Patients with acute myocardial infarction (AMI) usually require intensive care unit (ICU) admission and suffer from adverse prognosis (1–4). Early risk stratification is indispensable for appropriate clinical treatment and prognostic evaluation. The Global Registry of Acute Coronary Events (GRACE) score is widely recommended and is used to calculate clinical risk (1–4). It can predict in-hospital (5), 6-month (6), and even the longer term (7) (up to 4 years) mortality postacute coronary syndrome to help us discriminate high-risk patients. However, it is complex and hard to be obtained at the bedside.
Shock index (SI), an easily obtained bedside index, is defined as the ratio of heart rate and systolic blood pressure (SBP). Initially, it was used to evaluate hemorrhage and acute circulatory failure (8, 9). Recent studies found that SI was also a useful clinical parameter for quick risk assessment in AMI (10–18). It could predict short-term adverse prognosis in patients with ST-segment elevation myocardial infarction (STEMI) (10). Also, it was a predictor for short-term mortality (11–13), long-term mortality (13–15), microvascular damage (14), and myocardial injury size (16) in STEMI patients undergoing percutaneous coronary intervention (PCI). Another study confirmed the association between SI and higher in-hospital mortality in patients with non-ST-segment elevation myocardial infarction (NSTEMI) (17). Moreover, elevated SI was associated with poorer 5-year prognosis in patients with AMI undergoing PCI (18). In recent years, two derivatives of SI are created to improve the prognostic value of SI: modified shock index (MSI) is defined as the ratio of heart rate and mean arterial pressure (MAP) (19); age shock index (age SI) is defined as age multiplied by SI (20). The studies have found that both SI and MSI could predict the adverse prognosis in patients with STEMI, and MSI may be more accurate than SI (12, 14). However, no study focuses on the prognostic value of age SI and MSI in AMI patients undergoing PCI. And, we also do not know whether age SI can identify patients at high risk of death. So far, the prognostic performance of age SI is not compared with SI, MSI, and GRACE score for predicting long-term prognosis in patients with AMI undergoing PCI.
In this study, we aimed to assess whether admission age SI and MSI were useful clinical parameters to predict long-term prognosis in AMI patients undergoing PCI. Moreover, the prognostic performance of admission age SI was compared with SI, MSI, and GRACE score.
PATIENTS AND METHODS
Study design and setting
This study was based on a retrospective cohort that has been previously described (21). In brief, from January 1, 2010 to October 31, 2014, 2060 consecutive patients with AMI, who were hospitalized and underwent PCI at a large-scale hospital in Northeast China (Shengjing Hospital of China Medical University, Shenyang, China), were included in the cohort. Clinical data and procedural data of all cases were gained by the investigators from electronic medical records, Picture Archiving and Communication Systems of the interventional imaging data and operation records of PCI cases. Left ventricular ejection fraction (LVEF) was determined during hospitalization by echocardiography. GRACE score was determined as defined previously (5). Exclusion criteria included atrial fibrillation or other obvious arrhythmia at blood pressure measurement (91 cases), loss of GRACE score (31 cases), and no follow-up (74 cases). The final study cohort consisted of 1,864 patients. Clinical follow-up was assessed in October 2015 by direct hospital visits or phone interviews of patient's general practitioner/cardiologist, patient himself, or his family. All patients were followed for a mean duration of 32 months (12–69 months). All-cause mortality was identified from the patients’ medical records or the patient's referring hospital physician. All events were validated by two independent event-judge physicians. This study complies with the Declaration of Helsinki, and the Shengjing Hospital of China Medical University Research Ethics Committee approved the research protocol. Written informed consent was formally obtained from all participants.
Participants and procedures
AMI was defined according to current guidelines (1, 2). Briefly, NSTEMI was defined as chest discomfort or anginal equivalent, ST-segment depression, transitory ST-segment elevation or prominent T-wave inversion, and positive biomarkers of necrosis (CKMB, T/I troponin); STEMI was defined as chest pain present less than 12 h from onset of pain to time of catheterization, significant ST-segment elevation (at least 0.1 mV in two or more standard leads or at least 0.2 mV in two or more contiguous precordial leads) or a new left bundle branch block. PCI was urgently undertaken according to current guideline recommendations (1, 2). The use of aspiration thrombectomy and glycoprotein IIb/IIIa inhibitor was decided by operators. According to current guidelines, operators gave patients periprocedural and postprocedural antiplatelet treatments and other cardiovascular medications (1, 2). Admission SI is defined as the ratio of heart rate and SBP on admission (8, 9); admission MSI is defined as the ratio of heart rate and mean arterial pressure on admission (19); admission age shock index (age SI) is defined as age multiplied by SI on admission (20).
Quantitative variables were represented as mean ± standard deviation (SD) or median [interquartile range] and categorical variables were represented as counts and proportions (%). Cox proportional-hazards regression modeling was used to analyze the effect of variables on event-free survival. The variables that showed significances in univariate analysis (Appendix S1, https://links.lww.com/SHK/A586, P < 0.05) were “entered” into the final model (Table 1). Results were reported as hazard ratios (HRs) with associated 95% confidence intervals (CIs). The predictive performance of admission age SI, admission SI, admission MSI, and GRACE score was assessed by indexes of discrimination (C-statistic) and calibration (the Hosmer–Lemeshow test, the Nagelkerke-R2, and the Brier scores). The C-statistic, which was defined by the area under the receiver operating characteristic (ROC-AUC) curve in relation to long-term all-cause mortality, was compared using a nonparametric test developed by DeLong et al. (22) with the use of MedCalc software for Windows, version 220.127.116.11 (MedCalc Software, Mariakerke, Belgium). Each risk score was entered into a logistic regression model to get the individual risk probability of all-cause death, respectively. The Hosmer–Lemeshow (HL) test and the Nagelkerke-R2 from the regression modeling was used as an indicator of goodness-of-fit of each risk score and to assess the calibration ability of them (23). In the test, higher HL P values and higher Nagelkerke-R2 indicate better calibration. The Brier scores of admission age SI, admission SI, admission MSI, and GRACE score were also calculated (24). Lower Brier scores indicate better calibration (24). The Brier scores admission age SI, admission SI, admission MSI, and GRACE score were evaluated by the Spiegelhalter z-test, respectively. In the Spiegelhalter z test, P values ≤0.05 indicated poor model calibration (24). We also used the absolute integrated discrimination improvement (IDI) and category-free net reclassification improvement (NRI) to evaluate improvements in risk predictions quantify (25). All tests were two-sided, and the statistical significance was defined as P < 0.05. All statistical analyses, except for IDI and NRI calculated by the Statistical Analysis System version 9.4 (SAS, SAS Institute Inc, Cary, NC), were performed with the use of SPSS version 19 (SPSS Inc, Chicago, Ill).
Clinical characteristics of the study population
Of 2,060 patients, 196 were excluded because of incomplete data, atrial fibrillation, or other obvious arrhythmia at admission, and 1,864 remaining patients were analyzed. Baseline clinical characteristics are shown in Table 2. All-cause mortality of the whole group during the follow-up was 3.4% (64 patients; Table 2).
Univariate analysis found that multiple variables had significant effects on all-cause mortality: age, gender, current/recent smoker, history of MI, prior peripheral arterial disease, Killip class III/IV on admission, heart rate on admission, LVEF, troponin-I on admission, three-vessel disease, intra-aortic balloon pump, thrombolysis in myocardial infraction (TIMI) flow grade 3 post PCI, admission SI, admission MSI, admission age SI, GRACE, and discharge prescription of beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (P < 0.05; Appendix S1, https://links.lww.com/SHK/A586)Table 1. After adjusting for covariates, higher admission SI, admission MSI, admission age SI were still associated with a higher rate of long-term all-cause mortality [admission SI: HR = 3.466, 95% CI = 1.014–11.852, P = 0.048; admission MSI: HR = 2.902, 95% CI = 1.180–7.137, P = 0.020; admission age SI: HR = 1.025, 95% CI = 1.010–1.040, P = 0.001] (Table 1).
Prognostic performance and comparison of different parameters
The ROC-AUC of admission SI, admission MSI, admission age SI, and GRACE for predicting all-cause mortality were 0.614 (95% CI 0.591–0.636), 0.635 (95% CI 0.613–0.657), 0.708 (95% CI 0.687–0.729), and 0.694 (95% CI 0.673–0.715) (Table 3 and Fig. 1). The cut-off values for admission SI, admission MSI, and admission age SI for the prediction of all-cause mortality were 0.51 with a sensitivity of 0.922 and a specificity of 0.296; 0.71 with a sensitivity of 0.557 and a specificity of 0.347; 41 with a sensitivity of 0.594 and a specificity of 0.722, respectively. Also, the calibration of admission SI, admission MSI, admission age SI, and GRACE was good. The Hosmer–Lemeshow P values and the Nagelkerke-R2 were higher, and the Brier scores were lower. In the Spiegelhalter z test for the Brier scores of admission SI, admission MSI, admission age SI, and GRACE, all P values were larger than 0.05, which indicated good model calibration (Table 3).
The prognostic performance of admission age SI was similar to the GRACE systems for predicting all-cause mortality (C-statistic: z = 0.437, P = 0.662; IDI: −0.005, P = 0.474; NRI: −0.028, P = 0.257), but better than admission SI (C-statistic: z = 3.944, P < 0.001; IDI: 0.012, P = 0.016; NRI: 0.472, P < 0.001) and admission MSI (C-statistic: z = 3.214, P = 0.001; IDI: 0.011, P = 0.001; NRI: 0.561, P < 0.001) (Table 4).
This is the first study investigating the predictive value of admission age SI on the long-term prognosis in AMI patients undergoing PCI. The main findings were as follows: high admission SI, admission MSI, and admission age SI were all independent predictors of all-cause mortality; the prognostic performance of admission age SI was similar to the GRACE systems for predicting all-cause mortality; the prognostic performance of admission age SI was better than admission SI and admission MSI for predicting all-cause mortality in AMI patients undergoing PCI.
The incidence of cardiogenic shock was nearly 5% to 15% in patients with AMI (26). Even now treatment for AMI advanced, and cardiogenic shock remains the chief cause of death in AMI patients (26). Early in 1967, shock index was developed for assessing the degree of hemodynamic stability (8). It was an accurate and easily assessable risk index for circulatory failure (9). Bilkova et al. (11) first studied the prognostic value of SI for the prognosis in patients undergoing PCI. They found that SI was an independent predictor of in-hospital mortality in STEMI patients undergoing PCI (11). This observation then was verified and extended by other researches that elevated SI was an independent predictor of adverse outcome in STEMI (12–16), NSTEMI (17), and AMI (18) patients undergoing PCI. Recently, in a subgroup analysis of the Zwolle Myocardial Infarction Study Group, Hemradj et al. (15) also found that SI seemed to be a more sensitive prognostic predictor than cardiogenic shock for predicting 1-year mortality in patients with STEMI undergoing PCI. Although the detailed pathophysiological association between SI and adverse outcome in AMI patients needs further evaluation, some theories may explain the nature of such an association. First, SI may be associated with the deterioration of cardiac index, stroke volume, and LV stroke work (9). Furthermore, patients with AMI usually suffer from over-activity of the sympathetic nervous system, which regulates heart rate and blood pressure (27). The sympathetic nerve hyperactivity had also association with the degree of LV dysfunction (27). Therefore, SI may reflect an integrated cardiovascular and nervous system. Our study also confirmed that high admission SI was an independent predictor of all-cause mortality for AMI patients undergoing PCI.
Mean blood pressure, defined as (systolic blood pressure+2 multiplied diastolic blood pressure) divided by 3, could represent tissue perfusion status. MAP of less than 75 mm Hg at 6 h of ICU management was found to be an independent predictive factor of in-hospital mortality in AMI patients with cardiogenic shock and TIMI three flow after PCI (28). The data from GISSI-Prevenzione trial confirmed that low MAP was a predictor of death after a myocardial infarction (29). The results from the AMI-Kyoto Multi-Center Risk Study Group also suggested that low admission MBP might be associated with in-hospital death in Japanese AMI patients undergoing PCI (30). In cardiogenic shock or AMI, lower MBP might embody decreased cardiac output rather than peripheral vascular resistance. Myocardium gets the blood supply mainly during the diastolic phase, and previous studies have suggested that extremely low diastolic blood pressure (DBP) might damage coronary autoregulation and cause adverse prognosis through exacerbated myocardial ischemia in AMI patients (31). So, integration of mean blood pressure and SI as a new index is necessary and possible. Some evidence suggests that modified shock index, defined as the ratio of heart rate and mean arterial pressure, could predict the adverse prognosis in patients with STEMI (12, 14) and might be an even better predictor compared to shock index (12). However, no study focuses on the prognostic value of MSI in AMI patients undergoing PCI. This study for the first time demonstrates that admission MSI is an independent predictor of adverse outcome in AMI patients undergoing PCI. But different from the previous study (12), in our study, the prognostic performance of admission MSI was not better than admission SI for predicting long-term all-cause mortality (C-statistic: z = 1.575, P = 0.115; IDI: 0.002, P = 0.032; NRI: 0.346, P = 0.007) (Table 4).
It is reasonable that the incidence of all-cause death is higher in elderly patients because of a higher prevalence of comorbidity. So, age is an important predictor of outcome in patients with PCI (32), and therefore integrated into most risk score models (33). Age SI, integration of age and SI, is a new index and has been confirmed that it could predict mortality in patients with Emergency Severity Index level 3 (34). Moreover, age SI could provide better discriminating power than SI and MSI (34). However, to the best of our knowledge, there is no study focusing on the prognostic value of age SI or comparing the predictive capability of SI, MSI, and age SI specifically in AMI patients undergoing PCI. For the first time, this study demonstrates that admission age SI is an independent predictor of adverse outcome in AMI patients undergoing PCI.
In this study, the prognostic performance of admission age SI was also compared with admission SI and MSI. It was found that the prognostic performance of admission age SI outperformed admission SI and admission MSI. However, the further ROC analysis found that the sensitivity and specificity of admission SI, admission MSI, and admission age SI were not remarkable. This may be partly because heart rate and blood pressure are fluctuating in real time and the measures at only one time point have limitations. Furthermore, admission SI, admission MSI, and admission age SI contain only three indexes: age, hart rate, and blood pressure, but the long-term prognosis in AMI patients undergoing PCI is usually influenced by more factors (1, 2, 32, 33). In the case of GRACE score, a widely recommended and used clinical risk score, it contains eight variables (1, 2). So, during the clinical use of admission SI, admission MSI, and admission age SI, the clinicians should notice the potential impacts of false positives and false negatives on the clinical judgments. However, GRACE score is difficult and easily failed to be calculated, although there is GRACE 2.0, a revised GRACE algorithm, and “Mini-GRACE” algorithm. When the clinicians fail to get GRACE score, the indexes in our study could help them quickly discriminate high-risk patients at the bedside, since our study has confirmed that admission age SI was proven to have the same prognostic performance as GRACE score for predicting all-cause mortality in AMI patients undergoing PCI. These results taken together suggest that it is worth emphasizing the prognostic impact of admission age SI for long-term prognosis in patients with AMI undergoing PCI. More importantly, admission age SI is obtained easily and quickly.
This study had several limitations. First, this study was retrospective and observational, so potential confounders and selection bias could not be completely adjusted. Second, data about admission medication treatment that influenced admission heart rate and/or blood pressure, such as beta-blockers, were not complete. Third, heart rate and blood pressure were measured at only one time point. These data might be much different than those obtained in subsequent measures. However, the subsequent measures could be influenced by the later interventions and may be still the most reliable indicator. Fourth, in this study, patients with obvious arrhythmia, such as atrial fibrillation, were excluded, because blood pressure measurement was not well done. However, the study has found an association of atrial fibrillation with short- and long-term mortality among patients with AMI (35). At last, in our study, the sensitivity and the specificity of admission SI, admission MSI, and admission age SI for predicting all-cause mortality were not remarkable. So, despite the superior performance of these parameters, the potential impacts of false positives and false negatives on the clinical judgments still exist.
Admission MSI and age SI are both independent predictors of adverse outcome in AMI patients undergoing PCI. Admission age SI alone can identify patients at high risk of death in AMI patients undergoing PCI. Admission age SI was similar to the GRACE score but better than admission SI and admission MSI for predicting all-cause mortality in AMI patients undergoing PCI. However, age SI is easier to calculate than GRACE score.
The authors thank the patients who participated in this study.
1. Amsterdam EA, Wenger NK, Brindis RG, Casey DE Jr, Ganiats TG, Holmes DR Jr, Jaffe AS, Jneid H, Kelly RF, Kontos MC, et al. American College of Cardiology; American Heart Association Task Force on Practice Guidelines; Society for Cardiovascular Angiography and Interventions; Society of Thoracic Surgeons; American Association for Clinical Chemistry. 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol
2014; 64 24:e139–228.
2. O’Gara PT, Kushner FG, Ascheim DD, Casey DE Jr, Chung MK, de Lemos JA, Ettinger SM, Fang JC, Fesmire FM, Franklin BA, et al. American College of Emergency Physicians; Society for Cardiovascular Angiography and Interventions. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol
2013; 61 4:e78–e140.
3. Guillaume A, Pili-Floury S, Chocron S, Delabrousse E, De Parseval B, Koch S, Samain E, Capellier G, Piton G. Acute mesenteric ischemia among postcardiac surgery patients presenting with multiple organ failure. Shock
2017; 47 3:296–302.
4. Bagate F, Lellouche N, Lim P, Moutereau S, Razazi K, Carteaux G, de Prost N, Dubois-Randé JL, Brun-Buisson C, Mekontso Dessap A. Prognostic value of relative adrenal insufficiency during cardiogenic shock: a prospective cohort study with long-term follow-up. Shock
2017; 47 1:86–92.
5. Granger CB, Goldberg RJ, Dabbous O, Pieper KS, Eagle KA, Cannon CP, Van De Werf F, Avezum A, Goodman SG, Flather MD, et al. Global Registry of Acute Coronary Events Investigators. Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med
2003; 163 19:2345–2353.
6. Eagle KA, Lim MJ, Dabbous OH, Pieper KS, Goldberg RJ, Van de Werf F, Goodman SG, Granger CB, Steg PG, Gore JM, et al. GRACE Investigators. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry JAMA
2004; 291 22:2727–2733.
7. Tang EW, Wong CK, Herbison P. Global Registry of Acute Coronary Events (GRACE) hospital discharge risk score accurately predicts long-term mortality post acute coronary syndrome. Am Heart J
2007; 153 1:29–35.
8. Allgöwer M, Burri C. Shock index. Dtsch Med Wochenschr
1967; 92 43:1947–1950.
9. Rady MY, Nightingale P, Little RA, Edwards JD. Shock index: a re-evaluation in acute circulatory failure. Resuscitation
1992; 23 3:227–234.
10. Huang B, Yang Y, Zhu J, Liang Y, Tan H, Yu L, Gao X, Li J. Usefulness of the admission shock index for predicting short-term outcomes in patients with ST-segment elevation myocardial infarction. Am J Cardiol
2014; 114 9:1315–1321.
11. Bilkova D, Motovska Z, Widimsky P, Dvorak J, Lisa L, Budesinsky T. Shock index: a simple clinical parameter for quick mortality risk assessment in acute myocardial infarction. Can J Cardiol
2011; 27 6:739–742.
12. Shangguan Q, Xu JS, Su H, Li JX, Wang WY, Hong K, Cheng XS. Modified shock index is a predictor for 7-day outcomes in patients with STEMI. Am J Emerg Med
2015; 33 8:1072–1075.
13. Spyridopoulos I, Noman A, Ahmed JM, Das R, Edwards R, Purcell I, Bagnall A, Zaman A, Egred M. Shock-index as a novel predictor of long-term outcome following primary percutaneous coronary intervention. Eur Heart J Acute Cardiovasc Care
2015; 4 3:270–277.
14. Reinstadler SJ, Fuernau G, Eitel C, de Waha S, Desch S, Metzler B, Schuler G, Thiele H, Eitel I. Shock index as a predictor of myocardial damage and clinical outcome in ST-elevation myocardial infarction. Circ J
2016; 80 4:924–930.
15. Hemradj VV, Ottervanger JP, de Boer MJ, Suryapranata H. Zwolle Myocardial Infarction Study Group. Shock index more sensitive than cardiogenic shock in ST-elevation myocardial infarction treated by primary percutaneous coronary intervention. Circ J
2017; 81 2:199–205.
16. Hwang JK, Jang WJ, Song YB, Lima JA, Guallar E, Choe YH, Choi S, Kim EK, Hahn JY, Choi SH, et al. Shock index as a predictor of myocardial injury in ST-segment elevation myocardial infarction. Am J Med Sci
2016; 352 2:574–581.
17. Kobayashi A, Misumida N, Luger D, Kanei Y. Shock Index as a predictor for In-hospital mortality in patients with non-ST-segment elevation myocardial infarction. Cardiovasc Revasc Med
2016; 17 4:225–228.
18. Abe N, Miura T, Miyashita Y, Hashizume N, Ebisawa S, Motoki H, Tsujimura T, Ishihara T, Uematsu M, Katagiri T, et al. Long-term prognostic implications of the admission shock index in patients with acute myocardial infarction who received percutaneous coronary intervention. Angiology
19. Liu YC, Liu JH, Fang ZA, Shan GL, Xu J, Qi ZW, Zhu HD, Wang Z, Yu XZ. Modified shock index and mortality rate of emergency patients. World J Emerg Med
2012; 3 2:114–117.
20. Zarzaur BL, Croce MA, Fischer PE, Magnotti LJ, Fabian TC. New vitals after injury: shock index for the young and age x shock index for the old. J Surg Res
2008; 147 2:229–236.
21. Yu T, Dong Y, Sun Z, Sun Z. Comparison of prognostic significance of mean platelet volume/platelet count with GRACE for long-term prognosis in patients with non-ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention. Int J Cardiol
22. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics
1988; 44 3:837–845.
23. Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol
1982; 115 1:92–106.
24. Redelmeier DA, Bloch DA, Hickam DH. Assessing predictive accuracy: how to compare Brier scores. J Clin Epidemiol
1991; 44 11:1141–1146.
25. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med
2008; 27 2:157–172.
26. Thiele H, Ohman EM, Desch S, Eitel I, de Waha S. Management of cardiogenic shock. Eur Heart J
2015; 36 20:1223–1230.
27. Graham LN, Smith PA, Stoker JB, Mackintosh AF, Mary DA. Sympathetic neural hyperactivity and its normalization following unstable angina and acute myocardial infarction. Clin Sci (Lond)
2004; 106 6:605–611.
28. Popovic B, Fay R, Cravoisy-Popovic A, Levy B. Cardiac power index, mean arterial pressure, and Simplified Acute Physiology Score II are strong predictors of survival and response to revascularization in cardiogenic shock. Shock
2014; 42 1:22–26.
29. Avanzini F, Alli C, Boccanelli A, Chieffo C, Franzosi MG, Geraci E, Maggioni AP, Marfisi RM, Nicolosi GL, Schweiger C, et al. GISSI-Prevenzione Investigators. High pulse pressure and low mean arterial pressure: two predictors of death after a myocardial infarction. J Hypertens
2006; 24 12:2377–2385.
30. Shiraishi J, Nakamura T, Shikuma A, Shoji K, Nishikawa M, Yanagiuchi T, Ito D, Kimura M, Kishita E, Nakagawa Y, et al. AMI-Kyoto multi-center risk study group. relationship between mean blood pressure at admission and in-hospital outcome after primary percutaneous coronary intervention for acute myocardial infarction. Int Heart J
2016; 57 5:547–552.
31. D’Agostino RB, Belanger AJ, Kannel WB, Cruickshank JM. Relation of low diastolic blood pressure to coronary heart disease death in presence of myocardial infarction: the Framingham Study. BMJ
1991; 303 6799:385–389.
32. Lee JH, Bae MH, Yang DH, Park HS, Cho Y, Jeong MH, Kim YJ, Kim KS, Hur SH, Seong IW, et al. Korea Acute Myocardial Infarction Registry Investigators. Prognostic value of the age, creatinine, and ejection fraction score for 1-year mortality in 30-day survivors who underwent percutaneous coronary intervention after acute myocardial infarction. Am J Cardiol
2015; 115 9:1167–1173.
33. Filipiak KJ, Koltowski L, Grabowski M, Karpinski G, Glowczynska R, Huczek Z, Kochman J, Majstrak F, Opolski G. Prospective comparison of the 5 most popular risk scores in clinical use for unselected patients with acute coronary syndrome. Circ J
2011; 75 1:167–173.
34. Torabi M, Moeinaddini S, Mirafzal A, Rastegari A, Sadeghkhani N. Shock index, modified shock index, and age shock index for prediction of mortality in Emergency Severity Index level 3. Am J Emerg Med
2016; 34 11:2079–2083.
35. Lopes RD, Pieper KS, Horton JR, Al-Khatib SM, Newby LK, Mehta RH, Van de Werf F, Armstrong PW, Mahaffey KW, Harrington RA, et al. Short- and long-term outcomes following atrial fibrillation in patients with acute coronary syndromes with or without ST-segment elevation. Heart
2008; 94 7:867–873.