TY - CHAP M1 - Book, Section TI - Severity Scoring and Risk Prediction A1 - Liu, Vincent A2 - Schmidt, Gregory A. A2 - Kress, John P. A2 - Douglas, Ivor S. Y1 - 2023 N1 - T2 - Hall, Schmidt and Wood’s Principles of Critical Care, 5th Edition AB - Over the last five decades, critical care researchers and clinicians have amassed a great body of pathophysiologic and clinical knowledge that has advanced the care of critically ill patients through the use of data. These data have informed the development of severity-of-illness scores designed to risk stratify the outcomes of critically ill patients.The most well-known severity-of-illness scores are the Acute Physiology and Chronic Health Evaluation (APACHE), the Mortality Probability Models (MPM), Simplified Acute Physiology Score (SAPS), and Sequential Organ Failure Assessment (SOFA). Most of these have been updated over time and evaluated in diverse populations.The primary uses of these scores have been for clinical investigation (to compare severity of illness between patient cohorts) and ICU performance assessment (to compare process and outcome metrics over time or between health care settings).An emerging frontier in the care of critically ill patients seeks to use real-time risk prediction tools to inform bedside clinical decisions that can mitigate further patient deterioration, identify patients at risk for organ failure, and inform longer-term outcomes following intensive care.The use of innovative statistical, machine learning, and artificial intelligence algorithms that leverage detailed data from electronic health records and physiologic monitors is expected to continue to produce significant advances in clinicians’ capabilities to risk stratify, phenotype, and intervene in critical illness.However, a number of key challenges related to risk prediction accuracy and bias; clinician training and workflow integration; and patient engagement remain largely unsolved. Thus, as of today, only a paucity of these predictive models have produced robust evidence of benefit in critical care outcomes. SN - PB - McGraw Hill CY - New York, NY Y2 - 2024/11/02 UR - accessanesthesiology.mhmedical.com/content.aspx?aid=1201799249 ER -