Abstract
Introduction Acute coronary syndrome (ACS) remains a major contributor to cardiovascular morbidity and mortality, necessitating efficient and accurate risk-stratification tools. Widely used models such as the Thrombolysis in Myocardial Infarction (TIMI), Global Registry of Acute Coronary Events (GRACE), and Mehran scores require separate manual inputs, leading to workflow inefficiencies and transcription errors. This study introduces an integrated digital tool that consolidates these scoring systems into a single interface and explores the feasibility of AI-assisted development in clinical software design. Objective The objective of this study is to develop and technically validate an integrated ACS risk calculator that streamlines the computation of TIMI, GRACE, and Mehran scores while demonstrating the utility of AI-assisted coding in clinical applications. Methods A web-based calculator was developed using Hypertext Markup Language (HTML)/JavaScript, with AI-assisted code prototyping via ChatGPT (o3-mini-high) (OpenAI, San Francisco, CA). The interface standardizes shared clinical variables for all three risk scores. Validation was conducted using 226 ACS cases from the Sheikh Khalifa Specialty Hospital registry. TIMI and GRACE scores generated by the tool were compared against Get With The Guidelines-Coronary Artery Disease (GWTG-CAD) registry values using Pearson correlation. The Mehran score was internally validated through manual review. Congestive heart failure was inferred using Killip class > I to align inputs across models. Results The tool showed complete agreement with registry-based TIMI and GRACE scores (TIMI: r = 1.000, p < 0.001; GRACE: r = 1.000, p < 0.001). Manually reviewed Mehran scores demonstrated consistent output. The calculator reduced data entry duplication and preserved computational accuracy. Conclusion This study validates an integrated ACS risk calculator that unifies three established models within a single digital tool. It enhances workflow efficiency and demonstrates the practical value of AI-assisted, clinician-led development in cardiovascular decision support. Further clinical and usability validation is warranted.