Effort Oxygen Saturation and Effort Heart Rate to Detect Exacerbations of Chronic Obstructive Pulmonary Disease or Congestive Heart Failure

Abstract

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.

Is the 1-Minute Sit-To-Stand Test a Good Tool to Evaluate Exertional Oxygen Desaturation in Chronic Obstructive Pulmonary Disease?

“…This suggests that exercise-induced oxygen desaturation may be a key element in the prognostic evaluation and follow-up in COPD [ 20 , 21 , 22 ]. Moreover, some studies have demonstrated that regular monitoring of physiological variables during effort, such as oxygen desaturation and heart rate, may help to detect an exacerbation earlier [ 3 , 23 ].…”

<p>Remote Patient Monitoring for the Detection of COPD Exacerbations</p>

“…Previous studies have associated changes in breathing frequency, oxygen saturation, end-of-effort heart rate, and walking distance with exacerbation events. 19,[29][30][31] Other studies have used machine learning to associate imagebased measures of airway volume and resistance 20 or hospitalization history 21 with exacerbation risk. Despite these different approaches, several systematic reviews have cast doubt on the benefits of telemonitoring interventions for COPD on mortality, quality of life, exercise capacity, and exacerbation-related outcomes.…”

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions

“…In the healthcare field, the opportunity to identify abnormalities in RR is fundamental to forecast cardiac arrest, 22 exacerbations, 23 admissions to the Intensive Care Unit, and other adverse clinical events. 39 Despite the relevance of RR as prognostic factor, the current gold standard for measuring RR is the number of breaths performed in one minute, identified through auscultation or observation, which is not suitable for prolonged monitoring outside the clinical environment.…”

“…In the healthcare field, the opportunity to identify abnormalities in RR is fundamental to forecast cardiac arrest, 22 exacerbations, 23 admissions to the Intensive Care Unit, and other adverse clinical events. 39 …”