The Ratio Serum Creatinine/Serum Cystatin C (a Surrogate Marker of Muscle Mass) as a Predictor of Hospitalization in Chronic Obstructive Pulmonary Disease Outpatients.

<bold><italic>Background:</italic></bold> In chronic obstructive pulmonary disease (COPD), low muscle mass has been associated with several clinical outcomes such as low exercise capacity, hospital admission, and mortality. The Sarcopenia Index (SI) is a novel way to estimate muscle mass based on the ratio of serum creatinine (produced exclusively by muscle)/cystatin C (produced by all nucleated body cells). <bold><italic>Objectives:</italic></bold> This study aims to assess the SI in stable COPD outpatients, as compared with a healthy control group, to quantify its relationship with several important clinical features in COPD, and to study its potential usefulness to predict COPD exacerbations and hospital admissions. <bold><italic>Methods:</italic></bold> The SI was calculated in 18 healthy control subjects and 65 stable COPD outpatients were included in the study. Patients were prospectively followed for 1 year after being enrolled in the study. <bold><italic>Results:</italic></bold> COPD patients had a lower SI than controls, that is lower muscle mass. Furthermore, patients with a modified Medical Research Council dyspnea score ≥2, patients with a COPD Assessment Test score ≥10, and patients with a high risk of exacerbation had lower levels of SI compared with patients without these characteristics. SI correlated with FEV1 (<italic>r</italic> = 0.491, <italic>p</italic> < 0.001), the 6-min walking test (<italic>r</italic> = 0.560, <italic>p</italic> = 0.001), and the Fat-Free Mass Index (<italic>r</italic> = 0.431, <italic>p</italic> = 0.017). Univariate and multivariate Cox proportional risk analysis showed that a low SI is an independent predictor of hospital admission in COPD outpatients followed for 1 year (HR 5.16, <italic>p</italic> = 0.025). <bold><italic>Conclusions:</italic></bold> The ratio serum creatinine/serum cystatin C correlates with several COPD characteristics, and it can be used to predict COPD hospitalization. [ABSTRACT FROM AUTHOR]