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

Glasbey J.

LANCET DIGITAL HEALTH, cilt.6, sa.7, 2024 (SCI-Expanded) identifier identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 6 Sayı: 7
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/s2589-7500(24)00065-7
  • Dergi Adı: LANCET DIGITAL HEALTH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır

Özet

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 20% in derivation data, and 39% (CovidSurg-Cancer) and 47% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0786, 95% CI 0774-0798 vs 0785, 0772-0797), 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 0773, 95% CI 0751-0795; Brier score 0020, calibration in the large [CITL] 0034, slope 0954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0746, 95% CI 0733-0760; Brier score 0036, CITL 0109, slope 1056), but with some miscalibration in RECON data (AUROC 0716, 95% CI 0689-0744; Brier score 0045, CITL 1040, slope 1009). 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.