(Circulation. 1999;100:599-607.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
PREDICT: A Simple Risk Score for Clinical Severity and Long-Term Prognosis After Hospitalization for Acute Myocardial Infarction or Unstable Angina
The Minnesota Heart Survey
From the Division of Epidemiology, School of Public Health (D.R.J., C.K., R.C., H.B.) and Division of Laboratory Medicine and Pathology (L.G.), University of Minnesota, Minneapolis; Stratis Health, Bloomington, Minn (M.D.); and Department of Epidemiology, Cardiovascular Institute and Fu Wai Hospital, Chinese Academy of Medical Sciences, Beijing, PRC (D.F.G.).
Correspondence to David R. Jacobs, Jr, PhD, Division of Epidemiology, School of Public Health, University of Minnesota, 1300 S 2nd St, Suite 300, Minneapolis, MN 55454. E-mail jacobs{at}epivax.epi.umn.edu
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Abstract |
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Background—We evaluated short- and long-term mortality risks in 30- to 74-year-old patients hospitalized for acute myocardial infarction or unstable angina and developed a new score called PREDICT.
Methods and Results—PREDICT was based on information routinely
collected in hospital. Predictors abstracted from hospital record
items pertaining to the admission day, including shock, heart failure,
ECG findings, cardiovascular disease history, kidney
function, and age. Comorbidity was assessed from discharge diagnoses,
and mortality was determined from death certificates. For 1985 and 1990
hospitalizations, the 6-year death rate in 6134 patients with 0 to 1
score points was 4%, increasing stepwise to 89% for 
16 points.
Score validity was established by only slightly attenuated mortality
prediction in 3570 admissions in 1970 and 1980. When case severity was
controlled for, 6-year risk declined 32% between 1970 and 1990. When
PREDICT was held constant, 24% of those treated with
thrombolysis died in 6 years compared with 31% of
those not treated.
Conclusions—The simple PREDICT risk score was a powerful prognosticator of 6-year mortality after hospitalization.
Key Words: myocardial infarction • angina • cardiovascular diseases • thrombolysis
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Prognosis after a myocardial infarction (MI) varies widely, depending on patient characteristics before the event and the amount and functional state of viable myocardium after the event. Of the several useful scores exist for evaluating prognosis after MI, many use invasive diagnostic assessments not available for all patients,1 2 3 4 and others use only noninvasive measures.5 6 7 8 9 10 11 12 13 14 15 16 17 18 Generally, these scores are based on short-term risk,1 2 3 4 5 6 7 8 9 13 14 16 17 18 making them most useful for immediate decisions about hospital therapy. Long-term outcome after hospitalization has rarely been evaluated.4 10 11 12 15
In our epidemiological surveillance of MI in Minnesota, we needed a simple, long-term prognostic score for patients hospitalized for MI or unstable angina. Such a score would reflect the severity of the event and could be used to reduce confounding bias in epidemiological studies of hospital treatment effects, long-term prognosis, trend surveillance, and quality assessment of care.18 Additional use would be for guidance to the patient, family, and medical staff soon after admission. This article proposes such a score that is based on 30-day, 2-year, and 6-year mortality experience after hospitalization for MI or unstable angina through the use of data collected in the Minnesota Heart Survey (MHS), with abstraction of hospital records from 1985 and 1990 and validation of risk prediction in an independent sample of 1970 or 1980 admissions. We also examined severity-adjusted time trends in long-term mortality after hospitalization for MI or unstable angina and severity-adjusted differences in long-term risk among patients treated or not treated with thrombolytic agents.
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Methods |
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Data Abstracted From Hospital Charts of MI and Unstable Angina Patients
MHS was initiated in 1979 to examine community trends in coronary heart disease (CHD) mortality, morbidity, risk factors, and behaviors to study the components and causes of the decline in CHD death rate.19
In the present study, we examined the hospital records of a 50% random sample of patients (100% of women in 1990) who were residents of the Twin Cities metropolitan area, were 30 to 74 years of age, and were discharged with a diagnosis of acute MI (International Classification of Disease [ICD] 8 or 9, code 410) or unstable angina (ICD 8 or 9, code 411) for 1970, 1980, 1985, and 1990. Records of such patients were obtained from 35 of 36 Twin Cities hospitals in 1970, 30 of 31 hospitals in 1980 and 1985, and all 25 hospitals in 1990.
In 1980, when abstraction forms were created for 1970 and 1980 admissions, MHS cardiologists identified variables that they believed would reflect event severity. Abstracted information pertaining to the day of admission included history of disease, blood pressure, heart rate, ECG (by the Minnesota Code20 ), digitalis use, and chest x-ray findings. Comorbidity, much of which is apparent on the day of admission, was assessed by use of the Charlson Comorbidity Score,21 22 which is based on discharge diagnoses. Data obtained after the day of admission were not included in these analyses because a downward clinical course might reflect, rather than predict, in-hospital mortality. Abstraction was done by trained nurses under physician supervision. If no information was found in a hospital chart concerning a particular item, "not recorded" was checked.
Follow-Up and End Points
The vital status at the time of hospital discharge was
ascertained from medical records and subsequently by computerized
linkage with the Minnesota death certificate database (MINNDEX).
MINNDEX had 98% agreement with the National Death Index for death
certificates for 1980 through 1983.23 All-cause death was
the study end point. Underlying cause based on nosologic coding of the
death certificate diagnoses was also examined. Follow-up started at the
hospital admission date and went out 6 years from each hospitalization
year.
Statistical Analysis
The analytic goal was to form a predictive score, PREDICT
(Predicting Risk of Death in Cardiac Disease Tool), from noninvasive
markers available in the MHS database that was based on information
routinely collected in hospital and was available on the day of
admission. Development of PREDICT was based on 1985 and 1990
hospitalizations for precision in current data, with validation based
on an independent sample of hospitalizations from 1970 or 1980.
Mortality within 30 days, 2 years, and 6 years of the day of admission
was related to 35 single items; both "yes" and "not
recorded" responses connoted higher risk among many variables
(data not shown). The average number of items not recorded of a
possible 17 items decreased from 8 to 3 from 1970 to 1990
(P<0.0001). Information not recorded was predictive of
excess mortality in 1985 and 1990; we used the number of not
recorded items as a covariate in subsequent analyses.
In the first stage of development, for simplicity, the score was
assigned 1 point for a "yes" for each item. However, the data
confirmed that indicators such as shock and congestive heart failure
were far more predictive than other indicators such as history and age,
so we considered other weightings, forming PREDICT components by equal
weighting of clinically related items. The first 4 components were
specifically CHD related (shock, congestive heart failure, ECG, and
clinical cardiovascular disease [CVD] history), and 3
others were not (comorbidity, kidney function, and age). Specific items
used in each component are listed in Figure 1
. We evaluated each
component by linear regression of mortality on each potential PREDICT
component, adjusting for age and sex. We ran multiple linear
regressions with death in different follow-up intervals as the
dependent variable and potential components as the independent
variables, adjusting for sex, age, and the number of not
recorded items to determine optimal weights. Because adjustment had
little effect on regression coefficients for PREDICT components, we
returned to the unadjusted model. For simplicity, the final score
assigns integer points and adds them across components. Thus, PREDICT
includes 7 clinically understandable factors measured on the day of
admission (except discharge comorbidity codes) and thought a
priori to indicate clinical severity and to predict long-term mortality
after hospital admission for an acute coronary event.
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To evaluate goodness of fit, we used logistic regression to regress
mortality on PREDICT with SAS PROC LOGISTIC.24 We report
the C-statistic (area under the receiver-operating characteristic
curve) and the Hosmer-Lemeshow 
2 statistic for
goodness of fit (high probability value corresponds to good fit).
Epidemiological Applications
To analyze time trends in mortality rates from 1970 to
1990, we interpreted PREDICT as a measure of severity, using linear
regression analysis to regress mortality on year of death, and
computed mortality rates adjusted for age and sex; for age, sex, and
severity; and for age, sex, severity, and count of not recorded
items. We similarly evaluated mortality rates according to use of
thrombolytic agents.
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Results |
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Potential PREDICT Components
The components in Figure 2
generally
showed a consistent gradient of mortality risk in short- and
long-term follow-up. However, clinical history was inversely associated
with short-term mortality. Shock was most predictive in the short term.
The relative mortality risk of those with 3 shock points versus those
with 0 points was 11.7 in the first 30 days, whereas in 30-day
survivors, relative risk from 31 days to 6 years dropped to 2.3.
Moderately reduced kidney function, defined as blood urea nitrogen of
18 to 29 mg/dL, became predictive only after 2 years. Left
bundle-branch block, intraventricular block, and
right bundle-branch block accompanied by Q waves were the most
predictive ECG abnormalities. Risks for anterior or anterolateral
Q-wave infarction or for anterior, anterolateral, or
inferior non–Q-wave infarction were similar to each other
and less than risk for ventricular conduction defect. No
excess risk was noted for inferior Q-wave infarction. Given
the Q/ST score defined in Figure 1
, no excess risk was noted for
negative T wave, ST elevation, or reciprocal changes (data not shown).
The ECG severity score defined in Figure 1 is based on location,
extent, and severity of Q-wave or non–Q-wave infarction and viability
of the electrical conduction system. Risk generally increased as the
ECG severity score increased.
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Figure 3 presents age- and
sex-adjusted mortality rates for several additional variables not
included in PREDICT that added little to prediction of 6-year mortality
after adjustment for PREDICT: sex, any elevated enzyme, transport to
the hospital in an ambulance, the presence of cardiomegaly among those
who had an x-ray, race, medical insurance, and marital status. High
systolic blood pressure was inversely related to mortality,
although this effect largely disappeared by 6 years. Diagnosis of MI
(ICD 9, discharge diagnosis code 410) had a 36% 6-year risk compared
with 23% for unstable angina (ICD 9, code 411); the same was true for
MI defined by a standardized diagnostic algorithm. However,
this diagnostic category added little to prediction of
6-year mortality, given knowledge of PREDICT (data not shown).
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PREDICT Score
PREDICT was formed by adding points over components (Figure 1) and assigning the risk observed in MHS (Table 1). The mean±SD of PREDICT was
6.8±4.0. Distribution (Figure 4) peaked
at 4 points and was skewed to the right; only 4% had 16 points.
PREDICT showed a 22-fold, graded, monotonic increase in 6-year
mortality (Figure 5); 30-day and 2-year
mortality rates were also graded and nearly monotonic. Each point
corresponds to an 
5% increase in 6-year death rate. In logistic
regression of mortality on PREDICT, the C-statistic was 0.79 for 30-day
mortality, 0.81 for 2-year mortality, and 0.81 for 6-year mortality.
The Hosmer-Lemeshow goodness-of-fit statistic indicated relatively poor
fit for 30-day mortality (2 with 8
df=23.4, P=0.003), better fit for 2-year
mortality (2 with 8 df=18.1,
P=0.02), and excellent fit for 6-year mortality
(2 with 8 df=3.7,
P=0.88).
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In 1985 and 1990 admissions, 52% of deaths were attributed to CHD, and 74% were attributed to CVD. The higher the PREDICT score was, the more likely was the cause of death to be attributable to CVD. Generally, early deaths were attributed to CHD or CVD (74% and 88%). Among deaths occurring 2 to 6 years after hospital admission, 42% were CHD deaths, and 65% were attributable to CVD. Conversely, other non-CVD causes increased 3-fold, and cancer mortality increased 7-fold from 2% in the first 30 days after admission to 12% and 15% in 31 days to 2 years and in 2 to 6 years, respectively.
Prediction of Mortality in Those Discharged Alive
The PREDICT score estimated a strong gradient of risk among the
5709 hospital survivors. Mortality risk in 6 years ranged from 4% for
the 6% who had a PREDICT score of 0 to 1 to 85% for the 3% who had a
PREDICT score 16 (P<0.0001).
Validation of PREDICT in 1970 and 1980 Hospitalizations
PREDICT performed nearly as well in 1970 and 1980 admissions as in
1985 and 1990 admissions. The relative 6-year mortality risk was
9-fold, graded, and nearly monotonic over its range (10% to 85%
dead). As in 1985 and 1990, the distribution peaked at 4 points and was
skewed to the right (Figures 6 and 7). In logistic regression, the
C-statistic was 0.76 for 30-day mortality, 0.77 for 2-year mortality,
and 0.77 for 6-year mortality. The Hosmer-Lemeshow goodness-of-fit
statistic indicated relatively poor fit for 30-day mortality
(2 with 8 df=22.1,
P=0.002), better fit for 2-year mortality
(2 with 8 df=15.1,
P=0.03), and excellent fit for 6-year mortality
(2 with 8 df=8.2,
P=0.31).
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Comparison to Killip Score
For comparison with existing scores, we computed the Killip
score,9 approximated as closely as possible by use of MHS
abstracted data from the day of admission. We formed subcategories,
given in Table 2, to illustrate
ambiguities in recreating the original definition.9
Maximal 30-day death rate with use of the Killip score was 24%; in a
Killip score subcategory, the death rate was 34% (data not shown). A
broad range of 6-year death rates is seen across levels of the Killip
score. There is also substantial variation across the subcategories
within each Killip class (Table 2). In logistic regression of
mortality on the Killip score, the C-statistic was 0.70 for 30-day,
2-year, and 6-year mortality.
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Epidemiological Applications of PREDICT
Mortality Time Trends
The mean age- and sex-adjusted PREDICT score increased from 6.3 in
1970 to 7.0 in 1990 (Table 3), whereas
the number of not recorded items decreased. Age- and sex-adjusted
mortality decreased 43% from 1970 to 1990, with the largest drop
occurring between 1980 and 1985 (Table 3). Adjusting for PREDICT
and the number of items not recorded attenuated the decrease to
32%. A decrease of 32% was consistently seen for each
individual PREDICT score (data not shown).
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Risk Differential According to Use of Thrombolytic Agents
Although 30-day mortality rates were similar in the 669 patients
treated with thrombolytic agents versus the 5450 not
treated (8.5% versus 8.2%, respectively; P=0.8), those
treated had reduced 2-year mortality (13.1% versus 18.8%,
respectively; P=0.0002). The risk difference expanded during
years 2 through 6 of follow-up (Table 4),
even after controlling for PREDICT score. After further adjustment for
both severity and number of items not recorded, those treated with
thrombolytic agents had a 25.3% 6-year mortality rate
versus 30.9% in those not treated (P=0.0006).
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Discussion |
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PREDICT has epidemiological utility both for evaluating trends of disease severity and for examining whether other factors in diagnosis and care are predictive of mortality risk, controlling for severity. Although the mean PREDICT score changed little across study years of 1970, 1980, 1985, and 1990, we are mindful that severity may have been underestimated in the earlier years when hospital charts were less complete. Six-year mortality rates after hospital admission declined 32% between 1970 and 1990, controlling for severity and improvements in hospital chart record keeping. In agreement with clinical trials, we observed that use of thrombolytic agents was associated with improved long-term survival, even though those who received thrombolysis had less severe disease and more complete hospital charts.
Because the factors used in PREDICT have been shown to be predictive in other settings,5 6 7 8 9 10 11 12 13 14 15 16 17 18 it is reasonable to believe that the PREDICT score would perform well if formed by the physician's assignment of clinical severity levels, with the sense of the rules we used in MHS. For example, elevated serum creatinine can be substituted for blood urea nitrogen in determining the kidney score, and other signs of congestive heart failure, such as auscultatory findings, may help to classify the level of severity. Clinically, PREDICT may be useful in reassuring those patients with low scores, whose long-term risk is little different from people who have neither MI nor unstable angina. It also can help to identify those patients with intermediate levels of risk for whom additional clinical watchfulness may be appropriate.
Although PREDICT was designed to estimate long-term mortality risk, it did reasonably well in short-term prediction. A score more predictive of short-term risk would have reversed the weighting of clinical history and admission systolic blood pressure and increased the weighting of shock. Other clinical and invasive measures would be useful in a score designed to predict in-hospital or other short-term outcome.
We found 5 long-term noninvasive scores in the literature that included factors known to be predictive of mortality. Scores such as those developed by Norris et al14 or Killip and Kimball9 focused on subsets of variables well known to be directly related to CHD, most of which are included in PREDICT. Differences in prognostic scores depend on the particular combination of factors included (limited by data availability in a particular study) and by the way factors are defined and implemented. This ambiguity is illustrated in our implementation of the Killip score. The PREDICT score has a broader range of values and provides a finer gradation of outcome probabilities than existing clinical severity scores; PREDICT specifically adds renal function and comorbidity as predictors. One score, the Myocardial Infarction Severity Score,18 has a great deal in common with PREDICT and adds 3 factors not abstracted in MHS: state of consciousness, heart murmur, and respiratory rate. These scores are applicable to prediction in those patients who survive hospital; PREDICT is applicable from the day of admission.
We were hampered in our evaluation of other clinical severity scores5 6 7 8 10 14 because we did not abstract predictors in the specific form required to implement each score. We did estimate Killip score9 and found PREDICT to perform better, even after 30 days of follow-up. Furthermore, therapeutic success was far greater in 1985 and 1990 than in 1965, when the Killip score was created. The sample of 250 patients presented by Killip and Kimball9 included 19% judged to be in cardiogenic shock compared with <6% of MHS patients with shock and congestive heart failure recorded. The patients in cardiogenic shock suffered 78% in-hospital mortality in 1965 compared with only 24% 30-day mortality in the MHS patients and 58% in the GUSTO-I trial.25 Thus, the probabilities associated with the Killip score9 may be high, given present-day therapeutics.
Conclusions
PREDICT is a risk score for acute coronary patients that
uses information routinely obtained on the day of hospital admission
for acute MI or unstable angina. It is a simple and powerful
discriminator of 30-day, 2-year, and 6-year total mortality risk and is
largely independent of sex and CHD manifestation. It reflects severity
of the event. In an independent validation, there was little loss of
power. Because the hospital samples selected are
representative of the Twin Cities metropolitan
statistical area, it has broad generalizability among white men and
women. PREDICT expands on predictors used in earlier scores and updates
risk assessment for particular conditions by use of recent information.
Further evaluation of the score in other clinical settings and among
ethnic minorities would be useful.
Received January 13, 1999; revision received May 19, 1999; accepted May 19, 1999.
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J.-P. Collet, G. Montalescot, G. Agnelli, F. Van de Werf, E. P. Gurfinkel, J. Lopez-Sendon, C. V. Laufenberg, M. Klutman, N. Gowda, D. Gulba, et al. Non-ST-segment elevation acute coronary syndrome in patients with renal dysfunction: benefit of low-molecular-weight heparin alone or with glycoprotein IIb/IIIa inhibitors on outcomes. The Global Registry of Acute Coronary Events Eur. Heart J., November 1, 2005; 26(21): 2285 - 2293. [Abstract] [Full Text] [PDF]
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R. P. Giugliano and E. Braunwald The Year in Non--ST-Segment Elevation Acute Coronary Syndromes J. Am. Coll. Cardiol., September 6, 2005; 46(5): 906 - 919. [Full Text] [PDF]
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B Lagerqvist, E Diderholm, B Lindahl, S Husted, F Kontny, E Stahle, E Swahn, P Venge, A Siegbahn, and L Wallentin FRISC score for selection of patients for an early invasive treatment strategy in unstable coronary artery disease Heart, August 1, 2005; 91(8): 1047 - 1052. [Abstract] [Full Text] [PDF]
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G. C. Fonarow, K. F. Adams Jr, W. T. Abraham, C. W. Yancy, W. J. Boscardin, and for the ADHERE Scientific Advisory Committee, Stud Risk Stratification for In-Hospital Mortality in Acutely Decompensated Heart Failure: Classification and Regression Tree Analysis JAMA, February 2, 2005; 293(5): 572 - 580. [Abstract] [Full Text] [PDF]
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T. J. Gluckman, M. Sachdev, S. P. Schulman, and R. S. Blumenthal A Simplified Approach to the Management of Non-ST-Segment Elevation Acute Coronary Syndromes JAMA, January 19, 2005; 293(3): 349 - 357. [Abstract] [Full Text] [PDF]
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S. D. Wiviott, D. A. Morrow, P. D. Frederick, R. P. Giugliano, C.M. Gibson, C. H. McCabe, C. P. Cannon, E. M. Antman, and E. Braunwald Performance of the thrombolysis in myocardial infarction risk index in the National Registry of Myocardial Infarction-3 and -4: A simple index that predicts mortality in ST-segment elevation myocardial infarction J. Am. Coll. Cardiol., August 18, 2004; 44(4): 783 - 789. [Abstract] [Full Text] [PDF]
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D. A. Morrow, E. M. Antman, S. A. Murphy, S. F. Assmann, R. P. Giugliano, C. P. Cannon, C. Michael Gibson, C. H. McCabe, H. V. Barron, F. Van de Werf, et al. The Risk Score Profile: a novel approach to characterising the risk of populations enrolled in clinical studies Eur. Heart J., July 1, 2004; 25(13): 1139 - 1145. [Abstract] [Full Text] [PDF]
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G. De Luca, H. Suryapranata, A. W.J. van't Hof, M.-J. de Boer, J. C.A. Hoorntje, J.-H. E. Dambrink, A.T. M. Gosselink, J. P. Ottervanger, and F. Zijlstra Prognostic Assessment of Patients With Acute Myocardial Infarction Treated With Primary Angioplasty: Implications for Early Discharge Circulation, June 8, 2004; 109(22): 2737 - 2743. [Abstract] [Full Text] [PDF]
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M. A. Hlatky Comorbidity andoutcome in patientswith coronary artery disease J. Am. Coll. Cardiol., February 18, 2004; 43(4): 583 - 584. [Full Text] [PDF]
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E. J. Velazquez and M. A. Pfeffer Acute Heart Failure Complicating Acute Coronary Syndromes: A Deadly Intersection Circulation, February 3, 2004; 109(4): 440 - 442. [Full Text] [PDF]
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J.-P. Collet, G. Montalescot, E. Fine, J.-L. Golmard, M. Dalby, R.e. Choussat, A. Ankri, R. Dumaine, C. Lesty, N. Vignolles, et al. Enoxaparin in unstable angina patients who would have been excluded from randomized pivotal trials J. Am. Coll. Cardiol., January 1, 2003; 41(1): 8 - 14. [Abstract] [Full Text] [PDF]
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W. S. Weintraub Prediction Scores After Myocardial Infarction: Value, Limitations, and Future Directions Circulation, October 29, 2002; 106(18): 2292 - 2293. [Full Text] [PDF]
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M. Singh, G. S. Reeder, S. J. Jacobsen, S. Weston, J. Killian, and V. L. Roger Scores for Post-Myocardial Infarction Risk Stratification in the Community Circulation, October 29, 2002; 106(18): 2309 - 2314. [Abstract] [Full Text] [PDF]
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T. D. Rea, S. R. Heckbert, R. C. Kaplan, N. L. Smith, R. N. Lemaitre, and B. M. Psaty Smoking Status and Risk for Recurrent Coronary Events after Myocardial Infarction Ann Intern Med, September 17, 2002; 137(6): 494 - 500. [Abstract] [Full Text] [PDF]
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C. P. Cannon, A. Battler, R. G. Brindis, J. L. Cox, S. G. Ellis, N. R. Every, J. T. Flaherty, R. A. Harrington, H. M. Krumholz, M. L. Simoons, et al. American College of Cardiology key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes: A report of the American College of Cardiology Task Force on Clinical Data Standards (Acute Coronary Syndromes Writing Committee) Endorsed by the American Association of Cardiovascular and Pulmonary Rehabilitation, American College of Emergency Physicians, American Heart Association, Cardiac Society of Australia & New Zealand, National Heart Foundation of Australia, Society for Cardiac Angiography and Interventions, and the Taiwan Society of Cardiology J. Am. Coll. Cardiol., December 1, 2001; 38(7): 2114 - 2130. [Full Text] [PDF]
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D. H. Solomon, P. H. Stone, R. J. Glynn, D. A. Ganz, C. M. Gibson, R. Tracy, and J. Avorn Use of risk stratification to identify patients with unstable angina likeliest to benefit from an invasive versus conservative management strategy J. Am. Coll. Cardiol., October 1, 2001; 38(4): 969 - 976. [Abstract] [Full Text] [PDF]
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W. G. Kussmaul III Should we catheterize all patients with unstable angina? No--only the ones with coronary artery disease J. Am. Coll. Cardiol., October 1, 2001; 38(4): 977 - 978. [Full Text] [PDF]
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D. A. Morrow, E. M. Antman, L. Parsons, J. A. de Lemos, C. P. Cannon, R. P. Giugliano, C. H. McCabe, H. V. Barron, and E. Braunwald Application of the TIMI Risk Score for ST-Elevation MI in the National Registry of Myocardial Infarction 3 JAMA, September 19, 2001; 286(11): 1356 - 1359. [Abstract] [Full Text] [PDF]
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H. M. Krumholz, J. Chen, Y.-T. Chen, Y. Wang, and M. J. Radford Predicting one-year mortality among elderly survivors of hospitalization for an acute myocardial infarction: results from the Cooperative Cardiovascular Project J. Am. Coll. Cardiol., August 1, 2001; 38(2): 453 - 459. [Abstract] [Full Text] [PDF]
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K. K. Teo and D. J. Catellier Risk prediction after myocardial infarction in the elderly J. Am. Coll. Cardiol., August 1, 2001; 38(2): 460 - 463. [Full Text] [PDF]
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P. G. McGovern, D. R. Jacobs Jr, E. Shahar, D. K. Arnett, A. R. Folsom, H. Blackburn, and R. V. Luepker Trends in Acute Coronary Heart Disease Mortality, Morbidity, and Medical Care From 1985 Through 1997 : The Minnesota Heart Survey Circulation, July 3, 2001; 104(1): 19 - 24. [Abstract] [Full Text] [PDF]
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D. A. Morrow, E. M. Antman, A. Charlesworth, R. Cairns, S. A. Murphy, J. A. de Lemos, R. P. Giugliano, C. H. McCabe, and E. Braunwald TIMI Risk Score for ST-Elevation Myocardial Infarction: A Convenient, Bedside, Clinical Score for Risk Assessment at Presentation : An Intravenous nPA for Treatment of Infarcting Myocardium Early II Trial Substudy Circulation, October 24, 2000; 102(17): 2031 - 2037. [Abstract] [Full Text] [PDF]
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