• Anesthesiology · May 2014

    Multicenter Study Observational Study

    Predicting Risk of Postoperative Lung Injury in High-risk Surgical Patients: A Multicenter Cohort Study.

    The SLIP-2 score (Surgical Lung Injury Prediction) was found to offer acceptable accuracy in predicting the risk of post-operative ARDS in high-risk surgical patients (ROC 0.84).

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    • Daryl J Kor, Ravi K Lingineni, Ognjen Gajic, Pauline K Park, James M Blum, Peter C Hou, J Jason Hoth, Harry L Anderson, Ednan K Bajwa, Raquel R Bartz, Adebola Adesanya, Emir Festic, Michelle N Gong, Rickey E Carter, and Daniel S Talmor.
    • From the Department of Anesthesiology (D.J.K.), Department of Health Sciences Research (R.K.L., R.E.C.), and Department of Medicine, Division of Pulmonary and Critical Care Medicine (O.G.), Mayo Clinic, Rochester, Minnesota; Department of Surgery (P.K.P.) and Department of Anesthesiology (J.M.B.), University of Michigan School of Medicine, Ann Arbor, Michigan; Department of Surgery (H.L.A.), St Joseph Mercy Hospital, Ann Arbor, Michigan; Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts (P.C.H.); Department of Surgery, Wake Forest University Health Sciences, Winston-Salem, North Carolina (J.J.H.); Departme nt of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.K.B.); Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina (R.R.B.); Department of Anesthesiology, University of Texas Southwestern Medical Center, Dallas, Texas (A.A.); Department of Critical Care, Mayo Clinic, Jacksonville, Florida (E.F.); Department of Medicine, Albert Einstein College of Medicine, Bronx, New York (M.N.G.); and Department of Anaesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts (D.S.T.).
    • Anesthesiology. 2014 May 1; 120 (5): 1168-81.

    BackgroundAcute respiratory distress syndrome (ARDS) remains a serious postoperative complication. Although ARDS prevention is a priority, the inability to identify patients at risk for ARDS remains a barrier to progress. The authors tested and refined the previously reported surgical lung injury prediction (SLIP) model in a multicenter cohort of at-risk surgical patients.MethodsThis is a secondary analysis of a multicenter, prospective cohort investigation evaluating high-risk patients undergoing surgery. Preoperative ARDS risk factors and risk modifiers were evaluated for inclusion in a parsimonious risk-prediction model. Multiple imputation and domain analysis were used to facilitate development of a refined model, designated SLIP-2. Area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test were used to assess model performance.ResultsAmong 1,562 at-risk patients, ARDS developed in 117 (7.5%). Nine independent predictors of ARDS were identified: sepsis, high-risk aortic vascular surgery, high-risk cardiac surgery, emergency surgery, cirrhosis, admission location other than home, increased respiratory rate (20 to 29 and ≥30 breaths/min), FIO2 greater than 35%, and SpO2 less than 95%. The original SLIP score performed poorly in this heterogeneous cohort with baseline risk factors for ARDS (area under the receiver operating characteristic curve [95% CI], 0.56 [0.50 to 0.62]). In contrast, SLIP-2 score performed well (area under the receiver operating characteristic curve [95% CI], 0.84 [0.81 to 0.88]). Internal validation indicated similar discrimination, with an area under the receiver operating characteristic curve of 0.84.ConclusionsIn this multicenter cohort of patients at risk for ARDS, the SLIP-2 score outperformed the original SLIP score. If validated in an independent sample, this tool may help identify surgical patients at high risk for ARDS.

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    The SLIP-2 score (Surgical Lung Injury Prediction) was found to offer acceptable accuracy in predicting the risk of post-operative ARDS in high-risk surgical patients (ROC 0.84).

    Daniel Jolley  Daniel Jolley
     
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