• David A McAllister, Sarah H Wild, John D MacLay, Andrew Robson, David E Newby, William MacNee, J Alastair Innes, Vipin Zamvar, and Nicholas L Mills.
• Centre for Population Health Sciences, University of Edinburgh, Midlothian, Edinburgh, United Kingdom. david.mcallister@ed.ac.uk
• Plos One. 2013 Jan 1;8(5):e64565.

ObjectiveAn aging population and increasing use of percutaneous therapies have resulted in older patients with more co-morbidity being referred for cardiac surgery. Objective measurements of physiological reserve and severity of co-morbid disease are required to improve risk stratification. We hypothesised that FEV1 would predict mortality and length of stay following cardiac surgery.MethodsWe assessed clinical outcomes in 2,241 consecutive patients undergoing coronary artery bypass grafting and/or valve surgery from 2001 to 2007 in a regional cardiac centre. Generalized linear models of the association between FEV1 and length of hospital stay and mortality were adjusted for age, sex, height, body mass index, socioeconomic status, smoking, cardiovascular risk factors, long-term use of bronchodilators or steroids for lung disease, and type and urgency of surgery. FEV1 was compared to an established risk prediction model, the EuroSCORE.ResultsSpirometry was performed in 2,082 patients (93%) whose mean (SD) age was 67 (10) years. Median hospital stay was 3 days longer in patients in the lowest compared to the highest quintile for FEV1, 1.35-fold higher (95% CI 1.20-1.52; p<0.001). The adjusted odds ratio for mortality was increased 2.11-fold (95% CI 1.45-3.08; p<0.001) per standard deviation decrement in FEV1 (800 ml). FEV1 improved discrimination of the EuroSCORE for mortality. Similar associations were found after excluding people with known pulmonary disease and/or airflow limitation on spirometry.ConclusionsReduced FEV1 strongly predicted increased length of stay and in-hospital mortality following cardiac surgery. FEV1 is a widely available measure of physiological health that may improve risk stratification of complex patients undergoing cardiac surgery and should be evaluated for inclusion in new prediction tools.

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