Background Acute lung injury (ALI) is a serious postoperative complication with limited treatment options. A preoperative risk prediction model would assist both 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 This is 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 Characteristics Curve and the Hosmer and 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 which were maintained in the final SLIP model included high-risk cardiac, vascular, and thoracic surgery, diabetes mellitus, chronic obstructive pulmonary disease, gastroesophageal reflux disease, and alcohol abuse. The SLIP score discriminated 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) and was well calibrated (Hosmer Lemeshow p = 0.55). Internal validation using 10-fold cross-validation noted minimal loss of diagnostic accuracy with a mean +/− standard deviation 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 developing early postoperative ALI.

OBJECTIVE:To develop and validate time-efficient automated electronic search strategies for identifying preoperative risk factors for postoperative acute lung injury. PATIENTS AND METHODS:This secondary analysis of a prospective cohort study included 249 patients undergoing high-risk surgery between November 1, 2005, and August 31, 2006. Two independent data-extraction strategies were compared. The first strategy used a manual review of medical records and the second a Webbased query-building tool. Web-based searches were derived and refined in a derivation cohort of 83 patients and subsequently validated in an independent cohort of 166 patients. Agreement between the 2 search strategies was assessed with percent agreement and Cohen κ statistics. RESULTS:Cohen κ statistics ranged from 0.34 (95% confidence interval, 0.00-0.86) for amiodarone to 0.85 for cirrhosis (95% confidence interval, 0.57-1.00). Agreement between manual and automated electronic data extraction was almost complete for 3 variables (diabetes mellitus, cirrhosis, H 2 -receptor antagonists), substantial for 3 (chronic obstructive pulmonary disease, proton pump inhibitors, statins), moderate for gastroesophageal reflux disease, and fair for 2 variables (restrictive lung disease and amiodarone). Automated electronic queries outperformed manual data collection in terms of sensitivities (median, 100% [range, 77%-100%] vs median, 87% [range, 0%-100%]). The specificities were uniformly high (≥96%) for both search strategies.CONCLUSION: Automated electronic query building is an iterative process that ultimately results in accurate, highly efficient data extraction. These strategies may be useful for both clinicians and researchers when determining the risk of time-sensitive conditions such as postoperative acute lung injury. Mayo Clin © 2011 Mayo Foundation for Medical Education and Research For editorial comment, see page 373A cute lung injury (ALI) is a devastating postoperative respiratory complication and a leading cause of postoperative respiratory failure, 1-3 with a mortality rate of up to 45% in certain surgical populations.4,5 Moreover, treatment options are limited once the condition is fully established. Earlier identification of at-risk populations may allow the implementation of effective ALI prevention strategies. Recognizing that numerous baseline factors can modify a patient's response to illness or injury and the likelihood of developing ALI, we recently developed an ALI risk prediction model for mixed medical and surgical populations. 6,7 This score assigns points both for conditions that predispose patients to ALI (eg, shock, aspiration, sepsis, pancreatitis, pneumonia, high-risk surgery, high-risk trauma) and ALI-modifying factors (eg, sex, excess alcohol use, obesity, chemotherapy, diabetes mellitus [DM], smoking) at the time of hospital admission. We have shown the cumulative score to be a reliable predictor of the risk of developing ALI during hospitalization.A key remaining limitation to the early identification of patients at h...

T he fi nding of acute hypoxemia and bilateral lung infi ltrates on frontal chest radiograph is common in the ICU setting, and differentiation between cardiogenic pulmonary edema (CPE) and noncardiogenic pulmonary edema (acute lung injury [ALI]) is diffi cult and challenging in the early stages of illness. 1,2 Left atrial hypertension (LAH) as a principal cause of acute pulmonary edema must be excluded before making a diagnosis of ALI and its more severe form, ARDS. 3 Conversely, to exclude ALI, one needs not only the evidence of LAH but also the absence of signifi cant ALI risk factors. Traditionally, a pulmonary artery occlusion pressure (PAOP) . 18 mm Hg has been used as a surrogate marker of LAH. It is rarely used in current clinical practice because it is invasive, the effi cacy of pulmonary artery catheter-guided therapy in critically ill patients has not been proven, 4 and some studies have suggested increased morbidity and mortality associated with its use. Abbreviations: AECC 5 American European Consensus Conference; ALI 5 acute lung injury; AUC 5 area under curve; BNP 5 brain natriuretic peptide; CAD 5 coronary artery disease; CPE 5 cardiogenic pulmonary edema; CVP 5 central venous pressure; DC 5 development cohort; E/E 9 5 ratio of mitral peak velocity of early fi lling (E) to early diastolic mitral annular velocity (E 9 ); HL 5 Hosmer-Lemeshow test; IQR 5 interquartile range; LAH 5 left atrial hypertension; LBBB 5 left bundle branch block; PAOP 5 pulmonary occlusion pressure; Sp o 2 5 peripheral oxygen saturation; VC 5 validation cohort

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