7-Day Risk Prediction for Major Adverse Cardiac Events in Acute Coronary Syndrome Patients

7-Day Risk Prediction for Major Adverse Cardiac Events in Acute Coronary Syndrome Patients | 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 7-Day Risk Prediction for Major Adverse Cardiac Events in Acute Coronary Syndrome Patients Dede Moeswir, Sally A Nasution, Vika K Gliselda, Idrus Alwi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5667887/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Major Adverse Cardiac Events (MACE) increase illness and death rate among patients with acute coronary syndrome (ACS). Prediction scores have been utilized as prognostic to predict MACE. This study aims to develop a simple risk score that is easy to calculate and applicable for identifying ACS patients at risk for MACE. Methods: A retrospective cohort study involving 1,002 ACS patients in an intensive coronary care unit from January 1 st , 2021, until July 31 st , 2024. Sex, age, family history, diabetes, hemoglobin, leukocyte, creatinine, uric acid, cardiac enzyme, systolic blood pressure, heart rate, cardiac arrest, ST segment deviation, and Killip class were assessed as risk factors for MACE. Results: MACE was found in 112 (9.21%) of ACS patients. Predictors such as female, leukocyte, creatinine, uric acid, cardiac enzyme, systolic blood pressure, heart rate, cardiac arrest, and Killip class in multivariate logistic regression analysis were associated with MACE with (RR 95% CI) 2.66 (1.35-5.25), 2.06 (1.02-4.16), 2.84 (1.43-5.66), 3.79 (1.90-7.54), 3.26 (1.51-7.05), 3.48 (1.57-7.70), 2.46 (1.20-5.01), 42.04 (18.90-93.51), and 6.31 (3.19-12.50), respectively. The best predictive accuracy was obtained by an area under the curve of 0.95, 95% CI, 0.93-0.97. Conclusions: In ACS patients, the probability of MACE was found to be 3.6% for those with scores 0-6 and 83.5% for those with scores greater than 6, based on the following predictor factors: female (score 1), leukocytosis (score 1), elevated creatinine (score 1), hyperuricemia (score 2), elevated cardiac enzyme (score 1), hypotension (score 2), tachycardia (score 1), cardiac arrest (score 5), and Killip class III-IV (score 3). Major Adverse Cardiac Events acute coronary syndrome prediction score risk factors Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Cardiovascular diseases (CVDs) remain the leading cause of death globally, significantly impacting health and contributing to healthcare costs. In 2017, it accounted for 17 million deaths, representing 30% of deaths, and was responsible for 360 million Disability Adjusted Life Years Lost (DALYs). 1 WHO estimated around 17.7 million deaths of patients with CVD. Additionally, CVD imposes a significant financial burden, being the most expensive disease, surpassing Alzheimer’s and diabetes. 2 This trend is similar to what has been observed in developing countries like Indonesia, where there has been a notable rise in the incidence, prevalence, and mortality rates of CVD, according to RISKESDAS data. Previous studies have shown that approximately 470,000 individuals die from CVD in Indonesia each year. 3 It has been reported that there has been an increase in ACS cases in the Intensive Coronary Care Unit (ICCU) of Cipto Mangunkusumo Hospital (RSCM) every year with a mortality 12.1%. 4–5 MACE, which consists of cardiovascular and non-cardiovascular death, recurrent myocardial infarction, stroke, and recurrent percutaneous coronary intervention revascularization in hospitals, ranges from 20.4–36.7% in patients with ACS. 6 MACE complications in ACS patients are influenced by several risk factors, including age, gender, family history of coronary heart disease, diabetes, hemoglobin value, leukocyte count, creatinine value, uric acid value, cardiac enzyme value, systolic blood pressure, heart rate, cardiac arrest, ST segment deviation, and Killip class. 6 – 7 Early stratification of ACS patients at risk of MACE is crucial to identifying those in the high-risk group that requires early invasive strategy-based preventive measures. This stratification involves identifying individual demographic risk factors and clinical characteristics, along with assessing multiple other factors simultaneously to enhance the accuracy of risk assessment. This approach aims to reduce the rate of MACE in ACS patients. In ACS patients with a high risk of MACE, more aggressive therapeutic management is needed, but it turns out that most MACE occurs in individuals with intermediate risk, so an accurate risk assessment is needed to help reduce the incidence of MACE by achieving therapeutic targets, and the prediction score is a representation of an easy, simple, and accurate form of early risk stratification in patients with ACS undergoing the acute care phase. 8 Currently, there are several prediction scores that are often utilized to forecast the occurrence of MACE in patients with ACS. In the TIMI (Thrombolysis in Myocardial Infarction) score, the study population was only in patients with unstable angina pectoris and non-ST elevation myocardial infarction, and in this TIMI score, several significant independent predictor variables for estimating the occurrence of MACE in ACS patients were excluded, such as gender, heart rate, systolic blood pressure, serum creatinine, and cardiac arrest, and this TIMI score only has discriminative power with a c-statistic of 0.62, which indicates the weak predictive power. 9 In the GRACE (Global Registry of Acute Coronary Events) score, although it has a discriminative power with a c-statistic of 0.82 which is quite strong in predicting the occurrence of MACE in ACS patients, several strong independent predictor variables are not used. This GRACE score also requires calculating tools and handheld device systems in its operation, so it is not easy and simple. 9 – 10 In ACS patients with complex profiles, an initial stratification tool is needed to estimate the risk of MACE in ACS patients, such as the GRACE score, with the addition of several strong independent predictor variables such as gender, family history of coronary heart disease, diabetes, hemoglobin value, leukocyte count, and uric acid levels. This nomogram prediction score is an integration of various multivariable models of independent risk factors that are analyzed multivariately and simultaneously so that they are more accurate, simple, and have a balance between completeness and accuracy. 10 – 11 This study aims to develop a simple, easy-to-use, and comprehensive prediction score for the early risk stratification of ACS patients. This score seeks to improve the predictability of MACE, making it easier for clinicians to determine optimal management strategies, ultimately reducing morbidity and mortality in ACS patients. METHODS This was a retrospective cohort study based on prognostic research, aiming to evaluate the predictive ability of demographic, clinical, laboratory, and electrocardiographic parameters in predicting the occurrence of MACE within 7 days in patients with ACS. Sampling involved collecting medical record data starting on July 31th, 2024 and going back until the desired sample size was reached. MACE was evaluated for 7 days during hospitalization to determine outcomes through the medical record data registry. The results were recorded, and data analysis was subsequently carried out. Patients who met the inclusion criteria were those diagnosed with ACS and treated in the ICCU between January 1st, 2021, until July 31th, 2024. The study sample comprised the accessible population that met the research criteria. During this period, 1,204 ACS patients were identified; of these, 1,002 patients met the criteria to participate in the study, while 202 patients had incomplete medical record data. The exclusion criteria included patients who had incomplete medical record data. These criteria were established to ensure the selection of a specific patient population that met the study's objectives and to maintain data integrity throughout the research process. The sampling technique used was non-probability sampling, starting from July 2024 and going retrospectively backwards. In this study, 14 prognostic variables will be studied, namely age, gender, family history of coronary heart disease, diabetes, hemoglobin value, leukocyte count, creatinine value, uric acid value, cardiac enzyme marker value, systolic blood pressure, heart rate, cardiogenic shock, ST segment deviation, and Killip class. In previous studies, it was known that the prevalence of Major Adverse Cardiac Events in patients with acute coronary syndrome was 12.1%, so the sample size needed was 1,157 subjects. 4 The research data is recorded in a research form that has been tested in advance. After editing regarding the completeness of filling out the research form, this data is coded to be recorded on a computer. The data validation process is carried out to ensure the validity of the recorded data, and then the data processing process is carried out. The calculation of the arithmetic mean value and standard distribution is carried out for quantitative data, while the range of values ​​is calculated according to the 95% confidence interval. To find sensitivity, specificity, positive predictive value, and negative predictive value, analysis is used with a 2x2 table; bivariate analysis is carried out between each variable with MACE with Chi-Square analysis accompanied by the calculation of relative risk (RR) and its confidence limits. Variables that have p < 0.25 in the bivariate analysis will be included in the multivariate analysis (logistic regression). After obtaining which variables are significant through multivariate analysis (logistic regression), the ability to discriminate prognostic values ​​is determined using the area under the receiver operating characteristic curve, and then an analysis of which prognostic model will be recommended is carried out. To improve the usefulness of the obtained prognostic model, the model will be transformed into a nomogram with the S-plus statistics package. The prediction score system that has been created is assessed for its performance through calibration (with the Hosmer-Lemeshow test) and its discrimination ability (by looking at the AUC). The equation model for calculating the probability of MACE is tested for its internal validity using the Bootstrapping method. RESULTS 1,204 ACS patients were treated in the intensive care unit. A total of 1,002 patients met the criteria to participate in the study, while 202 patients did not have complete medical record data. Among the 1,002 subjects analyzed, MACE was reported in 112 subjects (9.21%). There were 650 male subjects (64.9%) and 352 female subjects (35.1%); MACE occurred in 63 male subjects (56.2%) and 49 female subjects (43.8%). There were 297 subjects aged ≥ 65 years (29.6%) and 705 subjects aged < 65 years (70.4%) with a median age of 58 years (range 18–90), MACE occurred in 44 subjects aged ≥ 65 years (39.3%) and 68 subjects aged < 65 years (60.7%). Patients with a family history of CHD were found in 162 subjects (16.2%), and 840 subjects (83.8%) were not accompanied by a family history of CHD, MACE occurred in 14 subjects (12.5%) with a family history of CHD and 98 subjects (87.5%) without a family history of CHD. Patients with diabetes were found in 332 subjects (33.1%), and 670 subjects (66.9%) were not accompanied by diabetes. MACE occurred in 44 subjects (39.3%) with diabetes and 68 subjects (60.7%) without a history of diabetes. All of 1.002 subjects with a diagnosis of ACS, 489 subjects (48.8%) were diagnosed with UAP accompanied by MACE in 17 subjects (15.2%), 243 subjects (24.3%) with a diagnosis of NSTEMI accompanied by MACE in 40 subjects (35.7%), and 270 subjects (26.9%) with a diagnosis of STEMI accompanied by MACE in 55 subjects (49.1%). More about the characteristics of the study subjects can be seen in Table 1 . Table 1 Characteristics of Research Subjects Variable All subject (%) MACE Yes No Age (years) ≥ 65 years 297 (29.6) 44 (39.3) 253 (28.4) < 65 years 705 (70.4) 68 (60.7) 637 (71.6) Gender Female 32.5 (35.1) 49 (43.8) 303 (34.0) Male 650 (64.9) 63 (56.2) 587 (66.0) Family history of CHD Yes 162 (16.2) 14 (12.5) 148 (16.6) No 840 (83.8) 98 (87.5) 742 (83.4) Diabetes Yes 332 (33.1) 44 (39.3) 288 (32.4) No 670 (66.9) 68 (60.7) 602 (67.6) Hemoglobin value Decrease 308 (30.7) 45 (40.2) 263 (29.6) Normal 694 (69.3) 67 (59.8) 627 (70.4) Leukocyte count Increase 476 (47.5) 78 (69.6) 398 (44.7) Normal 526 (52.5) 34 (30.4) 492 (55.3) Creatinine value Increase 404 (40.3) 78 (69.6) 325 (36.6) Normal 598 (59.7) 34 (30.4) 564 (63.4) Uric acid value Increase 392 (39.1) 87 (77.7) 305 (34.3) Normal 610 (60.9) 25 (22.3) 585 (65.7) Cardiac enzyme Increase 507 (50.6) 94 (83.9) 413 (46.4) Normal 495 (49.4) 18 (16.1) 477 (53.6) Systolic blood pressure Decrease 98 (9.8) 50 (51.0) 48 (49.0) Normal 904 (90.2) 62 (6.9) 842 (93.1) Heart rate Positive 151 (15.1) 53 (47.3) 98 (11.0) Negative 851 (84.9) 59 (52.7) 792 (89.0) Cardiac arrest Positive 88 (8.8) 70 (62.5) 18 (2.0) Negative 914 (91.2) 42 (37.5) 872 (98.0) ST segment deviation Positive 587 (58.6) 85 (75.9) 502 (56.7) Negative 415 (41.4) 27 (24.1) 384 (43.3) Killip class III-IV 194 (19.4) 84 (75.0) 110 (12.4) I-II 808 (80.6) 28 (25.0) 780 (87.6) Diagnosis UAP 489 (48.8) 17 (15.2) 472 (53.0) NSTEMI 243 (24.3) 40 (35.7) 203 (22.8) STEMI 270 (26.9) 55 (49.1) 215 (24.2) In the bivariate analysis, predictor factors associated with MACE in ACS patients were age ≥ 65 years, female gender, decreased hemoglobin value, increased leukocyte count, increased creatinine level, increased uric acid value, increased cardiac enzymes, decreased systolic blood pressure, increased heart rate, cardiac arrest, ST segment deviation, and increased Killip class. The relative risk of each predictor factor with a 95% confidence interval (CI) can be seen in Table 2 . Table 2 Bivariate analysis Variable MACE RR (95% CI) P Yes (%) (No%) Sex Female 49 (13.9) 303 (86.1) 1.43 0.043 Male 63 (9.7) 587 (90.3) (1.01–2.03) Age (Years) ≥ 65 years 44 (14.8) 253 (85.2) 1.53 0.024 < 65 years 68 (9.6) 637 (90.4) (1.07–2.18) Family History CHD Yes 14 (8.6) 148 (91.4) 0.74 0.326 No 98 (11.7) 742 (88.3) (0.43–1.26) Diabetes Yes 44 (13.3) 288 (86.7) 1.30 0.173 No 68 (10.1) 602 (89.9) (0.91–1.86) Hemoglobin value Decrease 45 (14.6) 263 (85.4) 1.51 0.029 Normal 67 (9.7) 627 (90.3) (1.06–2.15) Leukocyte count Increase 78 (16.4) 398 (83.6) 2.53 < 0.001 Normal 34 (6.5) 492 (93.5) (1.72–3.71) Creatinine value Increase 78 (19.3) 326 (80.7) 3.39 < 0.001 Normal 34 (5.7) 564 (94.3) (2.59–6.07) Uric acid value Increase 49 (13.9) 303 (86.1) 5.41 < 0.001 Normal 63 (9.7) 587 (90.3) (3.53–8.29) Cardiac enzymes Increase 109 (11.5) 835 (88.5) 5.09 < 0.001 Normal 3 (5.3) 54 (94.7) (3.12–8.31) Systolic blood pressure Decrease 50 (51.0) 48 (49.0) 7.43 < 0.001 Normal 62 (6.9) 842 (93.1) (5.46–10.13) Heart rate Increase 53 (35.1) 98 (64.9) 5.06 < 0.001 Normal 59 (6.9) 792 (93.1) (3.64–7.02) Cardiac arrest Positive 70 (79.5) 18 (20.5) 17.31 < 0.001 Negative 42 (4.6) 872 (95.4) (12.64–23.69) ST segment deviation Positive 85 (14.5) 502 (85.5) 2.22 < 0.001 Negative 27 (6.6) 384 (93.4) (1.47–3.36) Killip class III-IV 84 (43.3) 110 (56.7) 12.49 < 0.001 I-II 28 (3.5) 780 (96.5) (8.39–18.60) Variables that in bivariate analysis gave a p-value < 0.25, namely age, gender, diabetes, hemoglobin value, leukocyte count, creatinine value, uric acid value, cardiac enzymes, systolic blood pressure, heart rate, cardiac arrest, and Killip class, were included in the multivariate analysis. Multivariate analysis was performed using logistic regression. The results of each multivariate analysis can be seen in Table 3 . Table 3 Multivariate analysis Variable OR (95% CI) P Sex 2.66 (1.35–5.25) 0.005 Leukocyte count 2.06 (1.02–4.16) 0.044 Creatinine value 2.84 (1.43–5.66) 0.003 Uric acid value 3.79 (1.90–7.54) < 0.001 Cardiac enzymes 3.26 (1.51–7.05) 0.003 Systolic blood pressure 3.48 (1.57–7.70) 0.002 Heart rate 2.46 (1.20–5.01) 0.013 Cardiac arrest 42.04 (18.90-93.51) < 0.001 Killip class 6.31 (3.19–12.50) < 0.001 To simplify and facilitate the use of significant MACE probability prediction in daily clinical practice, a scoring system is created based on the results of the logistic regression analysis above. With this scoring system, identification and stratification of ACS patients with a high risk of MACE can be done more easily and accurately. The significant MACE prediction score can be made based on the rounding of the results of dividing the regression coefficient (B) by the standard error of each predictor variable. Thus we can obtain a score for each variable, which can be seen in Table 4 . Table 4 Prediction score for MACE Variable B SE B/SE Score Rounding Gender 0.980 0.346 2.829 1.40 1 Leukocyte count 0.723 0.358 2.018 1.00 1 Creatinine value 1.045 0.351 2.979 1.47 1 Uric acid value 1.332 0.351 3.793 1.88 2 Cardiac enzymes 1.183 0.363 3.008 1.49 1 Systolic blood pressure 1.249 0.404 3.089 1.53. 2 Heart rate 0.899 0.363 2.478 1.23 1 Cardiac arrest 3.738 0.407 9.167 4.54 5 Killip class 1.843 0.348 5.287 2.62 3 Based on the results of the logistic regression analysis above, we can also obtain category scores for each variable for MACE prediction, as can be seen in Table 5 . Table 5 Variable category scores for prediction of MACE Variable Category Score Sex Female 1 Male 0 Leukocyte count Decrease 1 Normal 0 Creatinine value Increase 1 Normal 0 Uric acid value Increase 2 Normal 0 Cardiac enzymes Increase 1 Normal 0 Systolic blood pressure Decrease 2 Normal 0 Heart rate Increase 1 Normal 0 Cardiac arrest Positive 5 Negative 0 Killip class III-IV 3 I-II 0 Based on the existing scores for each variable, using logistic regression analysis, a total score can also be created so that the probability of MACE occurring can be calculated based on the total score for one individual. Table 6 Probability of MACE based on total score Total Score OR (95% CI) 0–3 2.66 (1.35–5.25) 4–7 2.06 (1.02–4.16) 8–11 2.84 (1.43–5.66) > 11 3.79 (1.90–7.54) The MACE prediction score that has been created is assessed for its quality and performance through calibration (with the Hosmer-Lemeshow test) and its discrimination ability by looking at the area under the receiver operating characteristic curve (AUC) value. In the MACE prediction score, the Hosmer-Lemeshow test value was obtained with a value of p = 0.694 (p > 0.05), which means that the prediction model created has quite good precision. Meanwhile, the discrimination ability of this scoring system to distinguish patients who are predicted to experience MACE from those who will not experience MACE is good ([AUC] 0.95 with 95% CI, 0.93–0.97). Internal validation of the two scoring systems was carried out using the Bootstrapping method. After validation was carried out, the Hosmer-Lameshow test value for MACE, p = 0.664 was obtained. The Hosmer-Lameshow test value p > 0.05 after the Bootstrapping method was carried out showed that the prediction score above had good internal validation. In the sensitivity and specificity analysis of the prediction score, an optimal cutoff point was obtained with a sensitivity of 86% and a specificity of 92%, which means that if the score is > 6, there is a high probability that MACE will occur in ACS patients. In the prediction score for the occurrence of MACE, the R square value was obtained as 0.690, which illustrates the ability of the risk factor variables of gender, leukocyte count, creatinine value, uric acid value, cardiac enzymes, systolic blood pressure, heart rate, cardiac arrest, and Killip class in explaining the variance of the occurrence of MACE in ACS patients by 69%, and there are 31% other factors that explain the variance of the occurrence of MACE. DISCUSSION In multivariate analysis with logistic regression, a scoring system was obtained that can calculate the probability of MACE in ACS patients with a certain score number. In the MACE prediction score, scores of 0–3, 4–7, 8–11, and > 11 are each associated with a probability of MACE of 0.8%, 8.9%, 63.8%, and 97.3%. Based on the MACE prediction score above, the researcher categorized the prediction score into mild, moderate, and high risk. For the MACE prediction score, a score of 0 to 3 can be categorized as low risk with a probability of finding MACE of up to 0.8%. A score of 4 to 7 with a probability of finding MACE of 8.9% can be categorized as moderate risk. A score of > 8 is categorized as high risk with a probability of finding MACE of 82.2%. For low risk, it is recommended to be treated in the intensive care unit, and an elective invasive strategy is planned. For those with moderate risk, it is recommended to undergo treatment in the intensive care room and plan an invasive strategy within 24 hours, whereas if a high risk is found, it is recommended to undergo intensive treatment in a tertiary intensive care unit that has invasive hemodynamic monitoring facilities, ventilators, and the availability of an intra-aortic balloon pump and immediate early invasive strategy action Table 6 . Score Risk MACE Probability Recommendation 0–3 Low 0.8% Intensive care, elective care 4–7 Moderate 8.9% Intensive care, 24-hour invasive > 8 High 82.2% Tertiary intensive care, early invasive The internal validation of this prognostic study was conducted using the bootstrapping method using SPSS statistical software. After repeating the sample 1000 times, each Hosmer- Lameshow value was obtained p = 0.694 for the occurrence of MACE, the p-value obtained was > 0.05, so it can be assumed that the internal validation of this study is good in its ability as a predictive factor for the occurrence of MACE in ACS patients. For external validation, a multicenter study with a similar design is needed in the ACS population in other places. By being conducted in many places, it is expected that the inference and generalization capabilities of this study in the target population of ACS sufferers in Indonesia can be known so that the suggestions and recommendations that will be made can be better. CONCLUSION The proportion of MACE in ACS patients was found to be 9.2%. Factors such as gender, leukocyte count, creatinine value, uric acid value, cardiac enzymes, systolic blood pressure, heart rate, cardiac arrest, and Killip class are sequentially predictive factors for MACE in ACS patients. The recommended scoring system is as follows: low risk (0–3) probability 0.8%, moderate risk (4–7) probability 8.9% and high risk (> 8) probability 82.2%. The precision of the scoring system was quite good with p = 0.694 and AUC 0.95 (95% CI, 0.93–0.97). Declarations CONFLICT OF INTEREST The authors have no conflict of interest. FUNDING This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. ETHICAL STATEMENT This study was approved by the Health Research Ethics Committee of Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital No. 186/H2.1/ETIK, and received research permit approval from the research department. All data obtained from stored medical records will be kept confidential. AUTHOR CONTRIBUTION All authors contributed equally to this study. References Li Z, Lin L, Wu H, et al. 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Epub 2019 Jun 12. Ashburn NP, O'Neill JC, Stopyra JP, Mahler SA. Scoring systems for the triage and assessment of short-term cardiovascular risk in patients with acute chest pain. Rev Cardiovasc Med. 2021 Dec 22;22(4):1393-1403. Yanqiao L, Shen L, Yutong M, Linghong S, Ben H. Comparison of GRACE and TIMI risk scores in the prediction of in-hospital and long-term outcomes among East Asian non-ST-elevation myocardial infarction patients. BMC Cardiovasc Disord. 2022 Jan 7;22(1):4. Sofidis G, Otountzidis N, Stalikas N, Karagiannidis E, Papazoglou AS, Moysidis DV, Panteris E, Deda O, Kartas A, Zegkos T, Daskalaki P, Theodoridou N, Stefanopoulos L, Karvounis H, Gika H, Theodoridis G, Sianos G. Association of GRACE Risk Score with Coronary Artery Disease Complexity in Patients with Acute Coronary Syndrome. J Clin Med. 2021 May 20;10(10):2210. Kawamura Y, Yoshimachi F, Murotani N, Karasawa Y, Nagamatsu H, Kasai S, Ikari Y. Comparison of Mortality Prediction by the GRACE Score, Multiple Biomarkers, and Their Combination in All-comer Patients with Acute Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Intern Med. 2023 Feb 15;62(4):503-510. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5667887","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414546992,"identity":"5984561e-da57-4c1e-a423-5059bcfcdcac","order_by":0,"name":"Dede Moeswir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDCCAwwGEAYzA+ODhAobObDgAyK1MBt8OJNmDBZMIEoLAwOb5My2w4kNICY+LXzHmzc+ulFhY29wnPeBNA8bc/r8sMMPgbbYyek2YNcieeZYsXHOmbTEDYfZDYx5eNhyN95OMwBqSTY2O4Bdi8GNHDPp3LbDCWaH2RiSeSR4cjfOTgBpOZC4DZeW+2/Mf+e2/bcHaTnMYyCRbjg7/QN+LTd4zJhz2w4wbjvMxtg4I8EgQV46B78tkmfSiqVzziQn7j/MxswANN5wg3ROwYEEA9x+4Tt+eOPnnAo7e8n+Y+w/Ev/9l5efnb75w4cKOzlcWrA4FazSgIAqFCDfQIrqUTAKRsEoGAkAAAJeZg6Sx4GlAAAAAElFTkSuQmCC","orcid":"","institution":"Syarif Hidayatullah State Islamic University Jakarta","correspondingAuthor":true,"prefix":"","firstName":"Dede","middleName":"","lastName":"Moeswir","suffix":""},{"id":414546993,"identity":"661b0710-5150-410f-8cc2-97239f38ccd7","order_by":1,"name":"Sally A Nasution","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Sally","middleName":"A","lastName":"Nasution","suffix":""},{"id":414546994,"identity":"d1704476-6c12-496b-8c66-18f1fe07f031","order_by":2,"name":"Vika K Gliselda","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vika","middleName":"K","lastName":"Gliselda","suffix":""},{"id":414546995,"identity":"581980ef-9527-466e-b7f2-1ff1f26ce263","order_by":3,"name":"Idrus Alwi","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Idrus","middleName":"","lastName":"Alwi","suffix":""}],"badges":[],"createdAt":"2024-12-18 09:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5667887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5667887/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76194075,"identity":"2eeb0544-8a39-4db9-90ed-f994a596554a","added_by":"auto","created_at":"2025-02-13 10:06:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36376,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of MACE\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5667887/v1/fdfa4b5f566935887659d4b8.png"},{"id":76194077,"identity":"745ef1b5-d05f-4e3e-a58a-62ede9c8cc67","added_by":"auto","created_at":"2025-02-13 10:06:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29083,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve score predicts MACE\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5667887/v1/c047d34b0a06bb252b273454.png"},{"id":76194076,"identity":"c3956be9-24d6-4652-875f-80a1d26f084f","added_by":"auto","created_at":"2025-02-13 10:06:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33115,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity and specificity of prediction scores\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5667887/v1/9deceb13d94812905343cef5.png"},{"id":76194966,"identity":"3f843623-f1a4-4001-b012-fb40894d6b00","added_by":"auto","created_at":"2025-02-13 10:14:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":807907,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5667887/v1/677209a0-8ca9-486f-a246-d6a372961ad7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"7-Day Risk Prediction for Major Adverse Cardiac Events in Acute Coronary Syndrome Patients","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiovascular diseases (CVDs) remain the leading cause of death globally, significantly impacting health and contributing to healthcare costs. In 2017, it accounted for 17\u0026nbsp;million deaths, representing 30% of deaths, and was responsible for 360\u0026nbsp;million Disability Adjusted Life Years Lost (DALYs).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e WHO estimated around 17.7\u0026nbsp;million deaths of patients with CVD. Additionally, CVD imposes a significant financial burden, being the most expensive disease, surpassing Alzheimer\u0026rsquo;s and diabetes.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This trend is similar to what has been observed in developing countries like Indonesia, where there has been a notable rise in the incidence, prevalence, and mortality rates of CVD, according to RISKESDAS data. Previous studies have shown that approximately 470,000 individuals die from CVD in Indonesia each year.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e It has been reported that there has been an increase in ACS cases in the Intensive Coronary Care Unit (ICCU) of Cipto Mangunkusumo Hospital (RSCM) every year with a mortality 12.1%.\u003csup\u003e4\u0026ndash;5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMACE, which consists of cardiovascular and non-cardiovascular death, recurrent myocardial infarction, stroke, and recurrent percutaneous coronary intervention revascularization in hospitals, ranges from 20.4\u0026ndash;36.7% in patients with ACS.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e MACE complications in ACS patients are influenced by several risk factors, including age, gender, family history of coronary heart disease, diabetes, hemoglobin value, leukocyte count, creatinine value, uric acid value, cardiac enzyme value, systolic blood pressure, heart rate, cardiac arrest, ST segment deviation, and Killip class.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEarly stratification of ACS patients at risk of MACE is crucial to identifying those in the high-risk group that requires early invasive strategy-based preventive measures. This stratification involves identifying individual demographic risk factors and clinical characteristics, along with assessing multiple other factors simultaneously to enhance the accuracy of risk assessment. This approach aims to reduce the rate of MACE in ACS patients. In ACS patients with a high risk of MACE, more aggressive therapeutic management is needed, but it turns out that most MACE occurs in individuals with intermediate risk, so an accurate risk assessment is needed to help reduce the incidence of MACE by achieving therapeutic targets, and the prediction score is a representation of an easy, simple, and accurate form of early risk stratification in patients with ACS undergoing the acute care phase.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCurrently, there are several prediction scores that are often utilized to forecast the occurrence of MACE in patients with ACS. In the TIMI (Thrombolysis in Myocardial Infarction) score, the study population was only in patients with unstable angina pectoris and non-ST elevation myocardial infarction, and in this TIMI score, several significant independent predictor variables for estimating the occurrence of MACE in ACS patients were excluded, such as gender, heart rate, systolic blood pressure, serum creatinine, and cardiac arrest, and this TIMI score only has discriminative power with a c-statistic of 0.62, which indicates the weak predictive power.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the GRACE (Global Registry of Acute Coronary Events) score, although it has a discriminative power with a c-statistic of 0.82 which is quite strong in predicting the occurrence of MACE in ACS patients, several strong independent predictor variables are not used. This GRACE score also requires calculating tools and handheld device systems in its operation, so it is not easy and simple.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003eIn ACS patients with complex profiles, an initial stratification tool is needed to estimate the risk of MACE in ACS patients, such as the GRACE score, with the addition of several strong independent predictor variables such as gender, family history of coronary heart disease, diabetes, hemoglobin value, leukocyte count, and uric acid levels. This nomogram prediction score is an integration of various multivariable models of independent risk factors that are analyzed multivariately and simultaneously so that they are more accurate, simple, and have a balance between completeness and accuracy.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study aims to develop a simple, easy-to-use, and comprehensive prediction score for the early risk stratification of ACS patients. This score seeks to improve the predictability of MACE, making it easier for clinicians to determine optimal management strategies, ultimately reducing morbidity and mortality in ACS patients.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis was a retrospective cohort study based on prognostic research, aiming to evaluate the predictive ability of demographic, clinical, laboratory, and electrocardiographic parameters in predicting the occurrence of MACE within 7 days in patients with ACS. Sampling involved collecting medical record data starting on July 31th, 2024 and going back until the desired sample size was reached. MACE was evaluated for 7 days during hospitalization to determine outcomes through the medical record data registry. The results were recorded, and data analysis was subsequently carried out. Patients who met the inclusion criteria were those diagnosed with ACS and treated in the ICCU between January 1st, 2021, until July 31th, 2024. The study sample comprised the accessible population that met the research criteria. During this period, 1,204 ACS patients were identified; of these, 1,002 patients met the criteria to participate in the study, while 202 patients had incomplete medical record data. The exclusion criteria included patients who had incomplete medical record data. These criteria were established to ensure the selection of a specific patient population that met the study's objectives and to maintain data integrity throughout the research process. The sampling technique used was non-probability sampling, starting from July 2024 and going retrospectively backwards.\u003c/p\u003e \u003cp\u003eIn this study, 14 prognostic variables will be studied, namely age, gender, family history of coronary heart disease, diabetes, hemoglobin value, leukocyte count, creatinine value, uric acid value, cardiac enzyme marker value, systolic blood pressure, heart rate, cardiogenic shock, ST segment deviation, and Killip class. In previous studies, it was known that the prevalence of Major Adverse Cardiac Events in patients with acute coronary syndrome was 12.1%, so the sample size needed was 1,157 subjects.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The research data is recorded in a research form that has been tested in advance. After editing regarding the completeness of filling out the research form, this data is coded to be recorded on a computer. The data validation process is carried out to ensure the validity of the recorded data, and then the data processing process is carried out. The calculation of the arithmetic mean value and standard distribution is carried out for quantitative data, while the range of values ​​is calculated according to the 95% confidence interval. To find sensitivity, specificity, positive predictive value, and negative predictive value, analysis is used with a 2x2 table; bivariate analysis is carried out between each variable with MACE with Chi-Square analysis accompanied by the calculation of relative risk (RR) and its confidence limits. Variables that have p\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in the bivariate analysis will be included in the multivariate analysis (logistic regression). After obtaining which variables are significant through multivariate analysis (logistic regression), the ability to discriminate prognostic values ​​is determined using the area under the receiver operating characteristic curve, and then an analysis of which prognostic model will be recommended is carried out. To improve the usefulness of the obtained prognostic model, the model will be transformed into a nomogram with the S-plus statistics package. The prediction score system that has been created is assessed for its performance through calibration (with the Hosmer-Lemeshow test) and its discrimination ability (by looking at the AUC). The equation model for calculating the probability of MACE is tested for its internal validity using the Bootstrapping method.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e1,204 ACS patients were treated in the intensive care unit. A total of 1,002 patients met the criteria to participate in the study, while 202 patients did not have complete medical record data. Among the 1,002 subjects analyzed, MACE was reported in 112 subjects (9.21%). There were 650 male subjects (64.9%) and 352 female subjects (35.1%); MACE occurred in 63 male subjects (56.2%) and 49 female subjects (43.8%). There were 297 subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (29.6%) and 705 subjects aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years (70.4%) with a median age of 58 years (range 18\u0026ndash;90), MACE occurred in 44 subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (39.3%) and 68 subjects aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years (60.7%). Patients with a family history of CHD were found in 162 subjects (16.2%), and 840 subjects (83.8%) were not accompanied by a family history of CHD, MACE occurred in 14 subjects (12.5%) with a family history of CHD and 98 subjects (87.5%) without a family history of CHD. Patients with diabetes were found in 332 subjects (33.1%), and 670 subjects (66.9%) were not accompanied by diabetes. MACE occurred in 44 subjects (39.3%) with diabetes and 68 subjects (60.7%) without a history of diabetes. All of 1.002 subjects with a diagnosis of ACS, 489 subjects (48.8%) were diagnosed with UAP accompanied by MACE in 17 subjects (15.2%), 243 subjects (24.3%) with a diagnosis of NSTEMI accompanied by MACE in 40 subjects (35.7%), and 270 subjects (26.9%) with a diagnosis of STEMI accompanied by MACE in 55 subjects (49.1%). More about the characteristics of the study subjects can be seen in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Research Subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll subject (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e253 (28.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e705 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e637 (71.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.5 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e303 (34.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e650 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e587 (66.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e148 (16.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e840 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e742 (83.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e288 (32.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e670 (66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e602 (67.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e263 (29.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e694 (69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e627 (70.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e476 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e398 (44.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e526 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e492 (55.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e325 (36.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e598 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e564 (63.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e392 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e305 (34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e610 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e585 (65.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e507 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e413 (46.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e495 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e477 (53.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48 (49.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e904 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e842 (93.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98 (11.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e851 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e792 (89.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e914 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e872 (98.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST segment deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e587 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e502 (56.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e384 (43.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKillip class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e808 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e780 (87.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e489 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e472 (53.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSTEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e203 (22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e270 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e215 (24.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the bivariate analysis, predictor factors associated with MACE in ACS patients were age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, female gender, decreased hemoglobin value, increased leukocyte count, increased creatinine level, increased uric acid value, increased cardiac enzymes, decreased systolic blood pressure, increased heart rate, cardiac arrest, ST segment deviation, and increased Killip class. The relative risk of each predictor factor with a 95% confidence interval (CI) can be seen in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(No%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e303 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e587 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.01\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e637 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.07\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily History CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e742 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.43\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e288 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e602 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.91\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e263 (85.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e627 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.06\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e398 (83.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e492 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.72\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e326 (80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e564 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(2.59\u0026ndash;6.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e303 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e587 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(3.53\u0026ndash;8.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac enzymes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e835 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(3.12\u0026ndash;8.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e842 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(5.46\u0026ndash;10.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e792 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(3.64\u0026ndash;7.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e872 (95.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(12.64\u0026ndash;23.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST segment deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e502 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e384 (93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.47\u0026ndash;3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKillip class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e780 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(8.39\u0026ndash;18.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVariables that in bivariate analysis gave a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25, namely age, gender, diabetes, hemoglobin value, leukocyte count, creatinine value, uric acid value, cardiac enzymes, systolic blood pressure, heart rate, cardiac arrest, and Killip class, were included in the multivariate analysis. Multivariate analysis was performed using logistic regression. The results of each multivariate analysis can be seen in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.66 (1.35\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.06 (1.02\u0026ndash;4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.84 (1.43\u0026ndash;5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.79 (1.90\u0026ndash;7.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac enzymes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26 (1.51\u0026ndash;7.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.48 (1.57\u0026ndash;7.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.46 (1.20\u0026ndash;5.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.04 (18.90-93.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKillip class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.31 (3.19\u0026ndash;12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo simplify and facilitate the use of significant MACE probability prediction in daily clinical practice, a scoring system is created based on the results of the logistic regression analysis above. With this scoring system, identification and stratification of ACS patients with a high risk of MACE can be done more easily and accurately. The significant MACE prediction score can be made based on the rounding of the results of dividing the regression coefficient (B) by the standard error of each predictor variable. Thus we can obtain a score for each variable, which can be seen in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction score for MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB/SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRounding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac enzymes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKillip class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the results of the logistic regression analysis above, we can also obtain category scores for each variable for MACE prediction, as can be seen in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable category scores for prediction of MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac enzymes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKillip class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the existing scores for each variable, using logistic regression analysis, a total score can also be created so that the probability of MACE occurring can be calculated based on the total score for one individual.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProbability of MACE based on total score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.66 (1.35\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.06 (1.02\u0026ndash;4.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.84 (1.43\u0026ndash;5.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.79 (1.90\u0026ndash;7.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MACE prediction score that has been created is assessed for its quality and performance through calibration (with the Hosmer-Lemeshow test) and its discrimination ability by looking at the area under the receiver operating characteristic curve (AUC) value. In the MACE prediction score, the Hosmer-Lemeshow test value was obtained with a value of p\u0026thinsp;=\u0026thinsp;0.694 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which means that the prediction model created has quite good precision. Meanwhile, the discrimination ability of this scoring system to distinguish patients who are predicted to experience MACE from those who will not experience MACE is good ([AUC] 0.95 with 95% CI, 0.93\u0026ndash;0.97). Internal validation of the two scoring systems was carried out using the Bootstrapping method. After validation was carried out, the Hosmer-Lameshow test value for MACE, p\u0026thinsp;=\u0026thinsp;0.664 was obtained. The Hosmer-Lameshow test value p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 after the Bootstrapping method was carried out showed that the prediction score above had good internal validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity and specificity analysis of the prediction score, an optimal cutoff point was obtained with a sensitivity of 86% and a specificity of 92%, which means that if the score is \u0026gt;\u0026thinsp;6, there is a high probability that MACE will occur in ACS patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the prediction score for the occurrence of MACE, the R square value was obtained as 0.690, which illustrates the ability of the risk factor variables of gender, leukocyte count, creatinine value, uric acid value, cardiac enzymes, systolic blood pressure, heart rate, cardiac arrest, and Killip class in explaining the variance of the occurrence of MACE in ACS patients by 69%, and there are 31% other factors that explain the variance of the occurrence of MACE.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn multivariate analysis with logistic regression, a scoring system was obtained that can calculate the probability of MACE in ACS patients with a certain score number. In the MACE prediction score, scores of 0\u0026ndash;3, 4\u0026ndash;7, 8\u0026ndash;11, and \u0026gt;\u0026thinsp;11 are each associated with a probability of MACE of 0.8%, 8.9%, 63.8%, and 97.3%. Based on the MACE prediction score above, the researcher categorized the prediction score into mild, moderate, and high risk. For the MACE prediction score, a score of 0 to 3 can be categorized as low risk with a probability of finding MACE of up to 0.8%. A score of 4 to 7 with a probability of finding MACE of 8.9% can be categorized as moderate risk. A score of \u0026gt;\u0026thinsp;8 is categorized as high risk with a probability of finding MACE of 82.2%. For low risk, it is recommended to be treated in the intensive care unit, and an elective invasive strategy is planned. For those with moderate risk, it is recommended to undergo treatment in the intensive care room and plan an invasive strategy within 24 hours, whereas if a high risk is found, it is recommended to undergo intensive treatment in a tertiary intensive care unit that has invasive hemodynamic monitoring facilities, ventilators, and the availability of an intra-aortic balloon pump and immediate early invasive strategy action Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACE Probability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecommendation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensive care, elective care\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensive care, 24-hour invasive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTertiary intensive care, early invasive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe internal validation of this prognostic study was conducted using the bootstrapping method using SPSS statistical software. After repeating the sample 1000 times, each Hosmer- Lameshow value was obtained p\u0026thinsp;=\u0026thinsp;0.694 for the occurrence of MACE, the p-value obtained was \u0026gt;\u0026thinsp;0.05, so it can be assumed that the internal validation of this study is good in its ability as a predictive factor for the occurrence of MACE in ACS patients.\u003c/p\u003e \u003cp\u003eFor external validation, a multicenter study with a similar design is needed in the ACS population in other places. By being conducted in many places, it is expected that the inference and generalization capabilities of this study in the target population of ACS sufferers in Indonesia can be known so that the suggestions and recommendations that will be made can be better.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe proportion of MACE in ACS patients was found to be 9.2%. Factors such as gender, leukocyte count, creatinine value, uric acid value, cardiac enzymes, systolic blood pressure, heart rate, cardiac arrest, and Killip class are sequentially predictive factors for MACE in ACS patients. The recommended scoring system is as follows: low risk (0\u0026ndash;3) probability 0.8%, moderate risk (4\u0026ndash;7) probability 8.9% and high risk (\u0026gt;\u0026thinsp;8) probability 82.2%. The precision of the scoring system was quite good with p\u0026thinsp;=\u0026thinsp;0.694 and AUC 0.95 (95% CI, 0.93\u0026ndash;0.97).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Health Research Ethics Committee of Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital No. 186/H2.1/ETIK, and received research permit approval from the research department. All data obtained from stored medical records will be kept confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLi Z, Lin L, Wu H, et al. Global, Regional, and National Death, and Disability-Adjusted Life-Years (DALYs) for Cardiovascular Disease in 2017 and Trends and Risk Analysis From 1990 to 2017 Using the Global Burden of Disease Study and Implications for Prevention. \u003cem\u003eFront Public Health\u003c/em\u003e. 2021;9:559751. Published 2021 Oct 29. doi:10.3389/fpubh.2021.559751\u003c/li\u003e\n \u003cli\u003eDunbar SB, Khavjou OA, Bakas T, Hunt G, Kirch RA, Leib AR, Morrison RS, Poehler DC, Roger VL, Whitsel LP., American Heart Association. Projected Costs of Informal Caregiving for Cardiovascular Disease: 2015 to 2035: A Policy Statement From the American Heart Association.\u0026nbsp;Circulation.\u0026nbsp;2018 May 08;137(19):e558-e577.\u003c/li\u003e\n \u003cli\u003eHarmadha WSP, Muharram FR, Gaspar RS, Azimuth Z, Sulistya HA, Firmansyah F, Multazam CECZ, Harits M, Putra RM. Explaining the increase of incidence and mortality from cardiovascular disease in Indonesia: A global burden of disease study analysis (2000-2019). PLoS One. 2023 Dec 15;18(12):e0294128.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSetyawan, W. Validasi skor TIMI dalam memprediksi mortalitas pasien sindrom koroner akut di Indonesia [Tesis]. Jakarta: Universitas Indonesia, 2011.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMeutia RS, Nasution SA, Makmun LH. Validity of Sample Risk Index and Evaluation of Methods and Management of Acute Coronary Events to Predict Mortality in Acute Coronary Syndrome Patients in Intensive Coronary Care Unit at Mangunkusumo Hospital. Jurnal Penyakit Dalam Indonesia. 2017. Vol. 4: Iss. 43.\u003c/li\u003e\n \u003cli\u003eOkkonen M, Havulinna AS, Ukkola O, Huikuri H, Pietil\u0026auml; A, Koukkunen H, Lehto S, Mustonen J, Ketonen M, Airaksinen J, Kes\u0026auml;niemi YA, Salomaa V. Risk factors for major adverse cardiovascular events after the first acute coronary syndrome. Ann Med. 2021 Dec;53(1):817-823.\u003c/li\u003e\n \u003cli\u003eChoi BG, Rha SW, Yoon SG, Choi CU, Lee MW, Kim SW. Association of Major Adverse Cardiac Events up to 5 Years in Patients With Chest Pain Without Significant Coronary Artery Disease in the Korean Population. J Am Heart Assoc. 2019 Jun 18;8(12):e010541. doi: 10.1161/JAHA.118.010541. Epub 2019 Jun 12.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAshburn NP, O\u0026apos;Neill JC, Stopyra JP, Mahler SA. Scoring systems for the triage and assessment of short-term cardiovascular risk in patients with acute chest pain. Rev Cardiovasc Med. 2021 Dec 22;22(4):1393-1403.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYanqiao L, Shen L, Yutong M, Linghong S, Ben H. Comparison of GRACE and TIMI risk scores in the prediction of in-hospital and long-term outcomes among East Asian non-ST-elevation myocardial infarction patients. BMC Cardiovasc Disord. 2022 Jan 7;22(1):4.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSofidis G, Otountzidis N, Stalikas N, Karagiannidis E, Papazoglou AS, Moysidis DV, Panteris E, Deda O, Kartas A, Zegkos T, Daskalaki P, Theodoridou N, Stefanopoulos L, Karvounis H, Gika H, Theodoridis G, Sianos G. Association of GRACE Risk Score with Coronary Artery Disease Complexity in Patients with Acute Coronary Syndrome. J Clin Med. 2021 May 20;10(10):2210.\u003c/li\u003e\n \u003cli\u003eKawamura Y, Yoshimachi F, Murotani N, Karasawa Y, Nagamatsu H, Kasai S, Ikari Y. Comparison of Mortality Prediction by the GRACE Score, Multiple Biomarkers, and Their Combination in All-comer Patients with Acute Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Intern Med. 2023 Feb 15;62(4):503-510.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Major Adverse Cardiac Events, acute coronary syndrome, prediction score, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-5667887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5667887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Major Adverse Cardiac Events (MACE) increase illness and death rate among patients with acute coronary syndrome (ACS). Prediction scores have been utilized as prognostic to predict MACE. This study aims to develop a simple risk score that is easy to calculate and applicable for identifying ACS patients at risk for MACE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective cohort study involving 1,002 ACS patients in an intensive coronary care unit from January 1\u003csup\u003est\u003c/sup\u003e, 2021, until July 31\u003csup\u003est\u003c/sup\u003e, 2024. Sex, age, family history, diabetes, hemoglobin, leukocyte, creatinine, uric acid, cardiac enzyme, systolic blood pressure, heart rate, cardiac arrest, ST segment deviation, and Killip class were assessed as risk factors for MACE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e MACE was found in 112 (9.21%) of ACS patients. Predictors such as female, leukocyte, creatinine, uric acid, cardiac enzyme, systolic blood pressure, heart rate, cardiac arrest, and Killip class in multivariate logistic regression analysis were associated with MACE with (RR 95% CI) 2.66 (1.35-5.25), 2.06 (1.02-4.16), 2.84 (1.43-5.66), 3.79 (1.90-7.54), 3.26 (1.51-7.05), 3.48 (1.57-7.70), 2.46 (1.20-5.01), 42.04 (18.90-93.51), and 6.31 (3.19-12.50), respectively. The best predictive accuracy was obtained by an area under the curve of 0.95, 95% CI, 0.93-0.97.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In ACS patients, the probability of MACE was found to be 3.6% for those with scores 0-6 and 83.5% for those with scores greater than 6, based on the following predictor factors: female (score 1), leukocytosis (score 1), elevated creatinine (score 1), hyperuricemia (score 2), elevated cardiac enzyme (score 1), hypotension (score 2), tachycardia (score 1), cardiac arrest (score 5), and Killip class III-IV (score 3).\u003c/p\u003e","manuscriptTitle":"7-Day Risk Prediction for Major Adverse Cardiac Events in Acute Coronary Syndrome Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 10:06:54","doi":"10.21203/rs.3.rs-5667887/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4487261-d365-45ba-9802-2ae0c1feb567","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-13T10:06:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 10:06:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5667887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5667887","identity":"rs-5667887","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}