Continuous positive airway pressure adherence trajectories in sleep apnea: Clustering with summed discrete Fréchet and dynamic time warping dissimilarities

• Published in: Statistics in medicine 2021-10, Vol.40 (24), p.5373-5396
• Main Authors: Bottaz‐Bosson, Guillaume, Hamon, Agnès, Pépin, Jean‐Louis, Bailly, Sébastien, Samson, Adeline
• Format: Article
• Language: English
• Subjects: Applications; cluster validation; Continuous positive airway pressure; CPAP adherence trajectories; discrete Fréchet distance; dynamic time warping; Human health and pathology; Life Sciences; Pulmonology and respiratory tract; Sleep apnea; Statistics; time series clustering
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.9130

Background Obstructive sleep apnea (OSA) is a chronic disease characterized by recurrent pharyngeal collapses during sleep. In most severe cases, continuous positive airway pressure (CPAP) consists in keeping the airways open by administering mild air pressure. This treatment faces adherence issues. Objectives Eight hundred and forty‐eight subjects were equipped with CPAP prescribed at the Grenoble University Hospital between 2016 and 2018. Their daily CPAP uses have been recorded during the first 3 months. Our aim is to cluster these adherence time series. With hierarchical agglomerative clustering, we focused on the choices of the dissimilarity measure and the internal cluster validation index (CVI). Methods The Euclidean distance, the dynamic time warping (DTW) and the generalized summed discrete Fréchet dissimilarity were implemented with three linkage strategies (“average,” “complete,” and “Ward”). The performances of each method (dissimilarity and linkage) were evaluated on a simulation study through the adjusted Rand index (ARI). The Ward linkage with DTW dissimilarity provided the best ARI. Then six different internal CVIs (Silhouette, Calinski Harabasz, Davies Bouldin, Modified Davies Bouldin, Dunn, and COP) were compared on their ability to choose the best number of clusters. The Dunn index beat the others. Results CPAP data were clustered with the Ward linkage, the DTW dissimilarity and the Dunn index. It identified six clusters, from a cluster of patients (N = 29 subjects) whose stopped the therapy early on to a cluster (N = 105) with increasing adherence over time. Other clusters were extremely good users (N = 151), good users (N = 150), moderate users (N = 235), and poor adherers (N = 178).