Front. Med.Frontiers in MedicineFront. Med.2296-858XFrontiers Media S.A.10.3389/fmed.2025.1635357MedicineOriginal ResearchPrediction of mechanical complications post-acute myocardial infarction in individuals with type 2 diabetes mellitusZhaoChangying1†WangHan2†YuanWei3YanYang1*1Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China2Xi’an Jiaotong University Health Science Center, Xi’an, China3Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
Edited by: Guo-wei Tu, Fudan University, China
Reviewed by: Haixia Xu, Affiliated Hospital of Nantong University, China
Lishui Shen, Chinese Academy of Medical Sciences and Peking Union Medical College, China
*Correspondence: Yang Yan, yangyan3@xjtu.edu.cn
†These authors have contributed equally to this work and share first authorship
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Background
Acute myocardial infarction (AMI) patients with type 2 diabetes mellitus (T2DM) represent a unique population characterized by poorer prognoses, which may be further exacerbated by mechanical complications. This study aims to develop a predictive model to identify high-risk individuals within this populations.
Methods
This study enrolled AMI patients with T2DM and categorized them into complication and control groups. The mechanical complications were defined as papillary muscle rupture (with or without acute mitral regurgitation), ventricular septal defect, left ventricular pseudoaneurysm or aneurysm (with or without thrombus) and free wall rupture. Characteristics were selected using relaxed least absolute shrinkage and selection operator (LASSO) logistic regression, multivariate logistic regression and random forest model. Selected variables were utilized to construct a nomogram to predict the possibility of mechanical complications.
Results
A total of 2,816 patients were enrolled, with 191 individuals classified into the complication group. Baseline analysis identified 31 factors exhibiting potential differences, which were subsequently employed for LASSO-logistic regression, multivariate logistic regression and random forest model. After comprehensive evaluation, nine variables emerged as predictive factors for mechanical complications, including gender, pulmonary hypertension, ST-segment elevation myocardial infarction, body mass index, N-terminal pro-brain natriuretic peptide, creatine kinase, left ventricle ejection fraction and hemoglobin A1c, which were used to construct a reliable nomogram. The complication group also showed higher in-hospital mortality rates compared to controls, alerting the worse prognosis of these populations.
Conclusion
This study identified nine factors upon admission that may be associated with mechanical complications during the hospitalization. A nomogram was developed based on these factors for clinical application. T2DM patients should emphasize glucose control, which may offer benefits following the onset of AMI.
acute myocardial infarctiontype 2 diabetes mellitusmechanical complicationsprediction modelhemoglobin A1csection-at-acceptanceIntensive Care Medicine and AnesthesiologyIntroduction
Acute myocardial infarction (AMI) is a disease characterized by the rupture of atherosclerotic plaques, resulting in coronary artery occlusion and subsequent myocardial ischemia and infarction (1). Currently, direct percutaneous coronary intervention has emerged as the standard treatment for AMI, effectively reducing infarct size and significantly improving early survival rates among AMI patients (2). Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for AMI (3). The incidence of cardiovascular diseases in adults with T2DM is two to three times higher than that in their non-diabetic counterparts. Furthermore, studies indicate that the mortality rate within 5 years post-AMI for T2DM patients is twice that of other individuals, potentially reaching as high as 50% (4). Consequently, AMI patients with a history of T2DM represent a distinct population at elevated risk, necessitating heightened attention to enhance prognosis.
The overall incidence of mechanical complications following AMI ranges from approximately 0.1 to 0.3%, mainly including papillary muscle rupture (with or without acute mitral regurgitation), ventricular septal defect, pseudoaneurysm, among others (5, 6). Despite advancements in reperfusion therapies, continue to serve as critical determinants of clinical results in AMI patients (7, 8). Researches indicated that the risks associated with in-hospital death, cardiogenic shock, acute kidney injury and respiratory complications are all markedly higher among patients who experience mechanical complications compared to those who do not (5, 9). Identifying high-risk AMI patients is essential for the early prevention and management of these mechanical complications.
Given the lack of literature addressing the prediction of mechanical complications, this study aims to construct a model to predict the possibility of mechanical complications during the hospitalization in AMI patients with T2DM at admission. This study may assist clinicians in distinguishing high-risk individuals more effectively, helping hierarchical management of patients and early interventions.
MethodsPatient enrollment
Patients who were hospitalized at the First Affiliated Hospital of Xi’an Jiaotong University from January 2020 to December 2024 due to AMI combined with T2DM were included in this study. The diagnosis of AMI was established according to the guidelines set forth by European Society of Cardiology. The exclusion criteria mainly consisted of: (1) patients with a history of severe comorbidities that may affect the occurrence of the outcomes, such as hepatic dysfunction; (2) other subtypes of diabetes or unclassifiable diabetes (e.g., type 1 diabetes mellitus, gestational diabetes mellitus); (3) patients with incomplete clinical data that could not be addressed through statistical methods.
This study was approved by the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (XJTU1AF2025LSYY-505; date: 9 May 2025), in accordance with the Declaration of Helsinki. The Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University granted a waiver for informed consent to this study due to its retrospective design.
Data collection and grouping
The basic demographic information, biochemical test results, imaging data and in-hospital outcomes were obtained from the Biobank of the First Affiliated Hospital of Xi’an Jiaotong University. Enrolled patients were categorized into complication and control groups based on the occurrence of in-hospital mechanical complications. To enhance the selection of high-risk patients, the definition of mechanical complications was broadened to encompass papillary muscle rupture (with or without acute mitral regurgitation), ventricular septal defect, left ventricular pseudoaneurysm or aneurysm (with or without thrombus) and free wall rupture. These definitions included both traditional acute complications and chronic conditions associated with mechanical complications (5, 10).
Statistical analysis
Baseline variables with more than 20% missing values were excluded from further analysis, such as platelet counts (30.0%), left ventricular end-diastolic and end-systolic diameter (44.5% and 44.5%), and cardiac output (53.8%). The remaining variables were missing at random and were addressed using multiple interpolation by chained equations techniques with 5 iterations. A total of five imputed datasets generated, and the fifth dataset without missing values was used for following analysis. Continuous variables, following normal distribution, were presented as mean ± standard deviation and compared using Student’s t-tests. Other continuous variables were represented as median with interquartile range and were analyzed using Mann–Whitney U tests. Categorical variables were reported as absolute counts with relative frequencies. Chi-square tests and Fisher’s exact tests were employed for comparisons. The relaxed least absolute shrinkage and selection operator (LASSO) logistic regression model was used for preliminary screening of variables which had a p value less than 0.10 in the baseline characteristics. Screened variables underwent further examination through multivariate logistic regression and random forest model. Random forest modeling included 500 trees, with variable importance ranked by the arithmetic means of the accuracy and Gini index values. Factors demonstrating significant differences in either the multivariate or random forest models were included to construct a nomogram. The receiver operating characteristic curve, calibration curve and decision curve were employed to evaluate the performance of nomogram. Data analysis was conducted using SPSS software (version 27.0) and R software (version 4.1.1). P-value less than 0.05 was identified as having statistical difference.
ResultsBaseline characteristics and clinical outcomes
A total of 2,816 patients with AMI complicated by T2DM were enrolled in this study, among whom 191 (6.78%) developed in-hospital mechanical complications. The baseline characteristics of both groups were summarized in Table 1. In comparison to the control group, the complication group exhibited higher proportions of female gender, ST-segment elevation myocardial infarction (STEMI), and Killip class III/IV classification. Additionally, this group demonstrated elevated levels of age, body mass index (BMI), hemoglobin A1c (HbA1c), neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio, aminotransferase, globulin, albumin/globulin ratio, lipoprotein (a), N-terminal pro-brain natriuretic peptide (NT-ProBNP), high-sensitivity cardiac troponin T, lactate dehydrogenase, fibrinogen, D-dimer, and fibrin degradation products. Conversely, this group displayed lower levels of left ventricular ejection fraction (LVEF) and triglycerides when compared to the control group. Furthermore, correlation analysis of the incidence of mechanical complications and parameters which might relate to mechanical complications (p < 0.10) in baseline was illustrated in Figure 1.
Baseline characteristics and in-hospital clinical outcomes between the two groups.
Heatmap showing correlation between various medical parameters, with a dendrogram indicating clustering. Red indicates positive correlation, blue indicates negative. Parameters include LYMPHO, AST, CK-MB, hs-cTnT, and more, with correlation values ranging from -1 to 1. Red signifies strong positive, blue signifies strong negative.
Additionally, the complication group exhibited a higher proportion of intra-aortic balloon pump [23 (12.0) vs. 101 (3.8), p < 0.001] and extracorporeal membrane oxygenation [6 (3.1) vs. 26 (1.0), p = 0.007] utilization, along with an elevated levels of in-hospital mortality [5 (2.6) vs. 26 (1.0), p = 0.037] (Table 1). Cardiac rupture was confirmed or suspected in all fatal cases in complication group. One patient developed ventricular septal rupture complicated by ventricular aneurysm and was supported with intra-aortic balloon pump. During the discussion regarding surgical intervention of the family, the patient experienced sudden ventricular fibrillation followed by cardiogenic shock and eventually died. Another patient presented with ventricular aneurysm and developed acute cardiogenic shock. Despite combined support with extracorporeal membrane oxygenation and intra-aortic balloon pump, the patient did not survive. Three additional patients were diagnosed with ventricular aneurysm, one of whom received extracorporeal membrane oxygenation support. However, all three patients suddenly lost consciousness during hospitalization, with concomitant hypotension. Echocardiography revealed large pericardial effusion, and cardiac rupture was suspected as the cause of death.
LASSO-logistic regression results
Considering the correlation among potential variables, characteristics demonstrating statistical differences were incorporated into the LASSO-logistic regression analysis. When lambda.min = 0.0045 and log (lambda.min) = −5.3468, 13 variables were preliminary selected for further evaluation (Figure 2; Table 2).
Results of LASSO-logistic regression. (a) Cross-validation plot of LASSO-logistic regression. (b) Selection process of LASSO-logistic regression model by cross-validation method. BMI, body mass index; STEMI, ST-segment elevation myocardial infarction; HbA1c, hemoglobin A1c; WBC, white blood cell; NEU, neutrophil count; LYMPH, lymphocyte count; NLR, neutrophil-to-lymphocyte ratio; PCT, procalcitonin; AST, aspartate aminotransferase; A/G, albumin/globulin ratio; BUN, blood urea nitrogen; HDL, high-density lipoprotein; TG, triglycerides; NT-ProBNP, N-terminal pro-brain natriuretic peptide; hs-cTnT, high-sensitivity cardiac troponin T; CK-MB, creatine kinase isoenzymes MB; CK, creatine kinase; LDH, lactate dehydrogenase; FDP, fibrin degradation products; LVEF, left ventricle ejection fraction; PH, pulmonary hypertension.
Graph a shows a plot of error versus log lambda in a LASSO regression analysis, with error bars and highlighted points for lambda minimum and lambda LSE. Graph b illustrates coefficient paths for various variables, showing how they shrink toward zero as log lambda increases.
Results pf LASSO-logistic regression model.
Items
LASSO-logistic regression
Assignment
Coefficient
Age (years)
Continuous variable
Gender (male, %)
Male = 1, Female = 0
−0.1245
BMI (kg/m2)
Continuous variable
−0.2571
STEMI (%)
Yes = 1, No = 0
0.3920
Killip III/IV (%)
Yes = 1, No = 0
HbA1c (%)
Continuous variable
0.1797
WBC (10^9/L)
Continuous variable
NEUT (10^9/L)
Continuous variable
NEUT% (%)
Continuous variable
LYMPH (10^9/L)
Continuous variable
−0.2476
LYMPH% (%)
Continuous variable
NLR
Continuous variable
PCT (ng/ml)
Continuous variable
−0.1671
AST (U/L)
Continuous variable
Globulin (g/L)
Continuous variable
A/G
Continuous variable
BUN (mmol/L)
Continuous variable
HDL (mmol/L)
Continuous variable
TG (mmol/L)
Continuous variable
−0.1389
Lipoprotein (a) (mg/L)
Continuous variable
0.1096
NT-ProBNP (pg/mL)
Continuous variable
0.0800
hs-cTnT (ng/mL)
Continuous variable
CK-MB (U/L)
Continuous variable
CK (U/L)
Continuous variable
0.0825
LDH (U/L)
Continuous variable
Fibrinogen (g/L)
Continuous variable
D-dimer (mg/L)
Continuous variable
FDP (mg/L)
Continuous variable
LVEF (%)
Continuous variable
−0.8338
PH (%)
Yes = 1, No = 0
0.1335
Pericardial effusion (%)
Yes = 1, No = 0
0.2133
Ventricular arrhythmia (%)
Yes = 1, No = 0
LASSO, least absolute shrinkage and selection operator; other abbreviations as in Table 1.
Multivariate logistic regression and random forest model
After the LASSO-logistic regression analysis, multivariate logistic regression analysis was conducted (Table 3). The results indicated that male gender (OR = 0.66, 95%CI: 0.47–0.94; p = 0.020); BMI (OR = 0.93, 95%CI: 0.89–0.98; p = 0.004); STEMI (OR = 2.23, 95%CI: 1.55–3.20; p < 0.001); HbA1c (OR = 1.11, 95%CI: 1.03–1.21; p = 0.010); LVEF (OR = 0.92, 95%CI: 0.91–0.94; p < 0.001); pulmonary hypertension (PH) (OR = 2.36, 95%CI: 1.36–4.09; p = 0.002); pericardial effusion (OR = 2.23, 95%CI: 1.43–3.47; p < 0.001) were independent risk factors for prediction.
Results of multivariate logistic regression model.
Items
Multivariate logistic regression
OR (95% CI)
p value
Gender (male, %)
0.66 (0.47, 0.94)
0.020
BMI (kg/m2)
0.93 (0.89, 0.98)
0.004
STEMI (%)
2.23 (1.55, 3.20)
<0.001
HbA1c (%)
1.11 (1.03, 1.21)
0.010
LYMPH (10^9/L)
0.81 (0.63, 1.04)
0.103
PCT (ng/dL)
0.97 (0.92, 1.03)
0.387
TG (mmol/L)
0.90 (0.77, 1.06)
0.210
Lipoprotein (a) (mg/L)
1.00 (0.99, 1.00)
0.162
NT-ProBNP (pg/mL)
1.00 (1.00, 1.00)
0.348
CK (U/L)
1.00 (1.00, 1.00)
0.205
LVEF (%)
0.92 (0.91, 0.94)
<0.001
PH (%)
2.36 (1.36, 4.09)
0.002
Pericardial effusion (%)
2.23 (1.43, 3.47)
<0.001
OR, odds ratio; CI, confidence interval; other abbreviations as in Table 1.
Furthermore, a random forest model analysis was performed following the LASSO-logistic regression (Figure 3). The mean accuracy and Gini index for each variable were calculated and subsequently ranked based on the arithmetic means of the accuracy and Gini index values. Given the numerical character of the arithmetic mean, the top four variables were included in subsequent analyses, which encompassed LVEF, NT-ProBNP, CK and BMI.
Results of random forest model. (a) Mean decrease accuracy and mean decrease Gini index of all variables. (b) Arithmetic mean value between mean decrease accuracy and mean decrease Gini index of all variables. LVEF, left ventricle ejection fraction; NT-ProBNP, N-terminal pro-brain natriuretic peptide; CK, creatine kinase; BMI, body mass index; PCT, procalcitonin; TG, triglycerides; LYMPH, lymphocyte count; HbA1c, hemoglobin A1c; PH, pulmonary hypertension; STEMI, ST-segment elevation myocardial infarction.
Two graphs display variable importance in a dataset. Graph (a) features a dot plot with variables like "Pericardial effusion" and "Male" plotted against mean decrease accuracy and Gini. Graph (b) is a bar chart showing the arithmetic mean of accuracy and Gini for various variables, with "LVEF" having the highest value at 19.09, followed by "NT-ProBNP" at 14.72 and "CK" at 14.59. Other variables include "BMI," "PCT," and "STEMI," among others, with "STEMI" having the lowest value at 2.11.Construction of nomogram and its evaluation
Based on variable selection by multivariate logistic regression and random forest modeling, nine variables were used to construct a nomogram, including gender, pulmonary hypertension (PH), STEMI, pericardial effusion, BMI, NT-ProBNP, creatine kinase (CK), LVEF and HbA1c (Figure 4). For continuous variables, the shaded areas under the curve visually represent the distribution of individuals across different ranges, whereas for categorical variables, color blocks illustrate the relative proportions within each category. According to biochemical results and characteristics of patients, each variable was assigned specific points. The total score, calculated by summing all nine component scores, corresponded to the probability of experiencing in-hospital mechanical complications. The receiver operating characteristic curve indicated an area under the curve of 0.817 (95% CI: 0.787–0.847). Both the decision curve and calibration curve confirmed that the nomogram possessed good accuracy and consistency for clinical assessment (Figure 5).
Nomogram for the prediction of mechanical complications in acute myocardial infarction patients with type 2 diabetes mellitus. PH, pulmonary hypertension; STEMI, ST-segment elevation myocardial infarction; BMI, body mass index; NT-ProBNP, N-terminal pro-brain natriuretic peptide (reference range for healthy populations: 0–125 ng/mL; diagnosis range for acute heart failure in people less than 50 years old: >450 ng/mL; diagnosis range for chronic heart failure: >2,000 ng/mL; upper detection limit: 35,000 pg/mL); CK, creatine kinase (reference range: 40–200 U/L); LVEF, left ventricle ejection fraction (reference range: 52–75%); HbA1c, hemoglobin A1c (reference range: 4.0–6.0%).
Graphical representation of a scoring system with points assigned to various factors: gender, PH, STEMI, pericardial effusion, BMI, NT-ProBNP, CK, LVEF, and HbA1c. The plot shows density curves for each factor, total points, and predicted probability ranging from 0.002 to 0.8.
Evaluation of the nomogram. The receiver operating characteristic curve (a), decision curve (b) and calibration curve (c) of the nomogram. AUC, area under curve.
Three-panel image showing statistical evaluations for a model. Panel (a) is a ROC curve with an AUC of 0.817, plotting sensitivity versus 1-specificity. Panel (b) displays a decision curve analysis with standardized net benefit across high-risk thresholds. Panel (c) shows a calibration plot comparing actual and predicted probabilities with apparent, bias-corrected, and ideal lines.Discussion
Although the reperfusion therapies have significantly improved outcomes for AMI patients, mechanical complications continue to pose challenges regarding both short-and long-term prognosis, particularly in patients with concurrent T2DM. This study focused on AMI patients with T2DM, dividing them into complication and control groups. Following the analysis of their baseline characteristics upon admission, nine risk factors for in-hospital mechanical complications were identified. The complication group also exhibited a higher proportion of in-hospital mortality and extracorporeal life support device usage, indicating a poorer prognosis for this population.
T2DM is increasingly prevalent among AMI patients, with an incidence rate of approximately 25% (11). This trend may be attributed to the prolonged hyperglycemia experienced in T2DM patients, which can lead to platelet and endothelial dysfunction, thereby accelerating the atherosclerosis of coronary arteries. From a treatment perspective, somehow, it has been observed that the receiving rates of coronary angiography and percutaneous coronary intervention are lower in T2DM patients compared to their non-diabetic counterparts, which may contribute to worse outcomes, including mechanical complications (12). Furthermore, T2DM patients demonstrate limited left ventricular remodeling following AMI, which also elevates the risk of complications. A study indicated that even patients with transient hyperglycemia are more likely to develop in hospital complications (11). However, this study found that the long-term blood glucose control could benefit patients even after the onset of AMI, and elevated HbA1c levels could potentially serve as a predictor for mechanical complications, highlighting the importance of intensive glycemic management. Notably, the use of metformin post-AMI is also associated with reduced mortality rates (13). Emerging evidence also suggests that in T2DM patients, early and intensive glycemic control, along with the use of glucose-lowering medications, can help reduce the future risk of major adverse cardiovascular events, including stroke, AMI, and all-cause mortality (14). The implementation of timely and rapidly blood glucose control within 24 h of hospital admission may be advantageous in reducing the risk of 30-day mortality (15). Additionally, the triglyceride glucose index, calculated from triglycerides and fasting blood glucose, may serve as a predictor for major adverse cardiac and cerebral events over a median follow-up period of approximately 2 years (16).
There are also several studies focusing on the prediction of mechanical complications. A study found that the neutrophil-to-lymphocyte ratio, as an inflammatory marker, serves as an independent predictor for the occurrence of mechanical complications and left ventricular dysfunction in patients with ST-elevation myocardial infarction (17). In this study, although the neutrophil-to-lymphocyte ratio was included in the analysis, it did not demonstrate a significant difference. It is speculated that the influence of blood glucose may be more pronounced than inflammation in AMI patients combined with T2DM, further underscoring the importance of glucose control. Another study indicated that AMI patients experiencing mechanical complications might have fewer comorbidities such as hypertension, diabetes, and dyslipidemia. However, mechanical complications are more likely to be associated with patients who do not have malignancies or a history of AMI (18). These results contrast with our findings, as our patient cohort exhibited higher levels of blood lipids. Meanwhile, one study indicated that females have higher risk of mechanical complications and associated mortality, as females are less likely to receive necessary operations compared to males, which is consistent with results of this study (19). Overall, further research is needed to better elucidate the risk factors associated with mechanical complications.
After the onset of mechanical complications, prompt surgical interventions combined with preoperative or postoperative extracorporeal life support devices are essential. These may include intra-aortic balloon pumps, extracorporeal membrane oxygenation, implantation of ventricular assist devices, and even heart transplantation, as shown in this study (7, 20). However, due to limited ECMO cases (n = 6) in this study, its impact on survival could not be reliably quantified, warranting further investigation. It has been suggested that achieving hemodynamic stabilization prior to surgery may enhance survival rates postoperatively, particularly in patients with ventricular septal defects (20). However, despite receiving necessary therapies, the in-hospital mortality rate for AMI patients with mechanical complications remains around 50%, especially among older individuals, those with heart failure, cardiogenic shock or renal dysfunction (6, 21, 22). Furthermore, the literature assessing outcomes of surgical interventions presents considerable differences. Among all mechanical complications, ventricular septal defect repair appears to yield the most favorable results (23). Some studies have indicated that long-term survival rates for patients undergoing surgical procedures are promising. The 5-year and 10-year survival rates are approximately 48.1 and 41.0%, respectively (24). However, another study indicated that concomitant coronary artery bypass grafting may not necessarily result in lower early or late mortality, rendering the benefits of revascularization controversial (25). Meanwhile, a study found that the extracorporeal life support devices may only improve the clinical outcomes in patients with pseudoaneurysm and papillary muscle rupture, but do not significantly affect the overall survival (18). Overall, the management of AMI patients with mechanical complications requires further improvement, which includes the selection of appropriate life support devices and determination of optimal timing for surgical intervention.
This study also has certain limitations. Firstly, the conditions experienced during hospitalization were not included in the analysis, which may also influence the occurrence of mechanical complications. Furthermore, as this is a retrospective study, some relevant factors may be unable to obtain. Additionally, prospective analyses with long-term follow-up are necessary to more comprehensively evaluate the effectiveness of the nomogram.
Conclusion
This study found that gender, PH, STEMI, pericardial effusion, BMI, NT-ProBNP, CK, LVEF and HbA1c upon admission were associated with in-hospital mechanical complications among AMI patients who have a history of T2DM. A nomogram was constructed based on these risk factors to facilitate earlier identification and intervention of high-risk patients. It is imperative for individuals with T2DM to manage their blood glucose levels effectively since this may provide advantages even after experiencing an episode of AMI.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (XJTU1AF2025LSYY-505). The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because of the retrospective design of this study.
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Key Research and Development Program of Shaanxi Province (2024SF-YBXM-011) and the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University (XJTU1AF2021CRF-005 and XJTU1AFCRF2023-XK006).
We thank all participants of this study, and are grateful to the Biobank of the First Affiliated Hospital of Xi’an Jiaotong University for providing clinical data.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
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