Big data is a promising and innovative way to explore questions of clinical relevance, identify disease patterns and characteristics, and generate hypotheses in healthcare. A tremendous amount of data is now available—and growing exponentially—from a number of sources, including telemonitored medical devices that are connected to databases and provide information on device performance and patient status. Analysis of such data may provide new insights and support new approaches to healthcare problems.
In a cutting-edge analysis, real-world, de-identified data were used to investigate central sleep apnoea (CSA) during continuous positive airway pressure (CPAP) therapy of US telemonitored patients. The analysis was able to identify three patterns of CSA during CPAP therapy, all of which negatively affected CPAP therapy compliance and efficacy.1 A second analysis performed on the same database, found that switching patients with persistent or emergent CSA from CPAP to adaptive servo-ventilation (ASV)* therapy may improve compliance and thus, potentially patient outcomes.2
The trajectories of CSA during CPAP therapy study looked at de-identified data from 133,000 telemonitored patients treated for sleep disordered breathing (SDB) with ResMed Positive Airway Pressure (PAP) devices in the US in 2015. New information about the natural history of CSA during CPAP was discovered using repeated measures based on telemonitoring data rather than single day diagnostics like a home sleep test “snapshot” of CSA.
CSA occurred in 3.5% of patients; three categories of treatment associated CSA were identified1: emergent (20%), transient (55%), and persistent (25%) CSA.
The presence of CSA was associated with decreased CPAP usage hours and increased likelihood of treatment discontinuation, as compared with patients without relevant central apnoea during CPAP. The probability of continuing CPAP therapy on day 300 was 83% for OSA, and 79%, 76% and 72% for transient, persistent and emergent CSA, respectively. The hazard ratios for therapy termination for the three CSA groups were 1.3, 1.5, and 1.7, respectively. These findings were consistent using either the European Respiratory Society or the US definition of persistent CSA (AHI ≥15/h or CAI ≥5/h).
A secondary analysis showed that in patients with emergent or persistent CSA who switched from CPAP to ASV, compliance improved immediately after the switch was performed. There was a +22% adherence improvement in the two patient subgroups that switched from CPAP to either fixed (n=127, p<0.05) or variable (n=82, p<0.01) EPAP ASV.2 The average AHI before the CPAP to ASV switch among patients with emergent or persistent CSA was 17.34/h as compared with 4.1/h after the switch.
The data suggest that if CSA persists after 2 weeks, patients who experienced emergent or persistent CSA during CPAP therapy may benefit from a switch to ASV.
The study was led by an external international committee of sleep and respiratory experts in collaboration with ResMed: Jean-Louis Pépin (France), Holger Woehrle (Germany), Atul Malhotra (USA), and Peter Cistulli (Australia).
Support: This Insight article and accompanying videos were supported by ResMed. Read more: here.
1. Liu D, Armitstead J, Benjafield A, et al. Trajectories of emergent central sleep apnea during CPAP therapy. Chest. 2017;152:751–60.
2. Pépin JD, Woehrle H, Liu D, et al. Adherence to positive airway therapy after switching From CPAP to
ASV: a big data analysis. J Clin Sleep Med. 2018;14:57–63.
*ASV therapy may be contraindicated in patients with chronic, symptomatic heart failure with reduced left ventricular ejection fraction and moderate to severe predominant central sleep apnoea. Please refer to the user guide for more information.
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