DEVELOPMENT OF A SCORING SYSTEM FOR PREDICTING ACUTE CORONARY SYNDROME EVENTS: A SCOPING REVIEW - Ihsan Angga Anjarwadi; Azizah Khoiriyati; Erna Rochmawati (Contagion: Scientific Periodical Journal of Public Health and Coastal Health, 2026)
Clinical scoring systems are widely used to support early risk stratification and clinical decision-making in patients with acute coronary syndrome (ACS). However, the characteristics, development processes, and clinical applications of these scoring systems remain dispersed across the literature. This scoping review aims to map and synthesize the available evidence on scoring systems used to assess or predict ACS events in hospital settings. A scoping review methodology was conducted using electronic databases, including PubMed, Scopus, and Emerald. The search terms included “acute coronary syndrome,” “scoring system,” OR “score predictor,” and “in hospital.” Of 1,961 identified records, 18 studies met the inclusion criteria after screening. Data were extracted using a charting approach to summarize study characteristics, scoring system variables, and reported outcomes. The review identified eleven ACS scoring systems, including widely used models such as GRACE, TIMI, and HEART. These scoring tools primarily incorporate clinical indicators such as age, systolic blood pressure, electrocardiogram findings, cardiac biomarkers, and cardiovascular risk factors. The results indicate that ACS scoring systems can be broadly categorized according to their clinical objectives, including diagnostic risk assessment, prognostic prediction, and early risk stratification in emergency settings. This review highlights the variability of predictive models across different clinical contexts and emphasizes the importance of selecting practical scoring systems that rely on readily available clinical indicators. The findings provide a comprehensive overview of existing ACS scoring systems and offer a conceptual framework that may help clinicians select appropriate risk assessment tools and guide future research on the development of simplified prediction models.  Keywords: Acute Coronary Syndrome, Risk Score, Clinical Prediction Model, Risk Stratification