Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach

Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach | 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 cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach Hamidreza Soleimani, Reza Nikfar, Sahand Siami, Farhad Shaker, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8058920/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background: Although ST Elevation Myocardial Infarction (STEMI) diagnosis and therapy have improved, high-risk categories like elderly persons still have a significant chance of MACE despite treatment. Objectives: This study attempts to construct a predictive nomogram for MACE incidence using clinical data from a STEMI registry. Methods: Tehran Heart Center's computerized record recognized all 65-year-old STEMI primary PCI patients consecutively. This retrospective study examined demographic, laboratory, clinical, and intra-procedural factors. Post-PCI univariate and multivariate analyses identified MACE risk variables. Decision curve analysis, ROC, and calibration plots validated predictive nomograms. R Studio and R used “tidyverse” and “rms” packages for all analyses. Results: The 1946 study included 70% training and 30% testing patients. Basic demographic and clinical variables were identical for both groups. The average follow-up was 17 months. 8 factors were selected for the nomogram after univariate and multivariate analysis: left-ventricular ejection fraction (LVEF), serum creatinine, hemoglobin, and fasting blood glucose levels, presence of valvular heart disease, post-PCI TIMI flow grade, diameter of the culprit lesion stent, and presence or absence of shock after PCI. The post-PCI MACE prediction AUC was 71%. Calibration plots showed that the nomogram model was well-calibrated and close to observed outcomes. Decision curve analysis also revealed that the model predicted MACE discriminatively. Conclusion: A nomogram successfully predicts MACE risk in older STEMI patients using laboratory, clinical, and procedural parameters. This algorithm may identify vulnerable high-risk patients for more aggressive preventative interventions. Clinical trial number: not applicable. MACE STEMI PCI Model Prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ischemic heart diseases (IHDs), including acute coronary syndrome (ACS), are the leading cause of death worldwide, accounting for approximately 9 million deaths in 2021 [ 1 ]. Advanced age is one of the most important predictors of ACS morbidity and mortality one year following hospitalization [ 2 , 3 ]. The vast majority of elderly patients are typically undertreated compared to younger patients following ACS. Evidence indicates that younger patients with ACS undergo emergency reperfusion three times more frequently than their older counterparts, and antithrombotic medications, diuretics, and statins are significantly underused after older patients are discharged [ 3 , 4 ]. Recently, in various countries, efforts have been made to decrease in-hospital mortality and improve the incidence of adverse outcomes following ACS by applying evidence-based ACS therapies, early invasive surgery, and efficient post-percutaneous coronary intervention (PCI) care [ 4 ]. Also, evaluation and resolution of comorbidities and geriatric syndromes, including frailty, altered mental status, and cognitive disorders, and incorporating them into risk assessment models such as the combined model of GRACE risk score and clinical frailty scale (CFS) in elderly patients, can dramatically reduce in-hospital and long-term mortality after ACS [ 5 – 8 ]. A plethora of risk scores have been developed to predict poor outcomes and mortality in patients who have had ACS. However, few risk assessment tools have gained extensive credibility and validation to measure the risk score in patients following ST-elevation myocardial infarction (STEMI). Moreover, most studies have built their risk prediction models without focusing on the elderly population specifically. Some studies suggest that, despite their advantages in calculating the risk score prior to coronary angiography, prediction models merely based on clinical variables may miss the anatomical parameters that can increase discrimination and calibration for predicting adverse events. Therefore, A combined score model of clinical and angiographic variables, such as the Clinical SYNTAX Score (CSS), can significantly boost the model’s discriminative ability [ 9 ]. Other recent cohort studies have concluded that a nomogram-based prediction tool that includes both clinical and anatomical factors can stratify patients based on risk association and improve clinical decision-making by predicting major adverse cardiovascular events (MACE) incidence following ACS in the elderly [ 10 ]. Since there are only a limited number of reliable and validated risk stratification tools assessing MACE probability in STEMI patients over 65, in this retrospective registry study, we aimed to develop a precise and actionable nomogram with good calibration and discriminative ability to accurately predict MACE incidence in these patients. Methods Study population This study included patients aged 65 years and above diagnosed with STEMI and undergoing primary PCI at Tehran Heart Center. Clinical, biochemical, and procedural data for this research were meticulously extracted from the institutional database, which contains detailed patient records spanning from 2015 to 2020. A total of 1,946 patients who met the inclusion criteria were identified. To maintain consistent outcome distribution, the dataset was stratified by MACE incidence and these patients were stratified into a 70:30 ratio for training and testing purposes, resulting in 1,362 patients in the training set and 584 patients in the testing set. Institutional Review Board (IRB) and ethics committee at THC approved this study (IR.TUMS.THC.REC.1399.045). This study was retrospective in nature, utilizing de-identified data from THC’s electronic registry, so an informed consent waiver was granted by the IRB. Biochemical tests Before emergency coronary angiography, blood samples were analyzed to assess fasting blood glucose (FBS), hemoglobin (Hb), and serum creatinine levels. Lipid profile measurements, including total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), the TG/HDL ratio, and the triglyceride-glucose (Tyg) index, were retrieved from the database. The Tyg index, a proxy for insulin resistance, was calculated using the formula: [(Fasting triglycerides (mg/dL) × Fasting glucose (mg/dL)) / 2]. Coronary angiography Upon hospital admission, all patients underwent coronary angiography performed by senior cardiologists following standard treatment protocols. Procedural details, including pre-procedure administration of guideline-recommended loading doses of Ticagrelor/Clopidogrel and Aspirin, were documented in the database. Following PCI, patients were prescribed standard treatment regimens, including moderate-intensity statins and modern antiplatelet therapies. Follow-up schedules and treatment adherence were recorded as part of the institutional database. Follow-up and outcomes Outcomes were monitored through clinic visits and phone interviews, with all relevant data systematically logged in the database. Major adverse cardiac events (MACEs) were defined as a composite outcome encompassing all-cause mortality, nonfatal myocardial infarction, and stroke. Statistical analysis Variables with less than 10% missing data were selected from the database, resulting in 38 clinical, biochemical, and procedural factors included in the analysis. Variables with > 10% missingness were excluded; these were primarily procedural factors (e.g., stent brand, inflation pressure, operator name) without established prognostic relevance. For all other variables (< 10% missingness), missing values were managed using multiple imputation by chained equations under a missing-at-random assumption. Statistical analysis was done using R Studio version 1.1.463 (Posit PBC, Boston, MA, United States), utilizing packages tidyverse [ 11 ], pROC, survival , and rms. Means and standard deviations (SDs) were presented for continuous variables with normal distributions and medians and interquartile ranges (IQRs) for continuous variables with skewed distributions. For the analysis of continuous variables with normal distributions, the t-test was utilized. For the analysis of continuous variables with nonnormal distributions, the Mann-Whitney U-test or the Kruskal-Wallis test was utilized. Analysis of categorical variables was carried out utilizing the x2 -test. In all comparisons, a p-value below 0.05 indicated statistical significance. In the training set, a comprehensive univariate analysis was performed in this step, ensuring that all variables that were independently associated with MACE incidence and were statistically significant were selected. All variables were analyzed through a multivariate model to evaluate their association with MACE, with significance determined by a p-value threshold of 0.05. The proportional hazards assumption was evaluated using Schoenfeld residuals and log-minus-log plots, with no significant violations observed. The Cox model was specified to include main effects only, as interaction terms were not formally modeled to preserve parsimony and avoid unstable estimates. These predictors were subsequently used to construct a predictive nomogram for estimating MACE risk. R software was utilized to construct the nomogram using the Regression Modeling Strategies ( rms ) package. Risk nomograms were analyzed utilizing receiver characteristic curves (ROC) to evaluate their ability to discriminate MACEs based on the calculation of area under the ROC curve [ 12 ]. Utilizing the rms package, calibration curves were plotted and calculated to evaluate the calibration of the MACEs after the construction of the ACS risk nomograms. Bootstrapping was carried out by repeated random sampling (1000 times) for internal validation of model accuracy. The resulting optimism-corrected estimates were then used for Decision curve analysis (DCA) to quantify the clinical efficacy of the nomograms based on their net benefit for different threshold probabilities [ 12 ]. Results Population characteristics A total of 1946 elderly STEMI patients who underwent PCI were included in this study, with a mean follow-up period of 17.68 ± 14.50 months. The mean age of this population was 73.38 ± 6.99 years, and 68% were male. This population was divided into a train set (70% of the total population, 1362 cases) and a test set (30% of the total population, 584 cases). These two groups were statistically comparable regarding all investigated demographic, laboratory, clinical, and procedural characteristics except for the door-to-needle interval, which was longer in the train set ( Supplemental Table 1 ). Among the total population, the overall incidence of MACE was 18.29%, with a comparable rate in the two train and test sets. Probable predictors of MACE in the elderly with STEMI A stepwise method using univariate and multivariate Cox regression was employed to determine the independent predictor of MACE in elderly STEMI cases undergoing PCI ( Supplemental Table 2 ). In multivariate model 8 factors were identified to be significantly independently associated with MACE incidence: LVEF (OR = 0.96, 95%-CI: 0.94–0.98), serum creatinine (OR = 1.41, 95%-CI: 1.23–2.33), hemoglobin (OR = 1.01, 95%-CI: 1-1.01), fasting blood glucose levels (OR = 0.88, 95%-CI: 0.81–0.95), presence of valvular heart disease (OR = 2.39, 95%-CI: 1.24–5.87), post-PCI TIMI flow grade < 2 (OR = 0.43, 95%-CI: 0.19-1), diameter of the stent placed in the culprit lesion (OR = 0.69, 95%-CI: 0.49–0.98), and the presence or absence of shock in the post-PCI setting (OR = 28.4, 95%-CI: 9.45–123). Additionally, the overall incidence of MACE in the study cohort was 18.3%. Construction of a nomogram predicting MACE following STEMI Using the results of multivariate analysis and employing the eight identified independent predictive factors, a nomogram was constructed to predict the risk of MACE following STEMI in the elderly undergoing PCI ( Central illustration ). Drawing a vertical line from the value of each predictive factor to the “points” line could determine its numerical points. The total point could be calculated by summing up all of these points, representing the patient risk for MACE (ranging from 0 to 100). To assess the probability of MACE incidence, a vertical line from the calculated total point on the “Total points” line could be drawn to the “Risk of MACE incidence” scale on the bottom of the nomogram. Nomogram validation and predictive performance To assess the discriminatory ability of our nomogram, receiver operating characteristic (ROC) analysis was performed, which illustrated an area under cover (AUC) of 0.71 (95% CI: 0.65–0.77) (Fig. 1 ). At the optimal cutoff determined by Youden’s index, the model achieved a sensitivity of 72%, specificity of 63%, positive predictive value of 30%, and negative predictive value of 91%. A decision curve analysis (DCA) was performed based on optimism-corrected estimates to determine the net benefit of our developed prognostic model for clinical decision-making, which demonstrated a threshold probability of 23 to 89% for MACE incidence following PCI in patients > 65 years of age (Fig. 2 ), which suggests a wide range of applications. Additionally, a calibration plot was also generated to investigate the similarity and concordance of our nomogram’s predicted probability with the observed probability. In addition, Kaplan-Meier analysis was performed to investigate the prognostic reliability of the nomogram's predicted risk of MACE. We stratified our patients into four risk groups according to the nomogram’s predicted risk (0-0.25 as low risk, 0.25–0.5 as intermediate risk, 0.5–0.75 as high risk, 0.75-1 as very high-risk group). The results of the Kaplan-Meier analysis have been illustrated in the training set (Fig. 3 -A), test set (Fig. 3 -B), and total population (Fig. 3 -C). According to our results, very high-risk patients (predicted risk of > 0.75) demonstrated a 13.12 (95%-CI: 7.09–24.25) and 16.74 (95%-CI: 8.73–32.1) hazard ratio of MACE incidence compared to all other cases (predicted risk of < 0.75) and low-risk cases (predicted risk of < 0.25), respectively. A similar trend of higher observed MACE incidence in groups with higher nomogram’s predicted risk was observed through all other possible inter-group comparisons ( Supplemental Table 3 ), which suggests the clinical reliability of our nomogram’s predicted risk of MACE incidence. A mean absolute error of 0.018 was calculated for our model’s calibration plot, indicating that, on average, the predicted probabilities deviate from the actual outcomes by 1.8% ( Supplemental Fig. 1 ). The precision recall curve indicates excellent precision across various recall levels with an AUC-PR of 0.9 ( Supplemental Fig. 2 ). Discussion Our study developed and validated a nomogram for predicting MACE in a cohort of 1,946 elderly patients who underwent primary PCI. The nomogram, based on 8 significant predictors, was assessed for its discriminatory ability using ROC analysis, which yielded an AUC of 0.71. Kaplan-Meier analysis further evaluated its prognostic reliability by stratifying patients into four risk groups based on predicted MACE risk. This analysis, illustrated in both the train and test sets, revealed that very high-risk patients (predicted risk > 0.75) had significantly higher hazard ratios for MACE compared to lower-risk groups, affirming the nomogram's clinical reliability. Effective prognostic information is often obtained through scoring systems. The TIMI risk score is a straightforward tool originally designed to predict 30-day mortality in STEMI patients treated with fibrinolytics, and it has also proven effective for predicting in-hospital mortality in STEMI patients undergoing primary PCI [ 13 , 14 ]. The Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications (CADILLAC) risk score was developed to predict both 30-day and one-year mortality in STEMI and NSTEMI patients following primary PCI [ 15 ]. Compared with the mentioned and other traditional risk scores, including TIMI, PAMI, CADILLAC, and GRACE, which offer a modest discrimination in both the general STEMI (AUCs ranging from 0.68 to 0.70) [ 16 ] and elderly population, with an AUC of 0.70 and 0.69 for in-hospital and post-discharge mortality, respectively [ 17 ] our nomogram model demonstrates improved predictive power in the patient population aged ≥ 65 years, a population being rarely investigated in the existing literature. [ 18 , 19 ] The incorporation of the most robust clinical predictors for the elderly population following PCI, including age, diabetes, prior MI, LVEF, STEMI presentation, and eGFR, which were identified by Jalali et al.'s meta-analysis [ 20 ], alongside real-time procedural characteristics like post-PCI TIMI flow, stent diameter, and post-PCI shock, into our nomogram, has led to the development of a more bedside-friendly and homogeneous risk assessment tool for clinicians and patients, as other machine learning models such as iPROMPT [ 21 ] and XGBoost with a large sample size [ 22 ], rely more on heterogeneous predictors and “black-box” algorithms while showing more discriminative ability. Zhu et al. [ 18 ] have developed a similar nomogram in the elderly population post-PCI, but lacked the distinction between STEMI and NSTEMI populations, underestimating the possible clinical discernibility between these two. Although more discriminative ability was achieved by Zhu’s model with an AUC of 0.787, the high reliance of this model on biomarkers, overlooking the mechanical and hemodynamic dimensions of risk, which are addressed by procedural variables, and a smaller sample size of nearly 1000 patients compared with our nomogram, demonstrates the high clinical utility of the current model. Unlike our model, which addressed the risk of MACE following PCI in a specific age group, Yao et al. [ 23 ] developed another risk-prediction nomogram model, incorporating six variables, for 1-year readmission due to MACE in the general STEMI patients’ population with a considerably smaller sample size, lowering the generalizability of this tool in the clinical environment. Our model addresses some methodological shortcomings mentioned in a study that systematically explored the prediction models for MACE following PCI. With the inclusion of nearly 2,000 patients aged ≥ 65, we minimized heterogeneity and increased applicability for the elderly population while providing an events-per-variable (EPV) above the ≥ 20 threshold. The incorporation of real-time procedural characteristics into our model alongside the implementation of multivariable analysis addressed the lack of multivariable modeling for the selection of MACE predictors and the underestimation of procedural variables for risk stratification, as mentioned by Deng et al. [ 19 ] Moreover, we presented both calibration plots and Hosmer-Lemeshow statistics, directly addressing what 74% of models failed to report in Deng’s review. [ 19 ] We have enabled the risk stratification of elderly patients into four distinct risk groups based on the model’s predicted risk, which can inform individual care by directing high-risk patients to more intensive treatment plans, including earlier referral to specialized cardiology and heart failure clinics as well as extended monitoring and more intensive post-discharge follow-up. DCA is conducted to evaluate the clinical value of a prognostic model by quantifying the net benefit across a range of risk thresholds for developing a certain outcome, at which a clinician might choose to intervene. In our model, DCA delivers a positive net clinical benefit in a broad range of risk thresholds (23–89%), which translates into the applicability of the current model for both clinicians who only treat extremely high-risk patients and those who approach patients more conservatively in real-world settings. Chen et al.’s iPROMPT [ 21 ] shows a positive net benefit in a much narrower threshold range of 30–60% and Zhu’s model [ 18 ] demonstrates net benefit only when the threshold exceeds 15% which does not clarify how their net gain changes beyond moderate thresholds. Hamilton et al. [ 22 ] did not include a DCA, leaving clinicians and patients uncertain about whether this model leads to better clinical decision support in real-world settings. Fang et al.’s [ 24 ] DCA showed net benefit in a wide threshold range of 10–99% in their model, but included only 466 STEMI patients. Importantly, although we have not yet implemented our nomogram in the institution’s electronic medical record (EMR), the model is designed for integration into EMR and real-time use at the bedside. Its variables are readily available after PCI, and its point-based graphic format supports bedside decision-making and shared discussions with patients and families. This implementation plan reinforces the model’s potential utility for cardiologists and explains how we envision its applicability in daily care alongside its direct benefit to readers and clinicians. Our nomogram was constructed based on eight key factors identified as independent predictors of MACE in elderly STEMI patients undergoing PCI. These factors include LVEF, serum creatinine, hemoglobin, FBS, presence of VHD, post-PCI TIMI flow grade less than 2, diameter of the stent placed in the culprit lesion, and the presence or absence of shock in the post-PCI setting. LVEF, as a critical measure of cardiac function, is particularly important in the context of STEMI, as it helps to assess the severity of cardiac damage and guides treatment decisions. Previous studies have shown that low LVEF is associated with higher rates of MACE following STEMI [ 25 – 27 ]. VHD is both a risk and a complication of ACS, which is associated with a worse prognosis [ 28 , 29 ]. Our results align with the study of Hasdai et al., in which STEMI patients with pre-existing VHD had a worse prognosis compared to those without VHD [ 30 ]. Our nomogram incorporates easily obtainable blood test parameters, including creatinine, FBS, and baseline hemoglobin levels. Various studies have shown that higher creatinine and worse kidney function are associated with higher rates of MACE after acute MI [ 31 , 32 ]. Many patients with elevated creatinine levels have comorbid conditions such as diabetes, hypertension, and chronic kidney disease (CKD), which independently contribute to worse outcomes in STEMI. Moreover, renal impairment can exacerbate the inflammatory response during MI [ 33 ]. Additionally, renal dysfunction can lead to fluid overload and hypertension, further straining the heart during an acute event [ 34 ]. Furthermore, studies have shown that stress hyperglycemia is significantly associated with an increased risk of mortality in STEMI patients treated with PCI, regardless of diabetic status [ 35 , 36 ]. Stress hyperglycemia triggers the production of inflammatory factors, which can exacerbate atherosclerosis through various intracellular pathways [ 37 ]. Additionally, stress hyperglycemia increases thrombogenic activity, leading to a hypercoagulable state [ 38 ]. Anemia is another strong and independent predictor of MACE in patients with ACS, showing a notable dose-response relationship [ 39 ]. It significantly reduces oxygen delivery to the myocardium beyond coronary stenoses and increases myocardial oxygen demand by requiring a higher stroke volume and heart rate to ensure sufficient systemic oxygen delivery [ 40 ]. These factors may explain the progressively poorer outcomes seen in ACS patients with lower baseline hemoglobin levels. In the study of Liu et al., a higher hemoglobin level in the anemic group was associated with a decreased risk of 1-year mortality; however, a higher hemoglobin level in the erythrocytosis group was linked to an increased risk of 1-year mortality [ 41 ]. It is well established that peri-procedural complications during PCI can significantly impact mortality [ 42 ]. In our study, we observed that the characteristics of PCI and post-PCI events play a crucial role in patient outcomes. Notably, post-PCI TIMI flow, the occurrence of post-PCI shock, and the diameter of the PCI stent were significantly meaningful factors. Previous studies have shown that achieving TIMI flow grade 3 following PCI is linked to better short-term and long-term outcomes in these patients, including a decrease in MACE and cardiac mortality [ 43 , 44 ]. Although peri-procedural cardiogenic shock is reported to be low, it was linked to a significantly higher mortality [ 45 ]. Likely, in our study, post-PCI shock was the most powerful predictor of MACE following PCI. Stent diameter was another key factor in predicting MACE in this population. The benefits of stent oversizing on procedural and clinical outcomes have been documented. Specifically, small vessels treated with smaller stents experienced more adverse events [ 46 ]. Shugman et al. reported that deploying bare-metal stents in STEMI patients with infarct-related arteries measuring 3.5 mm or larger was associated with low rates of target vessel revascularization [ 47 ]. This indicates that choosing larger stents could enhance long-term outcomes. Our study has several strengths, including the use of a large cohort of 1,946 elderly STEMI patients, which enhances the robustness and generalizability of the findings. The rigorous analytical approach, employing both univariate and multivariate methods, allowed for the accurate identification of significant predictors of MACE. The nomogram was validated with an AUC of 0.71, demonstrating its effective discriminatory ability across train and test cohorts. Additionally, the inclusion of key clinical factors such as LVEF and post-PCI TIMI flow underscores its practical relevance. The DCA further confirmed the nomogram’s broad applicability and net benefit for clinical decision-making. However, the study has limitations such as the potential lack of generalizability due to being conducted in a single center, a follow-up period that may not capture long-term outcomes, and variability in some data points between cohorts. Other relevant factors not included in the nomogram might also influence MACE risk stratification. Limitation There are several important limitations to consider. Firstly, it was conducted at a single center, which may influence the generalizability of the findings to broader populations, as our results may not fully represent diverse patient demographics or different clinical settings. Additionally, the sample size of fewer than 2,000 participants, while adequate for preliminary findings, suggests that a larger cohort could enhance statistical power and improve the detection of significant differences or associations. The absence of external validation with a larger dataset indicates an opportunity for future research to strengthen the applicability of our results across various populations. Additionally, some clinically relevant parameters were not available in our dataset. These include details of antiplatelet therapy, anatomical vessel involvement (e.g., single- vs. multi-vessel disease), and post-discharge data such as attendance at cardiac rehabilitation or adherence to guideline-directed medical therapy (GDMT). While these variables are known to influence long-term outcomes, they could not be assessed in the current study. However, our model incorporates several intra-procedural predictors, including post-PCI TIMI flow, stent diameter, and post-PCI shock, which offer insight into procedural success and clinical severity. Notably, a recent systematic review of PCI-related MACE prediction models [ 19 ] identified these procedural variables as underexplored components of existing models. The inclusion of such factors in our nomogram provides added value and reflects an effort to address this methodological gap. Importantly, we recognize that important clinical parameters, including echocardiography and electrocardiogram, were not included in this investigation, and future studies that incorporate these factors may provide a more comprehensive understanding of the outcomes. Although the model demonstrated satisfactory discrimination (an AUC of 0.71, sensitivity of 72%, specificity of 63%), further improvement may be achievable through external validation in larger multicenter cohorts, incorporation of additional anatomical and post-discharge predictors (e.g., multi-vessel disease, adherence to guideline-directed medical therapy), and the application of rigorously validated machine-learning approaches. In addition, our Cox model was specified to include only main effects. Although this approach prioritizes interpretability and stability, it assumes no effect modification between predictors. Evaluation of clinically plausible interactions in larger or external datasets is an important direction for future research. Lastly, some study limitations, including the lack of external validation in multicenter studies and standardization across different systems, are potential barriers to the implementation of our nomogram model into the EMR. Assessment of clinician and care team acceptance, evaluation of workflow impact, and feasibility testing with medico-legal considerations will enable the integration of this nomogram into the EMR and real-time bedside settings. We will focus on pursuing each step to underline the practical implications of clinical applicability and to ensure the integration of this model into the EMR. Conclusion In conclusion, our nomogram, validated in 1,946 elderly STEMI patients undergoing PCI, showed an AUC of 0.71 and effectively predicted MACE risk. It integrates eight key factors and demonstrated strong clinical utility, with high-risk patients having significantly higher MACE rates. This tool offers valuable insights for personalized risk assessment and management in the elderly population with STEMI. Perspectives: Clinical Competencies: This study contributes primarily to the domains of Medical Knowledge, Patient Care, and Practice-Based Learning and Improvement. By developing and validating a predictive nomogram tailored to elderly patients with STEMI undergoing primary PCI, the work strengthens clinicians' ability to identify individuals at elevated risk of post-procedural adverse events. It promotes the application of evidence-based decision-making for a high-risk. The model leverages routinely collected clinical and procedural data, enhancing bedside applicability and facilitating more personalized post-PCI management. This supports quality improvement efforts by aligning individualized care with system-wide strategies for outcome monitoring. Translational Outlook: This study addresses a key translational gap by introducing a risk prediction model developed specifically for elderly patients with STEM, a population that has not been widely studied in isolation for dedicated post-PCI prognostic modeling. Most existing risk scores are derived from mixed-age cohorts, limiting their calibration and clinical relevance in older adults. By using readily available clinical and procedural variables, this model enables real-time application at the point of care without reliance on advanced diagnostics or long-term follow-up data. Future directions include external validation across broader populations, digital integration into clinical decision support systems, and incorporation of additional parameters such as anatomical burden and post-discharge adherence to optimize its performance and clinical utility. Abbreviations and Acronyms: ACS acute coronary syndrome IHD Ischemic heart diseases LVEF left-ventricular ejection fraction PCI percutaneous coronary intervention MACE major adverse cardiovascular events DCA Decision curve analysis FBS assess fasting blood glucose TIMI thrombolysis in myocardial infarction Declarations This study presents a validated nomogram predicting post-PCI major adverse cardiovascular events (MACE) in patients ≥65 with STEMI. Using 8 clinical & procedural variables (LVEF, creatinine, Hb, FBS, VHD, TIMI flow, stent diameter, post-PCI shock), the model stratifies risk to guide post-discharge care. Ethics approval and consent to participate This study was approved by the Institutional Review Board and Ethics Committee of Tehran Heart Center, Tehran University of Medical Sciences (IR.TUMS.THC.REC.1399.045). The study was conducted in accordance with the principles of the Declaration of Helsinki. Because this research used retrospective and fully de-identified registry data, the requirement for informed consent was waived by the institutional IRB. Consent for publication Figures are original and unpublished before. Competing interests The authors declare no competing interests. Disclosure: Dr. Nanna reports unrelated current research support from the American College of Cardiology Foundation supported by the George F. and Ann Harris Bellows Foundation, the Patient-Centered Outcomes Research Institute (PCORI), the Yale Claude D. Pepper Older Americans Independence Center (P30AG021342), and the National Institute on Aging (K76AG088428) as well as consulting fees from Heartflow, Merck, and Novo Nordisk. The remaining authors have nothing to disclose. Funding None. Author Contribution Hamidreza Soleimani conducted model training, along with manuscript revision, editing, and final draft preparation. Reza Nikfar participated in manuscript composition, revising, and editing the manuscript. Sahand Siami participated in manuscript composition and final draft preparation. Farhad Shaker was involved in manuscript writing and table preparation. Parisa Fallahtafti was involved in manuscript writing. Mehdi Mehrani performed critical revisions of the manuscript for important intellectual content and contributed to drafting the last version. Yaser Jenab executed essential edits of the manuscript for significant intellectual substance and assisted in composing the final edition. Adrian V. Hernandez executed essential edits of the manuscript for significant intellectual substance and assisted in composing the final edition. Diaa Hakim performed critical revisions of the manuscript for important intellectual content and contributed to drafting the last version. Michael G. Nanna conducted significant revisions to the manuscript, enhancing its intellectual substance, and assisted in composing the final edition. Sajjad Hosseini conducted essential modifications of the manuscript for significant intellectual substance and assisted in composing the final edition. Kaveh Hosseini provided the study concept and design. Acknowledgments Not applicable Data Availability The datasets used in the current study are available upon reasonable request. References Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021). Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2024. Barnett K, Mercer SW, Norbury M, Wattet alB G, Guthrie, et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43. https://doi.org/10.1016/s0140-6736(12)60240-2 . Gudnadottir GS, Gudnason T, Wilhelmson K, Ravn-Fischer A. Multimorbidity and Readmissions in Older People with Acute Coronary Syndromes. Cardiology. 2022;147(2):121–32. https://doi.org/10.1159/000522016 . Gale CP, Cattle BA, Woolston A, Baxteret PD, West alRM. Resolving inequalities in care? Reduced mortality in the elderly after acute coronary syndromes. The Myocardial Ischaemia National Audit Project 2003–2010. Eur Heart J. 2012;33(5):630–9. https://doi.org/10.1093/eurheartj/ehr381 . Hsieh, T.-H., J.-D. Wang, and L.-M. Tsai, 2012. Improving in-hospital mortality in elderly patients after acute coronary syndrome—A nationwide analysis of 97,220 patients in Taiwan during 2004–2008. International Journal of Cardiology, 155(1): pp. 149–154. https://doi.org/10.1016/j.ijcard.2011.10.009. El Amrawy A, Almaghraby A, Abd Elnabi M, Wagdy S. 2022. Pattern of cardiovascular risk factors and outcomes of patients older than 80 years and presenting with acute coronary syndromes. European Heart Journal, 43(Supplement_2). https://doi.org/10.1093/eurheartj/ehac544.2547 Fox KA, Fitzgerald G, Puymirat E, Huanget alF W, Anderson, et al. Should patients with acute coronary disease be stratified for management according to their risk? Derivation, external validation and outcomes using the updated GRACE risk score. BMJ Open. 2014;4(2):e004425. https://doi.org/10.1136/bmjopen-2013-004425 . Anand A, Cudmore S, Robertson S, Stephenet J, Denvir alMA. Frailty assessment and risk prediction by GRACE score in older patients with acute myocardial infarction. BMC Geriatr. 2020;20(1):102. https://doi.org/10.1186/s12877-020-1500-9 . Vassalli G, d'Angeli I, Scherff F, Sürderet D, Pedrazzini alGB. Comparison of clinical and angiographic prognostic risk scores in elderly patients presenting with acute coronary syndrome and referred for percutaneous coronary intervention. Swiss Med Wkly. 2015;145:w14049. https://doi.org/10.4414/smw.2015.14049 . Wang J, Wu X, Sun J, Xuet alH T, Jiang, et al. Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment. Front Cardiovasc Med. 2022;9. https://doi.org/10.3389/fcvm.2022.1053470 . Wickham H, Averick M, Bryan J, Changet alH W, Yutani, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686. https://doi.org/10.21105/joss.01686 . Lätti S, Niinivehmas S, Pentikäinen OT. 2016. Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization. J Cheminform, 8(1): p. 45. https://doi.org/10.1186/s13321-016-0158-y Morrow DA, Antman EM, Charlesworth A, Cairnset alE R, Braunwald, et al. TIMI risk score for ST-elevation myocardial infarction: A convenient, bedside, clinical score for risk assessment at presentation: An intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation. 2000;102(17):2031–7. https://doi.org/10.1161/01.cir.102.17.2031 . Morrow DA, Antman EM, Parsons L, de Lemoset alE JA, Braunwald, et al. Application of the TIMI risk score for ST-elevation MI in the National Registry of Myocardial Infarction 3. JAMA. 2001;286(11):1356–9. https://doi.org/10.1001/jama.286.11.1356 . Cox DA, Stone GW, Grines CL, Stuckeyet T, Griffin alJJ. Comparative early and late outcomes after primary percutaneous coronary intervention in ST-segment elevation and non-ST-segment elevation acute myocardial infarction (from the CADILLAC trial). Am J Cardiol. 2006;98(3):331–7. https://doi.org/10.1016/j.amjcard.2006.01.102 . Kumar R, Safdar U, Yaqoob N, Khanet alM SF, Karim, et al. Assessment of the prognostic performance of TIMI, PAMI, CADILLAC and GRACE scores for short-term major adverse cardiovascular events in patients undergoing emergent percutaneous revascularisation: a prospective observational study. BMJ Open. 2025;15(3):e091028. https://doi.org/10.1136/bmjopen-2024-091028 . Furnaz S, Karim M, Ashraf T, Aliet S, Siddiqi alTJ. Performance of the TIMI risk score in predicting mortality after primary percutaneous coronary intervention in elderly women: Results from a developing country. PLoS ONE. 2019;14(7):e0220289. https://doi.org/10.1371/journal.pone.0220289 . Zhu X-Y, Jiang Z-M, Li X, Su F-F, Tian J-W. Establishment and validation of post-PCI nomogram in elderly patients with acute coronary syndromes. Front Cardiovasc Med. 2025;12–2025. https://doi.org/10.3389/fcvm.2025.1529476 . Deng W, Wang D, Wan Y, Laiet alX S, Wang, et al. Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review. Front Cardiovasc Med. 2024;10–2023. https://doi.org/10.3389/fcvm.2023.1287434 . Jalali A, Hassanzadeh A, Najafi MS, Nayebiradet alA S, Shafiee, et al. Predictors of major adverse cardiac and cerebrovascular events after percutaneous coronary intervention in older adults: a systematic review and meta-analysis. BMC Geriatr. 2024;24(1):337. https://doi.org/10.1186/s12877-024-04896-4 . Chen W, Tan X, Du X, Liet alJ Q, Du, et al. Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance. Front Cardiovasc Med. 2023;10–2023. https://doi.org/10.3389/fcvm.2023.1181424 . Hamilton DE, Albright J, Seth M, Painteret I, Gurm alHS. Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions. Eur Heart J. 2024;45(8):601–9. https://doi.org/10.1093/eurheartj/ehad836 . Yao W, Li J. Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI. Clin Appl Thromb Hemost. 2022;28:10760296221137847. https://doi.org/10.1177/10760296221137847 . Fang C, Chen Z, Zhang J, Jin X, Yang M. Construction and evaluation of nomogram model for individualized prediction of risk of major adverse cardiovascular events during hospitalization after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction. Front Cardiovasc Med. 2022;9–2022. https://doi.org/10.3389/fcvm.2022.1050785 . Gavara J, Marcos-Garces V, Rios-Navarro C, Lopez-Lereuet alV MP, Bodi, et al. Sequential cardiovascular magnetic resonance assessment of left ventricular ejection fraction for prediction of subsequent events in a large multicenter STEMI registry. Eur Heart J - Cardiovasc Imaging. 2021;22(Supplement2). https://doi.org/10.1093/ehjci/jeab090.042 . p. jeab090.042. Ng VG, Lansky AJ, Meller S, Witzenbichleret B, Stone alGW. The prognostic importance of left ventricular function in patients with ST-segment elevation myocardial infarction: the HORIZONS-AMI trial. Eur Heart J Acute Cardiovasc Care. 2014;3(1):67–77. https://doi.org/10.1177/2048872613507149 . Halkin A, Stone GW, Dixon SR, Grineset CL, Lansky alAJ. Impact and determinants of left ventricular function in patients undergoing primary percutaneous coronary intervention in acute myocardial infarction. Am J Cardiol. 2005;96(3):325–31. https://doi.org/10.1016/j.amjcard.2005.03.069 . Crimi G, Montalto C, Ferri LA, Piattiet L, alS, De Servi. Clinical Impact of Valvular Heart Disease in Elderly Patients Admitted for Acute Coronary Syndrome: Insights From the Elderly-ACS 2 Study. Can J Cardiol. 2020;36(7):1104–11. https://doi.org/https://doi.org/10.1016/j.cjca.2019.11.014 . Singh GK, van der Bijl P, Goedemans L, Vollemaet alV EM, Delgado, et al. Prevalence of Aortic Valve Stenosis in Patients With ST-Segment Elevation Myocardial Infarction and Effect on Long-Term Outcome. Am J Cardiol. 2021;153:30–5. https://doi.org/10.1016/j.amjcard.2021.05.012 . Hasdai D, Lev EI, Behar S, Boykoet alA V, Battler, et al. Acute coronary syndromes in patients with pre-existing moderate to severe valvular disease of the heart: lessons from the Euro-Heart Survey of acute coronary syndromes. Eur Heart J. 2003;24(7):623–9. https://doi.org/10.1016/S0195-668X(02)00742-X . Islam A, Munwar S, Talukder S, Rezaet alA A, Karim, et al. Impact of Baseline Admission Serum Creatinine Level in ST Segment Elevated Myocardial Infarction (STEMI) Patient Undergoing Primary PCI: An Important Predictor of in-hospital and 12-month Survival Outcome. Cardiovasc J. 2020;12:135–42. https://doi.org/10.3329/cardio.v12i2.47991 . Seyfarth M, Kastrati A, Mann JF, Ndrepepaet alA G, Schömig, et al. Prognostic value of kidney function in patients with ST-elevation and non-ST-elevation acute myocardial infarction treated with percutaneous coronary intervention. Am J Kidney Dis. 2009;54(5):830–9. https://doi.org/10.1053/j.ajkd.2009.04.031 . Jankowski J, Floege J, Fliser D, Böhm M, Marx N. Cardiovascular Disease Chronic Kidney Disease Circulation. 2021;143(11):1157–72. https://doi.org/10.1161/CIRCULATIONAHA.120.050686 . Sarnak MJ, Levey AS, Schoolwerth AC, Coreshet J, Wilson alPW. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Hypertension. 2003;42(5):1050–65. https://doi.org/10.1161/01.HYP.0000102971.85504.7c . Wei Q-C, Chen Y-w, Gao Q-Y, Renet alJ K-D, Jiang, et al. Association of stress hyperglycemia with clinical outcomes in patients with ST-elevation myocardial infarction undergoing percutaneous coronary intervention: a cohort study. Cardiovasc Diabetol. 2023;22(1):85. https://doi.org/10.1186/s12933-023-01812-9 . Cinar H, Avci A, Gulen M, Avciet alS BS, Satar, et al. Does stress hyperglycemia affect mortality? Acute myocardial infarction – case control study. Archives Med Sci – Atherosclerotic Dis. 2019;4(1):201–7. https://doi.org/10.5114/amsad.2019.87303 . Stentz FB, Umpierrez GE, Cuervo R, Kitabchi AE. Proinflammatory cytokines, markers of cardiovascular risks, oxidative stress, and lipid peroxidation in patients with hyperglycemic crises. Diabetes. 2004;53(8):2079–86. https://doi.org/10.2337/diabetes.53.8.2079 . Nieuwdorp M, van Haeften TW, Gouverneur MC, Mooijet HL, Stroes alES. Loss of endothelial glycocalyx during acute hyperglycemia coincides with endothelial dysfunction and coagulation activation in vivo. Diabetes. 2006;55(2):480–6. https://doi.org/10.2337/diabetes.55.02.06.db05-1103 . Sabatine MS, Morrow DA, Giugliano RP, Burtonet alE PBJ, Braunwald, et al. Association of Hemoglobin Levels With Clinical Outcomes in Acute Coronary Syndromes. Circulation. 2005;111(16):2042–9. https://doi.org/10.1161/01.CIR.0000162477.70955.5F . Most AS, Ruocco NA Jr., Gewirtz H. Effect of a reduction in blood viscosity on maximal myocardial oxygen delivery distal to a moderate coronary stenosis. Circulation. 1986;74(5):1085–92. https://doi.org/10.1161/01.cir.74.5.1085 . Liu CW, Liao PC, Chen KC, Hsuet JC, Wu alYW. Baseline Hemoglobin Levels Associated with One-Year Mortality in ST-Segment Elevation Myocardial Infarction Patients. Acta Cardiol Sin. 2016;32(6):656–66. https://doi.org/10.6515/acs20160106a . Kinnaird T, Anderson R, Ossei-Gerning N, Cockburnet J, Mamas alMA. Coronary Perforation Complicating Percutaneous Coronary Intervention in Patients With a History of Coronary Artery Bypass Surgery: An Analysis of 309 Perforation Cases From the British Cardiovascular Intervention Society Database. Circ Cardiovasc Interv. 2017;10(9). https://doi.org/10.1161/circinterventions.117.005581 . Caixeta AM, Lansky AJ, Mehran R, Breneret SJ, Stone alGW. Predictors of suboptimal TIMI flow after primary angioplasty for acute myocardial infarction: results from the HORIZONS-AMI trial. EuroIntervention. 2013;9(2):220–7. https://doi.org/10.4244/EIJV9I2A37 . Kammler J, Kypta A, Hofmann R, Kerschneret alF K, Leisch, et al. TIMI 3 flow after primary angioplasty is an important predictor for outcome in patients with acute myocardial infarction. Clin Res Cardiol. 2009;98(3):165–70. https://doi.org/10.1007/s00392-008-0735-9 . Weipert KF, Bauer T, Nef HM, Hochadelet M, Hamm alCW. Incidence and outcome of peri-procedural cardiogenic shock: results from the international Euro Heart Survey PCI registry. Eur Heart J Acute Cardiovasc Care. 2020;9(2):120–7. https://doi.org/10.1177/2048872618822460 . Kitahara H, Okada K, Kimura T, Yocket alY PG, Honda, et al. Impact of Stent Size Selection on Acute and Long-Term Outcomes After Drug-Eluting Stent Implantation in De Novo Coronary Lesions. Circ Cardiovasc Interv. 2017;10(10). https://doi.org/10.1161/circinterventions.116.004795 . Shugman IM, Hee L, Mussap CJ, Diuet P, French alJK. Bare-metal stenting of large coronary arteries in ST-elevation myocardial infarction is associated with low rates of target vessel revascularization. Am Heart J. 2013;165(4):591–9. https://doi.org/https://doi.org/10.1016/j.ahj.2012.12.023 . Additional Declarations No competing interests reported. Supplementary Files SupplementalAppendix..docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 01 Jan, 2026 Reviewers agreed at journal 01 Jan, 2026 Reviews received at journal 14 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Editor invited by journal 28 Nov, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 27 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8058920","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560089495,"identity":"d03c99b0-4d09-4938-a0f1-8ad3dd01b104","order_by":0,"name":"Hamidreza Soleimani","email":"","orcid":"","institution":"Imam Khomeini Hospital Complex Tehran University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Soleimani","suffix":""},{"id":560089496,"identity":"3f8a63e9-b4fa-44ef-aec0-f5c0259f5567","order_by":1,"name":"Reza Nikfar","email":"","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Nikfar","suffix":""},{"id":560089497,"identity":"65afa7da-fba0-4f42-aa10-4add62ffdeff","order_by":2,"name":"Sahand Siami","email":"","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sahand","middleName":"","lastName":"Siami","suffix":""},{"id":560089498,"identity":"97d45b93-42a5-4a01-8830-f5e7f1faa61b","order_by":3,"name":"Farhad Shaker","email":"","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Shaker","suffix":""},{"id":560089499,"identity":"bc9ceaa6-431d-45c8-92bd-58cef294568a","order_by":4,"name":"Parisa Fallahtafti","email":"","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"","lastName":"Fallahtafti","suffix":""},{"id":560089500,"identity":"fe555973-4a02-4f80-9aa4-529b0179c666","order_by":5,"name":"Mehdi Mehrani","email":"","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Mehrani","suffix":""},{"id":560089501,"identity":"c7e02850-0c63-4c68-89c1-a8eb8a41327c","order_by":6,"name":"Yaser Jenab","email":"","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yaser","middleName":"","lastName":"Jenab","suffix":""},{"id":560089503,"identity":"5732c0da-6d19-404e-8948-53770b92ba03","order_by":7,"name":"Sajjad Hosseini","email":"","orcid":"","institution":"Imam Khomeini Hospital Complex Tehran University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Sajjad","middleName":"","lastName":"Hosseini","suffix":""},{"id":560089508,"identity":"7342df41-2576-472b-b457-5357d06a5003","order_by":8,"name":"Adrian V. Hernandez","email":"","orcid":"","institution":"University of Connecticut School of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Adrian","middleName":"V.","lastName":"Hernandez","suffix":""},{"id":560089511,"identity":"2e31edd7-fa14-4554-9536-43aa04f51853","order_by":9,"name":"Diaa Hakim","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Diaa","middleName":"","lastName":"Hakim","suffix":""},{"id":560089512,"identity":"0ab7fb8d-ceaa-45fb-8c89-e5d066dd82e1","order_by":10,"name":"Michael G. Nanna","email":"","orcid":"","institution":"Yale School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"G.","lastName":"Nanna","suffix":""},{"id":560089513,"identity":"8cf07e42-497a-46fc-b1d3-2f41f7872a46","order_by":11,"name":"Kaveh Hosseini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYBACCSBmZmA4AOYcYDCQsGNjbwCJEK/FIpmf5wAJWoCggnHmjAT8WiTbzxg+LmC4Iyfff/jg4YICCWaDm28MPxdU2DDwt3cnYNMizZNjbDyD4ZmxwY20hMMzDCT4DG7nGEvPOJPGIHHm7AZsWuQY0tKkeRgOJ26Q4DE4zGMAtOV2joE0b9thYFDkYtfC/yz9N0jL/P7zH0BaGDfcPGP8G58WaYnkY8wgLQ0HchjAWmbO4DHDa4vkjMeHQQ4D+QXsMGAgp5VZ85xJ48HlF4nziY2fgVpAIfb4M8+fOmBUHt58m6fCRo6/vRerFjBg/IfC5TAAkTw4lWMB7A9IUT0KRsEoGAXDHwAAo3leOcCMZ3sAAAAASUVORK5CYII=","orcid":"","institution":"Tehran Heart Center Research Institute, Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Kaveh","middleName":"","lastName":"Hosseini","suffix":""}],"badges":[],"createdAt":"2025-11-07 16:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8058920/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8058920/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98441078,"identity":"86e4b892-e682-41c9-88f9-14256c8689fe","added_by":"auto","created_at":"2025-12-17 17:04:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":947999,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript..docx","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/fc1c25820ffe6df8edfe4c5a.docx"},{"id":98377102,"identity":"00f15bf5-04ff-4f15-aab2-debd6d4707a1","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13343,"visible":true,"origin":"","legend":"","description":"","filename":"2a05f8660d3448d4a31a22b09728b67d.json","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/19e6bf3aa20cd3a2e00913c4.json"},{"id":98439731,"identity":"0f3d7bc4-8855-40e3-9f5a-363a17a284ba","added_by":"auto","created_at":"2025-12-17 17:02:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":378918,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalAppendix..docx","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/6f4bd0c263416c8b44b76ae7.docx"},{"id":98377111,"identity":"3a4c2479-c452-4818-b0b5-04724ffd2506","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139723,"visible":true,"origin":"","legend":"","description":"","filename":"2a05f8660d3448d4a31a22b09728b67d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/4644a6e114e6cf04d0ccbf50.xml"},{"id":98377099,"identity":"ad221ffa-9c42-44f0-8413-80d3dc35907f","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25440,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/fb0e60fa2d654a446b5ebb40.jpeg"},{"id":98377108,"identity":"23446cd4-ce53-41f4-82c7-8c5bb535488c","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34368,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/c326f143543411673737a1ca.jpeg"},{"id":98377105,"identity":"3d877edc-6b92-4a99-ab4f-8c5a4a5d9070","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46495,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/2bd1e3e8984320f83f68fc00.jpeg"},{"id":98439753,"identity":"cf38e429-757d-4cc9-9279-a2c4be25478b","added_by":"auto","created_at":"2025-12-17 17:02:53","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90720,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/02679ce55620f770ea1aebc1.jpeg"},{"id":98377115,"identity":"1e44289e-8e89-416e-895d-a2541b91a52c","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7011,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/f87ab725026fc7eb74433fc4.png"},{"id":98377116,"identity":"55e2b37a-bca2-437e-93d6-fb66a2dfb5dd","added_by":"auto","created_at":"2025-12-17 07:10:33","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13340,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/1fa87655cce303f00b2b6f6d.png"},{"id":98377114,"identity":"82805110-9e7b-498a-bb83-4d61b7a90900","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15506,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/21ff2d3d4312cab2d22e3195.png"},{"id":98377117,"identity":"690202c0-432c-49ad-acd3-1f62d256274a","added_by":"auto","created_at":"2025-12-17 07:10:33","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37049,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/5c1ffc8978c18d2a0a01451f.png"},{"id":98377120,"identity":"a63158e9-2b25-45d3-9455-516f58c8f11a","added_by":"auto","created_at":"2025-12-17 07:10:33","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137206,"visible":true,"origin":"","legend":"","description":"","filename":"2a05f8660d3448d4a31a22b09728b67d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/2aafb0b74cf9fe89bc60a120.xml"},{"id":98377119,"identity":"0abb07ff-c7fe-44ee-86f2-9affdc86cfd2","added_by":"auto","created_at":"2025-12-17 07:10:33","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152135,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/4886177c5302a92f90d153b4.html"},{"id":98377109,"identity":"ea5429f8-4c22-4928-abc0-0acb31829c11","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128576,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the accuracy of the nomogram in predicting MACE incidence in the development cohort.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/b17f56775cae5884c28dbc19.png"},{"id":98439702,"identity":"e5547187-1fd7-4c13-8778-26567ae17b90","added_by":"auto","created_at":"2025-12-17 17:02:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20156,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) was conducted to predict the incidence of MACE in the development cohort. The plot illustrates the correlation between threshold probability (abscissa) and the net benefit (ordinate) of the prediction model (blue line), the scenario of treating all patients (red line), and the scenario of treating none of the patients (green line).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/f55caf7f325c20fd4b973edf.jpg"},{"id":98439268,"identity":"d9eae479-88f9-423c-8933-7e0c555930e9","added_by":"auto","created_at":"2025-12-17 17:01:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28453,"visible":true,"origin":"","legend":"\u003cp\u003eThe incidence of cumulative MACE among patients categorized by different prognostic index quartiles.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/97c89227d6c75441bf2279c4.jpg"},{"id":98377103,"identity":"50fa9ce8-b5bb-45b8-b26f-7793bf19a622","added_by":"auto","created_at":"2025-12-17 07:10:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41471,"visible":true,"origin":"","legend":"\u003cp\u003eCentral illustration\u003c/p\u003e\n\u003cp\u003eA nomogram was developed for predicting MACE incidence using multivariate Cox regression analysis. Each patient was assigned points for each clinical characteristic by drawing a vertical line from its value to the top row. The scores for all eight characteristics were summed to calculate the total points in the middle row.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/d66ffe8526d02753534de310.jpg"},{"id":98445830,"identity":"1a1bf9bc-0d26-493e-9e37-84b2b20ea013","added_by":"auto","created_at":"2025-12-17 17:21:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/b4546e97-68de-4fa7-abd6-996928bc913b.pdf"},{"id":98441214,"identity":"9961ff75-5aff-4633-bc56-b4b88c02dc4b","added_by":"auto","created_at":"2025-12-17 17:05:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":378918,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalAppendix..docx","url":"https://assets-eu.researchsquare.com/files/rs-8058920/v1/758212e094370c47d2713938.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic heart diseases (IHDs), including acute coronary syndrome (ACS), are the leading cause of death worldwide, accounting for approximately 9\u0026nbsp;million deaths in 2021 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Advanced age is one of the most important predictors of ACS morbidity and mortality one year following hospitalization [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The vast majority of elderly patients are typically undertreated compared to younger patients following ACS. Evidence indicates that younger patients with ACS undergo emergency reperfusion three times more frequently than their older counterparts, and antithrombotic medications, diuretics, and statins are significantly underused after older patients are discharged [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, in various countries, efforts have been made to decrease in-hospital mortality and improve the incidence of adverse outcomes following ACS by applying evidence-based ACS therapies, early invasive surgery, and efficient post-percutaneous coronary intervention (PCI) care [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Also, evaluation and resolution of comorbidities and geriatric syndromes, including frailty, altered mental status, and cognitive disorders, and incorporating them into risk assessment models such as the combined model of GRACE risk score and clinical frailty scale (CFS) in elderly patients, can dramatically reduce in-hospital and long-term mortality after ACS [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA plethora of risk scores have been developed to predict poor outcomes and mortality in patients who have had ACS. However, few risk assessment tools have gained extensive credibility and validation to measure the risk score in patients following ST-elevation myocardial infarction (STEMI). Moreover, most studies have built their risk prediction models without focusing on the elderly population specifically. Some studies suggest that, despite their advantages in calculating the risk score prior to coronary angiography, prediction models merely based on clinical variables may miss the anatomical parameters that can increase discrimination and calibration for predicting adverse events. Therefore, A combined score model of clinical and angiographic variables, such as the Clinical SYNTAX Score (CSS), can significantly boost the model\u0026rsquo;s discriminative ability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Other recent cohort studies have concluded that a nomogram-based prediction tool that includes both clinical and anatomical factors can stratify patients based on risk association and improve clinical decision-making by predicting major adverse cardiovascular events (MACE) incidence following ACS in the elderly [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince there are only a limited number of reliable and validated risk stratification tools assessing MACE probability in STEMI patients over 65, in this retrospective registry study, we aimed to develop a precise and actionable nomogram with good calibration and discriminative ability to accurately predict MACE incidence in these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study included patients aged 65 years and above diagnosed with STEMI and undergoing primary PCI at Tehran Heart Center. Clinical, biochemical, and procedural data for this research were meticulously extracted from the institutional database, which contains detailed patient records spanning from 2015 to 2020. A total of 1,946 patients who met the inclusion criteria were identified. To maintain consistent outcome distribution, the dataset was stratified by MACE incidence and these patients were stratified into a 70:30 ratio for training and testing purposes, resulting in 1,362 patients in the training set and 584 patients in the testing set. Institutional Review Board (IRB) and ethics committee at THC approved this study (IR.TUMS.THC.REC.1399.045). This study was retrospective in nature, utilizing de-identified data from THC\u0026rsquo;s electronic registry, so an informed consent waiver was granted by the IRB.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBiochemical tests\u003c/h3\u003e\n\u003cp\u003eBefore emergency coronary angiography, blood samples were analyzed to assess fasting blood glucose (FBS), hemoglobin (Hb), and serum creatinine levels. Lipid profile measurements, including total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), the TG/HDL ratio, and the triglyceride-glucose (Tyg) index, were retrieved from the database. The Tyg index, a proxy for insulin resistance, was calculated using the formula: [(Fasting triglycerides (mg/dL) \u0026times; Fasting glucose (mg/dL)) / 2].\u003c/p\u003e\n\u003ch3\u003eCoronary angiography\u003c/h3\u003e\n\u003cp\u003eUpon hospital admission, all patients underwent coronary angiography performed by senior cardiologists following standard treatment protocols. Procedural details, including pre-procedure administration of guideline-recommended loading doses of Ticagrelor/Clopidogrel and Aspirin, were documented in the database. Following PCI, patients were prescribed standard treatment regimens, including moderate-intensity statins and modern antiplatelet therapies. Follow-up schedules and treatment adherence were recorded as part of the institutional database.\u003c/p\u003e\n\u003ch3\u003eFollow-up and outcomes\u003c/h3\u003e\n\u003cp\u003eOutcomes were monitored through clinic visits and phone interviews, with all relevant data systematically logged in the database. Major adverse cardiac events (MACEs) were defined as a composite outcome encompassing all-cause mortality, nonfatal myocardial infarction, and stroke.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eVariables with less than 10% missing data were selected from the database, resulting in 38 clinical, biochemical, and procedural factors included in the analysis. Variables with \u0026gt;\u0026thinsp;10% missingness were excluded; these were primarily procedural factors (e.g., stent brand, inflation pressure, operator name) without established prognostic relevance. For all other variables (\u0026lt;\u0026thinsp;10% missingness), missing values were managed using multiple imputation by chained equations under a missing-at-random assumption. Statistical analysis was done using R Studio version 1.1.463 (Posit PBC, Boston, MA, United States), utilizing packages \u003cem\u003etidyverse\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], \u003cem\u003epROC, survival\u003c/em\u003e, and \u003cem\u003erms.\u003c/em\u003e Means and standard deviations (SDs) were presented for continuous variables with normal distributions and medians and interquartile ranges (IQRs) for continuous variables with skewed distributions. For the analysis of continuous variables with normal distributions, the t-test was utilized. For the analysis of continuous variables with nonnormal distributions, the Mann-Whitney U-test or the Kruskal-Wallis test was utilized. Analysis of categorical variables was carried out utilizing the x2 -test. In all comparisons, a p-value below 0.05 indicated statistical significance.\u003c/p\u003e \u003cp\u003eIn the training set, a comprehensive univariate analysis was performed in this step, ensuring that all variables that were independently associated with MACE incidence and were statistically significant were selected. All variables were analyzed through a multivariate model to evaluate their association with MACE, with significance determined by a p-value threshold of 0.05. The proportional hazards assumption was evaluated using Schoenfeld residuals and log-minus-log plots, with no significant violations observed. The Cox model was specified to include main effects only, as interaction terms were not formally modeled to preserve parsimony and avoid unstable estimates. These predictors were subsequently used to construct a predictive nomogram for estimating MACE risk. R software was utilized to construct the nomogram using the Regression Modeling Strategies (\u003cem\u003erms\u003c/em\u003e) package. Risk nomograms were analyzed utilizing receiver characteristic curves (ROC) to evaluate their ability to discriminate MACEs based on the calculation of area under the ROC curve [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Utilizing the \u003cem\u003erms\u003c/em\u003e package, calibration curves were plotted and calculated to evaluate the calibration of the MACEs after the construction of the ACS risk nomograms. Bootstrapping was carried out by repeated random sampling (1000 times) for internal validation of model accuracy. The resulting optimism-corrected estimates were then used for Decision curve analysis (DCA) to quantify the clinical efficacy of the nomograms based on their net benefit for different threshold probabilities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation characteristics\u003c/h2\u003e \u003cp\u003eA total of 1946 elderly STEMI patients who underwent PCI were included in this study, with a mean follow-up period of 17.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.50 months. The mean age of this population was 73.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99 years, and 68% were male. This population was divided into a train set (70% of the total population, 1362 cases) and a test set (30% of the total population, 584 cases). These two groups were statistically comparable regarding all investigated demographic, laboratory, clinical, and procedural characteristics except for the door-to-needle interval, which was longer in the train set (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e). Among the total population, the overall incidence of MACE was 18.29%, with a comparable rate in the two train and test sets.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProbable predictors of MACE in the elderly with STEMI\u003c/h3\u003e\n\u003cp\u003eA stepwise method using univariate and multivariate Cox regression was employed to determine the independent predictor of MACE in elderly STEMI cases undergoing PCI (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). In multivariate model 8 factors were identified to be significantly independently associated with MACE incidence: LVEF (OR\u0026thinsp;=\u0026thinsp;0.96, 95%-CI: 0.94\u0026ndash;0.98), serum creatinine (OR\u0026thinsp;=\u0026thinsp;1.41, 95%-CI: 1.23\u0026ndash;2.33), hemoglobin (OR\u0026thinsp;=\u0026thinsp;1.01, 95%-CI: 1-1.01), fasting blood glucose levels (OR\u0026thinsp;=\u0026thinsp;0.88, 95%-CI: 0.81\u0026ndash;0.95), presence of valvular heart disease (OR\u0026thinsp;=\u0026thinsp;2.39, 95%-CI: 1.24\u0026ndash;5.87), post-PCI TIMI flow grade\u0026thinsp;\u0026lt;\u0026thinsp;2 (OR\u0026thinsp;=\u0026thinsp;0.43, 95%-CI: 0.19-1), diameter of the stent placed in the culprit lesion (OR\u0026thinsp;=\u0026thinsp;0.69, 95%-CI: 0.49\u0026ndash;0.98), and the presence or absence of shock in the post-PCI setting (OR\u0026thinsp;=\u0026thinsp;28.4, 95%-CI: 9.45\u0026ndash;123). Additionally, the overall incidence of MACE in the study cohort was 18.3%.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a nomogram predicting MACE following STEMI\u003c/h2\u003e \u003cp\u003eUsing the results of multivariate analysis and employing the eight identified independent predictive factors, a nomogram was constructed to predict the risk of MACE following STEMI in the elderly undergoing PCI (\u003cem\u003eCentral illustration\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eDrawing a vertical line from the value of each predictive factor to the \u0026ldquo;points\u0026rdquo; line could determine its numerical points. The total point could be calculated by summing up all of these points, representing the patient risk for MACE (ranging from 0 to 100). To assess the probability of MACE incidence, a vertical line from the calculated total point on the \u0026ldquo;Total points\u0026rdquo; line could be drawn to the \u0026ldquo;Risk of MACE incidence\u0026rdquo; scale on the bottom of the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNomogram validation and predictive performance\u003c/h2\u003e \u003cp\u003eTo assess the discriminatory ability of our nomogram, receiver operating characteristic (ROC) analysis was performed, which illustrated an area under cover (AUC) of 0.71 (95% CI: 0.65\u0026ndash;0.77) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the optimal cutoff determined by Youden\u0026rsquo;s index, the model achieved a sensitivity of 72%, specificity of 63%, positive predictive value of 30%, and negative predictive value of 91%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA decision curve analysis (DCA) was performed based on optimism-corrected estimates to determine the net benefit of our developed prognostic model for clinical decision-making, which demonstrated a threshold probability of 23 to 89% for MACE incidence following PCI in patients\u0026thinsp;\u0026gt;\u0026thinsp;65 years of age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which suggests a wide range of applications. Additionally, a calibration plot was also generated to investigate the similarity and concordance of our nomogram\u0026rsquo;s predicted probability with the observed probability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, Kaplan-Meier analysis was performed to investigate the prognostic reliability of the nomogram's predicted risk of MACE. We stratified our patients into four risk groups according to the nomogram\u0026rsquo;s predicted risk (0-0.25 as low risk, 0.25\u0026ndash;0.5 as intermediate risk, 0.5\u0026ndash;0.75 as high risk, 0.75-1 as very high-risk group). The results of the Kaplan-Meier analysis have been illustrated in the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A), test set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B), and total population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to our results, very high-risk patients (predicted risk of \u0026gt;\u0026thinsp;0.75) demonstrated a 13.12 (95%-CI: 7.09\u0026ndash;24.25) and 16.74 (95%-CI: 8.73\u0026ndash;32.1) hazard ratio of MACE incidence compared to all other cases (predicted risk of \u0026lt;\u0026thinsp;0.75) and low-risk cases (predicted risk of \u0026lt;\u0026thinsp;0.25), respectively. A similar trend of higher observed MACE incidence in groups with higher nomogram\u0026rsquo;s predicted risk was observed through all other possible inter-group comparisons (\u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e), which suggests the clinical reliability of our nomogram\u0026rsquo;s predicted risk of MACE incidence.\u003c/p\u003e \u003cp\u003eA mean absolute error of 0.018 was calculated for our model\u0026rsquo;s calibration plot, indicating that, on average, the predicted probabilities deviate from the actual outcomes by 1.8% (\u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e). The precision recall curve indicates excellent precision across various recall levels with an AUC-PR of 0.9 (\u003cb\u003eSupplemental Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study developed and validated a nomogram for predicting MACE in a cohort of 1,946 elderly patients who underwent primary PCI. The nomogram, based on 8 significant predictors, was assessed for its discriminatory ability using ROC analysis, which yielded an AUC of 0.71. Kaplan-Meier analysis further evaluated its prognostic reliability by stratifying patients into four risk groups based on predicted MACE risk. This analysis, illustrated in both the train and test sets, revealed that very high-risk patients (predicted risk\u0026thinsp;\u0026gt;\u0026thinsp;0.75) had significantly higher hazard ratios for MACE compared to lower-risk groups, affirming the nomogram's clinical reliability.\u003c/p\u003e \u003cp\u003eEffective prognostic information is often obtained through scoring systems. The TIMI risk score is a straightforward tool originally designed to predict 30-day mortality in STEMI patients treated with fibrinolytics, and it has also proven effective for predicting in-hospital mortality in STEMI patients undergoing primary PCI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications (CADILLAC) risk score was developed to predict both 30-day and one-year mortality in STEMI and NSTEMI patients following primary PCI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Compared with the mentioned and other traditional risk scores, including TIMI, PAMI, CADILLAC, and GRACE, which offer a modest discrimination in both the general STEMI (AUCs ranging from 0.68 to 0.70) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and elderly population, with an AUC of 0.70 and 0.69 for in-hospital and post-discharge mortality, respectively [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] our nomogram model demonstrates improved predictive power in the patient population aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, a population being rarely investigated in the existing literature. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe incorporation of the most robust clinical predictors for the elderly population following PCI, including age, diabetes, prior MI, LVEF, STEMI presentation, and eGFR, which were identified by Jalali et al.'s meta-analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], alongside real-time procedural characteristics like post-PCI TIMI flow, stent diameter, and post-PCI shock, into our nomogram, has led to the development of a more bedside-friendly and homogeneous risk assessment tool for clinicians and patients, as other machine learning models such as iPROMPT [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and XGBoost with a large sample size [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], rely more on heterogeneous predictors and \u0026ldquo;black-box\u0026rdquo; algorithms while showing more discriminative ability.\u003c/p\u003e \u003cp\u003eZhu et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have developed a similar nomogram in the elderly population post-PCI, but lacked the distinction between STEMI and NSTEMI populations, underestimating the possible clinical discernibility between these two. Although more discriminative ability was achieved by Zhu\u0026rsquo;s model with an AUC of 0.787, the high reliance of this model on biomarkers, overlooking the mechanical and hemodynamic dimensions of risk, which are addressed by procedural variables, and a smaller sample size of nearly 1000 patients compared with our nomogram, demonstrates the high clinical utility of the current model. Unlike our model, which addressed the risk of MACE following PCI in a specific age group, Yao et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] developed another risk-prediction nomogram model, incorporating six variables, for 1-year readmission due to MACE in the general STEMI patients\u0026rsquo; population with a considerably smaller sample size, lowering the generalizability of this tool in the clinical environment.\u003c/p\u003e \u003cp\u003eOur model addresses some methodological shortcomings mentioned in a study that systematically explored the prediction models for MACE following PCI. With the inclusion of nearly 2,000 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65, we minimized heterogeneity and increased applicability for the elderly population while providing an events-per-variable (EPV) above the \u0026ge;\u0026thinsp;20 threshold. The incorporation of real-time procedural characteristics into our model alongside the implementation of multivariable analysis addressed the lack of multivariable modeling for the selection of MACE predictors and the underestimation of procedural variables for risk stratification, as mentioned by Deng et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Moreover, we presented both calibration plots and Hosmer-Lemeshow statistics, directly addressing what 74% of models failed to report in Deng\u0026rsquo;s review. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e We have enabled the risk stratification of elderly patients into four distinct risk groups based on the model\u0026rsquo;s predicted risk, which can inform individual care by directing high-risk patients to more intensive treatment plans, including earlier referral to specialized cardiology and heart failure clinics as well as extended monitoring and more intensive post-discharge follow-up. DCA is conducted to evaluate the clinical value of a prognostic model by quantifying the net benefit across a range of risk thresholds for developing a certain outcome, at which a clinician might choose to intervene. In our model, DCA delivers a positive net clinical benefit in a broad range of risk thresholds (23\u0026ndash;89%), which translates into the applicability of the current model for both clinicians who only treat extremely high-risk patients and those who approach patients more conservatively in real-world settings. Chen et al.\u0026rsquo;s iPROMPT [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] shows a positive net benefit in a much narrower threshold range of 30\u0026ndash;60% and Zhu\u0026rsquo;s model [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] demonstrates net benefit only when the threshold exceeds 15% which does not clarify how their net gain changes beyond moderate thresholds. Hamilton et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] did not include a DCA, leaving clinicians and patients uncertain about whether this model leads to better clinical decision support in real-world settings. Fang et al.\u0026rsquo;s [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] DCA showed net benefit in a wide threshold range of 10\u0026ndash;99% in their model, but included only 466 STEMI patients.\u003c/p\u003e \u003cp\u003eImportantly, although we have not yet implemented our nomogram in the institution\u0026rsquo;s electronic medical record (EMR), the model is designed for integration into EMR and real-time use at the bedside. Its variables are readily available after PCI, and its point-based graphic format supports bedside decision-making and shared discussions with patients and families. This implementation plan reinforces the model\u0026rsquo;s potential utility for cardiologists and explains how we envision its applicability in daily care alongside its direct benefit to readers and clinicians.\u003c/p\u003e \u003cp\u003eOur nomogram was constructed based on eight key factors identified as independent predictors of MACE in elderly STEMI patients undergoing PCI. These factors include LVEF, serum creatinine, hemoglobin, FBS, presence of VHD, post-PCI TIMI flow grade less than 2, diameter of the stent placed in the culprit lesion, and the presence or absence of shock in the post-PCI setting.\u003c/p\u003e \u003cp\u003eLVEF, as a critical measure of cardiac function, is particularly important in the context of STEMI, as it helps to assess the severity of cardiac damage and guides treatment decisions. Previous studies have shown that low LVEF is associated with higher rates of MACE following STEMI [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. VHD is both a risk and a complication of ACS, which is associated with a worse prognosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our results align with the study of Hasdai et al., in which STEMI patients with pre-existing VHD had a worse prognosis compared to those without VHD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur nomogram incorporates easily obtainable blood test parameters, including creatinine, FBS, and baseline hemoglobin levels. Various studies have shown that higher creatinine and worse kidney function are associated with higher rates of MACE after acute MI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Many patients with elevated creatinine levels have comorbid conditions such as diabetes, hypertension, and chronic kidney disease (CKD), which independently contribute to worse outcomes in STEMI. Moreover, renal impairment can exacerbate the inflammatory response during MI [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, renal dysfunction can lead to fluid overload and hypertension, further straining the heart during an acute event [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, studies have shown that stress hyperglycemia is significantly associated with an increased risk of mortality in STEMI patients treated with PCI, regardless of diabetic status [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Stress hyperglycemia triggers the production of inflammatory factors, which can exacerbate atherosclerosis through various intracellular pathways [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, stress hyperglycemia increases thrombogenic activity, leading to a hypercoagulable state [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Anemia is another strong and independent predictor of MACE in patients with ACS, showing a notable dose-response relationship [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It significantly reduces oxygen delivery to the myocardium beyond coronary stenoses and increases myocardial oxygen demand by requiring a higher stroke volume and heart rate to ensure sufficient systemic oxygen delivery [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These factors may explain the progressively poorer outcomes seen in ACS patients with lower baseline hemoglobin levels. In the study of Liu et al., a higher hemoglobin level in the anemic group was associated with a decreased risk of 1-year mortality; however, a higher hemoglobin level in the erythrocytosis group was linked to an increased risk of 1-year mortality [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is well established that peri-procedural complications during PCI can significantly impact mortality [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In our study, we observed that the characteristics of PCI and post-PCI events play a crucial role in patient outcomes. Notably, post-PCI TIMI flow, the occurrence of post-PCI shock, and the diameter of the PCI stent were significantly meaningful factors. Previous studies have shown that achieving TIMI flow grade 3 following PCI is linked to better short-term and long-term outcomes in these patients, including a decrease in MACE and cardiac mortality [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Although peri-procedural cardiogenic shock is reported to be low, it was linked to a significantly higher mortality [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Likely, in our study, post-PCI shock was the most powerful predictor of MACE following PCI. Stent diameter was another key factor in predicting MACE in this population. The benefits of stent oversizing on procedural and clinical outcomes have been documented. Specifically, small vessels treated with smaller stents experienced more adverse events [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Shugman et al. reported that deploying bare-metal stents in STEMI patients with infarct-related arteries measuring 3.5 mm or larger was associated with low rates of target vessel revascularization [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This indicates that choosing larger stents could enhance long-term outcomes.\u003c/p\u003e \u003cp\u003eOur study has several strengths, including the use of a large cohort of 1,946 elderly STEMI patients, which enhances the robustness and generalizability of the findings. The rigorous analytical approach, employing both univariate and multivariate methods, allowed for the accurate identification of significant predictors of MACE. The nomogram was validated with an AUC of 0.71, demonstrating its effective discriminatory ability across train and test cohorts. Additionally, the inclusion of key clinical factors such as LVEF and post-PCI TIMI flow underscores its practical relevance. The DCA further confirmed the nomogram\u0026rsquo;s broad applicability and net benefit for clinical decision-making. However, the study has limitations such as the potential lack of generalizability due to being conducted in a single center, a follow-up period that may not capture long-term outcomes, and variability in some data points between cohorts. Other relevant factors not included in the nomogram might also influence MACE risk stratification.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThere are several important limitations to consider. Firstly, it was conducted at a single center, which may influence the generalizability of the findings to broader populations, as our results may not fully represent diverse patient demographics or different clinical settings. Additionally, the sample size of fewer than 2,000 participants, while adequate for preliminary findings, suggests that a larger cohort could enhance statistical power and improve the detection of significant differences or associations. The absence of external validation with a larger dataset indicates an opportunity for future research to strengthen the applicability of our results across various populations. Additionally, some clinically relevant parameters were not available in our dataset. These include details of antiplatelet therapy, anatomical vessel involvement (e.g., single- vs. multi-vessel disease), and post-discharge data such as attendance at cardiac rehabilitation or adherence to guideline-directed medical therapy (GDMT). While these variables are known to influence long-term outcomes, they could not be assessed in the current study. However, our model incorporates several intra-procedural predictors, including post-PCI TIMI flow, stent diameter, and post-PCI shock, which offer insight into procedural success and clinical severity. Notably, a recent systematic review of PCI-related MACE prediction models [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] identified these procedural variables as underexplored components of existing models. The inclusion of such factors in our nomogram provides added value and reflects an effort to address this methodological gap. Importantly, we recognize that important clinical parameters, including echocardiography and electrocardiogram, were not included in this investigation, and future studies that incorporate these factors may provide a more comprehensive understanding of the outcomes. Although the model demonstrated satisfactory discrimination (an AUC of 0.71, sensitivity of 72%, specificity of 63%), further improvement may be achievable through external validation in larger multicenter cohorts, incorporation of additional anatomical and post-discharge predictors (e.g., multi-vessel disease, adherence to guideline-directed medical therapy), and the application of rigorously validated machine-learning approaches. In addition, our Cox model was specified to include only main effects. Although this approach prioritizes interpretability and stability, it assumes no effect modification between predictors. Evaluation of clinically plausible interactions in larger or external datasets is an important direction for future research. Lastly, some study limitations, including the lack of external validation in multicenter studies and standardization across different systems, are potential barriers to the implementation of our nomogram model into the EMR. Assessment of clinician and care team acceptance, evaluation of workflow impact, and feasibility testing with medico-legal considerations will enable the integration of this nomogram into the EMR and real-time bedside settings. We will focus on pursuing each step to underline the practical implications of clinical applicability and to ensure the integration of this model into the EMR.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our nomogram, validated in 1,946 elderly STEMI patients undergoing PCI, showed an AUC of 0.71 and effectively predicted MACE risk. It integrates eight key factors and demonstrated strong clinical utility, with high-risk patients having significantly higher MACE rates. This tool offers valuable insights for personalized risk assessment and management in the elderly population with STEMI.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePerspectives:\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eClinical Competencies:\u003c/h2\u003e \u003cp\u003eThis study contributes primarily to the domains of Medical Knowledge, Patient Care, and Practice-Based Learning and Improvement. By developing and validating a predictive nomogram tailored to elderly patients with STEMI undergoing primary PCI, the work strengthens clinicians' ability to identify individuals at elevated risk of post-procedural adverse events. It promotes the application of evidence-based decision-making for a high-risk. The model leverages routinely collected clinical and procedural data, enhancing bedside applicability and facilitating more personalized post-PCI management. This supports quality improvement efforts by aligning individualized care with system-wide strategies for outcome monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTranslational Outlook:\u003c/h2\u003e \u003cp\u003eThis study addresses a key translational gap by introducing a risk prediction model developed specifically for elderly patients with STEM, a population that has not been widely studied in isolation for dedicated post-PCI prognostic modeling. Most existing risk scores are derived from mixed-age cohorts, limiting their calibration and clinical relevance in older adults. By using readily available clinical and procedural variables, this model enables real-time application at the point of care without reliance on advanced diagnostics or long-term follow-up data. Future directions include external validation across broader populations, digital integration into clinical decision support systems, and incorporation of additional parameters such as anatomical burden and post-discharge adherence to optimize its performance and clinical utility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv id=\"AGS1\" class=\"AbbreviationGroupSection\"\u003e \u003cdiv class=\"Heading\"\u003eand Acronyms:\u003c/div\u003e \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute coronary syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIschemic heart diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleft-ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epercutaneous coronary intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eassess fasting blood glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethrombolysis in myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study presents a validated nomogram predicting post-PCI major adverse cardiovascular events (MACE) in patients \u0026ge;65 with STEMI. Using 8 clinical \u0026amp; procedural variables (LVEF, creatinine, Hb, FBS, VHD, TIMI flow, stent diameter, post-PCI shock), the model stratifies risk to guide post-discharge care.\u003c/p\u003e\n \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Institutional Review Board and Ethics Committee of Tehran Heart Center, Tehran University of Medical Sciences (IR.TUMS.THC.REC.1399.045). The study was conducted in accordance with the principles of the Declaration of Helsinki. Because this research used retrospective and fully de-identified registry data, the requirement for informed consent was waived by the institutional IRB.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eFigures are original and unpublished before.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDisclosure:\u003c/h2\u003e \u003cp\u003eDr. Nanna reports unrelated current research support from the American College of Cardiology Foundation supported by the George F. and Ann Harris Bellows Foundation, the Patient-Centered Outcomes Research Institute (PCORI), the Yale Claude D. Pepper Older Americans Independence Center (P30AG021342), and the National Institute on Aging (K76AG088428) as well as consulting fees from Heartflow, Merck, and Novo Nordisk. The remaining authors have nothing to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHamidreza Soleimani conducted model training, along with manuscript revision, editing, and final draft preparation. Reza Nikfar participated in manuscript composition, revising, and editing the manuscript. Sahand Siami participated in manuscript composition and final draft preparation. Farhad Shaker was involved in manuscript writing and table preparation. Parisa Fallahtafti was involved in manuscript writing. Mehdi Mehrani performed critical revisions of the manuscript for important intellectual content and contributed to drafting the last version. Yaser Jenab executed essential edits of the manuscript for significant intellectual substance and assisted in composing the final edition. Adrian V. Hernandez executed essential edits of the manuscript for significant intellectual substance and assisted in composing the final edition. Diaa Hakim performed critical revisions of the manuscript for important intellectual content and contributed to drafting the last version. Michael G. Nanna conducted significant revisions to the manuscript, enhancing its intellectual substance, and assisted in composing the final edition. Sajjad Hosseini conducted essential modifications of the manuscript for significant intellectual substance and assisted in composing the final edition. Kaveh Hosseini provided the study concept and design.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used in the current study are available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eGlobal Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021). Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2024.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnett K, Mercer SW, Norbury M, Wattet alB G, Guthrie, et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(12)60240-2\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(12)60240-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGudnadottir GS, Gudnason T, Wilhelmson K, Ravn-Fischer A. Multimorbidity and Readmissions in Older People with Acute Coronary Syndromes. Cardiology. 2022;147(2):121\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000522016\u003c/span\u003e\u003cspan address=\"10.1159/000522016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGale CP, Cattle BA, Woolston A, Baxteret PD, West alRM. Resolving inequalities in care? Reduced mortality in the elderly after acute coronary syndromes. The Myocardial Ischaemia National Audit Project 2003\u0026ndash;2010. Eur Heart J. 2012;33(5):630\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehr381\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehr381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh, T.-H., J.-D. Wang, and L.-M. Tsai, 2012. \u003cem\u003eImproving in-hospital mortality in elderly patients after acute coronary syndrome\u0026mdash;A nationwide analysis of 97,220 patients in Taiwan during 2004\u0026ndash;2008.\u003c/em\u003e International Journal of Cardiology, 155(1): pp. 149\u0026ndash;154. https://doi.org/10.1016/j.ijcard.2011.10.009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Amrawy A, Almaghraby A, Abd Elnabi M, Wagdy S. 2022. \u003cem\u003ePattern of cardiovascular risk factors and outcomes of patients older than 80 years and presenting with acute coronary syndromes.\u003c/em\u003e European Heart Journal, 43(Supplement_2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehac544.2547\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehac544.2547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox KA, Fitzgerald G, Puymirat E, Huanget alF W, Anderson, et al. Should patients with acute coronary disease be stratified for management according to their risk? Derivation, external validation and outcomes using the updated GRACE risk score. BMJ Open. 2014;4(2):e004425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2013-004425\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2013-004425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnand A, Cudmore S, Robertson S, Stephenet J, Denvir alMA. Frailty assessment and risk prediction by GRACE score in older patients with acute myocardial infarction. BMC Geriatr. 2020;20(1):102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-020-1500-9\u003c/span\u003e\u003cspan address=\"10.1186/s12877-020-1500-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVassalli G, d'Angeli I, Scherff F, S\u0026uuml;rderet D, Pedrazzini alGB. Comparison of clinical and angiographic prognostic risk scores in elderly patients presenting with acute coronary syndrome and referred for percutaneous coronary intervention. Swiss Med Wkly. 2015;145:w14049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4414/smw.2015.14049\u003c/span\u003e\u003cspan address=\"10.4414/smw.2015.14049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Wu X, Sun J, Xuet alH T, Jiang, et al. Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment. Front Cardiovasc Med. 2022;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2022.1053470\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.1053470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H, Averick M, Bryan J, Changet alH W, Yutani, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21105/joss.01686\u003c/span\u003e\u003cspan address=\"10.21105/joss.01686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026auml;tti S, Niinivehmas S, Pentik\u0026auml;inen OT. 2016. \u003cem\u003eRocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization.\u003c/em\u003e J Cheminform, 8(1): p. 45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-016-0158-y\u003c/span\u003e\u003cspan address=\"10.1186/s13321-016-0158-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrow DA, Antman EM, Charlesworth A, Cairnset alE R, Braunwald, et al. TIMI risk score for ST-elevation myocardial infarction: A convenient, bedside, clinical score for risk assessment at presentation: An intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation. 2000;102(17):2031\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.cir.102.17.2031\u003c/span\u003e\u003cspan address=\"10.1161/01.cir.102.17.2031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrow DA, Antman EM, Parsons L, de Lemoset alE JA, Braunwald, et al. Application of the TIMI risk score for ST-elevation MI in the National Registry of Myocardial Infarction 3. JAMA. 2001;286(11):1356\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.286.11.1356\u003c/span\u003e\u003cspan address=\"10.1001/jama.286.11.1356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox DA, Stone GW, Grines CL, Stuckeyet T, Griffin alJJ. Comparative early and late outcomes after primary percutaneous coronary intervention in ST-segment elevation and non-ST-segment elevation acute myocardial infarction (from the CADILLAC trial). Am J Cardiol. 2006;98(3):331\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amjcard.2006.01.102\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2006.01.102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Safdar U, Yaqoob N, Khanet alM SF, Karim, et al. Assessment of the prognostic performance of TIMI, PAMI, CADILLAC and GRACE scores for short-term major adverse cardiovascular events in patients undergoing emergent percutaneous revascularisation: a prospective observational study. BMJ Open. 2025;15(3):e091028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2024-091028\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2024-091028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurnaz S, Karim M, Ashraf T, Aliet S, Siddiqi alTJ. Performance of the TIMI risk score in predicting mortality after primary percutaneous coronary intervention in elderly women: Results from a developing country. PLoS ONE. 2019;14(7):e0220289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0220289\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0220289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X-Y, Jiang Z-M, Li X, Su F-F, Tian J-W. Establishment and validation of post-PCI nomogram in elderly patients with acute coronary syndromes. Front Cardiovasc Med. 2025;12\u0026ndash;2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2025.1529476\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2025.1529476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng W, Wang D, Wan Y, Laiet alX S, Wang, et al. Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review. Front Cardiovasc Med. 2024;10\u0026ndash;2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2023.1287434\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.1287434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJalali A, Hassanzadeh A, Najafi MS, Nayebiradet alA S, Shafiee, et al. Predictors of major adverse cardiac and cerebrovascular events after percutaneous coronary intervention in older adults: a systematic review and meta-analysis. BMC Geriatr. 2024;24(1):337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-024-04896-4\u003c/span\u003e\u003cspan address=\"10.1186/s12877-024-04896-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Tan X, Du X, Liet alJ Q, Du, et al. Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance. Front Cardiovasc Med. 2023;10\u0026ndash;2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2023.1181424\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.1181424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton DE, Albright J, Seth M, Painteret I, Gurm alHS. Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions. Eur Heart J. 2024;45(8):601\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehad836\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehad836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao W, Li J. Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI. Clin Appl Thromb Hemost. 2022;28:10760296221137847. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/10760296221137847\u003c/span\u003e\u003cspan address=\"10.1177/10760296221137847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang C, Chen Z, Zhang J, Jin X, Yang M. Construction and evaluation of nomogram model for individualized prediction of risk of major adverse cardiovascular events during hospitalization after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction. Front Cardiovasc Med. 2022;9\u0026ndash;2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2022.1050785\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.1050785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavara J, Marcos-Garces V, Rios-Navarro C, Lopez-Lereuet alV MP, Bodi, et al. Sequential cardiovascular magnetic resonance assessment of left ventricular ejection fraction for prediction of subsequent events in a large multicenter STEMI registry. Eur Heart J - Cardiovasc Imaging. 2021;22(Supplement2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ehjci/jeab090.042\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeab090.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. p. jeab090.042.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg VG, Lansky AJ, Meller S, Witzenbichleret B, Stone alGW. The prognostic importance of left ventricular function in patients with ST-segment elevation myocardial infarction: the HORIZONS-AMI trial. Eur Heart J Acute Cardiovasc Care. 2014;3(1):67\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2048872613507149\u003c/span\u003e\u003cspan address=\"10.1177/2048872613507149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalkin A, Stone GW, Dixon SR, Grineset CL, Lansky alAJ. Impact and determinants of left ventricular function in patients undergoing primary percutaneous coronary intervention in acute myocardial infarction. Am J Cardiol. 2005;96(3):325\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amjcard.2005.03.069\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2005.03.069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrimi G, Montalto C, Ferri LA, Piattiet L, alS, De Servi. Clinical Impact of Valvular Heart Disease in Elderly Patients Admitted for Acute Coronary Syndrome: Insights From the Elderly-ACS 2 Study. Can J Cardiol. 2020;36(7):1104\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.cjca.2019.11.014\u003c/span\u003e\u003cspan address=\"10.1016/j.cjca.2019.11.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh GK, van der Bijl P, Goedemans L, Vollemaet alV EM, Delgado, et al. Prevalence of Aortic Valve Stenosis in Patients With ST-Segment Elevation Myocardial Infarction and Effect on Long-Term Outcome. Am J Cardiol. 2021;153:30\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amjcard.2021.05.012\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2021.05.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasdai D, Lev EI, Behar S, Boykoet alA V, Battler, et al. Acute coronary syndromes in patients with pre-existing moderate to severe valvular disease of the heart: lessons from the Euro-Heart Survey of acute coronary syndromes. Eur Heart J. 2003;24(7):623\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0195-668X(02)00742-X\u003c/span\u003e\u003cspan address=\"10.1016/S0195-668X(02)00742-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam A, Munwar S, Talukder S, Rezaet alA A, Karim, et al. Impact of Baseline Admission Serum Creatinine Level in ST Segment Elevated Myocardial Infarction (STEMI) Patient Undergoing Primary PCI: An Important Predictor of in-hospital and 12-month Survival Outcome. Cardiovasc J. 2020;12:135\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3329/cardio.v12i2.47991\u003c/span\u003e\u003cspan address=\"10.3329/cardio.v12i2.47991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeyfarth M, Kastrati A, Mann JF, Ndrepepaet alA G, Sch\u0026ouml;mig, et al. Prognostic value of kidney function in patients with ST-elevation and non-ST-elevation acute myocardial infarction treated with percutaneous coronary intervention. Am J Kidney Dis. 2009;54(5):830\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.ajkd.2009.04.031\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2009.04.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJankowski J, Floege J, Fliser D, B\u0026ouml;hm M, Marx N. Cardiovascular Disease Chronic Kidney Disease Circulation. 2021;143(11):1157\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.120.050686\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.120.050686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarnak MJ, Levey AS, Schoolwerth AC, Coreshet J, Wilson alPW. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Hypertension. 2003;42(5):1050\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.HYP.0000102971.85504.7c\u003c/span\u003e\u003cspan address=\"10.1161/01.HYP.0000102971.85504.7c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Q-C, Chen Y-w, Gao Q-Y, Renet alJ K-D, Jiang, et al. Association of stress hyperglycemia with clinical outcomes in patients with ST-elevation myocardial infarction undergoing percutaneous coronary intervention: a cohort study. Cardiovasc Diabetol. 2023;22(1):85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12933-023-01812-9\u003c/span\u003e\u003cspan address=\"10.1186/s12933-023-01812-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCinar H, Avci A, Gulen M, Avciet alS BS, Satar, et al. Does stress hyperglycemia affect mortality? Acute myocardial infarction \u0026ndash; case control study. Archives Med Sci \u0026ndash; Atherosclerotic Dis. 2019;4(1):201\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5114/amsad.2019.87303\u003c/span\u003e\u003cspan address=\"10.5114/amsad.2019.87303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStentz FB, Umpierrez GE, Cuervo R, Kitabchi AE. Proinflammatory cytokines, markers of cardiovascular risks, oxidative stress, and lipid peroxidation in patients with hyperglycemic crises. Diabetes. 2004;53(8):2079\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/diabetes.53.8.2079\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.53.8.2079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNieuwdorp M, van Haeften TW, Gouverneur MC, Mooijet HL, Stroes alES. Loss of endothelial glycocalyx during acute hyperglycemia coincides with endothelial dysfunction and coagulation activation in vivo. Diabetes. 2006;55(2):480\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/diabetes.55.02.06.db05-1103\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.55.02.06.db05-1103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabatine MS, Morrow DA, Giugliano RP, Burtonet alE PBJ, Braunwald, et al. Association of Hemoglobin Levels With Clinical Outcomes in Acute Coronary Syndromes. Circulation. 2005;111(16):2042\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.CIR.0000162477.70955.5F\u003c/span\u003e\u003cspan address=\"10.1161/01.CIR.0000162477.70955.5F\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMost AS, Ruocco NA Jr., Gewirtz H. Effect of a reduction in blood viscosity on maximal myocardial oxygen delivery distal to a moderate coronary stenosis. Circulation. 1986;74(5):1085\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.cir.74.5.1085\u003c/span\u003e\u003cspan address=\"10.1161/01.cir.74.5.1085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu CW, Liao PC, Chen KC, Hsuet JC, Wu alYW. Baseline Hemoglobin Levels Associated with One-Year Mortality in ST-Segment Elevation Myocardial Infarction Patients. Acta Cardiol Sin. 2016;32(6):656\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6515/acs20160106a\u003c/span\u003e\u003cspan address=\"10.6515/acs20160106a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinnaird T, Anderson R, Ossei-Gerning N, Cockburnet J, Mamas alMA. Coronary Perforation Complicating Percutaneous Coronary Intervention in Patients With a History of Coronary Artery Bypass Surgery: An Analysis of 309 Perforation Cases From the British Cardiovascular Intervention Society Database. Circ Cardiovasc Interv. 2017;10(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circinterventions.117.005581\u003c/span\u003e\u003cspan address=\"10.1161/circinterventions.117.005581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaixeta AM, Lansky AJ, Mehran R, Breneret SJ, Stone alGW. Predictors of suboptimal TIMI flow after primary angioplasty for acute myocardial infarction: results from the HORIZONS-AMI trial. EuroIntervention. 2013;9(2):220\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4244/EIJV9I2A37\u003c/span\u003e\u003cspan address=\"10.4244/EIJV9I2A37\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKammler J, Kypta A, Hofmann R, Kerschneret alF K, Leisch, et al. TIMI 3 flow after primary angioplasty is an important predictor for outcome in patients with acute myocardial infarction. Clin Res Cardiol. 2009;98(3):165\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00392-008-0735-9\u003c/span\u003e\u003cspan address=\"10.1007/s00392-008-0735-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeipert KF, Bauer T, Nef HM, Hochadelet M, Hamm alCW. Incidence and outcome of peri-procedural cardiogenic shock: results from the international Euro Heart Survey PCI registry. Eur Heart J Acute Cardiovasc Care. 2020;9(2):120\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2048872618822460\u003c/span\u003e\u003cspan address=\"10.1177/2048872618822460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitahara H, Okada K, Kimura T, Yocket alY PG, Honda, et al. Impact of Stent Size Selection on Acute and Long-Term Outcomes After Drug-Eluting Stent Implantation in De Novo Coronary Lesions. Circ Cardiovasc Interv. 2017;10(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circinterventions.116.004795\u003c/span\u003e\u003cspan address=\"10.1161/circinterventions.116.004795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShugman IM, Hee L, Mussap CJ, Diuet P, French alJK. Bare-metal stenting of large coronary arteries in ST-elevation myocardial infarction is associated with low rates of target vessel revascularization. Am Heart J. 2013;165(4):591\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.ahj.2012.12.023\u003c/span\u003e\u003cspan address=\"10.1016/j.ahj.2012.12.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MACE, STEMI, PCI, Model Prediction","lastPublishedDoi":"10.21203/rs.3.rs-8058920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8058920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough ST Elevation Myocardial Infarction (STEMI) diagnosis and therapy have improved, high-risk categories like elderly persons still have a significant chance of MACE despite treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study attempts to construct a predictive nomogram for MACE incidence using clinical data from a STEMI registry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTehran Heart Center's computerized record recognized all 65-year-old STEMI primary PCI patients consecutively. This retrospective study examined demographic, laboratory, clinical, and intra-procedural factors. Post-PCI univariate and multivariate analyses identified MACE risk variables. Decision curve analysis, ROC, and calibration plots validated predictive nomograms. R Studio and R used “tidyverse” and “rms” packages for all analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 1946 study included 70% training and 30% testing patients. Basic demographic and clinical variables were identical for both groups. The average follow-up was 17 months. 8 factors were selected for the nomogram after univariate and multivariate analysis: left-ventricular ejection fraction (LVEF), serum creatinine, hemoglobin, and fasting blood glucose levels, presence of valvular heart disease, post-PCI TIMI flow grade, diameter of the culprit lesion stent, and presence or absence of shock after PCI. The post-PCI MACE prediction AUC was 71%. Calibration plots showed that the nomogram model was well-calibrated and close to observed outcomes. Decision curve analysis also revealed that the model predicted MACE discriminatively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nomogram successfully predicts MACE risk in older STEMI patients using laboratory, clinical, and procedural parameters. This algorithm may identify vulnerable high-risk patients for more aggressive preventative interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:10:27","doi":"10.21203/rs.3.rs-8058920/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-06T07:32:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T13:00:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275264713526749121112823119132903370803","date":"2026-01-01T12:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96892516073998454432362656978663874927","date":"2026-01-01T12:02:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-14T15:47:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23239599618219612146856687379965823912","date":"2025-12-11T11:23:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134974315778687543612353676623751569176","date":"2025-12-11T08:17:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T04:12:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T04:10:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-28T13:58:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T17:44:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-11-27T17:39:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e5859b7-e6bf-46dd-b559-19bd1a39b7ac","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T07:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:10:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8058920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8058920","identity":"rs-8058920","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}