Background: current algorithms for the detection of heart failure (HF) and chronic obstructive pulmonary disease (COPD) exacerbations have poor performance.
Methods: this study was designed as a prospective longitudinal trial. Physiological parameters were evaluated at rest and effort (walking) in patients who were in the exacerbation or stable phases of HF or COPD. Parameters with relevant discriminatory power (sensitivity (Sn) or specificity (Sp) ≥ 80%, and Youden index ≥ 0.2) were integrated into diagnostic algorithms.
Results: the study included 127 patients (COPD: 56, HF: 54, both: 17). The best algorithm for COPD included: oxygen saturation (SaO₂) decrease ≥ 2% in minutes 1 to 3 of effort, end-of-effort heart rate (HR) increase ≥ 10 beats/min and walking distance decrease ≥ 35 m (presence of one criterion showed Sn: 0.90 (95%, CI(confidence interval): 0.75⁻0.97), Sp: 0.89 (95%, CI: 0.72⁻0.96), and area under the curve (AUC): 0.92 (95%, CI: 0.85⁻0.995)); and for HF: SaO₂ decrease ≥ 2% in the mean-of-effort, HR increase ≥ 10 beats/min in the mean-of-effort, and walking distance decrease ≥ 40 m (presence of one criterion showed Sn: 0.85 (95%, CI: 0.69⁻0.93), Sp: 0.75 (95%, CI: 0.57⁻0.87) and AUC 0.84 (95%, CI: 0.74⁻0.94)).
Conclusions: effort situations improve the validity of physiological parameters for detection of HF and COPD exacerbation episodes.
Keywords: chronic obstructive pulmonary disease; diagnostic algorithms; heart failure.