Comparison of the modified Singapore myocardial infarction registry risk score with GRACE 2.0 in predicting 1-year acute myocardial infarction outcomes & More Latest News Here – Up Jobs

In this real-world population-based study, we showed that the modified SMIR score performed similarly to the GRACE 2.0 score in a multi-ethnic Asian population in predicting 1-year all-cause mortality following STEMI and NSTEMI.

Inter-ethnic differences in the outcomes of STEMI patients have been published previously. Previous studies performed both locally^15,16,17 and abroad¹⁸ have suggested inter-ethnic differences in terms of outcomes such as mortality. While there are established coronary risk factors, such as smoking, hypertension, hyperlipidaemia and diabetes mellitus, these risk factors cannot fully account for the observed inter-ethnic variations in outcomes¹⁹. Ethnic differences also existed in possible pathophysiological factors such as economic, lifestyle, anthropometric, and patient susceptibility to cerebrovascular diseases^16,18. Of note, these factors are not included in contemporary risk scores such as the TIMI and GRACE 2.0 scores^3,4, and are also difficult to ascertain in the acute setting. As such, there is a need to assess the relevance of contemporary risk scores in predicting outcomes among multi-ethnic or ethnic-specific population.

The GRACE registry initially consisted of 123 hospitals from 14 countries in Europe, North and South America, Australia and New Zealand²⁰. This registry initially did not have participation from Asian countries, and consequently the derived original GRACE score was not obtained from Asian patient data². The subsequently updated GRACE 2 registry expanded recruitment to involve 154 hospitals, this time including hospitals from Asia (including China)²⁰. Nevertheless, the updated GRACE 2.0 score was only derived from the older registry and was validated in a French cohort³. In the Asian context, studies on the GRACE 2.0 score have been performed in ethnically homogenous populations such as in the Japanese⁷, Vietnamese⁸ and Chinese²¹ populations. The Japanese study was a single centre validation study of 412 STEMI patients who had undergone PPCI. This study showed a good AUC of 0.92 in predicting 360-day mortality⁷. The Vietnamese study was performed on 217 patients from a single centre diagnosed with unstable angina, NSTEMI and STEMI. The authors used the score to stratify their patients, but did not specifically study the predictive performance of the GRACE 2.0 score⁸. Fu et al. in China developed the CAMI-NSTEMI score based on 5775 patients from the China Acute Myocardial Infarction (CAMI) registry. They showed that the CAMI-NSTEMI score was superior to that of the GRACE score (AUC 0.81 vs 0.72, p < 0.01) in predicting in-hospital mortality in their Chinese population²¹. We found that the performance of the GRACE 2.0 and modified SMIR scores were similar, be it among all or ethnic-specific AMI patients.

In the modified SMIR score, we found that a higher LVEF was associated with a reduced 1-year all-cause mortality. LVEF is currently not one of the components of the TIMI and GRACE 2.0 scores. LVEF has previously been shown to be associated with an increased mortality in post-MI patients²². Therefore, it was worthwhile considering the use LVEF as a variable in risk prediction for AMI patients. Previously, it was difficult to perform a dedicated transthoracic echocardiogram study in the acute setting due to time constraints. However, with the advent of point-of-care echocardiography with portable handheld devices, the LVEF of the patient can be rapidly obtained by the bedside²³. Future risk scores may consider the use of variables that were previously not readily available.

In addition, notably there are emerging risk stratification tools for AMI patients beyond published risk scores. Emerging approaches, such as metabolomics-based risk stratification, may have a role in future risk stratification beyond current clinically available variables^24,25. Identified soluble biomarkers, such as those for myocardial fibrosis, may play a role in determining the severity of acute myocardial infarction²⁶. Authors have also reported machine-learning based methods for risk stratification of AMI patients using big data approaches, with results that seem to outperform traditional risk models^27,28. It is not improbable that in the future, risk prediction would incorporate a combination of clinical, haematological, biochemical, echocardiographic and electronic health records-based information, customized to the local context, to provide personalized risk stratification for each AMI patient. Nevertheless, until such technology becomes mature and widely available, and also in areas of practice with resource constraints²⁹, traditional risk scores will remain relevant.

Strengths and limitations

This study used a large national-level database of AMI patients based on mandatory reporting to ensure near-complete case coverage. This also minimized selection bias. Data linkage with the national Death Registry ensured accurate and objective ascertainment of outcomes. Another strength of this study is that this scoring system is based on the contemporaneous treatment population, both in terns of secondary prevention and revascularization.

Nevertheless, we acknowledge several limitations of this study. While Singapore’s ethnically diverse population is ideal for this study, no superiority in using the modified SMIR score compared to the popular and validated GRACE tool was demonstrated. Thus, the scientific and clinical contributions of our findings seem not to be high. Nevertheless, this study fills the literature gap by studying the GRACE 2.0 score in a multi-ethnic Asian population which is currently lacking and demonstrating that GRACE 2.0 is likely to be applicable to other Asian populations that are primarily of Chinese, Malay or Indian origin. As our study focused exclusively on PCI patients alone, our findings cannot be extrapolated to patients without PCI such as the thrombolysis population. However, thrombolysis as a reperfusion strategy is seldom used, at least in Singapore. Although we found that the GRACE 2.0 and modified SMIR scores were able to correctly classify patients broadly into low (< 10%), mid (10–20% and 20–40%) and high (40–60% and > 60%) risk, we were unable to compare the observed mortality for finer subgroups at different predicted risk level due to small sample sizes. Clinicians would need to apply their own clinical judgement should they need more granular risk stratification. Further studies are needed to optimize the performance of the stated scores in predicting 1-year all-cause mortality. Moreover, the points corresponding to categories of some prognostic components, such as age at onset of acute myocardial infarction and the Killip class, are nonlinear, but these components were used for regression models. Therefore, the clinical interpretability of these components needs to be done with cautions.

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