Rodrigo Octavio Deliberato, Stephanie Ko, Tejas Sundaresan, Aaron Russell Kaufman, and Leo Anthony Celi
Severity of illness scores are used for risk adjustment when comparing cohorts of critically ill patients in intensive care units (ICUs). Although these models have good discrimination, they are typically poorly calibrated, and over-predict mortality for low-risk patients and under-predict mortality for high-risk patients. ITherefore, clinicians have are skeptical of their accuracy for real-time patient prognostication. We propose a sequential modeling approach to improve these prediction models. We hypothesized that by first stratifying patients into high (mortality prediction ≥ 10%) and low-risk cohorts, then applying four standard machine learning tools on a much larger set of candidate variables on only on the high-risk cohort, we could improve discrimination and calibration of mortality risk prediction in critically ill patients.