Key result
GRACE, TIMI, and newer biomarker models effectively predict mortality and ischemic events in ACS.
Why the study?
Patients with ACS remain at high risk of adverse events after early invasive treatment and discharge, requiring accurate risk assessment to guide treatment plans and improve long-term survival.
Risk prediction models are essential tools for stratifying early risk in ACS patients and guiding appropriate treatment strategies to prevent recurrent adverse events.
Supports ACS risk stratification with GRACE/TIMI; leaves open incremental value of biomarker models pending prospective validation.
Acute coronary syndrome (ACS) has a high incidence of adverse cardiovascular events, even after early invasive treatment. Patients may still have a poor prognosis after discharge. The keys to the long-term survival of patients with ACS include effective treatment in a timely manner and identification of those patients who are at higher risk for long-term adverse events. Therefore, several nations have now devised a range of risk assessment models to provide data for accurately formulating treatment plans for patients with various risk levels following an ACS to prevent short and long-term cardiovascular events. The purpose of this article is to review the risk scores associated with mortality and ischemic events in patients with ACS. By using the clinical risk prediction score, we can accurately and effectively judge the prognosis of patients, so as to take a more reasonable treatment.
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Zhang et al. (2023) conducted a review in Acute Coronary Syndrome. Risk prediction models was evaluated. Clinical risk prediction scores, including GRACE, TIMI, and newer models incorporating biomarkers, effectively stratify early risk and predict mortality and ischemic events in patients with acute coronary syndrome.
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