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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:
      L. Bravo; J.F. Simoe; V.R. Cardoso; A. Adisa; M.L. Aguilera; A. Arnaud; B. Biccard; J. Calvache; S. Chernbumroong; M. Elhadi; D. Ghosh; R. Gujjuri; E. Harrison; M.W. Ho; V. Kasivisvanathan; O. Kouli; H. Lederhuber; E. Li; M.W. Loffler; A. Isik; H. Marcu; J. Martin; K.A. Mclean; A. Minaya-Bravo; M.M. Modolo; D. Nepogodiev; G. Pellino; M. Picciochi; P. Pockney; G. van Ramshorst; A. Riad; R. Sayyed; M. Sund; G. Gkouto; A.A. Bhangu; J.C. Glasbey; V. Cardoso; J. Glasbey; J.F.F. Simoe; B. Kadir; O. Omar; E. Revell; M. Bahrami-Hessari; W.-.-. Ahmed; L. Argu; A. Ball; A. Bhangu; E.P. Bywater; R. Blanco-Colino; A. Brar; D. Chaudhry; B.E. Dawson; I. Duran; R.R. Gujjuri; C.S. Jone; E.M. Harrison; S.K. Kamarajah; J.M. Keatley; S. Lawday; H. Mann; E.J. Marson; L. Norman; R. Ot; O. Outani; I. Santo; C. Shaw; E.H. Taylor; I.M. Trout; C. Varghese; M.L. Venn; W. Xu; I. Dajti; A. Gjata; S.E.O. Kacimi; L. Boccalatte; D. Cox; F. Aigner; I.E. Kronberger; E. Samadov; A. Alderazi; G. Padmore; I. Lawani; A. Cerovac; S. Delibegovic; G. Baiocchi; G.M.A. Gome; I. Lima Buarque; M. Gohar; M. Slavchev; C. Nwegbu; A. Agarwal; J. Ng-Kamstra; M. Olivo; W. Lou; D.-. Ren; J.A. Calvache; C.J. Perez Rivera; A. Danic Hadzibegovic
    • بيانات النشر:
      Elsevier
    • الموضوع:
      2024
    • Collection:
      The University of Milan: Archivio Istituzionale della Ricerca (AIR)
    • 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:e507; lastpage:e519; numberofpages:13; journal:THE LANCET. DIGITAL HEALTH; https://hdl.handle.net/2434/1081328; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85196614725
    • الرقم المعرف:
      10.1016/S2589-7500(24)00065-7
    • الدخول الالكتروني :
      https://hdl.handle.net/2434/1081328
      https://doi.org/10.1016/S2589-7500(24)00065-7
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.BD6D236A