Rationale: Accurate prediction of mortality helps select patients for interventions aimed at improving outcome.
Objectives: Because chronic obstructive pulmonary disease is characterized by low-grade systemic inflammation, we hypothesized that addition of inflammatory biomarkers to established predictive factors will improve accuracy.
Methods: A total of 1,843 patients enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints study were followed for 3 years. Kaplan-Meier curves, log-rank analysis, and Cox proportional hazards analyses determined the predictive value for mortality of clinical variables, while C statistics assessed the added discriminative power offered by addition of biomarkers.
Measurements and main results: At recruitment we measured anthropometrics, spirometry, 6-minute walk distance, dyspnea, BODE index, history of hospitalization, comorbidities, and computed tomography scan emphysema. White blood cell and neutrophil counts, serum or plasma levels of fibrinogen, chemokine ligand 18, surfactant protein D, C-reactive protein, Clara cell secretory protein-16, IL-6 and -8, and tumor necrosis factor-α were determined at recruitment and subsequent visits. A total of 168 of the 1,843 patients (9.1%) died. Nonsurvivors were older and had more severe airflow limitation, increased dyspnea, higher BODE score, more emphysema, and higher rates of comorbidities and history of hospitalizations. The best predictive model for mortality using clinical variables included age, BODE, and hospitalization history (C statistic of 0.686; P < 0.001). One single biomarker (IL-6) significantly improved the C statistic to 0.708, but this was further improved to 0.726 (P = 0.003) by the addition of all biomarkers.
Conclusions: The addition of a panel of selected biomarkers improves the ability of established clinical variables to predict mortality in chronic obstructive pulmonary disease. Clinical trial registered with www.clinicaltrials.gov (NCT00292552).