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Logistic risk model predicting postoperative respiratory failure in patients undergoing valve surgery

Department of Cardiothoracic Surgery, Mount Sinai School of Medicine, 1190 Fifth Avenue, New York, NY 10029-1028, United States

Received 16 November 2007; received in revised form 10 July 2008; accepted 14 July 2008.

^_* Corresponding author. Tel.: +1 212 659 6820; fax: +1 212 659 6818. (Email: farzan.filsoufi{at}mountsinai.org).

Background: Previous studies have been unable to identify independent valve-related risk factors for postoperative respiratory failure (RF) in patients undergoing valve surgery. This study was designed to determine the incidence and predictors of RF in these patients. We also aimed to create a model based on these risk factors that could serve as a tool for the prediction of this complication. Methods: We analyzed prospectively collected data of 2808 patients (mean age 63±15 years, 43% female) who underwent valve surgery from January 1998 to December 2006. Isolated valve surgery was performed in 2007 (72%) patients whereas 801 (28%) received concomitant coronary artery bypass grafting (CABG) procedures. The main outcome investigated was RF (ventilation >72 h). Other postoperative parameters included in the analysis were hospital mortality, morbidity, length of hospital stay, discharge and late survival. Results: Respiratory failure occurred in 12.2% (n = 342) of patients. The incidence of RF varied according to the procedures (single valve: 7.4–15.8%; multiple valves: 21.7–23.4%). The addition of CABG significantly increased the rate of RF (isolated valves: 10.8%, combined valve/CABG 15.7%, p < 0.001). Multivariate analysis revealed preoperative renal failure, ejection fraction <30%, age >70 years, active endocarditis, emergent procedures, reoperation, diabetes, congestive heart failure, previous myocardial infarction, female gender, double aortic and mitral valve procedures, and cardiopulmonary bypass time >180 min as independent predictors of RF. Hospital mortality among patients with RF was 22.2% (n = 76) versus 2.7% (n = 66) in the control group (p < 0.001). A logistic equation including the coefficients of the regression analysis was generated to calculate an individual patient’s risk for the development of RF. Predictive accuracy of the model and validation was measured (ROC area under the curve: 0.751). Long-term survival of discharged patients with RF was significantly decreased compared to those without RF. Conclusion: Respiratory failure is a common complication particularly in patients undergoing complex valve operations such as endocarditis or multiple valve procedures. The independent predictors of RF including valve-related factors allowed us to create a predictive model with great accuracy. The poor long-term survival of patients with RF underlines the need to direct more resources towards prevention and treatment of this complication.

Key Words: Respiratory failure • Valve surgery • Mortality • Morbidity • Long-term survival • Predicting model