Context Patients with unstable angina/non–ST-segment elevation myocardial
infarction (MI) (UA/NSTEMI) present with a wide spectrum of risk for death
and cardiac ischemic events.
Objective To develop a simple risk score that has broad applicability, is easily
calculated at patient presentation, does not require a computer, and identifies
patients with different responses to treatments for UA/NSTEMI.
Design, Setting, and Patients Two phase 3, international, randomized, double-blind trials (the Thrombolysis
in Myocardial Infarction [TIMI] 11B trial [August 1996–March 1998] and
the Efficacy and Safety of Subcutaneous Enoxaparin in Unstable Angina and
Non-Q-Wave MI trial [ESSENCE; October 1994–May 1996]). A total of 1957
patients with UA/NSTEMI were assigned to receive unfractionated heparin (test
cohort) and 1953 to receive enoxaparin in TIMI 11B; 1564 and 1607 were assigned
respectively in ESSENCE. The 3 validation cohorts were the unfractionated
heparin group from ESSENCE and both enoxaparin groups.
Main Outcome Measures The TIMI risk score was derived in the test cohort by selection of independent
prognostic variables using multivariate logistic regression, assignment of
value of 1 when a factor was present and 0 when it was absent, and summing
the number of factors present to categorize patients into risk strata. Relative
differences in response to therapeutic interventions were determined by comparing
the slopes of the rates of events with increasing score in treatment groups
and by testing for an interaction between risk score and treatment. Outcomes
were TIMI risk score for developing at least 1 component of the primary end
point (all-cause mortality, new or recurrent MI, or severe recurrent ischemia
requiring urgent revascularization) through 14 days after randomization.
Results The 7 TIMI risk score predictor variables were age 65 years or older,
at least 3 risk factors for coronary artery disease, prior coronary stenosis
of 50% or more, ST-segment deviation on electrocardiogram at presentation,
at least 2 anginal events in prior 24 hours, use of aspirin in prior 7 days,
and elevated serum cardiac markers. Event rates increased significantly as
the TIMI risk score increased in the test cohort in TIMI 11B: 4.7% for a score
of 0/1; 8.3% for 2; 13.2% for 3; 19.9% for 4; 26.2% for 5; and 40.9% for 6/7
(P<.001 by χ2 for trend). The pattern
of increasing event rates with increasing TIMI risk score was confirmed in
all 3 validation groups (P<.001). The slope of
the increase in event rates with increasing numbers of risk factors was significantly
lower in the enoxaparin groups in both TIMI 11B (P
= .01) and ESSENCE (P = .03) and there was a significant
interaction between TIMI risk score and treatment (P
= .02).
Conclusions In patients with UA/NSTEMI, the TIMI risk score is a simple prognostication
scheme that categorizes a patient's risk of death and ischemic events and
provides a basis for therapeutic decision making.
Patients presenting with an acute coronary syndrome without ST-segment
elevation are diagnosed as having unstable angina/non–ST elevation myocardial
infarction (MI) (UA/NSTEMI). Given the heterogeneous nature of UA/NSTEMI,
such patients have a wide spectrum of risk for death and cardiac ischemic
events.1-5
Many attempts to estimate a gradient of risk among patients with UA/NSTEMI
focus on a single variable, such as presence or absence of electrocardiographic
(ECG) changes6-9
or elevated serum cardiac markers.10-13
Prognostication schemes have been developed that categorize patients
qualitatively into high, intermediate, or low risk, but they do not provide
a quantitative statement about finer gradations of risk that exist clinically.2 Although univariate analyses are informative as an
initial assessment of the importance of a potential prognostic variable, because
of the complex profile of patients with an acute coronary syndrome, multivariate
analyses that adjust for several prognostic variables simultaneously provide
a more accurate tool for risk stratification.2,5,14
Reports of the results of randomized clinical trials of new therapeutic
strategies for UA/NSTEMI typically provide a statement of the overall effectiveness
of a treatment in a population that is a mixture of patients at varying risks
of the primary end point. Although univariate subgroup analyses are frequently
presented in clinical trial reports, these provide only a partial picture
of the effect of the new treatment in a given subgroup unless adjustment is
made for covariates. Given the spectrum of clinical presentations, it is plausible
that the magnitude of the treatment effect of a therapy may vary depending
on the profile of risk in any specific patient.15
Prognostication of patient risk, therefore, is useful not only for allowing
clinicians to triage patients to the optimum location for delivery of medical
care (eg, intensive care unit vs hospital ward vs outpatient care)16,17 but also for identification of patients
who may be best served by potent but expensive—and sometimes risky—new
therapies.5,18-20
To facilitate widespread use of a prognostic scoring system for patients with
UA/NSTEMI, it must be readily applicable using standard patient features that
are part of the routine medical evaluation of such patients.
The primary goal of this article is to report the development, testing,
and clinical utility of a risk stratification tool for evaluation of patients
with UA/NSTEMI. Previously, we reported that a risk stratification scheme
based on age 65 years or older, ST deviation on ECG, and positive serum cardiac
markers segregated patients with UA/NSTEMI into low-, intermediate-, and high-risk
groups, and the treatment effect of enoxaparin was greatest in the highest
risk group.21 However, that risk stratification
scheme used only a limited number of baseline characteristics. We developed
a new, more comprehensive risk score for UA/NSTEMI using the database of the
Thrombolysis in Myocardial Infarction (TIMI) 11B trial, a phase 3 trial comparing
low-molecular-weight heparin (enoxaparin) with unfractionated heparin.22 Our purpose in designing a simple risk score was
to provide a tool that potentially could be applied in clinical settings in
which patients with UA/NSTEMI present for evaluation.
The design and results of the TIMI 11B and Efficacy and Safety of Subcutaneous
Enoxaparin in Unstable Angina and Non-Q-Wave MI (ESSENCE) trials have been
reported previously.22,23 All
patients (n = 3910 in TIMI 11B and n = 3171 in ESSENCE) presented within 24
hours of an episode of UA/NSTEMI at rest. Additional enrollment criteria included
at least 1 of the following: ST-segment deviation on the qualifying ECG (either
transient ST elevation or persistent ST depression of ≥0.05 mV in TIMI
11B and ≥0.01 mV in ESSENCE), documented history of coronary artery disease,
and elevated serum cardiac markers. (In TIMI 11B, a history of coronary artery
disease was acceptable initially but was dropped later as the sole supportive
criterion for UA/NSTEMI.) Major exclusion criteria were planned revascularization
in 24 hours or less, a correctable cause of angina, and contraindications
to anticoagulation.
All patients received aspirin (100-325 mg/d) and, after providing written
informed consent, were randomly assigned to 1 of 2 antithrombotic strategies.
Both trials used a double dummy technique so that all patients received both
an intravenous infusion (unfractionated heparin or matched placebo) and subcutaneous
injections (enoxaparin or matched placebo). For the purposes of developing
the TIMI risk score for UA/NSTEMI, the prespecified primary efficacy end point
from TIMI 11B was applied to both trials in a fashion similar to that reported
for the TIMI 11B–ESSENCE meta-analysis.24
This end point was a composite of all-cause mortality, new or recurrent MI,
or severe recurrent ischemia prompting urgent revascularization. The analyses
shown herein are based on rates for the primary end point through 14 days
after randomization.
Initially, a multivariate model for prognostication of risk for experiencing
at least 1 element of the primary end point was developed. The model incorporated
baseline characteristics that could be readily identified at presentation
and was restricted to the cohort of patients assigned to unfractionated heparin
in TIMI 11B (test cohort). The rationale for this approach was to focus on
information that could be ascertained in a relatively short period after encountering
a patient and establishing a model that could be used for efficient triage
for patient care without waiting for additional tests or results of an initial
period of medical observation over several days. Baseline characteristics
that were evaluated include those previously reported to be important variables
predicting outcomes in patients with UA/NSTEMI and are shown in Table 1.4,5,14,25,26
A total of 12 baseline characteristics arranged in a dichotomous fashion
were screened as candidate predictor variables of risk of developing an end-point
event (Table 1). A multivariate
logistic regression model was then used to assess the statistical significance
of each candidate prognostic variable. After each factor was tested independently
in a univariate logistic regression model, those that achieved a significance
level of P<.20 were selected for testing in a
multivariate stepwise (backward elimination) logistic regression model. Variables
associated with P<.05 were retained in the final
model. Maximum likelihood estimates of the parameter coefficients were obtained
using SAS PROC LOGISTIC (SAS Institute Inc, Cary, NC). The goodness of fit
of the model to the observed event rates was evaluated by calculating the
Hosmer-Lemeshow statistic.27 Low χ2 values and high corresponding P values for
the Hosmer-Lemeshow statistic indicate that the data can be adequately fit
to a logistic function. The ability of the model to classify patients (ie,
its predictive performance) was evaluated using the C statistic, a term equivalent
to the area under a receiver operating characteristic curve for dichotomous
outcomes.28 Assessment of the impact of missing
information for predictor variables was carried out by Monte-Carlo simulations
that randomly set fixed proportions of the data to missing and then repeating
the logistic regression analyses.
After development of the multivariate model, the TIMI risk score for
UA/NSTEMI was developed for the test cohort using those variables that had
been found to be statistically significant predictors of events in the multivariate
analysis. The score was then constructed by a simple arithmetic sum of the
number of variables present. Differences in the event rates for increasing
TIMI risk score values were assessed using the χ2 test for
trend.
The risk score was then validated in 3 separate cohorts of patients:
the enoxaparin group from TIMI 11B (n = 1953), the unfractionated heparin
group from ESSENCE (n = 1564), and the enoxaparin group from ESSENCE (n =
1607). We tested for homogeneity of the unfractionated heparin control groups
in TIMI 11B and ESSENCE by comparing the slope of the increase in the rate
of events with increasing TIMI risk score using least squares linear regression
analysis. Differences between the unfractionated heparin and enoxaparin groups
in both TIMI 11B and ESSENCE were also assessed by comparing the slope of
the increase in rate of events with increasing TIMI risk score using least
squares linear regression analysis. In addition, using a merged database of
the TIMI 11B and ESSENCE studies, testing for a heterogeneous treatment effect
stratified by risk score was carried out by examining the statistical significance
of the interaction term in a multivariate logistic regression model of the
following form: outcome = constant + risk score + treatment (eg, unfractionated
heparin vs enoxaparin) + risk score ∗ treatment.
The asterisk in the model designates an interaction between the adjoining
terms. To explore whether the interaction of risk score ∗ treatment
was affected by the trial in which the patient was enrolled, we tested for
statistical significance of terms for trial (TIMI 11B vs ESSENCE) and interaction
of trial with risk score and treatment when added to the model.
As a secondary goal, we examined the ability of the TIMI risk score
to predict development of each of the individual components of the composite
primary end point as well as the composite end point of all-cause mortality
or nonfatal MI.
The test cohort for development of the TIMI risk score consisted of
the 1957 patients assigned to receive unfractionated heparin in TIMI 11B.22 The primary end point (all-cause mortality, MI, or
urgent revascularization) occurred by 14 days in 16.7% of patients in the
test cohort. Of the 12 original candidate variables, 7 remained statistically
significant in the multivariate analysis and formed the final set of predictor
variables (Table 1). The Hosmer-Lemeshow
statistic was 3.56df8,(P = .89). The C
statistic for the model in the test cohort was 0.65.
Since the parameter estimates for each of the 7 predictor variables
were of a similar magnitude (Table 1),
the risk score was calculated by assigning a value of 1 when a variable was
present and then categorizing patients in the test cohort by the number of
risk factors present, as shown in Figure 1. The pattern of the number of risk factors was normally distributed.
Because of the small number of patients with extreme risk scores, patients
with 0 or 1 risk factor(s) and 6 or 7 risk factors were combined. There was
a progressive, significant pattern of increasing event rates as the TIMI risk
score increased in the test cohort (P<.001 by χ2 for trend).
In the final model, an age cutoff of 65 years was used because this
value was close to the median age for the unfractionated heparin group (66
years) and was the median age for the enoxaparin group. Use of different age
cutoffs showed very little effect on performance of the model: the C statistic
ranged between 0.63 and 0.66 for varying age cutoffs in 5-year increments
from 50 to 80 years. Furthermore, treating age as a continuous variable (problematic
for the development of a simple risk score) also had little effect on model
performance: the C statistic was 0.66 in a model using age as a continuous
variable.
One of the 7 predictor variables shown in Table 1, prior coronary stenosis of 50% or more, requires knowledge
of the results of a prior cardiac catheterization. Construction of the TIMI
risk score using the TIMI 11B database was accomplished from the case report
form data for each patient and, therefore, complete information for the predictor
of prior coronary stenosis of 50% or more was available for all patients;
a value of 0 was assigned if no cardiac catheterization had been previously
performed or if a prior cardiac catheterization revealed no coronary stenoses
of 50% or more; a value of 1 was assigned if a prior cardiac catheterization
revealed at least 1 coronary stenosis of 50% or more.
Since the results of a prior cardiac catheterization might not be immediately
available to a clinician attempting to use the TIMI risk score when a patient
with UA/NSTEMI presents for evaluation, we investigated the effect of missing
values on the prior coronary stenosis of 50% or more variable. Using Monte-Carlo
simulation, a fixed proportion of data on prior coronary stenosis of 50% or
more was randomly set as missing. The model was reevaluated assuming 0 for
missing patients and then reevaluated once again excluding the missing patients.
When 10%, 30%, or 50% of the prior coronary stenosis data were randomly set
as missing and a 0 was assumed for the missing patients, the variable for
prior coronary stenosis of 50% or more remained a significant predictor of
the composite outcome at 14 days: for 10% missing, odds ratio (OR) = 1.44
(95% confidence interval [CI], 1.18-1.75; P<.001);
for 30% missing, OR = 1.35 (95% CI, 1.09-1.68; P
= .007); and for 50% missing, OR = 1.58 (95% CI, 1.25-2.01; P<.001). For the same assumptions of data randomly set as missing
but excluding missing patients, the variable for prior coronary stenosis of
50% or more also remained a significant predictor, with ORs of 1.53 (95% CI,
1.25-1.88), 1.50 (95% CI, 1.19-1.90), and 1.63 (95% CI, 1.25-2.13), respectively
(P<.001 for all). Therefore, under a variety of
assumptions about missing values, prior coronary stenosis of 50% or more remained
a significant predictor of outcome.
Validation of the TIMI risk score is shown in Figure 2. The unfractionated heparin control groups in TIMI 11B
and ESSENCE showed a homogeneous pattern when patients were stratified by
risk score since the slope of the increase in event rates with increasing
number of risk factors was not statistically different (P = .13) in the 2 unfractionated heparin groups (Figure 2). For all 3 validation cohorts (the enoxaparin group from
TIMI 11B, the unfractionated heparin group from ESSENCE, and the enoxaparin
group from ESSENCE) there was a significant increase in the rate of events
as the TIMI risk score increased (P<.001).
Application of TIMI Risk Score
As shown in Figure 2, the
relative rate of increase in events among patients with higher TIMI risk scores
was different for the unfractionated heparin and enoxaparin groups. For both
TIMI 11B and ESSENCE, the slope of the increase in event rates with increasing
numbers of risk factors was significantly lower in the enoxaparin groups (3.92
vs 6.41; P = .01 in TIMI 11B; 2.18 vs 4.36; P = .03 in ESSENCE). A generally consistent pattern of
increasing absolute risk difference and corresponding decrease in the number
of patients requiring treatment to prevent 1 end point event by 14 days after
randomization favoring enoxaparin was seen in both trials as the TIMI risk
score increased.
Using a merged database from the TIMI 11B and ESSENCE trials (N = 7081),
multivariate logistic regression analysis revealed that the TIMI risk score
and treatment (unfractionated heparin vs enoxaparin) were significant predictors
(P<.001 for both terms) of all-cause mortality,
MI, or urgent revascularization by 14 days after randomization (C statistic
= 0.63). An interaction term for TIMI risk score ∗ treatment was also
a significant predictor of the composite outcome at day 14 (P = .02). However, the following terms were not significant predictors
of the composite outcome at day 14: trial (P = .18),
trial ∗ treatment (P = .51), trial ∗
TIMI risk score (P = .13), and trial ∗ treatment ∗
TIMI risk score (P = .84); inclusion of these in
the logistic regression analysis had no effect on overall model performance
(C statistic = 0.63).
The ability of the TIMI risk score to predict outcomes other than all-cause
mortality, MI, or urgent revascularization was assessed in TIMI 11B. In the
entire trial population, there were progressive, significant (P<.001) increases in the rates of all-cause mortality, MI, urgent
revascularization, and the composite of all-cause mortality or nonfatal MI
as the TIMI risk score increased (Figure 3). The event rates stratified by risk score for the unfractionated
heparin and enoxaparin groups in TIMI 11B are shown in Table 2. For both treatment groups, there was a consistent, significant
increase in the rate of events for each outcome with increasing risk score.
Also, for each outcome, the slope of the increase in events with increasing
risk score was lower in the enoxaparin group: 68% lower for all-cause mortality
(P = .02), 25% lower for MI (P = .41), 38% lower for urgent revascularization (P = .05), and 39% lower for all-cause mortality or nonfatal MI (P = .15).
Our results indicate that standard clinical characteristics routinely
obtained during the initial medical evaluation of patients with UA/NSTEMI
can be used to construct a simple classification system that is predictive
of risk for death and cardiac ischemic events. The TIMI risk score includes
variables that can be easily ascertained when a patient with UA/NSTEMI presents
to the medical care system. The variables used to construct the score were
based on observations from prior studies of risk stratification and incorporate
demographic and historical features of the patient, measures of the tempo
and acuity of the presenting illness, and indicators of the extent of myocardial
ischemia and necrosis.2,9,17,29-31
The predictor variables were derived from a logistic regression model that
confirmed their independent predictive power after multivariate adjustment
in the TIMI 11B and ESSENCE data sets.
The simple arithmetic sum of the number of variables present that constitutes
the risk score can be calculated without the aid of a computer. This distinguishes
the TIMI risk score from other scoring systems that are more complex computationally
since they require weighting terms for the predictor variables and cannot
be implemented easily without computer assistance.25
The approach taken in developing the TIMI risk score is similar to that taken
by Centor et al,32 who introduced a scoring
system for assessment of the likelihood of streptococcal pharyngitis based
on clinical findings ascertained in the emergency department, and Croft et
al,33 who developed a simple clinical prediction
rule for identifying nerve function impairment in patients with leprosy.
The TIMI risk score appears statistically robust in that it was validated
internally within TIMI 11B as well as in 2 separate cohorts of patients from
the ESSENCE trial. The model is easy to recall and apply clinically since
a simple age cutoff of 65 years provided similar predictive ability to a more
complex model using age as a continuous variable. Also, variables such as
knowledge of whether the patient had a previously documented coronary stenosis
of 50% or more appeared relatively insensitive to missing information and
remained a significant predictor of events.
The TIMI risk score offers several promising applications for clinical
use. It categorizes patients with UA/NSTEMI into groups that span a wide range
of risk for clinical events—about a 5- to 10-fold range of risk. A contribution
of the TIMI risk score that has not been emphasized in other risk stratification
studies is the actual testing of its use for identifying patients who would
be expected to show particular benefit from new antithrombotic regimens such
as enoxaparin.4 As evidenced by the lower slope
of the increase in event rates with increasing risk score in Figure 2 and the statistical significance of the interaction term
between risk score and treatment, the benefit of enoxaparin was greatest in
those patients with higher TIMI risk scores. That the logistic regression
modeling did not indicate that the trial in which the patient was enrolled
was a predictor of outcome and that the interactions between trial and risk
score were not significant are consistent with an independent effect of enoxaparin
across the 2 trials and illustrates the use of the TIMI risk score for therapeutic
decision making. The absolute difference in event rates increased and the
corresponding number of patients needed to treat for prevention of 1 event
with enoxaparin decreased as the risk score increased (Figure 2). As shown in Figure 3, the risk score also appears useful for stratification of patients
at risk for the individual components of composite end points used in many
contemporary trials of therapies for UA/NSTEMI. The strength of the evidence
of a greater treatment effect of enoxaparin with increasing risk score is
not as strong for the individual components as for the composite primary end
point. This may reflect lower power to detect a treatment benefit from enoxaparin
due to lower absolute event rates for the individual elements of the end point,
although statistical significance favoring enoxaparin was observed for all-cause
mortality and for urgent revascularization (Table 2).
Several limitations of our analyses should be acknowledged. The TIMI
risk score was developed in cohorts of patients who qualified for enrollment
in 2 recent phase 3 trials of treatment for UA/NSTEMI. Its performance in
cohorts of patients who present to emergency departments and physicians' offices
with chest pain must be assessed to determine its generalizability to a variety
of clinical settings. The precise numerical relationship between the TIMI
risk score and event rates described for TIMI 11B and ESSENCE may be altered
as the risk score is applied to other populations. We did not have quantitative
data on the results of serum cardiac markers; instead, we used that predictor
as a dichotomous variable. Given the quantitative relationship between release
of cardiac biomarkers and prognosis, it is possible that the performance of
the model could be improved by incorporating a weighting term for small, moderate,
and large releases of biomarkers detected at the time of presentation.11,12 Other novel markers such as C-reactive
protein may provide additional prognostic information and may need to be incorporated
in future refinements of the risk score as such measurements become more widely
available. Although introduction of weighting factors for predictor variables
or expansion of the list of predictor variables may lead to improvement in
statistical measures of the predictive performance of the model (eg, C statistic),
this is likely to occur at the cost of a loss of simplicity. Risk score development
requires judgment to determine when a model predicts a sufficiently large
gradient of risk to be clinically useful, and further refinement of the model
produces unattractive levels of complexity.
Risk assessment of patients with UA/NSTEMI is a continuous process that
initially involves integration of data at presentation of the patient and
later incorporates hospital-phase data such as the results of noninvasive
and invasive testing, monitoring for episodes of spontaneous recurrent ischemia,
and response to initial therapeutic maneuvers.4
The TIMI risk score for UA/NSTEMI described herein was designed for prognostication
at the time of initial presentation. Updating of the risk score, as hospital-phase
data become available, is an area worthy of further investigation.
Since patients with an acute coronary syndrome are at increased risk
of death and nonfatal cardiac events, clinicians must assess prognosis on
an individual basis to formulate plans for evaluation and treatment. The TIMI
risk score for UA/NSTEMI is a simple prognostication scheme that enables a
clinician to categorize a patient's risk of risk of death and ischemic events
at the critical initial evaluation. A promising clinical application of this
score is identification of a patient for whom new antithrombotic therapies
would be especially effective. Other considerations may bear on the decision
to prescribe new therapies, even in lower risk groups, in whom the treatment
benefit may be smaller. Finally, the TIMI risk score for UA/NSTEMI offers
the opportunity for evaluation of cost-effectiveness of other drugs, such
as glycoprotein IIb/IIIa inhibitors, as well as an early invasive vs early
conservative strategy in patients with an acute coronary syndrome.
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