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
Summary 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 4·7% (RECON) in the validation
datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area
under the receiver operating curve [AUROC] 0·786, 95% CI 0·774–0·798 vs 0·785, 0·772–0·797),
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 0·773, 95% CI 0·751–0·795; Brier score 0·020, calibration
in the large [CITL] 0·034, slope 0·954). The model performance was acceptable on external
validation in CovidSurg-Cancer (AUROC 0·746, 95% CI 0·733–0·760; Brier score 0·036,
CITL 0·109, slope 1·056), but with some miscalibration in RECON data (AUROC 0·716,
95% CI 0·689–0·744; Brier score 0·045, CITL 1·040, slope 1·009). 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’ 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.
