| Title: |
A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts |
| Authors: |
NIHR Global Health Research Unit on Global Surgery; STARSurg Collaborative; Mauro Podda |
| Contributors: |
Global Health Research Unit on Global Surgery; STARSurg Collaborative, Nihr; Podda, Mauro |
| Publication Year: |
2024 |
| Collection: |
Università degli Studi di Cagliari: UNICA IRIS |
| Description: |
Background: Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery. Methods: Data from an international, prospective cohort study were used to develop a novel prognostic risk model for pulmonary complications after elective surgery in adult patients (aged ≥18 years) across all operation and disease types. The primary outcome measure was postoperative pulmonary complications at 30 days after surgery, which was a composite of pneumonia, acute respiratory distress syndrome, and unexpected mechanical ventilation. Model development with candidate predictor variables was done in the GlobalSurg-CovidSurg Week dataset (global; October, 2020). Two structured machine learning techniques were explored (XGBoost and the least absolute shrinkage and selection operator [LASSO]), and the model with the best performance (GSU-Pulmonary Score) underwent internal validation using bootstrap resampling. The discrimination and calibration of the score were externally validated in two further prospective cohorts: CovidSurg-Cancer (worldwide; February to August, 2020, during the COVID-19 pandemic) and RECON (UK and Australasia; January to October, 2019, before the COVID-19 pandemic). The model was deployed as an online web application. The GlobalSurg-CovidSurg Week and CovidSurg-Cancer studies were registered with ClinicalTrials.gov, NCT04509986 and NCT04384926. Findings: Prognostic models were developed from 13 candidate predictor variables in data from 86 231 patients (1158 hospitals in 114 countries). External validation included 30 492 patients from CovidSurg-Cancer (726 hospitals in 75 countries) and 6789 from RECON (150 hospitals in three countries). The overall rates of pulmonary complications were 2·0% in derivation data, and 3·9% (CovidSurg-Cancer) and ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/38906616; info:eu-repo/semantics/altIdentifier/wos/WOS:001260242800001; volume:6; issue:7; firstpage:507; lastpage:519; numberofpages:13; journal:THE LANCET. DIGITAL HEALTH; https://hdl.handle.net/11584/426453 |
| DOI: |
10.1016/S2589-7500(24)00065-7 |
| Availability: |
https://hdl.handle.net/11584/426453; https://doi.org/10.1016/S2589-7500(24)00065-7 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.EB86BC2F |
| Database: |
BASE |