Abstract
Aims
With an aging population and better survival rates, coronary artery disease (CAD) with multimorbidity has become more prevalent, complicating treatment and impacting life quality and longevity. This study identifies multimorbidity patterns in CAD patients and their effect on clinical outcomes, emphasizing treatment strategies.
Methods and results
The study analysed data from the DCEM registry (173 459 patients) and BleeMACS cohort (15 401 patients) to categorize CAD patients into three multimorbidity patterns. The focus was on how these patterns influence outcomes, especially concerning the efficacy and safety of dual antiplatelet therapy (DAPT). The study identified three distinct multimorbidity patterns: Class 1 encompassed cardiovascular–kidney–metabolic comorbidities indicating the highest risk; Class 2 included hypertension–dyslipidaemia comorbidities, reflecting intermediate risk; and Class 3 involved non-specific comorbidities, indicating the lowest risk. Class 1 patients demonstrated a six-fold increase in in-hospital mortality and a four-fold increase in severe in-hospital complications compared with Class 3. Over a 1-year period, Class 1 was associated with the highest risk, displaying a significant increase in all-cause mortality [adjusted hazard ratio (HR) 1.87, 95% confidence interval (CI) 1.52–2.31, P < 0.001] and a notable risk for major bleeding (adjusted HR 1.74, 95% CI 1.36–2.24, P < 0.001) compared with Class 3. The use of DAPT, particularly aspirin combined with clopidogrel, significantly reduced the 1-year all-cause mortality in Class 1 patients (adjusted HR 0.60, 95% CI 0.37–0.98, P = 0.04) without increasing in major bleeding.
Conclusion
Coronary artery disease patients with a cardiovascular–kidney–metabolic profile face the highest mortality risk. Targeted DAPT, especially aspirin and clopidogrel, effectively lowers mortality without significantly raising bleeding risks.
Registration
DCEM registry (NCT05797402) and BleeMACS registry (NCT02466854).
Keywords: Coronary artery disease, Multimorbidity, Dual antiplatelet therapy, Latent class analysis, Cardiovascular–kidney–metabolic comorbidities
Graphical Abstract
Graphical Abstract.
Introduction
The improved survival rates in coronary artery disease (CAD) patients, coupled with an aging population, have led to an increased prevalence of CAD with multimorbidity, defined as the coexistence of two or more chronic conditions within an individual.1,2 This multimorbidity not only elevates the risk and complexity of CAD management but also significantly impacts life expectancy and quality of life.3 Increasingly, studies are analysing the comorbidities of CAD across different populations, settings, and countries.4 Most of these studies have focused on the relationships between comorbidities such as hypertension, dyslipidaemia, congestive heart failure (CHF), diabetes, chronic kidney disease (CKD), or stroke and the prognosis of CAD.5–9 Clusters of these chronic diseases are not always random.10 Cooccurring chronic diseases may have similar underlying risk factors.11 In some cases, a disease may result from another chronic condition or treatment.12 The recognition of multimorbidity patterns is useful for the prevention, treatment, and improvement of prognosis.13,14 Both the World Health Organization and the National Institute for Health and Clinical Excellence consider understanding multimorbidity patterns in specific populations to be important for patient-oriented prevention, diagnosis, treatment, and prognosis.15
In previous studies of multimorbidity patterns, data focused on index diseases or proportions of comorbidities, rather than analysing their associations, have limited clinical value. Besides, these data analyses mainly relied on basic analytical techniques that consider composite additive or weighted comorbidity scores or focus on all possible combinations of conditions.16,17 In addition, nearly 100 patterns of comorbidities have been identified, mainly from the whole population while not for CAD patients.18,19 Coronary artery disease is one of the most common chronic medical conditions that is a leading cause of death and disability worldwide.20 Understanding how comorbidities cluster in individuals, and the impact of clustering of comorbidities on patient outcomes, is an essential step towards personalizing CAD treatment approaches for better outcomes. However, little is known about multimorbidity patterns and their association with outcomes in patients with CAD.
Therefore, we sought to identify the patterns of multimorbidity in CAD patients, as well as the association of specific multimorbidity patterns with patients’ short-term and long-term outcomes.
Methods
Study population and data
We utilized data from two registry studies including the Database of Chinese Elderly patients with Multiple diseases (DCEM, NCT05797402) and the Bleeding Complications in a Multicenter Registry of Patients Discharged With Diagnosis of Acute Coronary Syndrome (BleeMACS, NCT02466854).
The DCEM registry collected electronic medical records of patients admitted to Beijing Anzhen Hospital from January 2016 to October 2021. Data obtained from each record included patient demographics, diagnoses, procedures, hospital details, and discharge status. This data set constituted the derivation cohort for analysing multimorbidity patterns and short-term prognostic value. We selected patients aged ≥18 years discharged with CAD (ICD-10 codes I20–I25) as the primary diagnosis. Patients without ≥2 chronic conditions were excluded. The validity of ICD-10 codes for identifying CAD and comorbidities is well established. Multimorbidity was defined as ≥2 cooccurring chronic conditions; and CAD patients had CAD plus ≥1 secondary diagnosis.
The BleeMACS registry provided an external validation cohort to evaluate P2Y12 inhibitor choice and long-term prognostic value. It included acute coronary syndrome patients’ post-percutaneous coronary intervention (PCI) discharged 2003–2014 from Europe, Asia, and America.
The Ethics Committee of Beijing Anzhen Hospital approved this study (2021156X). All procedures adhered to the Declaration of Helsinki.
Outcomes and complications
The short-term outcome of this study included in-hospital severe complications and in-hospital death using data from the DCEM registry. Severe complications included gastrointestinal and/or intracerebral haemorrhage, transfusion, acute renal failure, haemodialysis, cardiopulmonary resuscitation (CPR), continuous invasive mechanical ventilation (CIMV ≥ 96 h), implantation of an intra-aortic balloon pump (IABP), and/or permanent pacemaker (PP) during hospitalization. Patients in this study who were discharged without a physician’s order usually died shortly after discharge, and this study considered these patients as in-hospital deaths. The long-term outcome for this analysis included all-cause death, reinfarction, and major bleeding events using data from the BleeMACS registry.21,22
Statistical analysis
Numerical variables were described as mean and standard deviation or median and interquartile range for normal and skewed distributions, respectively. Categorical variables were presented as frequencies and percentages. Continuous variables were compared by t-test, analysis of variance (ANOVA), Mann–Whitney U, or Kruskal–Wallis tests as appropriate. Categorical variables were compared using χ2 or Fisher’s exact tests.
Latent class analysis (LCA) identified multimorbidity clusters among CAD patients with CHF, hypertension, dyslipidaemia, cerebrovascular disease (CBD), diabetes mellitus (DM), CKD, peptic ulcer disease (PUD), and cancer. Latent class analysis models with two to five classes were evaluated to determine optimal classes based on Bayesian (BIC) and Akaike information criteria (AIC) and clinical interpretability. Patients were assigned to the class with the highest posterior probability. Full information maximum likelihood accounted for missing data. The LCA model parameters from the derivation cohort estimated class probabilities in the validation cohort. Analyses were conducted in R (poLCA package).
Multivariable logistic regression models assessed the associations between multimorbidity patterns and complications/mortality. Kaplan–Meier curves and log-rank tests compared prognosis by multimorbidity patterns. Multivariate Cox regression adjusted for patient characteristics, including age, sex, ST-segment elevation myocardial infarction (STEMI), and PCI, was used to evaluate the relationship between multimorbidity patterns and DAPT treatment and long-term prognosis. A P < 0.05 indicated statistical significance. Analyses were performed in R (version 4.1.3) and SPSS (version 24.0).
Results
Comorbidities in coronary artery disease patients
A total of 173,459 hospitalized patients with a primary diagnosis of CAD were finally included from DCEM data (see Supplementary material online, Figure S1). Among them, the majority were male (72.6%) with a mean age of 60.4 years (Table 1). The average hospital stay was 5.2 days, and patients had 4.8 comorbidities on average. Angina pectoris (79.1%), acute myocardial infarction (12.9%), and chronic ischaemic heart disease (8%) were the most common CAD causes. Hypertension, diabetes, CHF, and CBD were the most prevalent coexisting conditions. Correlation analysis revealed multidimensional relationships between these major comorbidities in CAD patients (Figure 1).
Table 1.
Clinical features and comorbidity conditions in the derivation cohort
| Variables | Class 1 (n = 19 209) | Class 2 (n = 93 646) | Class 3 (n = 60 604) | Total (n = 173 459) |
|---|---|---|---|---|
| Age | 63.34 ± 10.12 | 60.92 ± 9.60 | 58.73 ± 10.06 | 60.42 ± 9.93 |
| Male | 13 794 (71.81) | 65 231 (69.66) | 46 936 (77.45) | 125 961 (72.62) |
| Length of stay | 7.90 ± 6.93 | 4.75 ± 4.43 | 5.11 ± 4.86 | 5.22 ± 5.01 |
| CAD type | ||||
| Acute MI | 14 993 (21.95) | 83 524 (10.81) | 52 557 (13.28) | 22 385 (12.91) |
| Angina pectoris | 5106 (73.42) | 17 705 (81.09) | 13 386 (77.91) | 137 262 (79.13) |
| Comorbidity conditions | ||||
| No. of comorbidities | 7.26 ± 2.88 | 4.92 ± 1.86 | 5.11 ± 4.86 | 4.79 ± 2.24 |
| CHF | 17 040 (88.71) | 0 (0) | 7907 (13.05) | 24 947 (14.38) |
| Chronic obstructive pulmonary disease | 201 (1.05) | 410 (0.44) | 359 (0.59) | 970 (0.56) |
| Asthma | 113 (0.59) | 368 (0.39) | 233 (0.38) | 714 (0.41) |
| Dementia | 106 (0.55) | 130 (0.14) | 57 (0.09) | 293 (0.17) |
| PUD | 839 (4.37) | 3868 (4.13) | 2678 (4.42) | 7385 (4.26) |
| Moderate/severe anaemia | 1395 (7.26) | 1461 (1.56) | 1117 (1.84) | 3973 (2.29) |
| Atrial fibrillation | 1363 (7.10) | 2175 (2.32) | 1347 (2.22) | 4885 (2.82) |
| Chronic liver disease | 801 (4.17) | 1842 (1.97) | 1174 (1.94) | 3817 (2.20) |
| Depression | 148 (0.77) | 620 (0.66) | 384 (0.63) | 1152 (0.66) |
| CBD | 4108 (21.39) | 10 942 (11.68) | 1965 (3.24) | 17 015 (9.81) |
| PAD | 1296 (6.75) | 2911 (3.11) | 1364 (2.25) | 5571 (3.21) |
| Hypertension | 17 551 (91.37) | 92 600 (98.88) | 4 (0.01) | 110 155 (63.50) |
| DM | 8364 (43.54) | 35 565 (37.98) | 15 806(26.08) | 59 735 (34.44) |
| Cancer | 142 (0.74) | 283 (0.30) | 194 (0.32) | 619 (0.36) |
| RD | 118 (0.61) | 437 (0.47) | 287 (0.47) | 842 (0.49) |
| CKD | 3784 (19.70) | 0 | 0 | 3784 (2.18) |
| SA | 420 (2.19) | 1829 (1.95) | 853 (1.41) | 3102 (1.79) |
| Procedures | ||||
| DSA | 8204 (57.29) | 16 424 (82.46) | 13 099 (78.39) | 135 732 (78.25) |
| PTCA | 12 119 (36.91) | 46 979 (49.83) | 32 201 (46.87) | 82 160 (47.37) |
| PCI | 13 214 (31.21) | 52 705 (43.72) | 35 516 (41.4) | 72 024 (41.52) |
| CABG | 14 012 (27.06) | 83 609 (10.72) | 51 931 (14.31) | 23 907 (13.78) |
| Adverse events | ||||
| CPR | 18 905 (1.58) | 93 504 (0.15) | 60 434 (0.28) | 616 (0.36) |
| CIMV ≥ 96 h | 18 807 (2.09) | 93 390 (0.27) | 60 384 (0.36) | 878 (0.51) |
| IABP | 18 252 (4.98) | 92 992 (0.70) | 59 765 (1.38) | 2450 (1.41) |
| PP | 19 205 (0.02) | 93 626 (0.02) | 60 593 (0.02) | 35 (0.02) |
| Transfusion | 18 496 (3.71) | 92 431 (1.30) | 59 626 (1.61) | 2906 (1.68) |
| Haemodialysis | 18 976 (1.21) | 93 634 (0.01) | 60 587 (0.03) | 262 (0.15) |
| In-hospital death | 18 740 (2.44) | 93 438 (0.22) | 60 399 (0.34) | 882 (0.51) |
Figure 1.
Correlation of comorbidities in coronary artery disease patients. Correlation analysis revealed significant associations among comorbidities in patients with coronary artery disease. Congestive heart failure demonstrated a strong link to chronic kidney disease and atrial fibrillation. Diabetes mellitus showed a close connection with hypertension. Additionally, peripheral artery disease exhibited a significant correlation with cerebrovascular disease and chronic liver disease. *P < 0.05, **P < 0.01.
Multimorbidity patterns identified by latent class analysis
Latent class analysis identified three distinct patterns among hospitalized CAD patients with significant differences in demographics, clinical features, and in-hospital events. Patients in Class 1 had the oldest average age (63.34 years), longest hospital stays (7.90 days), and highest rates of acute myocardial infarction (21.95%), heart failure (88.71%), CKD (19.70%), adverse events, and comorbidity burden (average 7.26 conditions). This profile indicates Class 1 as highest risk. Class 2 patients were predominantly stable angina cases with very low heart failure but high hypertension prevalence and the lowest comorbidity count, conferring an intermediate-risk profile. Class 3 patients were the youngest and had balanced rates of various comorbidities, representing the lowest risk, non-specific multimorbidity pattern.
Multimorbidity patterns and outcomes
After adjusting for age and sex using multivariate logistic regression model, Class 1 patients had a six-fold higher risk of in-hospital mortality [adjusted odds ratio (OR) 6.2, 95% confidence interval (CI) 5.2–7.3, P < 0.001] and four-fold higher risk of severe in-hospital complications (adjusted OR 4.0, 95% CI 3.6–4.3, P < 0.001) compared with Class 3. Over 1-year follow-up, the all-cause mortality was 8.2% for Class 1, 2.8% for Class 2, and 2.7% for Class 3. Kaplan–Meier analysis showed Class 1 patients had significantly higher cumulative mortality vs. the other two classes (both log-rank P < 0.001, Figure 2A). In multivariate Cox regression, Class 1 conferred an elevated hazard for 1-year all-cause death [adjusted hazard ratio (HR) 1.87, 95% CI 1.52–2.31, P < 0.001] and major bleeding (adjusted HR 1.74, 95% CI 1.36–2.24, P < 0.001, Figure 2B). Class 2 patients had lower 1-year mortality (adjusted HR 0.77, 95% CI 0.62–0.95, P = 0.02) but higher major bleeding (adjusted HR 1.36, 95% CI 1.09–1.69, P = 0.01) vs. Class 3. Reinfarction risks were similar across three classes (Figure 2C).
Figure 2.
Kaplan–Meier estimates of 1-year adverse events in coronary artery disease patients with different multimorbidity patterns. Patients were categorized into three multimorbidity patterns based on the representative diseases determined by correlation and cluster analysis of coronary artery disease comorbidities: Kaplan–Meier analyses showed the risks of all-cause death (A), major bleeding (B), and reinfarction (C) among the three groups.
In the subgroup with less than three comorbidities, the 1-year all-cause mortality in Class 1 was higher than that in Class 2 (log-rank P < 0.001) and Class 3 (log-rank P = 0.002). In the subgroup with three or more comorbidities, the 1-year all-cause mortality of Class 1 was higher than that of Class 2 (log-rank P < 0.001) and Class 3 (log-rank P = 0.316; the difference was not significant). There was no significant difference between Class 2 and Class 3 in the two subgroups.
Multimorbidity patterns and treatment
Based on our analysis, there were notable distinctions in the utilization of antiplatelet drugs and oral anticoagulants (OACs) across the three multimorbidity patterns. In Class 1, aspirin (AAS) stood out as the most frequently prescribed medication at 91.72%, followed by clopidogrel at 36.07% and prasugrel at 5.66%. Ticagrelor had a usage rate of 5.32%, while OACs were employed in 9.23% of cases. Classes 2 and 3 exhibited similar medication utilization patterns, with AAS at 89.01 and 89.97%, clopidogrel at 39.33 and 38.93%, prasugrel at 3.80 and 4.48%, ticagrelor at 3.62 and 4.02%, and OAC at 4.35 and 3.82%, respectively.
Dual antiplatelet therapy (DAPT) involving AAS (100%) and clopidogrel (91.4%) was commonly employed. However, the use of ticagrelor was limited, accounting for only 4.0% of DAPT patients, compared with 4.5% for prasugrel and 1.2% for OACs. Among the 855 patients not on DAPT, this group included 664 cases prescribed AAS, 145 cases with clopidogrel, 5 cases with ticagrelor, and 4 cases with prasugrel; the remaining cases possibly involved triple antiplatelet therapy. Dual antiplatelet therapy demonstrated protective effects against mortality and reinfarction (HRs, 0.64 and 0.55, respectively; refer to Table 2). Further subgroup analysis within Class 1 revealed a significant reduction in all-cause mortality with DAPT (adjusted HR 0.60, 95% CI 0.37–0.98, P = 0.04) without an elevated risk of major bleeding.
Table 2.
Impact of multimorbidity patterns on adverse events
| Variables | All-cause death | Major bleeding | Reinfarction | |||
|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |
| Patterns | ||||||
| Class 1 | 1.87 (1.52–2.31) | 0.00 | 1.74 (1.36–2.24) | 0.00 | 1.02 (0.80–1.31) | 0.85 |
| Class 2 | 0.77 (0.62–0.95) | 0.02 | 1.36 (1.09–1.69) | 0.01 | 1.01 (0.82–1.24) | 0.96 |
| Class 3 | Ref | Ref | Ref | |||
| Age | 1.06 (1.06–1.07) | 0.00 | 1.04 (1.03–1.05) | 0.00 | 1.03 (1.02–1.04) | 0.00 |
| Female | 1.03 (0.86–1.24) | 0.75 | 0.99 (0.81–1.21) | 0.93 | 1.03 (0.84–1.27) | 0.76 |
| STEMI | 1.14 (0.96–1.35) | 0.13 | 1.12 (0.93–1.34) | 0.22 | 1.49 (1.23–1.82) | 0.00 |
| PCI | 0.96 (0.81–1.14) | 0.63 | 1.02 (0.86–1.23) | 0.79 | 0.63 (0.52–0.75) | 0.00 |
| DAPT | 0.64 (0.48–0.85) | 0.00 | 0.75 (0.53–1.04) | 0.09 | 0.55 (0.38–0.79) | 0.00 |
Discussion
As one of the most common chronic diseases worldwide, CAD often coexists with other diseases in patients, requiring comprehensive assessment of comorbidity relationships and patterns.23 Prior studies using exploratory factor analysis identified variable multimorbidity patterns in the general and elderly populations.18,24,25 However, few examined CAD-specific patterns.26 Our data-driven approach provides new insights into multimorbidity clustering and outcomes among hospitalized CAD patients. In this combined analysis of the DCEM registry (n = 173 459) and international BleeMACS cohort (n = 15 401), we utilized LCA to categorize three distinct comorbidity patterns in CAD patients. These were characterized by cardiovascular–kidney–metabolic (CKM) comorbidities (Class 1, high-risk); hypertension–dyslipidaemia comorbidities (Class 2, intermediate-risk); and non-specific comorbidities (Class 3, low-risk). Compared with Classes 2 and 3, Class 1 had the highest risks for in-hospital and 1-year all-cause mortality. Dual antiplatelet therapy demonstrated mortality and antithrombotic benefits without significantly increasing major bleeding, particularly in the highest-risk Class 1 patients.
Our study identified CKM multimorbidity as the highest-risk pattern in hospitalized CAD patients. The clustering of heart failure, CBD, CKD, and diabetes in Class 1 patients may relate to shared risk factors and pathophysiological pathways.27,28 In a recent American Heart Association (AHA) Presidential Advisory report published in Circulation, these adverse interactions were newly defined as CKM syndrome.29 Hypertension and dyslipidaemia also contribute to the risks in this cohort.30
Heart failure appears to be a major driver of adverse outcomes, with neurohormonal changes, vascular dysfunction, and treatment issues contributing.27,31 Cerebrovascular disease adds to the thrombosis burden and haemorrhagic risks.32 Chronic kidney disease further exacerbates mortality, bleeding, and complications through uraemic toxicity, electrolyte abnormalities, and drug clearance issues.33,34 Diabetes interacts deleteriously through macro- and microvascular damage, glucose variability, and polypharmacy.33,35 Therefore, the patterns likely reflect shared risk factors, cumulative damage, and pathophysiologic interactions between conditions.
Of note, DAPT demonstrated survival and antithrombotic benefits without significantly increasing major bleeding in the highest-risk Class 1 patients. With appropriate patient selection and management, Class 1 patients benefit from DAPT, especially the use of clopidogrel. It highlights the importance of CKM risk management and drug safety for these complex patients. The greater absolute risk reduction from preventing thrombotic events may offset marginal bleeding risks in these patients when therapy is individualized appropriately.36
In our study, the predominant use of clopidogrel among high-risk CAD patients with multimorbidity suggests a preference for established, cost-effective treatments in managing complex cases. This trend indicates a careful balance between efficacy and safety in a multimorbid context, potentially influenced by factors like cost, bleeding risks, and patient-specific considerations. The relatively lower use of newer P2Y12 inhibitors like ticagrelor and prasugrel across all classes highlights the need for personalized therapy choices, taking into account the overall health status and comorbidities of patients.
Our data provide contemporary, real-world quantification of the prognostic impact of multimorbidity patterns among hospitalized CAD patients and give reassurance on DAPT safety and efficacy in complex, multimorbid CAD patients. However, this study is not without limitations. Firstly, the differences in enrolment and prognosis between the DCEM and BleeMACS cohorts introduce a potential source of bias. We developed the LCA algorithm using DCEM data and subsequently applied it to BleeMACS, aiming to validate the robustness of comorbidity patterns for prognostic assessment. Secondly, the BleeMACS data set contains some variables with missing data, impacting the long-term prognostic analysis. To maintain data integrity, we opted to exclude these instances of missing values. Thirdly, our reliance on administrative data for pattern definition, coupled with the observational design, constrains our ability to establish causality. Future investigations should delve into the mechanisms and optimal management of comorbidity in CAD patients.
Conclusions
Our study reveals unique multimorbidity patterns in CAD, highlighting Class 1 as the highest-risk group with heightened mortality risks, predominantly associated with CKM comorbidities. Importantly, DAPT demonstrates efficacy in reducing mortality without significant bleeding risks in this high-risk cohort, specifically featuring AAS and clopidogrel. While our findings provide valuable insights, addressing potential biases and delving into mechanisms for optimized comorbidity management in CAD patients should be prioritized for future research.
Supplementary Material
Contributor Information
Wen Zheng, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Xin Huang, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China; Department of Cardiology, Beijing Shijitan Hospital, Capital Medical University, No.10 Tieyi Road, Haidian District, Beijing 100000, China.
Xiao Wang, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Min Suo, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Yan Yan, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Wei Gong, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Hui Ai, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Bin Que, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Shaoping Nie, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China.
Lead author biography
Dr Wen Zheng graduated with a PhD from the Jilin University in 2015. He is currently an attending physician and lecturer at Beijing Anzhen Hospital, focusing on interventional treatments for coronary heart disease. He has published over 20 papers, been involved in several national-level researches, coauthored three books, received the first prize in the Beijing Science and Technology Progress Awards, and holds four patents.
Data availability
The data underlying this article will be shared upon reasonable request to the corresponding author.
Supplementary material
Supplementary material is available at European Heart Journal Open online.
Ethical approval
Informed consent was obtained from all patients included in the study. The study protocol was reviewed and approved by the ethical committee of Beijing Anzhen Hospital. Patients were provided with detailed information regarding the purpose, procedures, potential risks, and benefits of the study. They were assured of the confidentiality of their personal information and had the right to withdraw their consent at any time without affecting their medical care. Written consent was obtained from each patient or their legal representative before their inclusion in the study.
Funding
This study was funded by grants from the National Key Research and Development Program of China (2022YFC2505600 and 2020YFC2004800), Beijing Hospitals Authority Youth Programme (QLM20230608), and National Natural Science Foundation of China (81970292, 82270258, 82100260, and 82200495).
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Data Availability Statement
The data underlying this article will be shared upon reasonable request to the corresponding author.