Forced Expiratory Volume in One Second Predicts Length of Stay and In-Hospital Mortality in Patients Undergoing Cardiac Surgery: A Retrospective Cohort Study

<div><p>Objective</p><p>An 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.</p><p>Methods</p><p>We 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.</p><p>Results</p><p>Spirometry 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.</p><p>Conclusions</p><p>Reduced 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.</p>