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Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study

Authors :
Kristen K. Rumer
Julien Hedou
Amy Tsai
Jakob Einhaus
Franck Verdonk
Natalie Stanley
Benjamin Choisy
Edward Ganio
Adam Bonham
Danielle Jacobsen
Beata Warrington
Xiaoxiao Gao
Martha Tingle
Tiffany N. McAllister
Ramin Fallahzadeh
Dorien Feyaerts
Ina Stelzer
Dyani Gaudilliere
Kazuo Ando
Andrew Shelton
Arden Morris
Electron Kebebew
Nima Aghaeepour
Cindy Kin
Martin S. Angst
Brice Gaudilliere
Source :
Ann Surg
Publication Year :
2021
Publisher :
Ovid Technologies (Wolters Kluwer Health), 2021.

Abstract

OBJECTIVE: To determine whether single-cell and plasma proteomic elements of the host’s immune response to surgery accurately identifies patients who develop a Surgical Site Complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients’ immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on post-operative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n=11) and without (n=30) an SSC (AUC = 0.86). Model features included co-regulated pro-inflammatory (e.g. IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (e.g. JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS: The multiomic analysis of patients’ immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

Details

ISSN :
15281140 and 00034932
Volume :
275
Database :
OpenAIRE
Journal :
Annals of Surgery
Accession number :
edsair.doi.dedup.....a0ccb9abe9fa6f062878a93d77873a53
Full Text :
https://doi.org/10.1097/sla.0000000000005348