Predicting long-term outcomes after primary PCI in Acute ST-segment elevation myocardial infarction patients with single-vessel disease

Predicting long-term outcomes after primary PCI in Acute ST-segment elevation myocardial infarction patients with single-vessel disease | 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 Predicting long-term outcomes after primary PCI in Acute ST-segment elevation myocardial infarction patients with single-vessel disease Hai-tao Yang, Jing-Kun Liu, xiang Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3866952/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to develop a predictive nomogram for long-term outcomes in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) for single-vessel disease, integrating the cholesterol-to-lymphocyte ratio (CLR) index with clinical data. Methods From April 2016 to December 2021, 1264 patients with acute STEMI were enrolled. They were divided into development (949 patients) and validation (315 patients) cohorts. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified potential risk factors, and multivariate Cox regression determined independent risk factors for the nomogram. The model was transformed into a web-based calculator for ease of use. Its performance was evaluated using ROC curve analysis, calibration curves,and C-index. In addition, individual risk assessment based on the model is conducted. Results The nomogram included age, diabetes, heart rate, and CLR index as variables. In the development cohort, ROC analysis yielded AUCs of 0.816, 0.812, and 0.751 for predicting major adverse cardiac events (MACEs) at 2, 3, and 4 years, respectively. In the validation cohort, the AUCs were 0.852, 0.773, and 0.806. The C-index was 0.76 in the development cohort and 0.79 in the validation cohort. Kaplan-Meier analysis indicated a higher likelihood of MACEs in the high-risk group. Conclusions This predictive model, incorporating CLR index and electronic health record (EHR) data, reliably and accurately forecasts adverse cardiac events post-primary PCI in patients with acute STEMI and single-vessel disease, aiding in improved risk stratification and management. Major adverse cardiovascular events Percutaneous coronary intervention Prediction nomogram ST-elevation myocardial infarction cholesterol-to-lymphocyte ratio index. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Acute Myocardial Infarction (AMI) is a medical emergency that demands swift and precise intervention to mitigate the risks of shock and fatality 1 . The current gold standard in treatment involves Percutaneous Coronary Intervention (PCI), a procedure designed to promptly restore blood flow in obstructed coronary vessels 2 . Despite the timely and successful implementation of PCI, there is a growing body of research indicating that certain AMI patients continue to face adverse outcomes 3–5 . This challenge emphasizes the importance of identifying modifiable risk factors to improve treatment strategies and prognosis for high-risk AMI patients. Moreover, a significant aspect of this issue is the early and precise identification of low-risk AMI patients. Understanding these risk factors has profound clinical implications. It allows us to tailor individualized comprehensive care and optimize healthcare resource allocation 6 . The primary cause of Acute Myocardial Infarction is the rupture of coronary atherosclerotic plaques. Multiple studies have underlined the pivotal role of cholesterol accumulation and immune system activation in initiating plaque rupture 7 . Cholesterol accumulation forms the cornerstone of plaque formation and progression. Over time, this accumulation leads to the development of cholesterol crystals, which incite sterile inflammation and disrupt immune cell homeostasis within atherosclerotic lesions 8 . Recent investigations have introduced the Cholesterol-to-Lymphocyte Ratio (CLR) as a quantifiable metric. It assesses both cholesterol accumulation and immune system activation and has demonstrated prognostic value in high-risk patients, particularly following cancer-related surgeries 9, 10 . Notably, the CLR index incorporates two easily measurable metabolic measures, offering cost-effectiveness, widespread availability in clinical practice, and ease of implementation for practical use. Given the intricate nature of AMI, which involves multiple pathophysiological mechanisms, a binary diagnosis of coronary artery disease falls short in capturing its complexity or adequately stratifying cardiovascular risk. Therefore, there is a pressing need for a quantitative model that leverages multimodal data to assess the risk of Acute Coronary Syndrome (ACS). Surprisingly, little research has explored the potential benefits of integrating the CLR with Electronic Health Record (EHR) data in predictive models that provide personalized risk stratification for AMI patients. Therefore, this study aims to evaluate the long-term prognostic value of CLR when combined with EHR data in individuals diagnosed with acute ST-segment elevation myocardial infarction (STEMI) following primary PCI. Methods Methods Study Design and Participants This study included patients admitted to the First Affiliated Hospital of Xinjiang Medical University with STEMI between April 2016 and December 2021. Coronary angiography was performed within 12 hours to assess single-vessel occlusion, and successful coronary stent implantation was carried out. A total of 1264 patients were enrolled and were randomly divided into Development and Validation cohorts at a 4:1 ratio. A nomogram was constructed and validated using this dataset to predict outcomes following PCI treatment. Since this study was a retrospective cohort analysis based on real-world scenarios, it was exempt from the requirement of obtaining informed consent from the patients. Inclusion Criteria: Newly diagnosed STEMI patients without pre-existing coronary artery disease (CAD) who underwent complete and successful revascularization through PCI. Patients were prescribed standard post-PCI treatment regimens in accordance with established guidelines. Regular clinical follow-up after hospital discharge. Availability of baseline clinical data, biochemical test results, and clinical endpoint data. Exclusion Criteria: Patients who underwent PCI more than 12 hours after the onset of myocardial infarction. Patients with stenosis exceeding 75% in two or more coronary vessels based on coronary angiography. Patients with left main coronary artery stenosis greater than 50% on coronary angiography. This study protocol received approval from the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval Number: NO.20160317-03) and was registered on the clinical trials website (NCT02737956). Definition of Cardiovascular Risk Factors Hypertension: Patients who were either taking antihypertensive medication to manage their blood pressure or consistently had blood pressure readings exceeding 140/90 mmHg on three or more separate occasions were classified as individuals with hypertension 11, 12 . Diabetes: Patients with a medical history of diabetes or whose fasting and/or postprandial blood glucose levels exceeded the recommended thresholds as per established guidelines were identified as diabetes patients 11, 12 . Alcohol Consumption: Individuals consuming more than 30 grams of alcohol per day were categorized as current drinkers. Blood pressure and heart rate measurements were collected in the morning after PCI by professional medical staff using electronic blood pressure monitors. During the measurements, patients were required to be in a quiet, rested state for at least 10 minutes. Biochemical Tests Before emergency coronary angiography, we conducted myocardial enzyme analysis and a complete blood count. Following PCI, we assessed fasting blood glucose (FBG), lipid profile, and albumin levels. Additionally, we calculated the CLR index by dividing the fasting total cholesterol by the lymphocyte count. Coronary Angiography and PCI Upon admission of patients with STEMI, our medical team adhered to the standard treatment protocol, and a senior cardiologist conducted coronary angiography. In cases of new-onset STEMI, we promptly administered the initial loading dose of aspirin and clopidogrel before primary PCI. Our in-house team of physicians performed each PCI procedure, tailoring it to the individual patient's coronary anatomy and clinical condition. We defined successful and complete revascularization as the successful implantation of stents with residual stenosis of less than 20% and achieving Thrombolysis in Myocardial Infarction (TIMI) flow grade 3 during the procedure. Discontinuation of dual antiplatelet therapy was discouraged unless a compelling and well-founded reason was provided. All patients received long-term advice to continue aspirin (100 mg/day) indefinitely and a P2Y12 inhibitor (75 mg/day) after their initial procedure, following the recommended duration as outlined in current guidelines. Clinical Endpoint For clinical follow-up, we conducted outpatient clinical visits or telephone interviews with our study participants. The median duration of follow-up within our study cohort was 30 months, ranging from 1 to 56 months. The primary clinical endpoint of our study was the occurrence of major adverse cardiac events (MACEs), which included all-cause mortality, recurrent myocardial infarction, in-stent restenosis, and the need for revascularization. Statistical Analysis We used SPSS version 29 (SPSS Inc., Chicago, IL, USA) and R version 3.2.4 (R Foundation for Statistical Computing, Vienna, Austria) for statistical analysis. Normality of data was assessed using the Shapiro-Wilk test. Results were presented as mean ± SEM or mean ± SD for normally distributed data, and as median ± interquartile range (25th to 75th percentiles) for non-normally distributed data. We used the 2-tailed Student , s t-test for normally distributed data or Mann-Whitney U test for non-normally distributed data to evaluate differences between two groups. Categorical variables were compared using the Fisher exact test or χ 2 test. A p-value of < 0.05 was considered statistically significant. For the selection of prognostic model indicators, the Least Absolute Shrinkage and Selection Operator (LASSO) was first applied to all variables. The non-zero parameters identified by LASSO were then further examined using multivariate COX regression. Variables with a P-value less than 0.05 were subsequently included in the model. Hazard ratios (HRs) were calculated using Cox regression analyses with 95% confidence intervals (CIs) or p-values. Variables were depicted in a nomogram, and three distinct methods were employed to validate the efficacy of the mode 13 : The ROC curve was used to assess the model using the area under the curve (AUC). Discriminatory ability was analyzed using Harrell’s concordance index (C-index) and its corresponding 95% CI. Model calibration was represented by calibration plots predicting the probability of MACE risk at 2, 3, and 4 years versus the observed probability. Finally, individual risk score was derived from the nomogram for patients in the development cohort. The optimal cutoff point for each model was calculated to stratify patients into low-risk and high-risk categories, determined by the largest discrepancy in MACE risk between the two groups. We used the log-rank statistic to compare the incidence of MACEs between these two groups, employing the Kaplan-Meier method. Results Patient Selection and Demographics A total of 1264 patients diagnosed with acute single-vessel myocardial infarction who underwent emergency PCI at the First Affiliated Hospital of Xinjiang Medical University from 2016 to 2021 were randomly divided into a development cohort and a validation cohort (Fig. 1 ). The median age of the study population was 58 years (range 48 to 68), with 82% being male. Among them, 53.96% were current smokers. The median resting blood pressure in the morning was 120/74 mmHg, and the median resting heart rate was 78 beats/min (range 70 to 88). The median follow-up duration was 30 months, ranging from 1 to 56 months, during which 170 cases (13.45%) of MACEs occurred. The demographic and event incidence data for both the development and validation cohorts were comparable, indicating successful matching (Table 1). Identification of Potential Predictors and Nomogram Construction To enhance the identification of potential predictors of MACEs, LASSO regression was employed, incorporating all 23 baseline patient characteristics variables from the development cohort into the analysis. Eight indicators with nonzero parameters were identified, specifically Age, Sex, Smoking, Heart Rate, Hypertension, Diabetes Mellitus, Neutrophil Count and Cholesterol-to-Lymphocyte Ratio Index (Fig. 2 ). Subsequently, multivariate Cox regression analysis further narrowed down the list to four independent risk factors for MACEs: age (HR = 1.050, 95% CI: 1.033 to 1.067, p < 0.001), heart rate (HR = 1.033, 95% CI: 1.022–1.045, p = 0.046), presence of diabetes mellitus (HR = 1.487, 95% CI: 1.008–2.195, p < 0.001), and CLR index (HR = 1.123, 95% CI: 1.022–1.045, p = 0.003) (Fig. 3 ). Using these four variables, a nomogram model was constructed to predict the risk of MACEs at 2, 3, and 4 years (Fig. 4 ). Finally, we uploaded the nomogram generation online tool to the shinyapps. io platform ( https://adh-clr.shinyapps.io/dynnomapp/ ). Clinicians can submit the 4 metrics in the full model to the appropriate text box for calculation of the risk of the patient long-term cardiovascular events via the web. Nomogram Performance Evaluation In order to evaluate the efficiency and stability of the model, the 4 variables built in the nomogram model were analysed together, then the operating characteristic curves of the recipient for 2, 3 and 4 years had been presented, and the area under the curves was calculated (Fig. 5 ). In the development cohort, the AUC for 2 years was 0.816. The AUC for 3 years stood at 0.812. For 4 years, the AUC was 0.751. In the validation cohort, the 2-year AUC was 0.852. The 3-year AUC was 0.773. The 4-year AUC was 0.806. Then, we use C-Index calculated from the calibration plots to observe the risk probability of the MACEs, in the development cohort the C-Index was 0.76,while in the validation cohort the C-Index was 0.79 (Fig. 6 ). Lastly, we used the median of the risk scores to determine high risk and low risk individuals within the population. We analysed the occurrence of MACEs according to Kaplan-Meier analysis. In comparison between the two cohorts, we found that the high-risk group was more likely to develop MACEs than the low-risk group (Fig. 7 ). Discussion Despite significant advancements in the treatment for acute myocardial infarction, there remains a subset of patients who fail to fully mitigate adverse prognosis resulting from ventricular remodeling, even when administered standardized therapeutic interventions 14, 15 . Recently, the need for a practical long-term prognosis model has arisen for this subset of AMI patients. In this study, we constructed a prediction nomogram of the MACEs occurrence in 2 years, 3 years and 4 years of the patients who received emergency PCI treatments due to acute single-vessel myocardial infarction. Total cholesterol, CLR (a biomarker of lymphocyte count), along with several traditional clinical risk factors (age, diabetes, heart rate) were included in the model. According to the evaluation of the model efficiency, the validity of the nomogram built in the development cohort had been proved by the validation cohort, which suggested that our predictive model had good predictive ability and stability, and it could render new insights to the STEMI risk stratification thus to support the development of new strategies. The CLR index is a crucial parameter, reflecting the dual aspects of the body's cholesterol accumulation and lymphocyte-mediated inflammatory response 9, 10 . Central to the dynamic formation and progression of coronary atherosclerosis is the interplay between cholesterol accumulation and the involvement of lymphocytes in the inflammatory response 16 . Serum total cholesterol, a well-established indicator of cholesterol accumulation, is recognized as an independent cardiovascular risk factor 17, 18 . Cholesterol accumulation can lead to membrane injury, potentially contributing to cellular dysfunction and amplifying the body's response to external stressors 19, 20 . Through this mechanism, cholesterol plays a role in modulating the immune response, especially the activation of lymphocytes, thereby impacting serum lymphocyte counts 21, 22 . Prior research has highlighted a significant correlation between lymphocyte count and the prognosis of acute myocardial infarction patients. The process of lymphocyte antigen recognition, triggering their activation and interaction with antigen-presenting cells, is believed to be instrumental in this context 23 . These activation pathways are closely linked to changes in lipoproteins and autoimmune reactions during atherosclerosis 24 . Additionally, some studies have suggested that excessive cholesterol may intensify inflammation in atherosclerotic plaques by interacting with specific lymphocyte subsets 25 . Our study emphasizes a marked association between the CLR index and poor prognosis in patients undergoing emergency PCI for acute myocardial infarction, thereby establishing it as a reliable indicator for long-term prognosis. Importantly, both serum cholesterol and lymphocyte levels are commonly measured in clinical practice. The CLR index, more easily obtainable and accessible than other indicators such as Osteopontin, trimethylamine N-oxide, and hyperuricemia, as noted in existing studies, offers valuable insights for the timely and effective personalized treatment of patients 26–28 . In our study, we noted that a history of diabetes emerged as a significant independent prognostic risk factor for patients, aligning with findings from previous research. Prior studies have consistently underlined the critical role of blood glucose levels in increasing the risk of stent restenosis, impacting both individuals with and without diabetes 29 . Notably, recent studies have demonstrated a marked improvement in long-term cardiovascular outcomes in acute myocardial infarction patients treated with oral Sodium-glucose cotransporter 2 inhibitors, compared to those in a control group, highlighting the potential advantages of such treatments in cardiovascular risk management 30 . Additionally, a growing body of evidence indicates that patients with diabetes experience an increase in mitochondrial reactive oxygen species (mtROS) production, significantly contributing to vascular endothelial cell dysfunction 31, 32 . This dysfunction can lead to intimal damage, promote platelet aggregation, and ultimately worsen vascular stenosis 33, 34 . Moreover, the increased production of reactive oxygen species, a consequence of elevated blood sugar levels, plays a crucial role in inducing oxidative stress, a fundamental element in the pathogenesis of cardiovascular diseases 35, 36 . Therefore, it is essential to refine therapeutic strategies for diabetic patients presenting with acute myocardial infarction. Additionally, advocating for active, long-term blood sugar control management in all patients, regardless of their diabetes status, is paramount. These strategies are vital in improving the prognosis and overall outcomes for individuals afflicted with acute myocardial infarction. This study's strengths are multifaceted. Foremost, the CLR index has been established as a validated prognostic tool for patients diagnosed with acute myocardial infarction. The rigorous internal validation process further enhances its credibility and applicability in clinical settings. Crucially, our inclusion of a large cohort comprising 1264 patients diagnosed with acute single-vessel myocardial infarction underscores the validity and robustness of our findings. In pursuit of precision, we meticulously excluded patients with severe multivessel lesions or significant left main coronary artery stenosis. Although complex lesions have the same pathophysiological mechanisms as single-vessel lesions in acute myocardial infarction, patients with complex lesions always need to implant stents for the second time, which led to data bias in the process of MACEs event statistics 37, 38 . The survival prognosis of patients with this type of complex acute myocardial infarction is worse than that of patients with single-vessel disease 39 . It is not practical for clinical guidance to analyze single-vessel lesions and complex lesions acute myocardial infarction, even if the results show significant statistical differences. There are some limitations in this study. First, the urgent nature of PCI in patients with acute myocardial infarction necessitated the postponement of cholesterol measurements until after the procedure. It is important to consider that both the PCI treatment and subsequent statin use may introduce a bias in the CLR index. Additionally, this study did not explore the relationship between changes in cholesterol levels and inflammation. For a more comprehensive analysis of patient prognosis following acute myocardial infarction, future research could benefit from incorporating multiple samplings. This approach would allow for a dynamic observation of the interplay among cholesterol reduction, inflammation, and the CLR index. Secondly, it is pertinent to note that this study is a retrospective analysis conducted at a single center. Although internal validation was conducted, there is a need for multi-center and large-sample prospective studies. Such studies would enable external validation and facilitate a comparison of the efficacy of our findings with existing prognostic models, thus providing a more comprehensive evaluation. Third, the predicted results of the nomogram remain the same over time, but in reality, the disease outcomes can vary with improvements in treatment, early detection, or changes in the natural course of the disease, which may lead to the performance of a nomogram inaccurate. Conclusions This study has developed a long-term model for predicting the prognosis of patients with acute single-vessel myocardial infarction who received PCI treatment, and the efficiency and stability of this model have been verified. This model, which combines the CLR index with traditional clinical risk factors, has been shown by our data to serve as a reliable long-term risk biomarker. The predictive nomogram that we have developed can be easily applied to clinical practice to better support individual management for patients with acute myocardial infarction. Declarations Acknowledgements We would like to express our deepest gratitude to the patients and their families who participated in this study, without whom this research would not have been possible. Special thanks are extended to the staff at the First Affiliated Hospital of Xinjiang Medical University for their invaluable support and assistance in data collection and patient care. We also appreciate the insightful comments and suggestions from our peer reviewers, which significantly improved the manuscript. Lastly, we are grateful to our colleagues and team members for their contributions and collaboration in this research project. Funding This research was funded by the National Natural Science Foundation of China, grant number 82170345. Conflict of Interest The authors declare that they have no conflicts of interest. Author Contribution HT.Y, JK.L and X.X contributed to the conception and design of the work. HT.Y contributed to the acquisition, analysis, or interpretation of data for the work. JK.L drafted the manuscript. X.X critically revised the manuscript. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy. Ethics approval and consent to participate This study received approval from the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval Number: 20160317-03) and was registered in the Chinese Clinical Trial Registry Information Network (Trial Registration Number: NCT02737956). Due to the retrospective design of the study, the requirement for obtaining informed consent from eligible patients was waived by the ethics committee. Data Availability Statement Individual participant data that support the findings of this article, following the process of de-identification, the data supporting the findings of this article are available from the corresponding author upon reasonable request. 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Hyperglycemia Drives Stent Restenosis in STEMI Patients. Diabetes Care. 2021;44(11):e192-e193. Paolisso P, Bergamaschi L, Gragnano F, et al. Outcomes in diabetic patients treated with SGLT2-Inhibitors with acute myocardial infarction undergoing PCI: The SGLT2-I AMI PROTECT Registry. Pharmacol Res. 2023;187:106597. Widlansky ME, Hill RB. Mitochondrial regulation of diabetic vascular disease: an emerging opportunity. Transl Res. 2018;202:83–98. Garg SS, Gupta J. Polyol pathway and redox balance in diabetes. Pharmacol Res. 2022;182:106326. Chang E, Abe JI. Kinase-SUMO networks in diabetes-mediated cardiovascular disease. Metabolism. 2016;65(5):623–633. Winer N, Sowers JR. Diabetes and arterial stiffening. Adv Cardiol. 2007;44:245–251. Davì G, Falco A, Patrono C. Lipid peroxidation in diabetes mellitus. Antioxid Redox Signal. 2005;7(1–2):256–268. Liu H, Wang X, Gao H, Yang C, Xie C. Physiological and pathological characteristics of vascular endothelial injury in diabetes and the regulatory mechanism of autophagy. Front Endocrinol (Lausanne). 2023;14:1191426. Yuoness SA, Goha AM, Romsa JG, et al. Very high coronary artery calcium score with normal myocardial perfusion SPECT imaging is associated with a moderate incidence of severe coronary artery disease. Eur J Nucl Med Mol Imaging. 2015;42(10):1542–1550. Zimbardo G, Cialdella P, Di Fusco P, et al. Acute coronary syndromes and multivessel coronary artery disease. Eur Heart J Suppl. 2023;25(Suppl C):C74-C78. Gaba P, Christiansen EH, Nielsen PH, et al. Percutaneous Coronary Intervention vs Coronary Artery Bypass Graft Surgery for Left Main Disease in Patients With and Without Acute Coronary Syndromes: A Pooled Analysis of 4 Randomized Clinical Trials. JAMA Cardiol. 2023;8(7):631–639. Table 1 Table.1 Baseline patient characteristics and major adverse cardiac events in the development and validation cohorts. Variable All cohort (N=1 264 ) Development cohort (N= 949 ) Validation cohort (N= 315 ) P Characteristic Male sex, N % 1048 (82.91 %) 794 (83.67 %) 254 (80.63 %) 0.227 Age, years 58 (48, 68) 57 (48, 68) 59 (48, 70) 0.709 Heart rate, bpm 78 (70, 88) 78 (70, 88) 77, (69, 90) 0.398 Systolic blood pressure, mmHg 120 (107, 131) 120 (108, 133) 117 (103, 130) 0.155 Diastolic blood pressure, mmHg 74 (67, 82) 74 (68, 82) 72 (64, 80) 0.355 Current smoking, N % 682 (53.96 %) 518 (54.58 %) 164 (52.06 %) 0.473 Current drinking, N % 333 (26.34 %) 252 (26.55 %) 81 (25.71 %) 0.825 Basic diseases Hypertension, N % 532 (42.09 %) 413 (43.52 %) 119 (37.78 %) 0.076 Diabetes Mellitus, N % 262 (20.73 %) 200 (21.07 %) 62 (19.68 %) 0.631 Laboratory results Neutrophil count, 10^9/L 4.83 (3.84, 6.32) 4.82 (3.69, 6.75) 4.86 (4.16, 5.66) 0.809 Lymphocyte count, 10^9/L 1.8 (1.41, 2.39) 1.82 (1.33, 2.4) 1.76 (1.3, 2.35) 0.292 Monocyte count, 10^9/L 0.56 (0.42, 0.78) 0.57 (0.42, 0.78) 0.54 (0.43, 0.81) 0.383 Hemoglobin, g/L 141 (129, 152) 141 (129, 151) 141 (129, 154) 0.381 Blood platelet count, 10^9/L 224 (182, 279) 225 (181, 279) 225 (175, 290) 0.826 Urea nitrogen, mmol/L 5.9 (4.8, 7.5) 5.9 (4.9, 7.5) 5.73 (4.69, 7.5) 0.332 Creatinine, μmol/L 77 (65.6, 87.62) 77 (65.5, 87) 76.88 (65.8, 91.77) 0.271 Aspartate aminotransferase, U/L 22 (18, 28) 22 (18, 27) 22.5 (17.5, 33.5) 0.121 Alanine aminotransferase, U/L 23.72 (16.65, 35.41) 23.9 (17.2 35.33) 23.12 (14.62, 35.92) 0.153 High-density lipoprotein cholesterol, mmol/L 0.91 (0.76, 1.11) 0.91 (0.77, 1.11) 0.89 (0.74, 1.11) 0.947 Low-density lipoprotein cholesterol, mmol/L 2.35 (1.86,2.98) 2.39 (1.86, 2.99) 2.33 (1.91, 2.95) 0.597 Triglyceride, mmol/L 1.5 (1.05, 2.23) 1.52 (1.06, 2.23) 1.46 (1.05, 2.08) 0.188 Total cholesterol, mmol/L 3.85 (3.15, 4.72) 3.9 (3.23, 4.72) 3.56 (3.03, 4.44) 0.618 Cholesterol-to-lymphocyte ratio (CLR) Index 2.1 (1.52, 3.2) 2.07 (1.52, 3.14) 2.12 (1.48, 3.09) 0.063 E nd point s MACEs, N % 170 (13.45 %) 124 (13.07 %) 46 (14.6 %) 0.505 All-cause mortality, N % 46 (3.64 %) 37 ( 3.9 %) 9 (2.86 %) 0.488 Re-myocardial infarction, N % 25 (1.98 %) 20 (2.11 %) 5 (1.59 %) 0.649 In-stent restenosis, N % 20 (1.58 %) 16 (1.69 %) 4 (1.27 %) 0.796 Revascularization, N % 102 (8.07 %) 82 (8.64 %) 20 (6.35 %) 0.232 Data are presented as median (interquartile range), mean ± SD, or number (%). 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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-3866952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267439269,"identity":"8de52a6c-aa8e-4d8a-97d4-35ff6f8f5dac","order_by":0,"name":"Hai-tao Yang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hai-tao","middleName":"","lastName":"Yang","suffix":""},{"id":267439270,"identity":"eaceefe7-8a8e-4a9f-a7c1-608ffc7a6430","order_by":1,"name":"Jing-Kun Liu","email":"","orcid":"","institution":"The Affiliated Tumor Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing-Kun","middleName":"","lastName":"Liu","suffix":""},{"id":267439271,"identity":"11e6b95e-67c2-4fc2-9cce-1e9fb66f6b1e","order_by":2,"name":"xiang Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZjApwcDP3nzwAYMBKVoke44lGxCnBQYMbuSYSRCn8jjzs4df2yzkQFoqfxTckWdgP3x0Az4tks1s5sYyZySMJc88K7vNY/DMsIEnLe0GPi38zAxm0hIVEol9x5O33WYwOMzYIMFjhlcLGzP7N2kJA4n6hgMJZoU/DA7bE9TCz8xjJvmhQiJB4ESKGQOPweFEglokm3nKpBnOSBjOBAayNFBLchshvxicP75N8mdbnTwoKj/++HPYtp/98DG8WkCAmQfFd4SUgwDjD2JUjYJRMApGwcgFAO1pR1nCAt2OAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":true,"prefix":"","firstName":"xiang","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-01-15 15:29:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3866952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3866952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49823623,"identity":"20bc5239-a1ac-4c03-aa76-9e7872719d25","added_by":"auto","created_at":"2024-01-18 15:30:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141586,"visible":true,"origin":"","legend":"\u003cp\u003eInclusion of New Onset ST-Elevation Myocardial Infarction (STEMI) Patients Following Primary Percutaneous Coronary Intervention (n = 1264) and Establishment of the Development Cohort (n = 949) and the External Validation Cohort (n = 315)\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/23a3894a6a718c340a436262.png"},{"id":49824605,"identity":"7020f587-46b2-46ad-8f99-0366eb524305","added_by":"auto","created_at":"2024-01-18 15:38:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198475,"visible":true,"origin":"","legend":"\u003cp\u003eShowing the trends of 23 variables for long-term prognosis. The abscissa represents the optimal tuning parameter λ, and the ordinate represents the regression coefficient (A). Illustrating the selection of the optimal tuning parameter λ and its relationship with the binomial deviation (binomial deviance) (B). The vertical line indicates the optimized four nonzero coefficients derived by 10-fold cross-validation, leading to further multivariate Cox regression analysis.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/ab45a077105aa399ee358d24.png"},{"id":49823620,"identity":"3a0a0c8b-8ce1-4d11-816b-04b21f61b3cf","added_by":"auto","created_at":"2024-01-18 15:30:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52006,"visible":true,"origin":"","legend":"\u003cp\u003eDisplaying HRs for the independent prognostic variables identified by multivariate Cox regression in the development cohort.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/917a95eb6a3edf489a8b3602.png"},{"id":49823624,"identity":"5ea2c8a0-4477-45ba-9e2d-d0cf64cbfc43","added_by":"auto","created_at":"2024-01-18 15:30:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188969,"visible":true,"origin":"","legend":"\u003cp\u003eDemonstrating the nomogram used for predicting MACEs. The figure illustrates how to assign points to each patient based on their clinical characteristics, which are then summed into a total point score to calculate the probability of MACEs.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/a1d44a38190db4c2b3e25263.png"},{"id":49823625,"identity":"fc356f80-09ca-4052-b40a-7b48a2c62470","added_by":"auto","created_at":"2024-01-18 15:30:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":232567,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluating the predictive accuracy of the nomogram for MACEs in both the development (A) and independent validation cohorts (B).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/a6c9b3353893129669686f64.png"},{"id":49823626,"identity":"1641deb2-169a-4418-8c31-c30c5fce09e6","added_by":"auto","created_at":"2024-01-18 15:30:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":155790,"visible":true,"origin":"","legend":"\u003cp\u003eDepicting calibration curves for MACE risk predictors in the development (A) and independent validation cohorts (B) .\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/98692bd1fb72a0b4aecd3408.png"},{"id":49824606,"identity":"2bd7f845-8acc-4e8a-a00c-e05b0595ab9c","added_by":"auto","created_at":"2024-01-18 15:38:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":92079,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrating the cumulative MACE-free survival of patients in the development (A) and independent validation cohorts (B), stratified based on MACE risk (high risk vs. low risk) using the median nomogram score.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/b5da8d1340f1a5517fde19c8.png"},{"id":50175561,"identity":"512e7d29-9281-4c25-9e8b-6fb5d667da0e","added_by":"auto","created_at":"2024-01-25 16:22:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":977464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3866952/v1/7d46bd86-03d5-4c26-9b17-60588ed569e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting long-term outcomes after primary PCI in Acute ST-segment elevation myocardial infarction patients with single-vessel disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute Myocardial Infarction (AMI) is a medical emergency that demands swift and precise intervention to mitigate the risks of shock and fatality \u003csup\u003e1\u003c/sup\u003e. The current gold standard in treatment involves Percutaneous Coronary Intervention (PCI), a procedure designed to promptly restore blood flow in obstructed coronary vessels \u003csup\u003e2\u003c/sup\u003e. Despite the timely and successful implementation of PCI, there is a growing body of research indicating that certain AMI patients continue to face adverse outcomes \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. This challenge emphasizes the importance of identifying modifiable risk factors to improve treatment strategies and prognosis for high-risk AMI patients. Moreover, a significant aspect of this issue is the early and precise identification of low-risk AMI patients. Understanding these risk factors has profound clinical implications. It allows us to tailor individualized comprehensive care and optimize healthcare resource allocation \u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary cause of Acute Myocardial Infarction is the rupture of coronary atherosclerotic plaques. Multiple studies have underlined the pivotal role of cholesterol accumulation and immune system activation in initiating plaque rupture \u003csup\u003e7\u003c/sup\u003e. Cholesterol accumulation forms the cornerstone of plaque formation and progression. Over time, this accumulation leads to the development of cholesterol crystals, which incite sterile inflammation and disrupt immune cell homeostasis within atherosclerotic lesions \u003csup\u003e8\u003c/sup\u003e. Recent investigations have introduced the Cholesterol-to-Lymphocyte Ratio (CLR) as a quantifiable metric. It assesses both cholesterol accumulation and immune system activation and has demonstrated prognostic value in high-risk patients, particularly following cancer-related surgeries \u003csup\u003e9, 10\u003c/sup\u003e. Notably, the CLR index incorporates two easily measurable metabolic measures, offering cost-effectiveness, widespread availability in clinical practice, and ease of implementation for practical use.\u003c/p\u003e \u003cp\u003eGiven the intricate nature of AMI, which involves multiple pathophysiological mechanisms, a binary diagnosis of coronary artery disease falls short in capturing its complexity or adequately stratifying cardiovascular risk. Therefore, there is a pressing need for a quantitative model that leverages multimodal data to assess the risk of Acute Coronary Syndrome (ACS). Surprisingly, little research has explored the potential benefits of integrating the CLR with Electronic Health Record (EHR) data in predictive models that provide personalized risk stratification for AMI patients. Therefore, this study aims to evaluate the long-term prognostic value of CLR when combined with EHR data in individuals diagnosed with acute ST-segment elevation myocardial infarction (STEMI) following primary PCI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMethods Study Design and Participants\u003c/h2\u003e \u003cp\u003eThis study included patients admitted to the First Affiliated Hospital of Xinjiang Medical University with STEMI between April 2016 and December 2021. Coronary angiography was performed within 12 hours to assess single-vessel occlusion, and successful coronary stent implantation was carried out. A total of 1264 patients were enrolled and were randomly divided into Development and Validation cohorts at a 4:1 ratio. A nomogram was constructed and validated using this dataset to predict outcomes following PCI treatment. Since this study was a retrospective cohort analysis based on real-world scenarios, it was exempt from the requirement of obtaining informed consent from the patients.\u003c/p\u003e \u003cp\u003eInclusion Criteria: Newly diagnosed STEMI patients without pre-existing coronary artery disease (CAD) who underwent complete and successful revascularization through PCI. Patients were prescribed standard post-PCI treatment regimens in accordance with established guidelines. Regular clinical follow-up after hospital discharge. Availability of baseline clinical data, biochemical test results, and clinical endpoint data. Exclusion Criteria: Patients who underwent PCI more than 12 hours after the onset of myocardial infarction. Patients with stenosis exceeding 75% in two or more coronary vessels based on coronary angiography. Patients with left main coronary artery stenosis greater than 50% on coronary angiography. This study protocol received approval from the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval Number: NO.20160317-03) and was registered on the clinical trials website (NCT02737956).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of Cardiovascular Risk Factors\u003c/h2\u003e \u003cp\u003eHypertension: Patients who were either taking antihypertensive medication to manage their blood pressure or consistently had blood pressure readings exceeding 140/90 mmHg on three or more separate occasions were classified as individuals with hypertension \u003csup\u003e11, 12\u003c/sup\u003e. Diabetes: Patients with a medical history of diabetes or whose fasting and/or postprandial blood glucose levels exceeded the recommended thresholds as per established guidelines were identified as diabetes patients \u003csup\u003e11, 12\u003c/sup\u003e. Alcohol Consumption: Individuals consuming more than 30 grams of alcohol per day were categorized as current drinkers. Blood pressure and heart rate measurements were collected in the morning after PCI by professional medical staff using electronic blood pressure monitors. During the measurements, patients were required to be in a quiet, rested state for at least 10 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical Tests\u003c/h2\u003e \u003cp\u003eBefore emergency coronary angiography, we conducted myocardial enzyme analysis and a complete blood count. Following PCI, we assessed fasting blood glucose (FBG), lipid profile, and albumin levels. Additionally, we calculated the CLR index by dividing the fasting total cholesterol by the lymphocyte count.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCoronary Angiography and PCI\u003c/h2\u003e \u003cp\u003eUpon admission of patients with STEMI, our medical team adhered to the standard treatment protocol, and a senior cardiologist conducted coronary angiography. In cases of new-onset STEMI, we promptly administered the initial loading dose of aspirin and clopidogrel before primary PCI. Our in-house team of physicians performed each PCI procedure, tailoring it to the individual patient's coronary anatomy and clinical condition. We defined successful and complete revascularization as the successful implantation of stents with residual stenosis of less than 20% and achieving Thrombolysis in Myocardial Infarction (TIMI) flow grade 3 during the procedure. Discontinuation of dual antiplatelet therapy was discouraged unless a compelling and well-founded reason was provided. All patients received long-term advice to continue aspirin (100 mg/day) indefinitely and a P2Y12 inhibitor (75 mg/day) after their initial procedure, following the recommended duration as outlined in current guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClinical Endpoint\u003c/h2\u003e \u003cp\u003e For clinical follow-up, we conducted outpatient clinical visits or telephone interviews with our study participants. The median duration of follow-up within our study cohort was 30 months, ranging from 1 to 56 months. The primary clinical endpoint of our study was the occurrence of major adverse cardiac events (MACEs), which included all-cause mortality, recurrent myocardial infarction, in-stent restenosis, and the need for revascularization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe used SPSS version 29 (SPSS Inc., Chicago, IL, USA) and R version 3.2.4 (R Foundation for Statistical Computing, Vienna, Austria) for statistical analysis. Normality of data was assessed using the Shapiro-Wilk test. Results were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normally distributed data, and as median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range (25th to 75th percentiles) for non-normally distributed data. We used the 2-tailed Student\u003csup\u003e,\u003c/sup\u003es t-test for normally distributed data or Mann-Whitney U test for non-normally distributed data to evaluate differences between two groups. Categorical variables were compared using the Fisher exact test or χ\u003csup\u003e2\u003c/sup\u003e test. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. For the selection of prognostic model indicators, the Least Absolute Shrinkage and Selection Operator (LASSO) was first applied to all variables. The non-zero parameters identified by LASSO were then further examined using multivariate COX regression. Variables with a P-value less than 0.05 were subsequently included in the model. Hazard ratios (HRs) were calculated using Cox regression analyses with 95% confidence intervals (CIs) or p-values. Variables were depicted in a nomogram, and three distinct methods were employed to validate the efficacy of the mode \u003csup\u003e13\u003c/sup\u003e: The ROC curve was used to assess the model using the area under the curve (AUC). Discriminatory ability was analyzed using Harrell\u0026rsquo;s concordance index (C-index) and its corresponding 95% CI. Model calibration was represented by calibration plots predicting the probability of MACE risk at 2, 3, and 4 years versus the observed probability. Finally, individual risk score was derived from the nomogram for patients in the development cohort. The optimal cutoff point for each model was calculated to stratify patients into low-risk and high-risk categories, determined by the largest discrepancy in MACE risk between the two groups. We used the log-rank statistic to compare the incidence of MACEs between these two groups, employing the Kaplan-Meier method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection and Demographics\u003c/h2\u003e \u003cp\u003eA total of 1264 patients diagnosed with acute single-vessel myocardial infarction who underwent emergency PCI at the First Affiliated Hospital of Xinjiang Medical University from 2016 to 2021 were randomly divided into a development cohort and a validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median age of the study population was 58 years (range 48 to 68), with 82% being male. Among them, 53.96% were current smokers. The median resting blood pressure in the morning was 120/74 mmHg, and the median resting heart rate was 78 beats/min (range 70 to 88). The median follow-up duration was 30 months, ranging from 1 to 56 months, during which 170 cases (13.45%) of MACEs occurred. The demographic and event incidence data for both the development and validation cohorts were comparable, indicating successful matching (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Potential Predictors and Nomogram Construction\u003c/h2\u003e \u003cp\u003eTo enhance the identification of potential predictors of MACEs, LASSO regression was employed, incorporating all 23 baseline patient characteristics variables from the development cohort into the analysis. Eight indicators with nonzero parameters were identified, specifically Age, Sex, Smoking, Heart Rate, Hypertension, Diabetes Mellitus, Neutrophil Count and Cholesterol-to-Lymphocyte Ratio Index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequently, multivariate Cox regression analysis further narrowed down the list to four independent risk factors for MACEs: age (HR\u0026thinsp;=\u0026thinsp;1.050, 95% CI: 1.033 to 1.067, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), heart rate (HR\u0026thinsp;=\u0026thinsp;1.033, 95% CI: 1.022\u0026ndash;1.045, p\u0026thinsp;=\u0026thinsp;0.046), presence of diabetes mellitus (HR\u0026thinsp;=\u0026thinsp;1.487, 95% CI: 1.008\u0026ndash;2.195, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CLR index (HR\u0026thinsp;=\u0026thinsp;1.123, 95% CI: 1.022\u0026ndash;1.045, p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using these four variables, a nomogram model was constructed to predict the risk of MACEs at 2, 3, and 4 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Finally, we uploaded the nomogram generation online tool to the shinyapps. io platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adh-clr.shinyapps.io/dynnomapp/\u003c/span\u003e\u003cspan address=\"https://adh-clr.shinyapps.io/dynnomapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Clinicians can submit the 4 metrics in the full model to the appropriate text box for calculation of the risk of the patient long-term cardiovascular events via the web.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNomogram Performance Evaluation\u003c/h2\u003e \u003cp\u003eIn order to evaluate the efficiency and stability of the model, the 4 variables built in the nomogram model were analysed together, then the operating characteristic curves of the recipient for 2, 3 and 4 years had been presented, and the area under the curves was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the development cohort, the AUC for 2 years was 0.816. The AUC for 3 years stood at 0.812. For 4 years, the AUC was 0.751. In the validation cohort, the 2-year AUC was 0.852. The 3-year AUC was 0.773. The 4-year AUC was 0.806. Then, we use C-Index calculated from the calibration plots to observe the risk probability of the MACEs, in the development cohort the C-Index was 0.76,while in the validation cohort the C-Index was 0.79 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Lastly, we used the median of the risk scores to determine high risk and low risk individuals within the population. We analysed the occurrence of MACEs according to Kaplan-Meier analysis. In comparison between the two cohorts, we found that the high-risk group was more likely to develop MACEs than the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite significant advancements in the treatment for acute myocardial infarction, there remains a subset of patients who fail to fully mitigate adverse prognosis resulting from ventricular remodeling, even when administered standardized therapeutic interventions \u003csup\u003e14, 15\u003c/sup\u003e. Recently, the need for a practical long-term prognosis model has arisen for this subset of AMI patients. In this study, we constructed a prediction nomogram of the MACEs occurrence in 2 years, 3 years and 4 years of the patients who received emergency PCI treatments due to acute single-vessel myocardial infarction. Total cholesterol, CLR (a biomarker of lymphocyte count), along with several traditional clinical risk factors (age, diabetes, heart rate) were included in the model. According to the evaluation of the model efficiency, the validity of the nomogram built in the development cohort had been proved by the validation cohort, which suggested that our predictive model had good predictive ability and stability, and it could render new insights to the STEMI risk stratification thus to support the development of new strategies.\u003c/p\u003e \u003cp\u003eThe CLR index is a crucial parameter, reflecting the dual aspects of the body's cholesterol accumulation and lymphocyte-mediated inflammatory response \u003csup\u003e9, 10\u003c/sup\u003e. Central to the dynamic formation and progression of coronary atherosclerosis is the interplay between cholesterol accumulation and the involvement of lymphocytes in the inflammatory response \u003csup\u003e16\u003c/sup\u003e. Serum total cholesterol, a well-established indicator of cholesterol accumulation, is recognized as an independent cardiovascular risk factor \u003csup\u003e17, 18\u003c/sup\u003e. Cholesterol accumulation can lead to membrane injury, potentially contributing to cellular dysfunction and amplifying the body's response to external stressors \u003csup\u003e19, 20\u003c/sup\u003e. Through this mechanism, cholesterol plays a role in modulating the immune response, especially the activation of lymphocytes, thereby impacting serum lymphocyte counts \u003csup\u003e21, 22\u003c/sup\u003e. Prior research has highlighted a significant correlation between lymphocyte count and the prognosis of acute myocardial infarction patients. The process of lymphocyte antigen recognition, triggering their activation and interaction with antigen-presenting cells, is believed to be instrumental in this context \u003csup\u003e23\u003c/sup\u003e. These activation pathways are closely linked to changes in lipoproteins and autoimmune reactions during atherosclerosis \u003csup\u003e24\u003c/sup\u003e. Additionally, some studies have suggested that excessive cholesterol may intensify inflammation in atherosclerotic plaques by interacting with specific lymphocyte subsets \u003csup\u003e25\u003c/sup\u003e. Our study emphasizes a marked association between the CLR index and poor prognosis in patients undergoing emergency PCI for acute myocardial infarction, thereby establishing it as a reliable indicator for long-term prognosis. Importantly, both serum cholesterol and lymphocyte levels are commonly measured in clinical practice. The CLR index, more easily obtainable and accessible than other indicators such as Osteopontin, trimethylamine N-oxide, and hyperuricemia, as noted in existing studies, offers valuable insights for the timely and effective personalized treatment of patients \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, we noted that a history of diabetes emerged as a significant independent prognostic risk factor for patients, aligning with findings from previous research. Prior studies have consistently underlined the critical role of blood glucose levels in increasing the risk of stent restenosis, impacting both individuals with and without diabetes \u003csup\u003e29\u003c/sup\u003e. Notably, recent studies have demonstrated a marked improvement in long-term cardiovascular outcomes in acute myocardial infarction patients treated with oral Sodium-glucose cotransporter 2 inhibitors, compared to those in a control group, highlighting the potential advantages of such treatments in cardiovascular risk management \u003csup\u003e30\u003c/sup\u003e. Additionally, a growing body of evidence indicates that patients with diabetes experience an increase in mitochondrial reactive oxygen species (mtROS) production, significantly contributing to vascular endothelial cell dysfunction \u003csup\u003e31, 32\u003c/sup\u003e. This dysfunction can lead to intimal damage, promote platelet aggregation, and ultimately worsen vascular stenosis \u003csup\u003e33, 34\u003c/sup\u003e. Moreover, the increased production of reactive oxygen species, a consequence of elevated blood sugar levels, plays a crucial role in inducing oxidative stress, a fundamental element in the pathogenesis of cardiovascular diseases \u003csup\u003e35, 36\u003c/sup\u003e. Therefore, it is essential to refine therapeutic strategies for diabetic patients presenting with acute myocardial infarction. Additionally, advocating for active, long-term blood sugar control management in all patients, regardless of their diabetes status, is paramount. These strategies are vital in improving the prognosis and overall outcomes for individuals afflicted with acute myocardial infarction.\u003c/p\u003e \u003cp\u003eThis study's strengths are multifaceted. Foremost, the CLR index has been established as a validated prognostic tool for patients diagnosed with acute myocardial infarction. The rigorous internal validation process further enhances its credibility and applicability in clinical settings. Crucially, our inclusion of a large cohort comprising 1264 patients diagnosed with acute single-vessel myocardial infarction underscores the validity and robustness of our findings. In pursuit of precision, we meticulously excluded patients with severe multivessel lesions or significant left main coronary artery stenosis. Although complex lesions have the same pathophysiological mechanisms as single-vessel lesions in acute myocardial infarction, patients with complex lesions always need to implant stents for the second time, which led to data bias in the process of MACEs event statistics \u003csup\u003e37, 38\u003c/sup\u003e. The survival prognosis of patients with this type of complex acute myocardial infarction is worse than that of patients with single-vessel disease \u003csup\u003e39\u003c/sup\u003e. It is not practical for clinical guidance to analyze single-vessel lesions and complex lesions acute myocardial infarction, even if the results show significant statistical differences.\u003c/p\u003e \u003cp\u003eThere are some limitations in this study. First, the urgent nature of PCI in patients with acute myocardial infarction necessitated the postponement of cholesterol measurements until after the procedure. It is important to consider that both the PCI treatment and subsequent statin use may introduce a bias in the CLR index. Additionally, this study did not explore the relationship between changes in cholesterol levels and inflammation. For a more comprehensive analysis of patient prognosis following acute myocardial infarction, future research could benefit from incorporating multiple samplings. This approach would allow for a dynamic observation of the interplay among cholesterol reduction, inflammation, and the CLR index. Secondly, it is pertinent to note that this study is a retrospective analysis conducted at a single center. Although internal validation was conducted, there is a need for multi-center and large-sample prospective studies. Such studies would enable external validation and facilitate a comparison of the efficacy of our findings with existing prognostic models, thus providing a more comprehensive evaluation. Third, the predicted results of the nomogram remain the same over time, but in reality, the disease outcomes can vary with improvements in treatment, early detection, or changes in the natural course of the disease, which may lead to the performance of a nomogram inaccurate.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study has developed a long-term model for predicting the prognosis of patients with acute single-vessel myocardial infarction who received PCI treatment, and the efficiency and stability of this model have been verified. This model, which combines the CLR index with traditional clinical risk factors, has been shown by our data to serve as a reliable long-term risk biomarker. The predictive nomogram that we have developed can be easily applied to clinical practice to better support individual management for patients with acute myocardial infarction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our deepest gratitude to the patients and their families who participated in this study, without whom this research would not have been possible. Special thanks are extended to the staff at the First Affiliated Hospital of Xinjiang Medical University for their invaluable support and assistance in data collection and patient care. We also appreciate the insightful comments and suggestions from our peer reviewers, which significantly improved the manuscript. Lastly, we are grateful to our colleagues and team members for their contributions and collaboration in this research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China, grant number 82170345.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHT.Y, JK.L and X.X contributed to the conception and design of the work. HT.Y contributed to the acquisition, analysis, or interpretation of data for the work. JK.L drafted the manuscript. X.X critically revised the manuscript. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval Number: 20160317-03) and was registered in the Chinese Clinical Trial Registry Information Network (Trial Registration Number: NCT02737956). Due to the retrospective design of the study, the requirement for obtaining informed consent from eligible patients was waived by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual participant data that support the findings of this article, following the process of de-identification, the data supporting the findings of this article are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThrane PG, Olesen KKW, Thim T, et al. Mortality Trends After Primary Percutaneous Coronary Intervention for ST-Segment Elevation Myocardial Infarction. J Am Coll Cardiol. 2023;82(10):999\u0026ndash;1010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang HT, Xiu WJ, Zheng YY, et al. 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JAMA Cardiol. 2023;8(7):631\u0026ndash;639.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable.1\u003c/strong\u003e \u003cstrong\u003eBaseline patient characteristics and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emajor adverse cardiac events\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein the development and validation cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"857\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N=1\u003c/strong\u003e\u003cstrong\u003e264\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevelopment cohort (N=\u003c/strong\u003e\u003cstrong\u003e949\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation cohort (N=\u003c/strong\u003e\u003cstrong\u003e315\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eMale sex,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e1048 (82.91 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e794 (83.67 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e254 (80.63 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e58 (48, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e57 (48, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e59 (48, 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eHeart rate, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e78 (70, 88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e78 (70, 88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e77, (69, 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e120 (107, 131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e120 (108, 133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e117 (103, 130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eDiastolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e74 (67, 82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e74 (68, 82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e72 (64, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eCurrent smoking,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e682 (53.96 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e518 (54.58 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e164 (52.06 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eCurrent drinking,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e333 (26.34 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e252 (26.55 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e81 (25.71 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eHypertension,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e532 (42.09 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e413 (43.52 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e119 (37.78 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eDiabetes Mellitus,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e262 (20.73 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e200 (21.07 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e62 (19.68 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eNeutrophil count, 10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e4.83 (3.84, 6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e4.82 (3.69, 6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e4.86 (4.16, 5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eLymphocyte count, 10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e1.8 (1.41, 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e1.82 (1.33, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e1.76 (1.3, 2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eMonocyte count, 10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e0.56 (0.42, 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e0.57 (0.42, 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e0.54 (0.43, 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e141 (129, 152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e141 (129, 151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e141 (129, 154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eBlood platelet count, 10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e224 (182, 279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e225 (181, 279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e225 (175, 290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eUrea nitrogen, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e5.9 (4.8, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e5.9 (4.9, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e5.73 (4.69, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eCreatinine, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e77 (65.6, 87.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e77 (65.5, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e76.88 (65.8, 91.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eAspartate aminotransferase, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e22 (18, 28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e22 (18, 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e22.5 (17.5, 33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eAlanine aminotransferase, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e23.72 (16.65, 35.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e23.9 (17.2 35.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e23.12 (14.62, 35.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eHigh-density lipoprotein cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e0.91 (0.76, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e0.91 (0.77, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e0.89 (0.74, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eLow-density lipoprotein cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e2.35 (1.86,2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e2.39 (1.86, 2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e2.33 (1.91, 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e1.5 (1.05, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e1.52 (1.06, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e1.46 (1.05, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e3.85 (3.15, 4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e3.9 (3.23, 4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e3.56 (3.03, 4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eCholesterol-to-lymphocyte ratio (CLR) Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e2.1 (1.52, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e2.07 (1.52, 3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e2.12 (1.48, 3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003end point\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eMACEs,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e170 (13.45 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e124 (13.07 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e46 (14.6 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eAll-cause mortality,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e46 (3.64 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e37 ( 3.9 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e9 (2.86 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eRe-myocardial infarction,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e25 (1.98 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e20 (2.11 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e5 (1.59 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eIn-stent restenosis,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e20 (1.58 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e16 (1.69 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e4 (1.27 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.098130841121495%\"\u003e\n \u003cp\u003eRevascularization,\u0026nbsp;N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.457943925233646%\"\u003e\n \u003cp\u003e102 (8.07 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39252336448598%\"\u003e\n \u003cp\u003e82 (8.64 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.523364485981308%\"\u003e\n \u003cp\u003e20 (6.35 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.52803738317757%\" valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as median (interquartile range), mean \u0026plusmn; SD, or number (%).\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Major adverse cardiovascular events, Percutaneous coronary intervention, Prediction nomogram, ST-elevation myocardial infarction, cholesterol-to-lymphocyte ratio index.","lastPublishedDoi":"10.21203/rs.3.rs-3866952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3866952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to develop a predictive nomogram for long-term outcomes in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) for single-vessel disease, integrating the cholesterol-to-lymphocyte ratio (CLR) index with clinical data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom April 2016 to December 2021, 1264 patients with acute STEMI were enrolled. They were divided into development (949 patients) and validation (315 patients) cohorts. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified potential risk factors, and multivariate Cox regression determined independent risk factors for the nomogram. The model was transformed into a web-based calculator for ease of use. Its performance was evaluated using ROC curve analysis, calibration curves,and C-index. In addition, individual risk assessment based on the model is conducted.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe nomogram included age, diabetes, heart rate, and CLR index as variables. In the development cohort, ROC analysis yielded AUCs of 0.816, 0.812, and 0.751 for predicting major adverse cardiac events (MACEs) at 2, 3, and 4 years, respectively. In the validation cohort, the AUCs were 0.852, 0.773, and 0.806. The C-index was 0.76 in the development cohort and 0.79 in the validation cohort. Kaplan-Meier analysis indicated a higher likelihood of MACEs in the high-risk group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis predictive model, incorporating CLR index and electronic health record (EHR) data, reliably and accurately forecasts adverse cardiac events post-primary PCI in patients with acute STEMI and single-vessel disease, aiding in improved risk stratification and management.\u003c/p\u003e","manuscriptTitle":"Predicting long-term outcomes after primary PCI in Acute ST-segment elevation myocardial infarction patients with single-vessel disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:30:52","doi":"10.21203/rs.3.rs-3866952/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"213cd828-9a17-4196-a8a6-a30ba0761775","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-02T14:33:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 15:30:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3866952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3866952","identity":"rs-3866952","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}