Derivation and validation of a clinical model to predict intensive care unit length of stay after cardiac surgery

Background — Across the globe, elective surgeries have been postponed to limit infectious exposure and preserve hospital capacity for COVID‐19. However, the ramp down in cardiac surgery volumes may result in unintended harm to patients who are at high risk of mortality if their conditions are left untreated. To help to optimize triage decisions, we derived and ambispectively validated a clinical score to predict intensive care unit (ICU) length of stay (LOS) after cardiac surgery.

Methods and Results — Following ethics approval, we derived and performed multicenter validation of clinical models to predict the likelihood of short (≤ 2 days) and prolonged ICU LOS (≥ 7 days) in patients ≥ 18 years of age, who underwent coronary artery bypass grafting and/or aortic, mitral, and tricuspid value surgery in Ontario, Canada. Multivariable logistic regression with backward variable selection was used, along with clinical judgment, in the modeling process. For the model that predicted short ICU stay, the c‐statistic was 0.78 in the derivation cohort and 0.71 in the validation cohort. For the model that predicted prolonged stay, c‐statistic was 0.85 in the derivation and 0.78 in the validation cohort. The models, together termed the “CardiOttawa LOS Score”, demonstrated a high degree of accuracy during prospective testing.

Conclusions — Clinical judgment alone has been shown to be inaccurate in predicting postoperative ICU LOS. The CardiOttawa LOS Score performed well in prospective validation and will complement the clinician’s gestalt in making more efficient resource allocation during the COVID‐19 period and beyond.