title: Derivation and diagnostic accuracy of the surgical lung injury prediction model.
abstract: BACKGROUND
  Acute lung injury (ALI) is a serious postoperative complication with limited treatment options. A preoperative risk-prediction model would assist clinicians and scientists interested in ALI. The objective of this investigation was to develop a surgical lung injury prediction (SLIP) model to predict risk of postoperative ALI based on readily available preoperative risk factors.
  
  
  METHODS
  Secondary analysis of a prospective cohort investigation including adult patients undergoing high-risk surgery. Preoperative risk factors for postoperative ALI were identified and evaluated for inclusion in the SLIP model. Multivariate logistic regression was used to develop the model. Model performance was assessed with the area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test.
  
  
  RESULTS
  Out of 4,366 patients, 113 (2.6%) developed early postoperative ALI. Predictors of postoperative ALI in multivariate analysis that were maintained in the final SLIP model included high-risk cardiac, vascular, or thoracic surgery, diabetes mellitus, chronic obstructive pulmonary disease, gastroesophageal reflux disease, and alcohol abuse. The SLIP score distinguished patients who developed early postoperative ALI from those who did not with an area under the receiver operating characteristic curve (95% CI) of 0.82 (0.78-0.86). The model was well calibrated (Hosmer-Lemeshow, P = 0.55). Internal validation using 10-fold cross-validation noted minimal loss of diagnostic accuracy with a mean ± SD area under the receiver operating characteristic curve of 0.79 ± 0.08.
  
  
  CONCLUSIONS
  Using readily available preoperative risk factors, we developed the SLIP scoring system to predict risk of early postoperative ALI.
authors.affiliation: Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, USA. kor.daryl@mayo.edu
authors.forename: Daryl J
authors.initials: DJ
authors.lastname: Kor
authors.forename: David O
authors.initials: DO
authors.lastname: Warner
authors.forename: Anas
authors.initials: A
authors.lastname: Alsara
authors.forename: Evans R
authors.initials: ER
authors.lastname: Fernández-Pérez
authors.forename: Michael
authors.initials: M
authors.lastname: Malinchoc
authors.forename: Rahul
authors.initials: R
authors.lastname: Kashyap
authors.forename: Guangxi
authors.initials: G
authors.lastname: Li
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
mesh_terms: D055371:Acute Lung Injury / Q000175:diagnosis* / Q000209:etiology; D000368:Aged; D006348:Cardiac Surgical Procedures / Q000009:adverse effects; D015331:Cohort Studies; D016208:Databases, Factual; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D020407:Internet; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011183:Postoperative Complications / Q000175:diagnosis*; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D012128:Respiratory Distress Syndrome / Q000175:diagnosis* / Q000209:etiology; D012307:Risk Factors; D013514:Surgical Procedures, Operative / Q000009:adverse effects*; D019616:Thoracic Surgical Procedures / Q000009:adverse effects; D016896:Treatment Outcome; D014656:Vascular Surgical Procedures / Q000009:adverse effects