Epidemiological classification of acute myocardial infarction: time for a change?

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1 European Heart Journal (1999) 20, Article No. euhj , available online at on Epidemiological classification of acute myocardial infarction: time for a change? P. Porela*, H. Helenius, K. Pulkki and L.-M. Voipio-Pulkki* *Departments of Medicine, Biostatistics, Clinical Chemistry, University of Turku, Turku, Finland Aims The classification of an acute ischaemic cardiac event is traditionally based on cardiac enzymes, electrocardiography (ECG) and clinical symptoms. The impact of new specific cardiac markers on the diagnostic classification of suspected acute myocardial infarction remains poorly studied. We therefore set out to compare the diagnostic and prognostic information provided by the MONICA code and a patient classification based on the maximal level of creatine kinase MB isoenzyme. The significance of typical pain and various ECG algorithms were separately analysed. Methods and Results The study population consisted of 311 consecutive patients who were evaluated for suspected acute myocardial infarction in a regional referral hospital. Patients were retrospectively classified according to the MONICA criteria, by a simplified code combining symptoms and creatine kinase MB, and solely using the maximal creatine kinase MB concentration. Total mortality was followed for 1 and 5 years. The creatine kinase MB based classification was shown to be the strongest predictor of mortality (OR= , p<0 001) for outcome both at 1 and 5 years. Typical pain and a positive Minnesota ECG had no prognostic relevance. However, an analysis algorithm of the admission ECG was predictive of 1- and 5-year survival. Conclusions The epidemiological classification of suspected acute myocardial infarction could be based solely on a specific cardiac marker, such as creatine kinase MB mass. This approach contains prognostic information and is accurate enough for the structured diagnosis of acute myocardial infarction. Other outcome predictors could be used to identify patient subgroups and assess therapy. (Eur Heart J 1999; 20: ) 1999 The European Society of Cardiology Key Words: Myocardial, infarction, classification, MONICA, creatine kinase MB. See page 1446 for the Editorial comment on this article Introduction The classification of acute myocardial infarction is the basis of all myocardial infarction studies. Nevertheless, the criteria of acute myocardial infarction vary from study to study. The WHO MONICA study classification [1] uses conventional enzyme activities, the Minnesota ECG code [2] and clinical symptoms (typical pain >20 min). The Minnesota ECG code is complicated and carries significant inter-observer variability [3]. The new cardiac injury markers [4 6] have dramatically changed the options available to rule in and rule out acute myocardial infarction in the emergency room. However, their use in the general epidemiological classification of suspected acute myocardial infarction patients is poorly characterized. Because the new markers also contain prognostic information [7 9], we set out to study how the basis of the Revision submitted 8 January 1999 and accepted 15 January 1999 Correspondence: Pekka Porela, MD, Department of Medicine, University of Turku, FIN Turku, Finland X/99/ $18.00/0 classification could be changed to incorporate such information. At the same time, it is essential to analyse what happens to the number of cases in each category if the basis of the classification changes, particularly in centres that have participated in long-term epidemiological studies. Because the ECG is used to select patients for thrombolytic therapy, we specially investigated the role of the admission ECG as a prognostic marker. Methods Subjects and design The study population consisted of 311 consecutive patients (204 men, 107 women) aged mean (SD) 67 (13) years who came to the only primary hospital in our district for the evaluation of a suspected acute ischaemic event. Patients with trauma and recent surgical procedures were excluded. At arrival 45/311 of the total population received thrombolysis. 97/311 (31%) patients had a previously documented myocardial infarction. A 1999 The European Society of Cardiology

2 1460 P. Porela et al. Table 1 Criteria of the new combination code, designed to mimic MONICA classification CK-MB Typical symptom Atypical symptom >40 μg.l 1 Definite AMI Definite AMI μg.l 1 Definite AMI Possible AMI <20 μg.l 1 Possible AMI No AMI 12-lead ECG and a serum sample for markers of myocardial injury were obtained immediately upon admission and then every 8 h for 48 h and thereafter daily for 3 days. Two patients were excluded because of technically incomplete data. Thus, the final study population consisted of 309 patients. All available information was used for the purpose of our study. Two hundred and seventy-one (88%) of the patients were hospitalized for at least one day. Mortality information was collected from the national register and was available in 305 out of 309 patients (99%) at 1 and 5 years follow-up. The total in-hospital mortality was 14%. The total 1 year mortality was 24% and the 5 year mortality 41%. Patient classification We studied three different classifications: the WHO MONICA, a combination of clinical symptoms and creatine kinase MB isoenzyme concentration data, and a creatine kinase MB based classification. The cut-off limits for the new combination code were interactively chosen to reach maximal concordance with the MONICA after the exclusion of ECG data (Table 1). The creatine kinase MB classification was based on the maximal value of creatine kinase MB during hospitalization regardless of the number of available samples. The limits 5 μg.l 1 and 10 μg.l 1 were used as cut-offs for possible and definite acute myocardial infarction. Cardiac injury markers Serum creatine kinase isoenzyme MB mass fraction was determined by Microparticle Enzyme Immunoassay (MEIA) using the IMX CK-MB assay (Abbot Laboratories, Abbot Park, IL, U.S.A.). The upper reference limit was 5 μg.l 1. Coefficient of variation in our laboratory was 4%. Serum creatine kinase, lactate dehydrogenase and its isoenzyme 1 activities were determined by using an Hitachi 704 or 717 analyser (Hitachi Ltd, Tokyo, Japan) as described previously [10]. The upper reference limit used for CK was 201 IU. l 1, for lactate dehydrogenase 451 IU. l and for lactate dehydrogenase IU. l 1. These enzymes were defined as raised when more than twice the upper reference limit was achieved, and as borderline when the maximal value was elevated but remained less than twice the upper reference range. ECG acquisition and analysis The Marquette automated 12-lead system (Marquette Electronics Inc., Milwaukee, WI, U.S.A.) was used for data collection and storage. The data matrix of Marquette Electronics was used for the durations and amplitudes of the Q, R and S waves as well as for the magnitudes of ST deviations. All available 12-lead ECGs were manually coded according to the Minnesota code in five categories: definite acute myocardial infarction, possible acute myocardial infarction, nondeveloping changes, no acute myocardial infarction and uncodable ECG. To categorize the admission ECGs, we first used the simplified Selvester QRS score to detect already existing Q wave infarctions [11]. The Selvester QRS score was originally developed to assess myocardial injury size, so that 1 point corresponds to a 3% loss of left ventricular mass. When the admission Selvester QRS score was <10, patients were classified based on the amount of ST elevation (modified Aldrich score) [12]. The Aldrich score was originally developed to predict the size of the injury from the admission ECG. ST depressions (horizontal or downsloping 1 mm) were recorded according to the MPIP code [13]. The presence of left bundle branch block or pacemaker rhythm was coded, as suggested by the Marquette automated diagnosis. If none of the above mentioned criteria were fulfilled, the classification of the admission ECG was no diagnostic changes. The categorization of the admission ECGs was fully automated and computerized and was based on the commercially available Marquette data matrix. Statistical methods Observer agreement of categorical assessment [14], a method to assess the inter-observer variation of categorical assessments, was used to study the concordance of the different classifications. The level of the proportion of agreement that signifies agreement is arbitrary [14]. Given the size of our population under consideration, we chose 0 70 to indicate agreement between two codes. The odds ratios (OR) and 95% confidence intervals (95% CI) were calculated, using the SAS system for Windows release 6.12/1996. P values less than 0 05 were interpreted as statistically significant. Results Table 2 shows the distribution of patients into the categories of definite, possible and no acute myocardial infarction using the three different classifications. The MONICA code classified almost half of the patients presenting with suspected myocardial infarction as possible acute myocardial infarctions. After omitting manual ECG Minnesota coding, we could, at best, achieve the same diagnosis as that given by the MONICA in

3 Classification of AMI 1461 Number of diagnoses with different classifica- Table 2 tions Definite Possible No AMI MONICA Combination code CK-MB CK-MB=creatine kinase MB. Table 3 The concordances between different classifications. The limit for statistically significant concordance was 0 70 MONICA Combination code CK-MB MONICA definite possible no AMI Combination code definite possible no AMI CK-MB definite possible no AMI CK-MB=creatine kinase MB. 267/309 patients (86%). This required the use of relatively high creatine kinase MB cut-off values (Table 1). An even more pronounced shift of cases from the possible to the definite and no acute myocardial infarction categories occurred when the maximal creatine kinase MB was used as the sole classification basis. As shown in Table 2, with creatine kinase MB classification the number of patients with definite acute myocardial infarction was the highest (144; 47%) and also the number of no acute myocardial infarction cases (130; 42%) was higher than with any other code. Differences between classifications Table 3 shows the concordance [14] among the three difference classifications. The concordance reached statistically significant levels in the case of MONICA vs the new combination code (0 73 for the definite, 0 74 for the possible and 0 91 for the no acute myocardial infarction group) as the new code was generated to mimic MONICA coding. The creatine kinase MB classification did not reach statistically significant concordance with any other classifications. In general, the greatest discrepancies were present in the possible acute myocardial infarction group. Prognostic significance of classifications We then studied mortality at 1 (Table 4A) and 5 years (Table 4B). The odds ratio 1 was selected to represent risk of death from all causes in the no acute myocardial infarction group. The creatine kinase MB classification had the strongest prognostic significance at both 1 (P<0 001) and 5 years (P<0 001, Table 4). All codes were statistically associated with outcome, but most of this association was due to the fact that the possible acute myocardial infarction group had good prognosis using both the MONICA and the new combination code at 1 as well as 5 years (Tables 4A and B). In contrast, both the definite and possible acute myocardial infarction classes based on creatine kinase MB were independently and statistically strongly associated with mortality at 1 year (OR= ) and at 5 years (OR= ). To further explore the cause of the poor prognostic significance of the MONICA classification, we studied separately the three basic variables (conventional enzyme activities, Minnesota ECG and symptoms) from which the final MONICA classification is constructed. Table 5 shows the variables that were statistically significantly associated with 1 and 5-year mortality. Odds ratio 1 was selected to signify association in the group with normal findings. Of the three variables, only enzymes (P=0 003 for 1 year and P<0 001 for 5 years) and the Minnesota ECG code (P=0 005 for 1 year and P<0 001 for 5 years) were associated with mortality. However, only definitely raised ( 2 upper reference limit) enzymes and non-developing or uncodable ECGs were predictive of 1 year mortality (Table 5). At 5 years, borderline (elevated, but <2 upper reference limit) enzymes and possible acute myocardial infarction ECGs also emerged as prognostically important variables. Paradoxically, typical symptoms tended to be associated with better prognosis, but this was of borderline statistical significance (P=0 085 and P=0 051). Automated analysis of admission ECG To investigate the possible prognostic role of the presenting ECG patterns, we used an automated ECG programme to classify the main findings in the admission ECG recordings (see Methods). Positive findings were compared with the no diagnostic changes group, which was given an odds ratio of 1. Total mortality in this group at 1 year was 13% and at 5 years 29%. As shown in Table 6, statistically significantly worse prognosis was associated with all other positive ECG findings except ST elevations (Table 6). Of the 59 patients presenting with computer-coded ST elevation, 25 (42%) received thrombolysis at admission. Discussion Our study demonstrates the fundamental differences in the behaviour of the MONICA code, developed for

4 1462 P. Porela et al. Table 4 Relative odds ratios for mortality at 1 year (panel A) and 5 years (panel B) using the three different classifications. OR=odds ratio, CI=confidence interval. P-value <0 05 indicates that the classification as a whole was statistically significantly associated with prognosis P-value Died/n (%) OR 95% CI A. 1 year MONICA Definite 31/97 (32) to 2 5 Possible 22/133 (17) to 1 1 No AMI 21/75 (28) 1 Combination code Definite 35/107 (33) to 2 3 Possible 15/113 (13) to 0 8 No AMI 24/85 (28) 1 CK-MB class <0 001 Definite ( 10 μg.l 1 ) 46/140 (33) to 6 5 Possible ( 5 μg.l 1 ) 12/35 (34) to 8 9 No AMI 16/130 (12) 1 B. 5 years MONICA Definite 47/97 (48) to 2 0 Possible 44/133 (33) to 1 03 No AMI 34/75 (45) 1 Combination code Definite 54/107 (50) to 2 0 Possible 31/113 (27) to 0 8 No AMI 40/85 (47) 1 CK-MB class <0 001 Definite ( 10 μg.l 1 ) 75/140 (54) to 5 7 Possible ( 5 μg.l 1 ) 17/35 (49) to 6 0 No AMI 33/130 (25) 1 CK-MB=creatine kinase MB. Table 5 Significant predictors of 1 year (panel A) and 5 year (panel B) mortality among to the MONICA variables. OR=relative odds ratio compared to the normal findings group, CI=confidence interval P-value Died/n (%) OR 95% CI A. Enzymes Raised 29/102 (28) to 4 1 Minnesota ECG Non-developing 34/99 (34) to 21 3 Uncodable 11/30 (37) to 28 6 Pain B. Enzymes <0 001 Raised 46/102 (45) to 4 4 Borderline 34/75 (45) to 4 6 Minnesota ECG <0 001 Possible 42/102 (41) to 10 2 Non-developing 50/99 (51) to 14 1 Uncodable 20/30 (67) to 29 4 Pain epidemiological purposes, the tailored ECG scores and specific cardiac markers. Our study was designed to emphasize the prognostic significance of an epidemiological acute myocardial infarction classification. Biochemical tissue damage was found to be the most powerful in the detection of patients with adverse outcome after an ischaemic event (Tables 4A and B). We chose the admission ECG to represent electrocardiographic information because this data is consistently available and is used to guide therapeutic choices. Regardless of the presence or absence of a classified ischaemic event, the admission ECG score was found to be a strong predictor of long-term survival (Table 6). Previous studies [7 9] have shown that with the use of the new specific cardiac injury markers the prognostic classification of suspected acute myocardial infarction patients can be enhanced, particularly in the unstable angina subgroups. Our approach was more epidemiological in nature. In fact, we originally set out to study the prognostic value of peak creatine kinase MB concentration in the entire study population. The cut-off limit of 5 μg.l 1 and 10 μg.l 1 bear no difference in predictive power (Tables 4A and B), but are presented separately because 10 μg.l 1 was the most accurate cut-off point for the MONICA definite acute myocardial infarction group. The analysis of several clinical and laboratory measures and their combinations (Tables 4 6) revealed no benefit over the simplistic approach based on maximum creatine kinase MB alone. The

5 Classification of AMI 1463 Table 6 Prognostic significance of automated classification of admission ECG, based on injury scores, for 1 (panel A) and 5 years (panel B) mortality. OR=relative odds ratio compared with the no diagnostic ECG changes group, CI=confidence interval P-value Died/n (%) OR 95% CI A. 1 year <0 001 Q-wave 17/33 (52) to 17 9 ST-elevation 13/59 (22) to 4.2 ST-depression 15/48 (31) to 6 9 LBBB or pacemaker 10/20 (50) to 18 2 No diagnostic changes 19/145 (13) 1 B. 5 years <0 001 Q-wave 19/33 (58) to 8 1 ST-elevation 22/59 (37) to 2 8 ST-depression 26/48 (54) to 6 1 LBBB or pacemaker 16/20 (80) to 31 3 No diagnostic changes 42/145 (29) 1 validity of our approach is of course dependent on the ultimate purpose of the classification. We suggest that prognosis, represented by the 1 and 5 years mortalities in our study, should be the central goal of an epidemiological classification of acute ischaemic syndromes. If this task is successfully fulfilled by biochemical data alone, any further characterization of patient subpopulations could rely on other methods, such as ECG, which can be specifically adjusted to the particular needs in question. In line with previous observations [15], the uncodable ECG population also had the highest mortality in our study. On the other hand, previous studies have associated specific ECG features with adverse outcome in the acute myocardial infarction population. The non-q wave acute myocardial infarction patients now have a better short- as well as long-term prognosis than patients who ultimately develop Q waves [16 22]. This evidently means that the non-q wave acute myocardial infarction patients with initial ST elevation have a relatively good prognosis after thrombolysis [16,17,21], but patients with ST-depression or left bundle branch block do worse [18,19,21]. Also the use and benefits of various treatments differ in the various ECG subgroups [22 24]. Thus, incorporation of ECG data in patient characterization algorithms appears justified, although we do not think that such information is needed to detect the presence of myocardial injury per se. This could now be based on biochemical information alone. The workload of ECG classification can be minimized by automating the ECG coding in epidemiological studies [25 27]. However, there is no consensus of optimal interpretation algorithms for that purpose [28]. In this study we provide an example of how difficult it can be to monitor long-term epidemiological trends in today s clinical environment. In principle, there are two ways to go. By comparing various classification algorithms it could be possible to analyse the changes induced by new markers and maintain coding stability, but this will probably not be possible in most centres. Therefore, we suggest that the time has come to redefine the goals and practices of epidemiological classification of acute ischaemic syndromes. 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