How this instrument works
MICA — short for myocardial infarction or cardiac arrest — is the common name for the Gupta Perioperative Risk calculator, developed by Gupta and colleagues and published in Circulation in 2011. It's a logistic regression model that estimates the probability that a patient undergoing a planned operation will have a perioperative MI or cardiac arrest, built from five variables assessed before surgery: age, functional status, ASA physical status class, kidney function, and the type of surgery planned, which this calculator lists as one of 21 procedure categories.
The model was derived from the American College of Surgeons' National Surgical Quality Improvement Program (ACS NSQIP), using a 2007 dataset of 211,410 patients across more than 250 hospitals, of whom 1,371 (0.65%) developed a perioperative MI or cardiac arrest, then validated against a separate 2008 dataset of 257,385 patients. In that validation, the model's discrimination (a C-statistic of 0.874) outperformed the older Revised Cardiac Risk Index (0.747) at distinguishing patients who did and didn't go on to have a cardiac event.
The five predictors combine into a single weighted linear predictor, x, which a logistic transform converts into a percentage risk. Surgery type alone carries a wide spread of coefficients — from -1.61 for breast surgery, the lowest, to 1.6 for aortic surgery, the highest — reflecting how much the procedure itself predicts cardiac risk independent of a patient's age, functional status, or kidney function.
One sourcing note worth being direct about: the 2011 paper's full text sits behind a journal paywall, so the cohort and validation details above come from its public abstract, but the coefficient table itself — the exact weights for functional status, ASA class, creatinine, the 21 surgery types, and the model's intercept — could not be checked against the original paper's full text while building this page. Those coefficients were instead cross-checked against six independent secondary clinical-calculator implementations, all of which agreed to two decimal places. This is a preoperative risk-stratification tool built for clinicians — surgeons, anesthesiologists, and other perioperative providers — to inform a discussion of surgical risk with a patient, not a patient self-assessment tool or a substitute for individualized clinical evaluation.
- Enter the patient's age in years (age).
- Select functional status: Independent, Partially dependent, or Totally dependent.
- Select the ASA physical status class, I through V.
- Set creatinine > 1.5 mg/dL to Yes or No — this adds 0.61 to the linear predictor when present.
- Select the surgery type from the list of 21 procedure categories.
- Read the logistic linear predictor (x) and the predicted risk percentage of MI or cardiac arrest.
Worked example — three surgical risk profiles
A 60-year-old, independent patient with ASA class II status, normal creatinine, having hernia surgery has x = 60×0.02 + 0 + (−3.29) + 0.61×0 + 0 − 5.25 = −7.34, giving a predicted risk of myocardial infarction or cardiac arrest of about 0.06%.
A higher-risk profile — a 75-year-old, totally dependent patient with ASA class IV status, elevated creatinine (above 1.5 mg/dL), undergoing aortic surgery — has x = 75×0.02 + 1.03 + (−0.95) + 0.61×1 + 1.6 − 5.25 = −1.46, giving a predicted risk of about 18.85%, illustrating how much surgery type and functional status can shift the estimate.
A 45-year-old, partially dependent patient with ASA class III status, normal creatinine, undergoing non-vascular extremity orthopedic surgery has x = 45×0.02 + 0.65 + (−1.92) + 0.61×0 + 0.8 − 5.25 = −4.82, giving a predicted risk of about 0.80%.
Questions
What does MICA stand for, and what does it estimate?
MICA stands for myocardial infarction or cardiac arrest. The Gupta Perioperative Risk calculator, developed by Gupta and colleagues and published in Circulation in 2011, estimates the probability that a patient undergoing a planned operation will have a perioperative MI or cardiac arrest, using age, functional status, ASA physical status class, kidney function, and the type of surgery planned.
Who is this calculator meant for?
This is a preoperative risk-stratification tool built for clinicians — surgeons, anesthesiologists, and other perioperative providers — to estimate cardiac risk before a planned operation and support a risk conversation with the patient. It is not a patient self-assessment tool, and it doesn't replace an individualized preoperative evaluation, an ECG, stress testing, or a cardiology consult when those are clinically indicated.
What data was the model built and tested on?
Gupta and colleagues developed the model using a 2007 American College of Surgeons NSQIP dataset of 211,410 patients across more than 250 hospitals, of whom 1,371 (0.65%) had a perioperative MI or cardiac arrest, then validated it against a separate 2008 dataset of 257,385 patients. In that validation, the model discriminated patients who did and didn't have an event better (C-statistic 0.874) than the older Revised Cardiac Risk Index (0.747).
Why does creatinine only matter above 1.5 mg/dL?
The model treats kidney function as a threshold rather than a continuous variable: creatinine above 1.5 mg/dL adds a fixed 0.61 to the linear predictor, and creatinine at or below that level adds nothing. This reflects how the original regression was built — as a binary elevated-versus-normal split on abnormal creatinine — rather than a graded effect across the full creatinine range.
Why does surgery type change the estimate so much?
Surgery type contributes one of 21 procedure-specific coefficients, and they vary widely — from −1.61 for breast surgery, the lowest, to 1.6 for aortic surgery, the highest. That spread reflects how strongly the type of operation alone predicts perioperative cardiac risk in the derivation data, independent of the patient's age, functional status, or kidney function.
How reliable is the source behind this calculator's coefficients?
The 2011 Circulation paper's full text sits behind a journal paywall, so its public abstract — which confirms the cohort size, validation dataset, and predictor list — could be checked directly, but the exact coefficient values were not verified against the original paper's full text while building this page. Instead, every coefficient here was cross-checked against six independent secondary clinical-calculator implementations, and all six agreed to two decimal places. That's strong cross-corroboration, but it isn't the same as confirming against the source itself, so treat it accordingly.
Can a patient use this to decide whether to have surgery?
No. This tool performs the published arithmetic on the values entered; it doesn't weigh the benefits of the planned procedure, alternative treatments, or the full clinical context a surgeon and anesthesiologist would consider. It's meant to support a clinician's discussion of perioperative cardiac risk with a patient, not to replace that discussion or stand in as a verdict on its own.
References
- Gupta PK, et al. — Risk calculator for cardiac risk after surgery, Circulation, 2011;124(4):381 (PMID 21730309)
- QxMD — Gupta Perioperative Cardiac Risk calculator (secondary cross-check source)
Read this first: This instrument computes a screening figure from population formulas — it is not a diagnosis, and it cannot see the whole picture a clinician can. Use it to inform a conversation, not to replace one.