The Gupta MICA Score is a clinical risk assessment tool developed to predict the likelihood of perioperative major adverse cardiac events, specifically myocardial infarction (MI) or cardiac arrest, in patients undergoing non-cardiac surgery. It was designed as a modern alternative to older risk indices like the Revised Cardiac Risk Index (RCRI), addressing the need for improved predictive accuracy in diverse surgical populations.

This tool draws upon data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and incorporates key patient and surgical variables to generate an individualized risk estimate. Unlike traditional indices that categorize patients broadly, the Gupta model applies logistic regression to provide a patient-specific percentage risk, making it more nuanced and clinically useful.

The inputs include variables such as type of surgery, functional status, age, serum creatinine, and ASA physical status classification. These variables have been shown to be independently associated with perioperative cardiac risk. For example, patients with impaired functional capacity, advanced age, or renal dysfunction are at significantly higher risk for perioperative cardiac events.