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.
Augmented intelligence can analyze complex data to predict patient responses to cardiovascular and pulmonary interventions. This approach aids critical care providers in tailoring treatments for conditions like shock and acute respiratory distress syndrome (ARDS).
Area of Science:
- Critical Care Medicine
- Biomedical Informatics
- Cardiovascular and Pulmonary Medicine
Background:
- Precision medicine often relies on novel biomarkers, but patient response data offers another avenue for individualized treatment.
- Understanding patient response to cardiovascular and pulmonary interventions is crucial for determining the level of organ support needed in critical care.
- Predicting treatment benefit or harm based on patient response can optimize care for critical illnesses like shock and ARDS, but data complexity is a challenge.
Purpose of the Study:
- To review the application of augmented intelligence in phenotyping patient responses to interventions in cardiovascular and pulmonary failure.
- To explore how augmented intelligence can analyze complex multimodal data for personalized critical care.
- To identify future research directions for implementing precision medicine approaches in clinical settings.
Main Methods:
- This narrative review synthesizes existing research on augmented intelligence in critical care.
- Focuses on the use of augmented intelligence for phenotyping patient responses to diagnostic and therapeutic interventions.
- Examines data collection and analysis complexities in predicting treatment outcomes.
Main Results:
- Augmented intelligence technologies demonstrate potential in analyzing complex multimodal data for patient phenotyping.
- These technologies can assist in interpreting patient responses to cardiovascular and pulmonary interventions.
- The review highlights current uses and future possibilities for AI in personalizing critical care.
Conclusions:
- Augmented intelligence offers a promising approach to phenotyping patient responses, enabling more precise cardiovascular and pulmonary interventions.
- Future research should focus on overcoming data complexities to integrate these precision approaches into routine clinical practice.
- This technology can help streamline care for common critical illnesses by predicting treatment efficacy.
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