APACHE II Score Calculator

APACHE II Score: Explanation and Clinical Context The APACHE II (Acute Physiology And Chronic Health Evaluation II) score is a widely used ICU severity-of-disease classification system that sums abnormalities in 12 physiologic variables (the Acute Physiology Score, APS), adds age points, and adds chronic health points to provide an admission severity score (range 0–71). Higher scores correlate with higher hospital mortality and can be used for case-mix adjustment, benchmarking, and research. The physiologic variables include oxygenation (PaO₂ or A–a gradient depending on FiO₂), temperature (rectal preferred), mean arterial pressure, arterial pH, heart rate, respiratory rate, serum sodium, serum potassium, serum creatinine (doubled if acute renal failure), hematocrit, white blood cell count, and neurologic status as GCS (scored as 15 − actual GCS).

Age points and chronic health points are added as per the original APACHE II definitions. For predicted probability of hospital death the original APACHE II approach used a logistic model combining the APACHE II score, diagnostic-category weights, and a postoperative emergency coefficient. The simplified logistic form used here (derived from the original paper and widely cited implementations) is: ln(R/(1−R)) = −3.517 + 0.146 × (APACHE II score) + 0.603 (if emergency surgery).

This calculator uses that logistic equation (with the emergency surgery coefficient) but does not apply the original paper’s diagnostic-category weights (a set of additive coefficients depending on the principal diagnosis), since those require a large diagnostic lookup and reduce portability. Omitting diagnostic-category weights will change the precise predicted probability for particular diagnoses; therefore interpret the mortality estimate as a group-level/simplified estimate rather than an exact per-diagnosis prediction. Clinical interpretation summary: APACHE II is appropriate for adults (not validated for children) and should be calculated using the worst physiologic values in the first 24 hours of ICU admission.

It is useful for benchmarking, risk adjustment, and research; its mortality prediction is best for groups and may be less precise for single patients or contemporary populations (newer models such as APACHE IV may be more calibrated to modern cohorts). Use clinical judgment when applying predicted risk to individual care decisions.