@article {43993,
	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.},
	journal = {Lancet Digit Health},
	volume = {6},
	year = {2024},
	month = {2024 Jul},
	pages = {e507-e519},
	abstract = {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{\textperiodcentered}0\% in derivation data, and 3{\textperiodcentered}9\% (CovidSurg-Cancer) and 4{\textperiodcentered}7\% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0{\textperiodcentered}786, 95\% CI 0{\textperiodcentered}774-0{\textperiodcentered}798 vs 0{\textperiodcentered}785, 0{\textperiodcentered}772-0{\textperiodcentered}797), was more explainable, and required fewer covariables. The final GSU-Pulmonary Score included ten predictor variables and showed good discrimination and calibration upon internal validation (AUROC 0{\textperiodcentered}773, 95\% CI 0{\textperiodcentered}751-0{\textperiodcentered}795; Brier score 0{\textperiodcentered}020, calibration in the large [CITL] 0{\textperiodcentered}034, slope 0{\textperiodcentered}954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0{\textperiodcentered}746, 95\% CI 0{\textperiodcentered}733-0{\textperiodcentered}760; Brier score 0{\textperiodcentered}036, CITL 0{\textperiodcentered}109, slope 1{\textperiodcentered}056), but with some miscalibration in RECON data (AUROC 0{\textperiodcentered}716, 95\% CI 0{\textperiodcentered}689-0{\textperiodcentered}744; Brier score 0{\textperiodcentered}045, CITL 1{\textperiodcentered}040, slope 1{\textperiodcentered}009).

INTERPRETATION: This novel prognostic risk score uses simple predictor variables available at the time of a decision for elective surgery that can accurately stratify patients{\textquoteright} risk of postoperative pulmonary complications, including during SARS-CoV-2 outbreaks. It could inform surgical consent, resource allocation, and hospital-level prioritisation as elective surgery is upscaled to address global backlogs.

FUNDING: National Institute for Health Research.},
	issn = {2589-7500},
	doi = {10.1016/S2589-7500(24)00065-7}
}
