title: Risk prediction model for respiratory complications after lung resection: An observational multicentre study.
abstract: BACKGROUND
  Patients undergoing lung surgery are at risk of postoperative pulmonary complications (PPCs). Identifying those patients is important to optimise individual perioperative management. The Clinical Prediction Rule for Pulmonary Complications (CPRPCs) after thoracic surgery, developed by the Memorial Sloan-Kettering Cancer Center, might be an ideal predictor. The hypothesis was that CPRPC performs well for the prediction of PPCs.
  
  
  OBJECTIVE
  The aim of our study was to provide the external validation of the CPRPC after lung resection for primary tumours, before universal acceptance. In case of poor discrimination, we planned, as a second objective, to derive a new predictive index for PPCs.
  
  
  METHODS
  Retrospective, observational multicentre study.
  
  
  METHODS
  A total of 559 adult consecutive patients who underwent pulmonary resection. Inclusion criteria were adult patients (aged over 17 years).
  
  
  METHODS
  Thirteen Spanish hospitals during the first half of 2011.
  
  
  METHODS
  A record of the PPCs defined, as in the original publication, as the presence of any of the following events: atelectasis; pneumonia; pulmonary embolism; respiratory failure; and need for supplemental oxygen at hospital discharge.
  
  
  METHODS
  The performance of the CPRPC was determined in order to examine its ability to discriminate and calibrate the presence of PPCs.
  
  
  RESULTS
  The study included 559 patients, of whom 75 (11.6%) suffered PPCs. The CPRPC did not show enough discriminatory power for our cohort area under the receiver operating characteristic (ROC) curve 0.47 (95% confidence interval 0.37 to 0.57)]. After a fitting step by stepwise multivariate logistic regression, we identified three main predictors of PPCs: age; smoking status; and predicted postoperative forced expiratory volume in 1 s. Combining them into a simple risk score, we were able to obtain an area under the ROC curve of 0.74 (95% confidence interval 0.68 to 0.79).
  
  
  CONCLUSIONS
  In this external validation, the CPRPC performed poorly despite its simplicity. The CPRPC was not a useful scale in our cohort. In contrast, we used a more accurate score to predict the occurrence of PPCs in our cohort. It is based on age, smoking status and predicted postoperative forced expiratory volume in 1 s. We propose that our formula should be externally validated.
authors.affiliation: From the Department of Anaesthesia and Intensive Care Medicine, University of Navarra; Clínica Universidad de Navarra (MJ-YT, PM); and Complejo Hospitalario de Navarra, Irunlarrea, Pamplona, Spain (JR-PV).
authors.forename: Maria J
authors.initials: MJ
authors.lastname: Yepes-Temiño
authors.forename: Pablo
authors.initials: P
authors.lastname: Monedero
authors.forename: José Ramón
authors.initials: JR
authors.lastname: Pérez-Valdivieso
mesh_terms: D000367:Age Factors; D000368:Aged; D019540:Area Under Curve; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D005541:Forced Expiratory Volume; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008168:Lung / Q000503:physiopathology / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011013:Pneumonectomy / Q000009:adverse effects*; D011237:Predictive Value of Tests; D017064:Process Assessment, Health Care; D012372:ROC Curve; D015203:Reproducibility of Results; D012120:Respiration Disorders / Q000175:diagnosis / Q000209:etiology* / Q000503:physiopathology; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects; D013030:Spain; D016896:Treatment Outcome