Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes.
Journal
Current opinion in critical care
ISSN: 1531-7072
Titre abrégé: Curr Opin Crit Care
Pays: United States
ID NLM: 9504454
Informations de publication
Date de publication:
01 12 2021
01 12 2021
Historique:
pubmed:
22
9
2021
medline:
15
12
2021
entrez:
21
9
2021
Statut:
ppublish
Résumé
Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges. Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures. The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients.
Identifiants
pubmed: 34545029
doi: 10.1097/MCC.0000000000000883
pii: 00075198-202112000-00026
pmc: PMC8585713
mid: NIHMS1739262
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
717-725Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM137936
Pays : United States
Organisme : NIA NIH HHS
ID : R33 AG065744
Pays : United States
Organisme : NINDS NIH HHS
ID : R61 NS114926
Pays : United States
Organisme : NINDS NIH HHS
ID : R33 NS114926
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG065744
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM138353
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG058417
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
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