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.

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 / Global Health Research Unit on Global Surgery, Nihr; Collaborative, Starsurg; Porcu, Alberto; Perra, Teresa; Scanu, Antonio Mario; Cossu, Maria Laura; Ginesu, Giorgio Carlo; Fancellu, Alessandro; Feo, Claudio; Giacomina Carta, Anna; Giuliani, Giuliana; Cottu, Pietrina; Madonia, Massimo; Tedde, Alessandro; Tedde, Matteo; Bussu, Francesco; Rizzo, Davide; Piras, Antonio; Scognamillo, Fabrizio; Piredda, Franco; Ciccarello, Sandro; Dettori, Salvatora; Soma, Damiano; Rita Tanca, Anna; Altana, Cristian; Dessole, Francesco; Petrillo, Marco. - In: THE LANCET. DIGITAL HEALTH. - ISSN 2589-7500. - 6:7(2024), pp. 507-519. [10.1016/S2589-7500(24)00065-7]

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

Alberto Porcu
Membro del Collaboration Group
;
Teresa Perra
Membro del Collaboration Group
;
Antonio Mario Scanu
Membro del Collaboration Group
;
Maria Laura Cossu
Membro del Collaboration Group
;
Giorgio Carlo Ginesu
Membro del Collaboration Group
;
Alessandro Fancellu
Membro del Collaboration Group
;
Claudio F Feo
Membro del Collaboration Group
;
Massimo Madonia
Membro del Collaboration Group
;
Francesco Bussu
Membro del Collaboration Group
;
Davide Rizzo
Membro del Collaboration Group
;
Fabrizio Scognamillo
Membro del Collaboration Group
;
Francesco Dessole
Membro del Collaboration Group
;
Marco Petrillo
Membro del Collaboration Group
2024-01-01

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 2·0% 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.
2024
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 / Global Health Research Unit on Global Surgery, Nihr; Collaborative, Starsurg; Porcu, Alberto; Perra, Teresa; Scanu, Antonio Mario; Cossu, Maria Laura; Ginesu, Giorgio Carlo; Fancellu, Alessandro; Feo, Claudio; Giacomina Carta, Anna; Giuliani, Giuliana; Cottu, Pietrina; Madonia, Massimo; Tedde, Alessandro; Tedde, Matteo; Bussu, Francesco; Rizzo, Davide; Piras, Antonio; Scognamillo, Fabrizio; Piredda, Franco; Ciccarello, Sandro; Dettori, Salvatora; Soma, Damiano; Rita Tanca, Anna; Altana, Cristian; Dessole, Francesco; Petrillo, Marco. - In: THE LANCET. DIGITAL HEALTH. - ISSN 2589-7500. - 6:7(2024), pp. 507-519. [10.1016/S2589-7500(24)00065-7]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/333489
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