The Risk Score Profile: a novel approach to characterising the risk of populations enrolled in clinical studies
David A. Morrowa,*,
Elliott M. Antmana,
Sabina A. Murphya,
Susan F. Assmannb,
Robert P. Giuglianoa,
Christopher P. Cannona,
C. Michael Gibsona,
Carolyn H. McCabea,
Hal V. Barronc,
Frans Van de Werfd and
Eugene Braunwalda
a TIMI Study Group, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
b New England Research Institutes, Watertown, MA, USA
c Department of Medicine, University of California San Francisco and Department of Medical Affairs, Genentech, Inc, San Francisco, CA, USA
d Department of Cardiology, UH Gasthuisberg, Leuven, Belgium
Received December 21, 2003;
revised April 8, 2004;
accepted April 29, 2004
* Corresponding author. Tel.: +1-617-278-0145; fax: +1-617-734-7329
E-mail address: dmorrow{at}partners.org
This paper was guest edited by C. Granger, Duke University Medical Center, Durham, USA
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Abstract
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Aims Interpreting the results and practice implications of clinical studies requires accurate characterisation of the baseline risk of the population. We evaluated the Thrombolysis in Myocardial Infarction (TIMI) risk score for STEMI as a tool to describe and compare the risk profile of populations enrolled in three clinical trials (InTIME-II, ASSENT-2 and MAGIC) and the National Registry of Myocardial Infarction.
Methods and Results The risk score was calculated for each patient
and the frequency distribution plotted for each population. The Risk Score Profiles were compared using the KolmogorovSmirnov test. The Risk Score Profile demonstrated a striking concordance between the baseline risk of patients in InTIME-II and ASSENT-2 (median scores in each=
,
. In contrast, the distributions in MAGIC (designed to enroll high risk) and NRMI (registry) were shifted significantly toward higher risk (median scores=
for MAGIC and
in NRMI,
for each vs. InTIME-II). A graded relationship between the risk score and mortality was evident in each study
.
Conclusions The frequency distribution of the TIMI Risk Score, or similar tools for risk assessment, may be used to quantify and readily compare the risk profile of populations enrolled in clinical studies.
Key Words: Clinical trials Prognosis Myocardial infarction Mortality Risk factors
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Introduction
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Interpreting the results and implications of findings from clinical studies requires an accurate characterisation of the baseline risk of the study population. Such an assessment may be important for evaluating the applicability of results to a general population, placing a study in the context of prior investigations, or for exploring possible explanations when the results of well designed trials of very similar interventions are discordant. The approach generally employed, i.e., a tabular comparison of the baseline characteristics across populations, may be informative but is difficult to translate into a quantitative, integrated assessment of risk.
The Thrombolysis in Myocardial Infarction (TIMI) risk score (the Risk Score) for ST-segment elevation myocardial infarction (STEMI) was developed in the Intravenous nPA for the Treatment of Infarcting Myocardium Early (InTIME-II) Trial as a simple integer score to assist clinicians in profiling the mortality risk of an individual patient with STEMI.1 The risk score provides strong discriminatory capacity among patients with STEMI enrolled into clinical trials, as well as in non-selected patients treated with acute reperfusion therapy in the United States.1,2 Using an approach that could be extrapolated to other scoring systems validated in the community, as an example, we evaluated whether this risk score could be employed to characterise and compare the risk profile of populations enrolled in three large clinical trials (InTIME-II, Assessment of the Safety and Efficacy of a New Thrombolytic (ASSENT)-2, and Magnesium in Coronaries (MAGIC) trials) as well as in the National Registry of Myocardial Infarction (NRMI)-3.
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Methods
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Study populations
The enrollment criteria and study designs for each trial have been described.35 The study populations examined in this analysis were purposely selected so as to include one from a trial (ASSENT-2) very similar in design to InTIME-II (reference population), one from a trial that aimed to enroll a very high risk population (MAGIC) and one from a community-based registry (NRMI).
The InTIME-II
and ASSENT-2
trials had similar inclusion criteria3,4 and were both multi-national, randomised clinical trials that enrolled fibrinolytic-eligible patients with STEMI within 6 h of symptom onset. Eligible participants were aged
18 years with chest pain and ST-elevation or left bundle branch block (LBBB) on the qualifying electrocardiogram. Exclusion criteria included any history of cerebrovascular disease, cardiogenic shock, or increased risk of severe bleeding. In each trial, patients were randomly allocated to one of two pharmacological reperfusion regimens.
MAGIC
also enrolled patients with STEMI within 6 h of symptom onset.5 Patients were randomised from two strata: Stratum-1,
, included candidates for fibrinolysis or primary percutaneous coronary intervention (PCI) age
65 years; Stratum-2,
, included patients of any age not considered candidates for fibrinolysis. Exclusion criteria included cardiogenic shock, severe renal impairment (serum creatinine
265 µmol/L) and sustained bradycardia (
50 beats/min). Randomised study therapy consisted of intravenous magnesium or matched-placebo.
NRMI is a prospective, observational database of demographics, practice patterns and outcomes for consecutive patients with acute myocardial infarction (AMI) from 1529 hospitals in the United States.2,6 NRMI 3, which collected data from April 1998 through June 2000, was used for the present analysis. All treatment decisions were at the discretion of the managing physicians. The analysis set from NRMI 3 included patients with ST-elevation or LBBB who completed their stay at the admitting hospital and were not in cardiogenic shock at the initial evaluation.
TIMI Risk Score for STEMI
The risk score is a weighted integer score based upon 10 clinical risk indicators at presentation (Table 1).1 For each patient, the score is calculated as the sum of points for each risk feature present (range 014). The risk score was developed using multi-variable methods among patients from InTIME-II. Based on independent predictors capturing
95% of the prognostic information from a full 16-term regression model, the discriminatory capacity (c-statistic 0.78) of the risk score compares favorably to alternative risk models, including sophisticated regression equations applied in the same dataset.1
Statistical analysis
The risk score was calculated for all patients with baseline data that were complete for the risk score variables (InTIME-II 94%, ASSENT-2 99%, MAGIC 99.7% and NRMI 94%). We have previously shown minimal difference in the discriminatory capacity of the risk score when applied among patients with randomly missing variables.1 As not all risk score variables were collected identically in each trial, the best possible surrogates were used. In ASSENT-2 and MAGIC, prior documented coronary artery disease (CAD) was employed as a surrogate for prior angina. Patients' body weights were not collected in MAGIC and thus did not contribute to their risk score. Exploratory analyses were performed both imputing weights for MAGIC based on the distribution in InTIME-II, as well as excluding the variable for patient weight from InTIME-II and ASSENT-2 (data not shown) without important quantitative or qualitative changes in the results of the inter-trial comparisons (InTIME-II vs. ASSENT-2 and InTIME-II vs. MAGIC). Patients in MAGIC and NRMI who were treated without reperfusion therapy were assessed one point for the variable for delay of reperfusion therapy. Vital status was available at 30 days for patients in each of the clinical trials and through hospital discharge in NRMI. The risk score predictions for mortality in NRMI are based on the in-hospital event rates in InTIME-II.2
The Risk Score Profile
The Risk Score Profile for each population was expressed graphically as a smoothed curve fit to the frequency distribution of the risk score. The risk profile of each population was also expressed as the cumulative distribution across risk score groups, as well as the median and 25th75th percentiles. The distribution of the risk scores in different populations was compared using the KolmogorovSmirnov Test, which measures the maximum difference between the two cumulative distribution functions and calculates the probability that the two observed distributions would exhibit a difference at least that large if the samples were drawn from identical populations.7 The distribution of risk score values in each trial was also compared using a non-parametric (Wilcoxon) rank sums test. The risk score distribution for InTIME-II was compared to ASSENT-2, as a trial with very similar entry criteria, and to that from MAGIC, a trial aimed at enrolling higher risk patients. In addition, based on documented differences in the risk score performance among patients treated without reperfusion therapy,2 we compared the Risk Score Profiles from each stratum in MAGIC.
The prognostic discriminatory capacity of the risk score was expressed as the c-statistic, representing the area under the receiver operating characteristic (ROC) curve for predicting 30-day death (or, in the case of NRMI, in-hospital death), modelling the risk score with indicator variables for each risk score group (0 to
8).8 Associations between mortality rates and the risk score were assessed using the
test for trend. The predicted mortality for each study population was derived from the expected mortality rates based on the risk score and the proportion of patients in each risk score group. Given no detectable difference between the randomised treatments in InTIME-II, ASSENT-2 and MAGIC,35 only the aggregate mortality rates are presented. Multi-variable logistic regression including the TIMI risk score and clinical variables known to be associated with mortality in MAGIC5 was performed to identify those variables which added prognostic information that was incremental to the risk score with respect to mortality at 30 days. P-values of
0.05 (two-tailed) were considered significant. Analyses were performed using SAS v8.0 (SAS Institute, Cary, NC) and STATA v7-inter-cooled (STATA Corp., College Station, TX).
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Results
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This analysis included 121,085 patients with STEMI across three clinical trials and one registry. The baseline characteristics of the patients from each study are summarised in Table 2. Patients enrolled in MAGIC and NRMI tended to be older, more often female and more frequently to have had a previous myocardial infarction than patients in InTIME-II or ASSENT 2.
The Risk Score Profile
The risk score was calculated for each patient and the frequency distribution plotted for each study, providing a graphical representation of the risk profile for the study populations.
Comparison between randomised trials of fibrinolysis
Comparison of the Risk Score Profile for two trials with very similar entry criteria, InTIME-II and ASSENT-2, showed a striking concordance between the baseline risk profile of the populations enrolled in these large multi-national trials of fibrinolysis (Fig. 1(a)). In both populations, the median risk score was 3 (inter-quartile range (IQR)=14;
). It can be readily appreciated from the graphical representation that the shapes of the risk score distribution were highly similar in the two populations. Moreover, assessed using the KolmogorovSmirnov test, the difference in the distribution of the risk score between the two populations was non-significant
.

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Fig. 1 Risk Score Profile for InTIME-II (red) and ASSENT-2 (blue). The smoothed curves represent the frequency distribution (a). The bars indicate the rate of death at 30 days (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Applied for mortality prediction in ASSENT-2, the risk score demonstrated a strong prognostic capacity (c-statistic=0.79) that was near identical to the risk score performance in InTIME-II (c-statistic=0.79).1 Notably, the overall mortality risk predicted by the risk score for ASSENT-2 (6.3%) was very similar to that observed (6.0%). In addition, the observed rates of death in each risk score group were strongly concordant with the predictions using the TIMI Risk Score (Fig. 1(b)).
Comparison to MAGIC
The Risk Score Profile from InTIME-II was also compared to that from MAGIC, a trial that was designed to enroll high-risk patients with STEMI being treated with and without reperfusion therapy, i.e., a population anticipated to be distinctly different from that in InTIME-II and ASSENT-2 (Fig. 2). Contrasted with the risk profile of InTIME-II, the distribution of the risk score in MAGIC was shifted significantly to the right toward higher mortality risk, median score=4 (IQR: 35),
. The shape of the risk profile of MAGIC also differed substantially from that of InTIME-II as assessed using the KolmogorovSmirnov test
. The two pre-specified strata enrolled in MAGIC (i.e., those treated with and without immediate reperfusion therapy) were also evaluated separately. Although the median risk scores in the two groups were the same (media
), examination of the shape of the Risk Score Profile revealed a substantive difference in the distribution of the risk score in these two sub-groups, with Stratum-I exhibiting a higher risk profile (Fig. 3, KolmogorovSmirnov test,
).

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Fig. 2 Risk Score Profile for InTIME-II (red) and MAGIC (green). The smoothed curves represent the frequency distribution of the risk score in each trial. The bars indicate the rate of death at 30 days. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3 Risk Score Profile for MAGIC Stratum-I (dashed) vs. Stratum-II (solid). The smoothed curves represent the frequency distribution of the risk score in each population. The bars indicate the rate of death at 30 days.
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A strong graded relationship between the risk score and mortality was also present in MAGIC
) but with an overall prognostic discriminatory capacity (c-statistic=0.68) that was lower than in InTIME-II or ASSENT-2. The mortality observed in the MAGIC population at any risk score was higher than that observed in InTIME-II and ASSENT-2 (Fig. 2). This excess mortality in MAGIC beyond that predicted by the risk score was evident within both strata. As such, only one half of the difference in the observed mortality between MAGIC (15.2%) and InTIME-II/ASSENT-2 (6.06.3%) could be attributed to differences in the baseline risk captured by the Risk Score Profile (expected mortality 10.5%). An exploratory analysis using multi-variable regression revealed that the geographical region, and patterns in the use of in-hospital medications (ß-blockers and angiotensin converting enzyme inhibitors) accounted for a significant component of the higher mortality in MAGIC, along with a smaller contribution from additional baseline characteristics, gender and history of prior stroke (c-statistic=0.73).
Comparison to NRMI
The risk profile from InTIME-II was also compared to patients followed in NRMI 3 revealing a significantly higher risk profile for this community-based population (KolmogorovSmirnov test,
). Based on expected differences between those treated with and without an immediate reperfusion strategy (fibrinolysis or primary PCI),2 these two groups were also profiled separately (Fig. 4). Notably, the difference in the risk profile of the patients treated with immediate reperfusion therapy in the community (NRMI,
) and those enrolled in InTIME-II was modest (KolmogorovSmirnov test,
), although statistically significant due to the large sample sizes
). In contrast, the difference in the risk profile for patients from InTIME-II and patients in NRMI treated without reperfusion therapy was substantially greater when quantified using the D-statistic from the KolmogorovSmirnov test (
, Fig. 4). We have previously shown the mortality among patients in NRMI treated with reperfusion therapy to be highly similar to InTIME-II at each risk score, with predicted and observed overall in-hospital mortality in NRMI of 6.7% and 6.3%, respectively.2

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Fig. 4 Risk Score Profile for InTIME-II (red) and patients from NRMI managed with (purple) and without an immediate reperfusion strategy (IRS, light blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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In the present analysis, the risk score was also applied as a framework for comparison of outcomes among patients treated with fibrinolysis vs. primary PCI in NRMI, stratified by baseline risk. Overall, patients treated with primary PCI vs. fibrinolytic showed a statistically significant but clinically modest difference in baseline risk as characterised by the risk profile (KolmogorovSmirnov test,
,
, Fig. 5). Compared within strata of a similar baseline risk, patients managed with primary PCI were found to be at a numerically lower risk of in-hospital mortality compared to patients treated with fibrinolysis in NRMI (Fig. 5).

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Fig. 5 Risk Score Profile for InTIME-II (red) and patients from NRMI treated with fibrinolysis (purple, solid) and primary percutaneous coronary intervention (purple, dashed). The smoothed curves represent the frequency distribution of the risk score in each population. The bars indicate the rate of in-hospital death. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Use of the cumulative distribution
Simultaneous evaluation of each of the clinical trial populations, along with the community-based registry of patients with STEMI in NRMI, was performed using the cumulative distribution of risk score values to characterise the baseline risk profile of each population (Fig. 6). Again the similarity of the risk profile of the populations from InTIME-II and ASSENT-2 is readily apparent. The higher risk profiles of patients enrolled in the MAGIC trial and those in NRMI managed without reperfusion therapy are also readily appreciated using this representation.
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Discussion
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Differences in baseline risk may underlie divergent results of clinical trials, confound comparisons across clinical populations or sub-groups and influence the implementation of findings from clinical trials. We have previously described application of the TIMI Risk Score for STEMI as an effective framework for analyses stratified by risk at presentation, such as risk-adjusted assessment of resource utilisation1 and regional variation in outcomes.9,10 We have also used the risk score for Unstable Angina/Non-ST elevation MI as a mechanism for comparison of the effects of a similar intervention across two different clinical trials.11 In the present analysis, we have demonstrated an approach to characterising the risk profile of populations participating in clinical studies by employing the risk score; such an approach is likely to be generalisable to other validated scoring systems, including those that may have superior performance in patients treated without reperfusion therapy.
Graphical representation of the risk score distribution provides an immediate and effective comparison of the baseline risk in different patient populations. While the median and inter-quartile range of risk score values provide some insight into the distribution of the risk score, the shape of the histogram may carry additional information regarding the baseline risk profile, as exemplified by differences in the two enrollment strata from MAGIC (Fig. 3). We have also described an approach to quantify the difference in risk profile based on statistical comparison of the distributions using the KolmogorovSmirnov test. Analogous quantitative approaches have previously been applied to comparative evaluations of cell populations and fluid dynamics,12 and to provide new insight into potential mechanisms underlying restenosis through careful analysis of frequency distributions.13
The present analysis illustrates several potential applications of the Risk Score Profile in the interpretation of clinical study results. It is reassuring to see that two large multi-national clinical trials with similar enrollment criteria (InTIME-II and ASSENT-2) have virtually indistinguishable Risk Score Profiles. Moreover, as anticipated based on the similar risk profile, the overall mortality rates at 30 days, as well as within each risk score group, were highly concordant between these two trials. In contrast, assessment of the risk profile in MAGIC confirms that the MAGIC investigators achieved their objective of recruiting a higher risk population. The higher baseline risk profile of Stratum-I vs. Stratum-II of MAGIC is not surprising given that patients under the age of 65 years were excluded from Stratum-I by trial design.
In addition, the Risk Score Profile provides a means to assess whether factors other than the baseline risk profile of the population may be contributing to a higher, or lower, than expected mortality. For example, as would be anticipated based on the baseline risk profile, the overall mortality in MAGIC was substantially higher than in the InTIME-II and ASSENT-2 trials. However, the observed rates of death in MAGIC were also higher than expected for each risk score category, pointing toward the contribution of factors other than those included in the risk score to the observed outcomes. In this case, additional, exploratory analysis indicates that regional differences in patterns of care and use of medical therapies may be important in explaining this excess risk. Specifically, geographical region and lower in-hospital use of ß-blockers and angiotensin converting enzyme inhibitors accounted for a significant component of this excess risk in the multi-variable model. Similarly, among patients in NRMI, those managed without reperfusion therapy were at higher baseline risk as characterised by the risk profile and also suffered even worse outcomes than was predicted by the risk score: a result contributed to, in part, by the deferral of immediate reperfusion therapy.2 As such, application of the Risk Score Profile to both MAGIC and NRMI provided incremental information to that obtained by simple review of the overall observed mortality rates and individual baseline characteristics. As a final example, we demonstrated the use of the risk score to account for potential differences in risk when performing analyses of specific treatment interventions (such as primary PCI vs. fibrinolysis) in observational studies (Fig. 5). Although provided only as an example, the comparison between primary PCI vs. fibrinolysis among patients grouped according to baseline risk both provides observational data in a community-based study that is supportive of the results from randomised clinical trials and demonstrates the utility of applying the risk score toward risk-adjusted analyses in clinical research. The strong relationship between the TIMI Risk Score and short-term mortality among patients undergoing treatment with primary PCI in this study is particularly relevant given the ongoing shift in contemporary practice toward primary PCI.
Limitations
The TIMI Risk Score for STEMI was designed for practical clinical application at the bedside using data immediately available at the time of presentation and thus does not capture some measures of medical co-morbidity that may offer incremental prognostic information.14 In some circumstances, it may be desirable to utilise a more complex instrument for risk assessment. However, others have shown that a simple model may be as effective as more complex risk assessment tools for standardising outcomes across different community populations and is less likely to be constrained by missing data.15 Moreover, the Risk Score Profile should be applicable with other scoring systems that include a broader spectrum of co-morbidities.16
Elements of the TIMI Risk Score for STEMI were missing from MAGIC and NRMI. While the absence of variables (e.g., weight) may reduce the discriminatory capacity of the risk score in these data sets, it does not alter the qualitative findings from our description of the methodology. This limitation is inherent to the application of any risk models for standardising outcomes across different populations and may be minimised when planned during the design phase so that all of the key components of the risk score are collected.15
As a method for comparing distributions, the KolmogorovSmirnov test is not as powerful as the t-test if the two distributions being compared have essentially normal distributions and similar variance. However, the KolmogorovSmirnov test allows detection of any type of difference in the two distributions, not simply a shift in location.17 In addition, the test is useful for distinguishing differences in the distributions when the data are clustered,18 such as in the application of integer risk scores. As is the case with any statistical test, with very large sample sizes, the KolmogorovSmirnov test may achieve statistical significance even for clinically unimportant differences in distributions. The actual differences in distributions, examined graphically and quantified by the test D-statistic, must be considered along with the P-value when comparing patient populations.
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Conclusion
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The frequency distribution of the TIMI Risk Score for STEMI, or other similar tools for risk assessment, may be used to characterise the risk profile of populations enrolled in clinical studies as an aid toward facilitating their comparative evaluation and providing insight toward the interpretation of clinical trial results.
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Acknowledgments
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In TIME II was supported by Bristol-Myers Squibb; MAGIC was supported by the NHLBI; ASSENT II was supported by Boeringer-Ingelheim and Genentech; and NRMI is supported by Genentech.
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