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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

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  • معلومة اضافية
    • Contributors:
      Bravo, L.; Simoes, J. F.; Cardoso, V. R.; Adisa, A.; Aguilera, M. L.; Arnaud, A.; Biccard, B.; Calvache, J.; Chernbumroong, S.; Elhadi, M.; Ghosh, D.; Gujjuri, R.; Harrison, E.; Ho, M. W.; Kasivisvanathan, V.; Kouli, O.; Lederhuber, H.; Li, E.; Loffler, M. W.; Isik, A.; Marcus, H.; Martin, J.; Mclean, K. A.; Minaya-Bravo, A.; Modolo, M. M.; Nepogodiev, D.; Pellino, G.; Picciochi, M.; Pockney, P.; van Ramshorst, G.; Riad, A.; Sayyed, R.; Sund, M.; Gkoutos, G.; Bhangu, A. A.; Glasbey, J. C.; Cardoso, V.; Glasbey, J.; Simoes, J. F. F.; Kadir, B.; Omar, O.; Revell, E.; Bahrami-Hessari, M.; Ahmed, W. -U. -R.; Argus, L.; Ball, A.; Bhangu, A.; Bywater, E. P.; Blanco-Colino, R.; Brar, A.; Chaudhry, D.; Dawson, B. E.; Duran, I.; Gujjuri, R. R.; Jones, C. S.; Harrison, E. M.; Kamarajah, S. K.; Keatley, J. M.; Lawday, S.; Mann, H.; Marson, E. J.; Norman, L.; Ots, R.; Outani, O.; Santos, I.; Shaw, C.; Taylor, E. H.; Trout, I. M.; Varghese, C.; Venn, M. L.; Xu, W.; Dajti, I.; Gjata, A.; Kacimi, S. E. O.; Boccalatte, L.; Cox, D.; Aigner, F.; Kronberger, I. E.; Samadov, E.; Alderazi, A.; Padmore, G.; Lawani, I.; Cerovac, A.; Delibegovic, S.; Baiocchi, G.; Gomes, G. M. A.; Lima Buarque, I.; Gohar, M.; Slavchev, M.; Nwegbu, C.; Agarwal, A.; Ng-Kamstra, J.; Olivos, M.; Lou, W.; Ren, D. -L.; Calvache, J. A.; Perez Rivera, C. J.; Danic Hadzibegovic, A.
    • بيانات النشر:
      London, EC2Y 5AS Regno Unito
      The Lancet Publishing Group
    • الموضوع:
      2024
    • Collection:
      Sapienza Università di Roma: CINECA IRIS
    • نبذة مختصرة :
      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 ...
    • 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/11573/1713963
    • الدخول الالكتروني :
      https://hdl.handle.net/11573/1713963
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.30DEB875