| Title: |
Development and external validation of the 'Global Surgical-Site Infection' (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery |
| Authors: |
NIHR Global Research Health Unit on Global Surgery and GlobalSurg Collaborative; Li, E.; Borg, E.; Koh, C.; Leppäniemi, Ari; Mentula, Panu; Sallinen, Ville; Sund, M.; Koskenvuo, L.; Lee, K.; Tolonen, Matti |
| Contributors: |
HUS Abdominal Center; II kirurgian klinikka; Clinicum; Pertti Panula / Principal Investigator; Department of Anatomy; Department of Surgery |
| Publisher Information: |
Oxford University Press |
| Publication Year: |
2024 |
| Collection: |
Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto |
| Subject Terms: |
Surgery; anesthesiology; intensive care; radiology |
| Description: |
Background: Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods: This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July-November 2014) and GlobalSurg-2 (January-July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator ('LASSO')) and machine learning (extreme gradient boosting ('XGBoost')). Final model selection was based on prognostic accuracy and clinical utility. Results: There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept -0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion: The 'Global Surgical-Site Infection' score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
This study was funded through support from a National Institute for Health Research (NIHR) Global Health Research Unit grant (NIHR 16.136.79) and a Royal College of Surgeons of Edinburgh (RCSEd) Robertson Trust research fellowship (RTRF/22/010). The funders had no role in conception, study design, data collection, analysis and interpretation, or writing of this article.; https://hdl.handle.net/10138/584841; 85200890886; 001287553200001 |
| Availability: |
https://hdl.handle.net/10138/584841 |
| Rights: |
cc_by ; info:eu-repo/semantics/openAccess ; openAccess |
| Accession Number: |
edsbas.984F44A2 |
| Database: |
BASE |