From the Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, MN.
Received for publication May 27, 2004; accepted for publication September 8, 2004.
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ABSTRACT |
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coronary disease; diagnostic techniques, cardiovascular; electrocardiography; myocardial infarction
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INTRODUCTION |
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The diagnostic techniques for detecting myocardial damage have changed greatly since the decline in cardiovascular disease mortality rates began in the late 1960s. More sensitive measures of cardiac necrosis, such as creatine phosphokinase (CPK), the myocardial band fraction of CPK (CPK-MB) (its isoenzyme), and cardiac troponin, have been introduced (4). The rate of autopsy used to confirm AMIs in rapidly fatal cases has substantially declined (5). The changes in financial incentives resulting from the use of diagnosis-related groups have likely influenced the assignment of discharge diagnostic codes away from those with lower reimbursement toward codes with higher reimbursement. In addition, the International Classification of Diseases (ICD) has been updated twice from the Eighth to the Tenth Revision (5).
The purpose of AMI validation criteria is to standardize AMI diagnosis over different time periods, different physicians, different diagnostic markers, and different hospitals. To determine the impact of the changes that have occurred in both primary and secondary prevention of coronary heart disease, as well as in diagnostic and therapeutic methods, standardized AMI criteria must be used.
The Minnesota Heart Survey (MHS) has abstracted hospital charts of potential myocardial infarction patients aged 3074 years in the seven-county Twin Cities metropolitan area in 1970, 1980, 1985, 1990, and 1995 (68). The major elements for documenting definite AMI are chest pain, enzymes or biomarkers, electrocardiogram, and autopsy. Among these, the only consistent element at all time points was the electrocardiogram, classified by the Minnesota Code (9). In the face of increasingly sensitive biomarkers, this paper investigates the interpretation of long-term trends in acute coronary heart disease morbidity when a consistent detection methodology, the electrocardiogram classified by Minnesota Code, is used.
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MATERIALS AND METHODS |
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Hospitalized acute coronary heart disease data collection
For 1970, 1980, 1985, 1990, and 1995, we obtained listings of all patients aged 3074 years who were discharged from the Twin Cities metropolitan area hospitals with a code of acute coronary heart disease among any of the discharge diagnoses. In 1970, AMI discharge was determined from ICD, Eighth Revision, codes and in all other years from ICD, Ninth Revision, codes. The screening ICD codes targeted for abstraction were equivalent to ICD, Ninth Revision, codes 410 (acute myocardial infarction) and 411 (other acute and subacute forms of ischemic heart disease). A 50 percent sample of men and women with target ICD discharge codes was randomly selected from each participating Twin City hospital in 1970, 1980, and 1985 (4). A 50 percent sample of men and a 100 percent sample of women were selected in 1990. In 1995, a 40 percent sample of men and an 80 percent sample of women were selected from those with ICD code 410. To enhance the efficiency of AMI case finding in 1995, a random subsample (approximately 15 percent) with ICD discharge code 411 was fully abstracted, and their electrocardiograms were classified by Minnesota Code (8, 9).
Using a standard protocol, trained nurses abstracted the medical records of the hospitalizations having ICD discharge code 410 or code 411. Information was obtained on signs and symptoms, medical history, cardiac enzyme levels, electrocardiogram findings, clinical complications, therapy, and when available, autopsy results. In 1970 and 1980, up to 12 electrocardiograms per patient were photocopied for classification (10). In 1985, 1990, and 1995, a maximum of four electrocardiograms per patient was photocopied. Electrocardiograms for each year were classified according to the Minnesota Code. These always included the first and the last electrocardiogram of the hospital stay.
The MHS applied a computer-based diagnostic algorithm to all abstracted acute coronary heart disease hospitalizations in each year using autopsy findings, Minnesota Code electrocardiograms, and peak cardiac enzyme levels. In 1970, the enzymes available were serum glutamic-oxaloacetic transaminase (SGOT)/lactate dehydrogenase (LDH); in 1980, SGOT/LDH and CPK; in 1985, SGOT/LDH and CPK/CPK-MB but with CPK dominating usage; while in 1990, CPK/CPK-MB was available but CPK-MB dominant; in 1995, CPK/CPK-MB and LDH were used with CPK-MB the dominant enzyme. The MHS algorithm relied more on enzymes and less on electrocardiograms as the study progressed (8). Incident (first-ever) and recurrent AMIs were distinguished by extensive searches of prior hospitalization records (8).
Electrocardiogram algorithm
We developed an electrocardiogram algorithm based on Minnesota Code findings applied consistently in all study years, using a maximum of three electrocardiograms per hospitalization. Specific Minnesota Code findings were posited to be more frequent among people hospitalized with AMI than in those hospitalized without AMI. A total of 27 criteria were selected on theoretical grounds as potential elements of an algorithm to identify the presence of AMI. Each of the 27 electrocardiogram criteria was a dichotomous indicator of the presence or absence of the electrocardiogram abnormality. In the absence of a "gold standard" for definite AMI, we used two diagnostic dichotomies, one lenient and one stringent, both accepted as markers of AMI. ICD code 410 indicating AMI is a lenient classification, whereas the diagnosis resulting from the MHS algorithm is stringent. Thus, the ICD code 410 yields higher rates than the MHS algorithm.
In-hospital morbidity data are available for the MHS Survey years 1970, 1980, 1985, 1990, and 1995; data from 1995 were reserved as a validation set. For those cases having only one electrocardiogram, we selected two electrocardiogram markers of ischemia (ST elevation or diagnostic Q waves), the presence of either of which is taken as evidence of AMI. For those cases having two or more electrocardiograms, separately for the data from 1970, 1980, 1985, and 1990, the other 25 electrocardiogram criteria were allowed to enter stepwise forward logistic regressions using either ICD code 410/411 or, alternatively, MHS computer diagnosis, as the diagnostic indicator of AMI. In this way, we identified a reduced set of eight electrocardiogram criteria that contributed to the logistic prediction function consistently across years and consistently for both ICD code 410/411 and MHS diagnosis (table 1).
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On this basis, we estimated an overall logistic predictor by regressing the abnormal MHS diagnosis on the eight electrocardiogram criteria using data from all years 19701990. We chose an epidemiologically meaningful cutpoint, where the slope of the receiver operator characteristic curve (logistic predictor) is one to equalize the "costs" for false-positive or false-negative classifications. The cutpoint of 0.3 and the coefficients (table 1) imply that positive status of any of the eight criteria is taken as evidence of AMI. We determined the sensitivity and specificity at this cutpoint against ICD code 410 (AMI) versus code 411 (non-AMI) and against MHS diagnosis in each study year from 1970 to 1990.
The electrocardiogram algorithm was validated against new data from 1995. The electrocardiogram predictor showed performance similar to that in previous years. We calculated sensitivities and specificities for AMI by electrocardiogram in the 1995 data against the ICD code and the MHS dichotomies. Together, the algorithm for only one electrocardiogram and the algorithm just described for more than one electrocardiogram constitute our procedure for defining electrocardiogram-based AMI (AMIECG).
Rates
Counts of AMIECG were converted to rates and standardized to the 1990 US Census for all study years 19701995. In strata of gender, we calculated rates of attack AMIECG, incident AMIECG, and recurrent AMIECG per 100,000 at risk, as well as by ICD code 410 and by MHS algorithm.
Subsampling method in 1995
For efficient use of resources, sampling and coding rates for electrocardiograms were lower in 1995 than in the previous surveys, leading to larger standard errors of the estimated rates in 1995. For our study, previously photocopied but as yet uncoded electrocardiograms from the 1995 survey were classified by Minnesota Code.
Time trends
Finally, we used the rates of AMIECG for the study years 19701995 to determine trends in AMI, and we compared the trends in the rates based on AMIECG with the rates based on ICD code 410 or on the MHS algorithm.
Statistical modeling
The graphical representation of the trends suggested that all the data, in four strata of incident or recurrent AMIECG in men or in women, might be summarized in a unified model. Logarithms of the rates were taken to impose proportional changes in the strata. Beyond the intercept, the starting model necessitates including main effects for men versus women and for recurrent versus incident AMI (two parameters). The full model adds possible discontinuities for incident and for recurrent AMI in 1985 (two parameters) and consistent slopes in each of two time periods, 19701980 and 19851995 (two parameters), for a total of six parameters. Conditional F tests were used to give a reduced model in which the slope and discontinuity aspects of the full model are simplified. Goodness of fit is reported as the adjusted R2 because of the limited number of data points (12).
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RESULTS |
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Recurrent AMI rates
Recurrent AMI trends in men and women were approximately two thirds of the incident AMI rates. The trends plots were approximately parallel (within survey periods for the MHS algorithm). The major difference between incident and recurrent AMI trends was a sharp increase in the recurrent rate in 1985 that was not present in the incident trends. As did incident rates, the recurrent rates declined from 1985 to 1995.
Attack AMI rates
The attack rates are the sum of the incident and recurrent AMI rates and show substantial concurrence between the incident and attack plots, except that the 1985 recurrent increase is blunted in the attack trend curves.
A simple unified model
Although at different levels, the graphs for the four strata of men versus women and incident versus recurrent AMIECG seem to show proportionate changes, leading us to consider a single model for the logarithm of the rates. We fit the full model using the six parameters described in Materials and Methods. The model fit well (12), giving an R2adjusted = 97.1 percent. Testing showed that the intercept in 1985 for incident log-rate was not different from zero and that the slope in 19701980 was not different from zero. The reduced model based on four parameters showed no degradation in goodness of fit (R2adjusted = 97.5 percent), and conditional F tests showed the necessity for retaining the shifted intercept in recurrent log-rates in 1985 and the downward slope in 19851995 (p < 0.001). The unified model shows that AMIECG rates in the MHS were flat from 1970 to 1980, that recurrent rates shifted upward by 32 percent (p = 0.002) in 1985, and that both incident and recurrent rates in men and in women declined 14.3 percent per 5-year period from 1985 to 1995 (p < 0.001). Figure 4 shows the observed and fitted rates; the biggest residual occurs as overestimation of recurrent events in women in 1970.
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Independence of AMIECG criteria from the MHS algorithm
Because the MHS algorithm uses the evolving Q wave as one of its diagnostic elements, we investigated whether the Q-wave component of the MHS algorithm performed similarly to the full AMIECG criterion. The estimated AMI levels by evolving Q-wave criteria were about one third of the corresponding AMIECG rates in both men and women. Further, these trends did not parallel those determined by the other methods. For example, attack rates showed an upward trend from 1970 to 1980 with a decline thereafter. Recurrent rates failed to show the significant decline from 1985 to 1990 (data not shown).
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DISCUSSION |
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Elements necessary for trend determinations
Utilization of standardized and consistent criteria over time periods is the most important element for reliably documenting trends in acute coronary heart disease events. In the MHS and other epidemiologic studies on long-term trends in acute coronary heart disease, AMI detection criteria have changed dramatically from 1970 to 1995. This diagnostic drift has been driven largely by the availability of new and more sensitive cardiac enzymes or biomarkers (4). Therefore, a major limitation of long-term trend analysis is the inability to consistently define AMI across time periods that are the framework for inferences about AMI trends (8).
The MHS has systematically examined trends in coronary heart disease mortality, morbidity, and medical care over a 25-year period (19701995). However, trend comparisons have been limited to specific periods (19871994 (3), 19801985 (4), 19851997 (8)) because of the differential availability of more sensitive enzymes. Future AMI trend comparisons for the year 2000 and beyond will become even more problematic with the introduction of the highly sensitive cardiac biomarker, troponin, in its several and evolving forms.
Electrocardiography
It is generally believed that the electrocardiogram lacks the sensitivity to be used as a stand-alone criterion for AMI detection. This is supported by the significant differential AMI detection rates by evolving Q wave versus cardiac enzymes. However, our proposed AMIECG has two important characteristics. First, the consistent availability of the electrocardiogram allows determination of long-term trends. Second, without greatly reducing specificity, the broader range of the electrocardiogram criteria used improves the sensitivity of AMIECG. A previous MHS publication reported that the MHS algorithm validated approximately 75 percent of ICD code 410-labeled patients as definite AMI and 12 percent of ICD code 411-labeled patients as definite AMI in each year from 1970 to 1990 (2). These results are not unexpected, because the MHS algorithm and ICD code 410 are both heavily dependent on cardiac enzyme elevation for labeling a case definite AMI. Both are vulnerable to diagnostic drift. If the MHS algorithm (or the ICD code) is defined to be the gold standard, although AMIECG is less sensitive and specific, it has the important advantage of using a constant validation likelihood for investigating long-term trends (8).
It could be argued that the AMIECG criterion is strongly associated with the electrocardiogram criteria used as one diagnostic element in the MHS algorithm and that the AMIECG criterion merely mirrors the performance of the MHS algorithms electrocardiogram criteria. The MHS algorithm uses seven discrete electrocardiogram criteria, each including a criterion for evolving Q waves with or without evolving ST elevation, ST depression, or T-wave inversion findings. In the MHS algorithm, evolving Q waves and evolving ST-T waves are required to be simultaneously present in the same lead group defined by the Minnesota Code as lateral (leads I, aVL, V6), inferior (leads II, III, aVF ), or anterior (leads V1V5). In contrast, the currently described AMIECG criterion uses two nonevolving criteria when only one electrocardiogram was available or eight evolving criteria when two or more electrocardiograms were available.
We compared incident, recurrent, and attack AMI trends generated by the MHS algorithms evolving Q-wave criteria (in individuals with MHS algorithm-validated definite AMI) with corresponding trends determined by AMIECG. The diagnosis of AMI by the evolving Q-wave criteria generated trend rates lower than and different in pattern from the AMIECG rates. Such comparisons strongly suggest that the AMIECG criterion contributes independent information beyond the evolving Q-wave criteria. Further support of this independence comes from the fact that only two of the 10 criteria used by AMIECG overlap the evolving Q-wave criteria.
Comparison of AMI trends by different methods
All three methods (ICD code, MHS algorithm, and AMIECG criteria) showed that age-adjusted attack trends were largely unchanged from 1970 to 1985 and then declined from 1985 to 1995. These findings agree with previous MHS findings that attributed the lack of change from 1970 to 1985 to improved AMI detection, increased survival to admission, increased elective admissions subsequently discharged with an AMI diagnosis, and changed AMI coding practices of hospitals (8). The decline in attack rates of hospitalized AMI patients from 1985 to 1995 was consistent with concurrent favorable trends in the population risk profile, as reported in Minnesota (2) and elsewhere (1315).
By all methods, incident AMI rates were flat from 1970 to 1985 and declined from 1985 to 1995. This suggests ongoing improvements in primary prevention (8, 16, 17). Trends in recurrent AMI rates showed little change from 1970 to 1980, a sharp upward shift from 1980 to 1985, and a decline from 1985 to 1995. MHS previously documented a substantial improvement in short- and long-term survival of hospitalized definite AMI in 1980 versus 1970 and no further improvement in 1985 versus 1980 (8). This may have led to an increased pool of AMI survivors that contributed to the increase in recurrent rates between 1980 and 1985. The decline in recurrent events in 1990 and 1995 compared with 1985 might be a result of increased use of thrombolytic therapy and emergent angioplasty (resulting in aborted AMIs), but it is more likely to be a result of improved secondary prevention.
Different studies have used different methods for validating AMI trends. Some have combined specific criteria with clinical judgment to validate potential cases of AMI, while others have used standardized computer-driven algorithms in an attempt to minimize the clinical judgment required for case validation (5). However, use of a standardized method to discriminate between definite AMI present or absent can be substantially affected if a shift in enzyme availability has occurred during the time periods studied. A previous MHS publication noted greater than a 50 percent increase in definite AMI rates when the MHS algorithm included CPK/CPK-MB in place of SGOT/LDH (5). When assessing trends in AMI over time, researchers must take care to ensure that the diagnostic elements are equally available at all points in time and, more importantly, of comparable sensitivity for detecting cases.
The greatest source of bias for any epidemiologic study concerned with reporting and making inferences about trends in AMI is the absence of a consistent algorithm to validate AMI at each time period. Therefore, studies need a method providing equal likelihood of AMI detection, without which they lack internal and external consistency.
Measurement of acute coronary heart disease trends in populations is crucial for determining whether incident and recurrent disease trends are rising or falling and whether primary or secondary prevention efforts are successful. Mortality alone is less adequate for trend evaluation because of lack of sensitivity to incident, recurrent, and attack rates for acute coronary heart disease (4).
Although numerous studies have provided information on acute coronary heart disease trends (1822), none (to our knowledge) has suggested a satisfactory procedure to overcome rapid advances in diagnostic technology (e.g., new biomarkers) combined with changes in disease presentation.
The emerging epidemic of AMI in developing countries also warrants additional attention to valid surveillance in these areas. Determinations of the magnitude and direction of AMI trends are essential for primary and secondary prevention efforts. Disparities between urban and rural hospitals and treatment options for AMI, such as emergency angioplasty or bypass surgery, are likely to be much greater in developing countries. These disparities highlight the need for using AMI validation methods that will be immune to the inevitable shifts in availability of diagnostic resources.
Study limitations
This study was restricted to hospitals in the Minneapolis/St. Paul, Minnesota, metropolitan area and does not well reflect AMI rates in African Americans and other ethnic groups. Therefore, the generalizability of our findings is uncertain. Other studies determining AMI trends may find a different set of electrocardiogram predictors from the set of 27 originally evaluated. It must be emphasized that this approach is not suitable for individual patients or clinicians caring for patients. Its value is for epidemiologic studies of group data trends. Despite these limitations, the electrocardiogram classified by Minnesota Code remains the only consistently available and reasonably sensitive method to validate AMI trends over long periods.
Conclusions
In spite of many sources of variability for the determination of reliable AMI trends, this study suggests that electrocardiographic criteria can provide a valid and reliable method for examining long-term trends in AMI. To achieve this, precise measurement and classification guidelines for electrocardiogram waveforms must be followed. Most events likely to be an AMI occur in settings not controlled by epidemiologic researchers, so that most electrocardiograms will be paper copies, with various levels of quality. The most extensively used measurement system for visual electrocardiogram findings in epidemiologic studies is the Minnesota Code (9). Our findings suggest that, when comparable enzymes are available, it is appropriate to compare AMI trends; however, studies in which enzyme, biomarker, or other diagnostic modalities are not equally available may find the electrocardiogram classified by Minnesota Code a useful method for determination of long-term trends in AMI.
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ACKNOWLEDGMENTS |
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The authors are indebted to many previous contributors to the design, initiation, and operation of this study; to the study programmers; and to the dedicated nurse abstractors of the Minnesota Heart Survey.
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APPENDIX |
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In the Minnesota Heart Study, Minnesota codes are grouped into lead groups: lateral includes leads I, aVL, and V6; inferior includes leads II, III, and aVF; or anterior includes leads V1V5.
Diagnostic Minnesota Q code = any 1-1-x
Major Minnesota Q code = any 1-2-x except 1-2-6/1-2-8
Equivocal Minnesota Q code = any 1-3-x or 1-2-8
Major Minnesota ST depression code = any 4-2/4-1-2/4-1-1
Minor Minnesota ST depression code = any 4-4 or 4-3
Major Minnesota T-wave inversion code = any 5-2/5-1
Minor Minnesota T-wave inversion code = any 5-4/5-3
Minnesota ST elevation code = any 9-2
Complete left bundle branch block (LBBB) = 7-1-1
Intraventricular conduction defect = 7-4
Complete right bundle branch block (RBBB) = 7-2-1
Minnesota Q codes and Minnesota ST elevation, ST depression, or T-wave inversion may be in different lead groups.
Number of available electrocardiograms and criteria
When only one electrocardiogram is available: there are two criteria:
1. Minnesota ST elevation code
2. Major or diagnostic Minnesota Q code
When two or more electrocardiograms are available, there are 25 criteria:
1. No Minnesota Q code on the first electrocardiogram and a major or diagnostic Minnesota Q code on another electrocardiogram.
2. An equivocal Minnesota Q code on the first electrocardiogram and a diagnostic Minnesota Q code on another electrocardiogram.
3. An equivocal Minnesota Q code on the first electrocardiogram and no major Minnesota ST depression code on the first electrocardiogram and a major Minnesota Q code plus a major Minnesota ST depression code on another electrocardiogram.
4. An equivocal Minnesota Q code and no major Minnesota T-wave inversion code on the first electrocardiogram and a major Minnesota Q code plus a major Minnesota T-wave inversion code on another electrocardiogram.
5. An equivocal Minnesota Q code and no Minnesota ST elevation code on the first electrocardiogram and a major Minnesota Q-wave code plus Minnesota ST elevation code on another electrocardiogram.
6. No Minnesota Q code and no major Minnesota ST depression code on the first electrocardiogram and an equivocal Minnesota Q code plus a major Minnesota ST depression code on another electrocardiogram.
7. No Minnesota Q code and no major Minnesota T-wave inversion code on the first electrocardiogram and an equivocal Minnesota Q code plus a major Minnesota T-wave code on another electrocardiogram.
8. No Minnesota Q code and no ST elevation code on the first electrocardiogram and an equivocal Minnesota Q code plus an ST elevation code on the next electrocardiogram.
9. Minnesota ST elevation code and no major ST depression or T-wave inversion code on the first electrocardiogram and no Minnesota ST elevation code plus a major ST depression or T-wave inversion code on another electrocardiogram.
10. No Minnesota ST depression code on the first electrocardiogram and a major Minnesota ST depression code on another electrocardiogram.
11. A major Minnesota ST depression code on the first electrocardiogram and no major Minnesota ST depression code on another electrocardiogram.
12. No major Minnesota T-wave inversion code on the first electrocardiogram and a major Minnesota T-wave inversion code on another electrocardiogram.
13. A major Minnesota T-wave inversion code on the first electrocardiogram and no major Minnesota T-wave inversion code on another electrocardiogram.
14. No Minnesota ST elevation code on the first electrocardiogram and a Minnesota ST elevation code on another electrocardiogram.
15. Minnesota ST elevation code on the first electrocardiogram and no Minnesota ST elevation code on another electrocardiogram.
16. No major Minnesota conduction code on the first electrocardiogram and a complete LBBB or significant intraventricular conduction delay on another electrocardiogram.
17. No major Minnesota conduction code on the first electrocardiogram and a complete RBBB on another electrocardiogram.
18. No Minnesota Q code and no Minnesota Code ST depression or T-wave inversion code on the first electrocardiogram and an equivocal Minnesota Q code plus a minor Minnesota Code T-wave inversion or ST depression code on another electrocardiogram.
19. An equivocal Minnesota Q code and no Minnesota T-wave inversion or ST depression code on the first electrocardiogram and a major Minnesota Q code plus a minor Minnesota T-wave inversion code on another electrocardiogram.
20. No Minnesota Q code on the first electrocardiogram and an equivocal Minnesota Q code on another electrocardiogram.
21. An equivocal Minnesota Q code on the first electrocardiogram and a major Minnesota Q code on another electrocardiogram.
22. No Minnesota ST depression code on the first electrocardiogram and a minor ST depression code on another electrocardiogram.
23. A minor Minnesota ST depression code on the first electrocardiogram and no ST depression code on another electrocardiogram.
24. No Minnesota T-wave inversion code on the first electrocardiogram and a minor T-wave inversion code on another electrocardiogram.
25. A minor Minnesota T-wave inversion code on the first electrocardiogram and no Minnesota T-wave inversion code on another electrocardiogram.
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NOTES |
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REFERENCES |
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