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Original Research: CRITICAL CARE |

Development and Validation of a Risk Calculator Predicting Postoperative Respiratory FailureRisk Calculator Predicting Respiratory Failure

Himani Gupta, MD; Prateek K. Gupta, MD; Xiang Fang, PhD; Weldon J. Miller, MS; Samuel Cemaj, MD; R. Armour Forse, MD, PhD; Lee E. Morrow, MD, FCCP
Author and Funding Information

From the Department of Medicine (Drs H. Gupta and Morrow), the Department of Surgery (Drs P. K. Gupta, Cemaj, and Forse), and Biostatistical Core (Dr Fang), Creighton University, Omaha, NE; and the School of Medicine (Mr Miller), University of Pittsburgh, Pittsburgh, PA.

Correspondence to: Lee E. Morrow, MD, FCCP, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Creighton University, 601 N 30th St, Ste 3820, Omaha, NE 68131; e-mail: lmorrow@creighton.edu


Funding/Support: The authors have reported to CHEST that no funding was received for this study.

Part of this article has been published in abstract form (Gupta P, Gupta H, Miller WJ, et al. Chest. 2009;136(4)(suppl 4):31S).

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


© 2011 American College of Chest Physicians


Chest. 2011;140(5):1207-1215. doi:10.1378/chest.11-0466
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Background:  Postoperative respiratory failure (PRF) (requiring mechanical ventilation > 48 h after surgery or unplanned intubation within 30 days of surgery) is associated with significant morbidity and mortality. The objective of this study was to identify preoperative factors associated with an increased risk of PRF and subsequently develop and validate a risk calculator.

Methods:  The American College of Surgeons National Surgical Quality Improvement Program (NSQIP), a multicenter, prospective data set (2007-2008), was used. The 2007 data set (n = 211,410) served as the training set and the 2008 data set (n = 257,385) as the validation set.

Results:  In the training set, 6,531 patients (3.1%) developed PRF. Patients who developed PRF had a significantly higher 30-day mortality (25.62% vs 0.98%, P < .0001). On multivariate logistic regression analysis, five preoperative predictors of PRF were identified: type of surgery, emergency case, dependent functional status, preoperative sepsis, and higher American Society of Anesthesiologists (ASA) class. The risk model based on the training data set was subsequently validated on the validation data set. The model performance was very similar between the training and the validation data sets (c-statistic, 0.894 and 0.897, respectively). The high c-statistics (area under the receiver operating characteristic curve) indicate excellent predictive performance. The risk model was used to develop an interactive risk calculator.

Conclusions:  Preoperative variables associated with increased risk of PRF include type of surgery, emergency case, dependent functional status, sepsis, and higher ASA class. The validated risk calculator provides a risk estimate of PRF and is anticipated to aid in surgical decision making and informed patient consent.

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