TY - JOUR
T1 - Derivation and diagnostic accuracy of the surgical lung injury prediction model
AU - Kor, Daryl J.
AU - Warner, David O.
AU - Alsara, Anas
AU - Fernández-Pérez, Evans R.
AU - Malinchoc, Michael
AU - Kashyap, Rahul
AU - Li, Guangxi
AU - Gajic, Ognjen
PY - 2011/7
Y1 - 2011/7
N2 - 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.
AB - 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.
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U2 - 10.1097/ALN.0b013e31821b5839
DO - 10.1097/ALN.0b013e31821b5839
M3 - Article
C2 - 21694510
AN - SCOPUS:79959513244
SN - 0003-3022
VL - 115
SP - 117
EP - 128
JO - Anesthesiology
JF - Anesthesiology
IS - 1
ER -