%)	206 (53.5%)		34 (25.6%)	145 (55.3%)		
Age (y)	70.5 ± 10.5	53.9 ± 12.6	 < 0.001	72.8 ± 9. 5	60.6 ± 12.1	 < 0.001	
DM	116 (41.4%)	156 (40.5%)	0.814	44 (33.1%)	93 (35.5%)	0.634	
BMI (kg/m2)	25.5 ± 3.2	23.9 ± 3.2	 < 0.001	25.1 ± 3.3	22.9 ± 3.1	 < 0.001	
Smoking	152 (54.2%)	66 (17.1%)	 < 0.001	71 (53.4%)	37 (14.2%)	 < 0.001	
Hypertension	175 (62.5%)	91 (23.6%)	 < 0.001	78 (58.7%)	60 (22.9%)	 < 0.001	
Metformin	59 (21.1%)	147 (38.2%)	 < 0.001	34 (25.6%)	83 (31.7%)	0.208	
NLR	4.9 ± 2.2	1.7 ± 0.5	 < 0.001	5.5 ± 2.4	1.8 ± 0.5	 < 0.001	
LDL-C (mmol/L)	3.4 ± 1.1	2.6 ± 0.6	 < 0.001	3.2 ± 1.1	3.0 ± 0.7	0.132	
TC (mmol/L)	4.1 ± 1.2	3.9 ± 1.0	0.173	3.8 ± 1.3	3.6 ± 1.0	0.127	
TyG	8.5 ± 0.3	6.6 ± 0.6	 < 0.001	9.2 ± 0.2	6.6 ± 0.5	 < 0.001	
UHR (%)	42.0 ± 29	12.1 ± 4.6	 < 0.001	36.8 ± 24.7	15.7 ± 3.7	 < 0.001	
Antiplatelet	99 (35.4%)	113 (29.4%)	 < 0.001	51 (38.4%)	65 (24.8%)	0.005	
Statins	89 (31.8%)	112 (29.1%)	0.455	38 (28.6%)	81 (30.9%)	0.631	

LASSO regression

The significant variables of univariate analysis were included in further analysis.. Predictor selection using the LASSO regression analysis with tenfold cross-validation. 8 variables were selected based on non-zero coefficients calculated by LASSO regression analysis (Fig. 1). These variables included sex, age, smoking, hypertension, metformin, NLR, TyG and UHR. Among them, metformin is a favorable factor, and the rest are unfavorable factors.Fig. 1 LASSO regression analysis for analytical screening of predictor variables. (a) Tuning parameter (lambda) selection of deviance in the LASSO regression based on the minimum criteria (left dotted line) and the 1-SE criteria (right dotted line). (b) A coefficient profile plot was created against the log (lambda) sequence. In the present study, predictor’s selection was according to the 1-SE criteria (right dotted line), where 8 nonzero coefficients were selected. LASSO, least absolute shrinkage and selection operator; SE, standard error.

Nomogram

Based on the above 8 variables screened by LASSO regression, multiple logistic regression was used to construct the prediction model and nomogram for AAA risk assessment. The C-index of the prediction model is 0.928, it shows that the model has good discrimination (Fig. 2).Fig. 2 Nomogram for predicting abdominal aortic aneurysm and its algorithm. First, a point was found for each variable of a patient on the uppermost rule; then all scores were added together and the total number of points were collected. Finally, the corresponding predicted probability of abdominal aortic aneurysm was found on the lowest rule.

ROC curve

The AUCs of the model in the training cohort and external validation cohort were 0.928(95%CI,0.907–0.950) and 0.902(95%CI,0.865–0.940), respectively, showing good predictive ability (Fig. 3). Sensitivity and specificity analysis to better understand the utility of the model, the sensitivity of AAA was 0.757 and a specificity of 0.800.Fig. 3 Evaluation of validity and reliability of the model. ROC curves of the nomogram prediction model in the training cohort (a), external validation cohort (b).

Calibration curves

The bootstrapping method (resampling = 1000) was employed for internal validation and external verification. The horizontal coordinate of the calibration curve is the predicted probability of a positive outcome occurring, and the vertical coordinate is the actual probability of occurrence. “Ideal” represents the curve under an ideal model where the probability of actual occurrence is equal to the probability of predicted occurrence. “Apparent” is the fit between the predicted probability and the actual probability. “Bias-corrected” is the fit between the corrected forecast probability and the actual probability. The closer “Apparent” and “Bias-corrected” are to “Ideal”, the closer the prediction probability is to the actual probability, the higher the prediction value of the model. The trend trajectories of the ideal curve and the actual curve are basically the same, and have a strong consistency (Fig. 4). The Hosmer–Lemeshow test yielded a nonsignificant P > 0.05, which indicates that there was no statistical departure from a perfect fit between the predicted and observed values.Fig. 4 Calibration curves: the horizontal coordinate is the probability of an event occurring as predicted by the model. The vertical coordinate is the proportion of events that actually occur within the predicted probability range. The calibration curve is used to assess the agreement between the predicted probability of an event and the actual probability of its occurrence. (a) Training cohort, (b) External validation cohort.

DCA curves

The internal training cohort and external validation external validation cohort DCA were drawn to further evaluate the clinical utility of the constructed model. In this study, the DCA curve demonstrated that the nomogram had good net benefits for clinical use (Fig. 5).Fig. 5 DCA of the nomogram. The horizontal coordinate is the high risk threshold, referring to the thresholds selected for the different predictor variables. The vertical coordinate is the standard net benefit, which refers to the standard net benefit calculated for the different predictor variables constituting the model and the line strategy. The DCA plot of the two makes it possible to assess the extent to which each model outperforms or underperforms the baseline strategy under different decision scenarios. (a) Training cohort, (b) External validation cohort.

CIC curves

The internal training cohort and external validation external validation cohort CIC were drawn to further evaluate the clinical utility of the constructed model. From the CIC, it is known that the model prediction is highly matched with the actual occurrence, and the effective rate of clinical prediction is high (Fig. 6).Fig. 6 CIC of the nomogram. The high-risk thresholds and cost–benefit ratios in the horizontal coordinate compare the costs of using the model with its benefits. The vertical coordinate represents the number of samples judged to be depressed at the selected high-risk threshold for a sample size of 1,000. The three form a net benefit curve that can be used to assess the benefits of each model across different predictor variables, helping policymakers to make optimal decisions. (a) Training cohort, (b) External validation cohort.

Discussion

In this study, a nomogram to predict AAA was established by retrospective analysis of clinical data. This nomogram incorporated 8 variables, including sex, age, smoking, hypertension, metformin, NLR, TyG and UHR. Among them, metformin is a favorable factor, and the rest are unfavorable factors. The model has been internally and externally validated and showed good discriminatory ability, calibration, and clinical usefulness.

The results of this study show that sex, age, smoking, hypertension, metformin, NLR, TyG and UHR are independent risk factors affecting the occurrence of AAA. The prevalence of AAA has obvious gender differences, astudy of 3.1 million people in the United States shows that the prevalence of AAA ranges from 1.3 to 8.9% in men, while only 1.0–2.2% in women. The prevalence rate in men is much higher than that in women. However, the prevalence rate of postmenopausal women is gradually close to that of men, which may be due to the fact that women may be protected from AAA by premenopausal estrogen levels12–14 . Our study also confirms the idea that AAA is more common in men than in women; Therefore, gender is also included in the model. Therefore, further exploration of the role of estrogen in AAA may provide new insights into the diagnosis and treatment of patients with AAA. The study of 1,728 million samples of inpatients and population census in the United States from 2012 to 2018 shows that the prevalence of AAA increases significantly with age, and rupture will lead to higher mortality15. An analysis of individual data on 15,475 AAA showed that AAA patients who smoking progressed on average 0.35 mm/y faster than those who quit or did not smoking, and the rate of AAA rupture in men who did not quit was double that of those who quit or did not smoking, and in women it was four times higher16. This may be related to the increase in the activity of matrix metalloproteinases (MMPs) induced by nicotine, which leads to a decrease in the elasticity of the aortic wall17,18. The longer patients smoking, the higher the incidence of AAA. Therefore, early cessation of smoking is very important for the prevention and treatment of AAA19,20. Hypertension is one of the main reasons for the occurrence of AAA, patients with hypertension need to correct blood pressure in time to avoid the occurrence of AAA21,22. In recent years, studies have shown that metformin can inhibit the expansion of AAA and reduce the risk of rupture and death of AAA23. Although studies have shown that the mechanism of inhibiting AAA by metformin is related to κB signaling pathway, AMPK signaling pathway, JAK/STAT3 and mTOR/STAT3 signaling pathway, etc., the specific mechanism is unknown24–29. The latest guidelines do not clearly indicate that metformin has an inhibitory effect. This study shows that metformin can inhibit the progression of AAA. This study can provide some reference value for the compilation of guidelines.. As a reliable and readily available marker, NLR has become a compelling area of biomedical research and is used in almost all medical disciplines 30. The normal range of NLR is between 1 and 2, and higher than 3.0 or lower than 0.7 in adults is pathological. As the NLR index increases, the risk of disease is increasing 31. A large number of studies have shown that the incidence and mortality of AAA increase with the increase of NLR32–34. This study is consistent with previous research results, indicating that increased NLR is closely associated with high-risk events of AAA, and may early warn clinicians of the risk course of AAA through dynamic changes in NLR. TyG was first proposed in 2008 as a comprehensive index composed of fasting triglyceride and fasting blood glucose. In a large cross-sectional study of seemingly healthy individuals35. The TyG index is considered to be a simple, cost-effective, valid and reliable surrogate marker for insulin resistance (IR)36,37. In recent years, TyG index has been considered to be related to cardio-cerebrovascular diseases38. Literature has shown that the increase of TyG index will significantly increase the postoperative mortality of AAA 39. As far as I know, there is currently no study on the correlation between TyG and AAA. This study reported the correlation between TyG and AAA for the first time, providing a simple reference index for the treatment of AAA. Recently, UHR has been recognized as a novel biomarker for evaluating inflammatory and anti-inflammatory interactions40. Previous studies have shown that an increase in HDL-C is associated with a lower risk of AAA, and impaired cholesterol outflow due to abnormal HDL transport function is also associated with the development of AAA41–43. The risk of being diagnosed as AAA in patients receiving anti-gout drugs is significantly lower than that in patients not receiving anti-gout drugs44. In addition, the experimental evidence in mice shows that hyperuricemia will aggravate the formation of AAA, and reveals that URAT1/ERK1/2/ROS/MMP-9 pathway is one of the pathways of uric acid activation45. This study demonstrated a significant positive correlation between UHR and AAA for the first time, suggesting that UHR may be closely related to inflammation.

This study innovatively adopted LASSO regression analysis to screen the most predictive factors. The advantage of LASSO regression is that while fitting the generalized linear model, it can filter variables and adjust their complexity to minimize the risk of overfitting, which is conducive to the establishment of clinical prediction models. In this study, 8 independent risk factors were selected by LASSO regression analysis to construct an easy-to-use nomogram model. All eight of these indicators are readily available clinically to maximize their clinical applicability and generality. This study visualized the complex formula by constructing a nomogram prediction model, which can easily, intuitively and dynamically estimate the risk of AAA in patients. The scores corresponding to all variables in the nomogram are added together to get the total score, and a vertical line is drawn downwards from the score to get the estimated probability of AAA in the patient. More importantly, it can also greatly facilitate patients and clinicians. In the effectiveness evaluation of the prediction model, the AUC of the training cohort and the external verification cohort are both > 0.90, which shows that the model constructed in this study has good prediction value for AAA. At the same time, the calibration curve of the model nomogram is very consistent with the ideal curve standard line, which shows that nomogram has sufficient statistical ability to predict the incidence of diseases. In addition, DCA and CIC curves show the clinical practicability of the nomogram in predicting AAA, showing superior benefits in a larger threshold probability range. In the external verification cohort, this model also performs well. Therefore, the nomogram we established is accurate and widely beneficial in clinical practice.

As far as we know, this is the first study on the disease diagnosis model of AAA patients. In the era of precision medicine, this model is a practical and simple clinical prediction tool, which fills the gap of AAA disease diagnosis model. In the specific clinical application, if the patient's score is high according to the model, it means that the risk of developing AAA is high. As a clinician, we can make a closer dynamic monitoring plan for the patient and give him more active treatment. Therefore, the construction of personalized model is of great significance for the timely identification and processing of AAA.

The study has several advantages. First of all, as far as I know, this is the first nomogram study of AAA. Secondly, all predictions are common demographic and laboratory indicators, and the final eight variables that are both statistically and clinically significant are included, which are relatively easy to obtain and can be done in the outpatient department, greatly reducing the burden on doctors and patients. Third, the results of this study can be used as an early warning examination for high-risk AAA patients. For patients with high scores, further ultrasound or CTA examination can be recommended, or they can go to a professional vascular surgery center for consultation, so as to reduce the waste of medical resources. Fourth, it is simple and fast to display the risk degree of each influence by using a nomogram, and patients can clearly see the risk factors of AAA disease through the nomogram, which will invisibly prompt them to change their lifestyle according. Fifth, for some patients with abdominal aortic dilation who do not meet the diagnostic criteria for abdominal aortic aneurysm, the nomogram can be used as a relative dynamic monitoring indicator. Sixth, it can help clinicians make better strategic decisions to meet the needs of their patients. Although this study provides valuable information, it also has some limitations, which are worthy of careful consideration. First of all, this is a retrospective study, and there are some inevitable biases. Therefore, a multi-center randomized controlled clinical study with a larger sample size can be carried out in the future to verify its clinical benefits. Second, the prediction model is based on known risk factors, and some factors affecting the incidence of AAA have not been studied and demonstrated. Therefore, with the development of molecular biology in the future, the relevant indicators can be refined continuously to further improve the diagnostic accuracy of the model. Thirdly, other high-risk factors of AAA, such as the annual growth rate of AAA, aneurysm shape, aneurysm hemodynamics, shear force and thickness of aneurysm wall, are incomplete and cannot be included in the study to build a prediction model. Due to this limitation and dilemma, the next step will be to integrate data with multi-centers, include more indicators, and add basic experiments to make this risk prediction model more perfect.

Conclusion

In this study, a nomogram was constructed that incorporated eight demographic and clinical characteristics of AAA patients, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.

Abbreviations

AAA Abdominal aortic aneurysm

LASSO Least absolute shrinkage and selection operator

ROC Receiver operating characteristic

AUC Area under Curve

DCA Decision curve analysis

CIC Clinical impact

BMI Body mass index

TC Total cholesterol

LDL-C Low-Density Lipoprotein Cholesterol

NLR Neutrophil to lymphocyte ratio

TyG Triglyceride-glucose

UHR Uric acid to high-density lipoprotein cholesterol ratio

CTA Computed tomography angiography

IR Insulin resistance

DM Diabetes mellitus

Author contributions

D.Z. and G.H. conceived and designed the study. H.S. and J.Z. collected and analyzed the data. G.H. and J.Z. wrote the main manuscript text and Y.Z. prepared figures together. Z.Y., J.C., Y.T., Z.L. and J.H. provided critical revisions to the manuscript.All authors reviewed the manuscript.

Funding

This work was supported by the Suzhou “Science and Education Revitalize Health” Youth Science and Technology Project (KJXW2021031).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Guijun Huo, Han Shen, Jin Zheng.
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