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Predicting risk of postoperative lung injury in high-risk surgical patients: a multicenter cohort study
Daryl J Kor1, Ravi K Lingineni, Ognjen Gajic
1From 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.).
Background:
Acute 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.
Methods:
This 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.
Results:
Among 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.
Conclusions:
In 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.
