Summary
Background
Risk assessment is fundamental in the management of acute coronary syndromes (ACS), enabling estimation of prognosis.
Aims
To evaluate whether the combined use of GRACE and CRUSADE risk stratification schemes in patients with myocardial infarction outperforms each of the scores individually in terms of mortality and haemorrhagic risk prediction.
Methods
Observational retrospective single-centre cohort study including 566 consecutive patients admitted for non-ST-segment elevation myocardial infarction. The CRUSADE model increased GRACE discriminatory performance in predicting all-cause mortality, ascertained by Cox regression, demonstrating CRUSADE independent and additive predictive value, which was sustained throughout follow-up. The cohort was divided into four different subgroups: G1 (GRACE < 141; CRUSADE < 41); G2 (GRACE < 141; CRUSADE ≥ 41); G3 (GRACE ≥ 141; CRUSADE < 41); G4 (GRACE ≥ 141; CRUSADE ≥ 41).
Results
Outcomes and variables estimating clinical severity, such as admission Killip-Kimbal class and left ventricular systolic dysfunction, deteriorated progressively throughout the subgroups (G1 to G4). Survival analysis differentiated three risk strata (G1, lowest risk; G2 and G3, intermediate risk; G4, highest risk). The GRACE + CRUSADE model revealed higher prognostic performance (area under the curve [AUC] 0.76) than GRACE alone (AUC 0.70) for mortality prediction, further confirmed by the integrated discrimination improvement index. Moreover, GRACE + CRUSADE combined risk assessment seemed to be valuable in delineating bleeding risk in this setting, identifying G4 as a very high-risk subgroup (hazard ratio 3.5; P < 0.001).
Conclusions
Combined risk stratification with GRACE and CRUSADE scores can improve the individual discriminatory power of GRACE and CRUSADE models in the prediction of all-cause mortality and bleeding. This combined assessment is a practical approach that is potentially advantageous in treatment decision-making.
Résumé
Contexte
L’évaluation des risques est fondamentale dans la gestion des syndromes coronariens aigus, permettant l’estimation du pronostic.
Objectifs
Le but de notre étude était d’évaluer l’utilisation combinée des scores GRACE et CRUSADE pour la stratification de la mortalité et du risque hémorragique des patients pris en charge pour un infarctus aigu du myocarde en comparaison à l’utilisation isolée de chacun de ces scores.
Méthodes
Cohorte rétrospective observationnelle monocentrique ayant inclus 566 patients consécutifs hospitalisés pour un syndrome coronarien aigu sans sus-décalage du segment ST. Le score CRUSADE a augmenté le pouvoir discriminant du score GRACE pour la prédiction de la mortalité globale, en utilisant la régression de Cox, ce qui démontre la valeur prédictive indépendante et additive du score CRUSADE, laquelle était maintenue tout au long du suivi. La cohorte a été divisée en 4 sous-groupes : G1 (GRACE < 141 ; CRUSADE < 41) ; G2 (GRACE < 141 ; CRUSADE ≥ 41) ; G3 (GRACE ≥ 141 ; CRUSADE < 41) ; G4 (GRACE ≥ 141 ; CRUSADE ≥ 41).
Résultats
Les événements et variables qui évaluaient la sévérité clinique, comme la classe Killip-Kimbal à l’admission et la dysfonction systolique du ventricule gauche étaient plus fréquents de manière linéaire en fonction des sous-groupes (G1–G4). L’analyse de la survie a montré 3 groupes de risque (G1, risque bas ; G2 et G3, risque intermédiaire ; G4, risque plus élevé). Le modèle GRACE + CRUSADE a montré une performance pronostique supérieure (AUC 0,76) au score GRACE utilisé de manière isolé (AUC 0,70) pour la prédiction de la mortalité, ce qui a été confirmé par l’amélioration de l’index de la discrimination intégrée. De plus, l’évaluation combinée des scores GRACE + CRUSADE semble avoir une valeur additionnelle pour la prédiction de risque de saignement et permet d’identifier le groupe G4 comme étant à risque très élevé (HR 3,5 ; p = 0,001).
Conclusion
L’utilisation combinée des scores GRACE et CRUSADE pourrait améliorer leur pouvoir discriminant en comparaison à leur utilisation isolée pour la prédiction de la mortalité globale ainsi que du risque hémorragique. Cette nouvelle approche semble apporter des avantages dans la pratique quotidienne et orienter la prise en charge thérapeutique.
Background
Risk assessment is fundamental in acute coronary syndrome (ACS) management, enabling estimation of patient prognosis – a key issue for communicating with patients and relatives, and for therapeutic decision-making. Current recommendations propose an aggressive treatment approach for high-risk non-ST-elevation myocardial infarction (NSTEMI), including more potent antithrombotic therapies and a rapid invasive strategy . Conversely, lower-risk cases may do well with less aggressive medical treatment and a more selective invasive strategy. Thus, it is essential to assess ischaemic risk on an individual basis, preferably using quantitative risk scoring systems such as the Global Registry of Acute Coronary Events (GRACE) model , use of which is favoured over other risk scores in the latest guidelines update . However, with the greater use of more potent antithrombotic drugs and early revascularization, bleeding occurs more frequently and has become a relevant clinical problem in the ACS setting, making haemorrhagic risk assessment a necessary tool to guide treatment strategies. The can rapid risk stratification of unstable angina patients suppress adverse outcomes with early implementation of the American College of Cardiology/American Heart Association guidelines (CRUSADE) risk score is one of the most popular bleeding risk algorithms, consisting of several recognized predictors of haemorrhage . As bleeding results not only in an immediate threat but also in increased risk of adverse outcomes during follow-up , it remains to be determined if ACS risk assessment with combined ischaemic and bleeding risk assessment will prove advantageous. Our aim was to establish the appropriateness of the combined use of GRACE and CRUSADE risk stratification in NSTEMI patients and to evaluate potential gains in outcome prediction, compared with the separate use of the traditional risk-scoring systems.
Methods
Patient selection
This was an observational retrospective single-centre cohort study including all patients consecutively admitted to our University Hospital’s Acute Cardiac Care Unit with a final diagnosis of myocardial infarction between 1 December 2006 and 31 May 2008. Myocardial infarction was defined according to the recently updated definition , excluding patients with unstable angina and those with myocardial injury (elevated cardiac biomarkers) without evidence of ischaemia (i.e. symptoms, electrocardiogram, imaging modalities). Furthermore, only NSTEMI cases were considered, with the final study cohort including a total of 566 patients.
Data collection and patient follow-up
Demographic and clinical features were collected at admission and during hospitalization. The electrocardiogram and analytical assessment (including complete blood count and biochemical and clotting tests) were performed according to the Acute Cardiac Care Unit standards: at admission and then at least daily, according to patient’s clinical evaluation. Troponin I measurements were taken at admission, between 12 and 24 hours after admission and daily thereafter. The measurement of troponin I was performed with the chemiluminescent technique (Ortho Clinical Diagnostics VITROS ® Troponin I ES Assay; Johnson & Johnson Ltd., Maidenhead, UK). The lower detection limit for this assay is 0.012 ng/mL. The 99th percentile upper reference limit is 0.034 ng/mL, with a reported imprecision of 10% coefficient of variation. Results > 0.034 ng/mL were considered positive. Creatinine clearance was estimated using the modification of diet in renal disease equation . The reference for coronary angiography and potential percutaneous myocardial revascularization was an individually tailored decision, involving the Acute Cardiac Care Unit and the interventional cardiologist’s clinical judgment, in accordance with the European Society of Cardiology guidelines for myocardial infarction management . Finally, left ventricular ejection fraction was obtained from the predischarge transthoracic echocardiogram, in accordance with European Association of Cardiovascular Imaging standards .
Patients were followed for 21.1 ± 7.5 months after discharge by means of patient’s clinical records, routine visits, consultation of the National Health System User Card database and telephone calls until the end of a 2-year period after discharge, and whenever clinical files were considered insufficient.
Risk assessment
We tested and compared the prognostic performance of GRACE and CRUSADE risk stratification models in this cohort, through evaluation of their overall discriminative performance and calibration in the prediction of all-cause mortality during the index event, follow-up and in-hospital bleeding, respectively. The traditional risk categories of GRACE and CRUSADE scores are depicted in Supplementary Table 1 . The GRACE score for in-hospital mortality (GRACE IH ) is more commonly used in clinical practice than the 6-month postdischarge GRACE score, because the former may guide revascularization timing in NSTEMI (i.e. patients at high ischaemic risk [GRACE ≥ 141] should be considered for an early invasive strategy) . Subsequently, the cohort was divided into four different groups according to the presence of at least one high-risk category (using in-hospital GRACE and CRUSADE cut-offs used in clinical practice) : group 1 (G1: GRACE < 141 non-high-risk class; CRUSADE < 41 non-high-risk class), group 2 (G2: GRACE < 141, non-high-risk class; CRUSADE ≥ 41, high-risk class); group 3 (G3: GRACE ≥ 141, high-risk class; CRUSADE < 41, non-high-risk class); group 4 (G4: GRACE ≥ 141, high-risk class; CRUSADE ≥ 41, high-risk class). Each group was evaluated in terms of baseline characteristics and study endpoints.
In this study, major bleeding was defined in accordance with the CRUSADE investigators : intracranial haemorrhage, documented retroperitoneal bleed, haematocrit drop ≥ 12% (from baseline), any red blood cell transfusion when baseline haematocrit was ≥ 28% or any red blood cell transfusion when baseline haematocrit was < 28% with witnessed bleed.
Study endpoints
The primary outcome measures were in-hospital all-cause mortality, all-cause mortality during follow-up and in-hospital major bleeding.
Statistical analysis
Statistical analyses were done using SPSS ® software, version 17.0 (StataCorp LP, College Station, Texas, USA). When needed, baseline characteristics were described with means ± standard deviations for continuous and counts and proportions for categorical data. The Kolmogorov–Smirnov test was used to test the normal distribution of continuous variables. The Chi 2 test and Student’s t test were used for quantitative and nominal comparisons between two groups, and non-parametric equivalent tests were used when appropriate. Regression estimation techniques were applied to replace missing values whenever the number of missing values was negligible, otherwise cases with missing values were omitted. P values < 0.05 (two-sided) were considered statistically significant.
Univariate analysis was performed to evaluate the potential association between each previously defined myocardial infarction group and the study endpoints. Cox regression was used to evaluate the predictive value of the CRUSADE score compared with the GRACE algorithm for follow-up mortality. The analysis of variance (or equivalent non-parametric test, when necessary) was used to determine differences among the predefined myocardial infarction subgroup means. Discrimination, measured in terms of the area under the receiver operating characteristic (ROC) curve (AUC), was performed to assess the predictive power of the GRACE score in-hospital (GRACE IH ) and 6 months postdischarge (GRACE 6M ) for in-hospital and follow-up mortality, respectively, and of the CRUSADE model for in-hospital major bleeding. Finally, the combined GRACE and CRUSADE model (GRACE + CRUSADE) was tested for in-hospital and follow-up mortalities and major bleeding. Other measures of incremental value have been proposed, which examine the extent to which a model reclassifies subjects, such as the net reclassification index (NRI) and the integrated discrimination improvement (IDI) . The NRI method, described by Pencina et al. , states that a positive and significant NRI translates a net overall successful reclassification of subjects into a more appropriate risk category. The IDI, which may be seen as a continuous form of the NRI, assesses improvement in risk discrimination by estimating the change in the difference of the average of predicted probabilities of an event between those with and without the event under consideration ; it is a more appropriate measure of risk reclassification when comparing scores with different risk categorization (e.g. GRACE stratifies patients into three risk strata and GRACE + CRUSADE stratifies patients into four risk categories). Calibration of each score was also assessed using the Hosmer–Lemeshow test. Finally, Kaplan–Meier curves were constructed to evaluate survival during follow-up according to each predefined myocardial infarction group.
Results
Cohort characteristics
The cohort included 566 patients with a mean age of 70.4 ± 12.3 years (range 31–92 years), 61.3% of whom were men. Patients’ baseline clinical, analytical and imaging characteristics are shown in Supplementary Table 1 . The cohort distribution according to GRACE and CRUSADE risk values/categories are given in Table 1 . In 270 (47.7%) cases there was overall concordance between GRACE IH and CRUSADE risk categories; 184 (32.5%) patients were classified as high-risk by both GRACE and CRUSADE risk models ( Table 1 ).
| Low-risk class | Intermediate-risk class | High-risk class | |
|---|---|---|---|
| GRACE IH | 77 (13.6) | 135 (23.9) | 354 (62.5) |
| GRACE 6M | 155 (27.3) | 219 (38.7) | 192 (34.0) |
| CRUSADE | 222 (39.2) | 126 (22.3) | 218 (38.5) |
| Cross-tabulation of CRUSADE and GRACE risk categories | |||
|---|---|---|---|
| GRACE | |||
| Low-risk class | Intermediate-risk class | High-risk class | |
| CRUSADE | |||
| Low-risk class | 58 (26.1) | 83 (37.4) | 81 (36.5) |
| Intermediate-risk class | 12 (9.5) | 28 (22.2) | 86 (68.3) |
| High-risk class | 6 (2.8) | 28 (12.8) | 184 (84.4) |
Risk score performance
The discrimination performances of GRACE IH (for in-hospital mortality), GRACE 6M (for follow-up mortality) and CRUSADE (for major bleeding) were tested in our cohort, and their discrimination performances are displayed in Table 2 . All scores showed good calibration, as demonstrated by Hosmer–Lemeshow test P values > 0.05.
| AUC (95% CI) | P | |
|---|---|---|
| Continuous variable | ||
| GRACE IH | 0.81 (0.75–0.88) | < 0.001 |
| GRACE 6M | 0.78 (0.73–0.83) | < 0.001 |
| CRUSADE | 0.70 (0.62–0.77) | < 0.001 |
| Categorical variable | ||
| GRACE IH | 0.70 (0.64–0.76) | < 0.001 |
| GRACE 6M | 0.74 (0.69–0.83) | < 0.001 |
| CRUSADE | 0.69 (0.62–0.76) | < 0.001 |
| GRACE + CRUSADE IH | 0.76 (0.70–0.82) | < 0.001 |
| GRACE + CRUSADE 6M | 0.78 (0.73–0.83) | < 0.001 |
| GRACE + CRUSADE bleed | 0.66 (0.59–0.74) | < 0.001 |
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