Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care
Andre L Holder1, Gwénolé Abgrall1, Ran Xiao1
1Division of Pulmonary, Critical Care, Allergy and Sleep Medicine (A. L. H.), Department of Medicine, Emory University School of Medicine, the Nell Hodgson Woodruff School of Nursing (R. X.), Emory University, Atlanta, GA; the Division of Cardiac Surgery (J. K.), Beth Israel Deaconess Medical Center, Boston, MA; the Department of Health Policy and Management (I. D.-M.), Milken Institute School of Public Health, George Washington University, Washington, DC; the Division of Pulmonary, Critical Care, Allergy and Sleep Medicine (A. Z.), Department of Medicine, the Cardiovascular Research Institute (A. Z.), University of California, San Francisco, San Francisco, CA; the AP-HP (G. A.), Service de Médecine Intensive-Réanimation, Hôpital de Bicêtre, DMU 4 CORREVE, Inserm UMR S_999, FHU SEPSIS, CARMAS, Université Paris-Saclay, Le Kremlin-Bicêtre; the Service de Médecine Intensive Réanimation (G. A.), Centre Hospitalier Universitaire Grenoble Alpes, La Tronche, France; the Department of Cardiology (S. N.), Alfred Hospital, the Department of Cardiology (S. N.), Cabrini Hospital, the Monash-Alfred-Baker Centre for Cardiovascular Research (S. N.), Monash University, Melbourne, VIC, Australia; the Department of Critical Care Medicine (A. Z.), University of Calgary and Alberta Health Services, Calgary; the Interdepartmental Division of Critical Care Medicine (E. C. G.), and the Department of Physiology (E. C. G.), University of Toronto, Toronto, ON, Canada.
Abstract:
The paradigm of precision medicine often focuses on novel biomarkers, but other types of data directly applicable to patient care could individualize treatment. The response to a cardiovascular and pulmonary intervention is important to critical care providers because it may determine the need and intensity of organ support. Using patient response to predict benefit (or no effect or harm) from an intervention could streamline care better for common syndromes of critical illness such as shock and ARDS, but the necessary data collection and analysis often are complex. Augmented intelligence technologies could assist with this kind of phenotyping because they can analyze and interpret complex multimodal data. In this narrative review, we summarize how augmented intelligence has been used to phenotype responses to diagnostic and therapeutic interventions in cardiovascular and pulmonary failure. We also discuss opportunities for future research that could make this precision approach useful in the clinical environment.
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