Sleep medicine · 2026
Clinical profile of responders to adaptive servo-ventilation: a cluster analysis from the READ-ASV European cohort.
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Pépin Jean-Louis, Heinzer Raphael, Bailly Sébastien, Lavergne Florent, Benjafield Adam V, Fischer Susanne, Woehrle Holger, Arzt Michael
Аннотация
BACKGROUND: Adaptive servo-ventilation (ASV) effectively treats central sleep apnea (CSA), with or without coexisting obstructive sleep apnea (CSA-OSA). However, this is a diverse patient group, and the characteristics of patients who are most likely to benefit from ASV therapy, outside of traditional etiology-based subgroups, are unknown.
OBJECTIVES: To identify clusters of ASV-treated patients based on anthropometric data and comorbidities, and to evaluate the differential response to ASV in these patient clusters.
METHODS: This analysis used data from the READ-ASV registry, which enrolled adults with CSA prescribed ASV from September 2017-March 2021. Evaluations included the Epworth Sleepiness Scale, Functional Outcomes of Sleep Questionnaire, Pittsburg Sleep Quality Index, EuroQol-5-dimension, and sleep study data. Latent class analysis was used to identify patient clusters based on baseline anthropometric data and comorbidities.
RESULTS: Of 812 patients, 421 (52%) were in Cluster (C) 1 (older, high rate of cardiovascular comorbidities [including stroke], high body mass index [BMI]), 239 (29%) were in C2 (elderly/middle-aged, average-low cardiovascular comorbidity rate, high stroke rate, lower BMI, low diabetes rate), and 152 (19%) were in Cluster 3 (younger, low cardiovascular comorbidity rate, higher BMI, high depression rate). Patients from C3 versus C1/C2 had the highest apnea-hypopnea index at baseline (55 vs. 48/39.5 events/h), were the most symptomatic, had the worst quality of life (QoL), and had the greatest symptomatic and QoL improvement on ASV. They remained the most symptomatic cluster after treatment.
CONCLUSIONS: The identification of subgroups of ASV users with differing responses to therapy and residual symptom burden could facilitate a more personalized approach to ASV prescription and therapy management.
CLINICAL TRIAL REGISTRATION: https://www.
CLINICALTRIALS: gov; NCT03032029.
OBJECTIVES: To identify clusters of ASV-treated patients based on anthropometric data and comorbidities, and to evaluate the differential response to ASV in these patient clusters.
METHODS: This analysis used data from the READ-ASV registry, which enrolled adults with CSA prescribed ASV from September 2017-March 2021. Evaluations included the Epworth Sleepiness Scale, Functional Outcomes of Sleep Questionnaire, Pittsburg Sleep Quality Index, EuroQol-5-dimension, and sleep study data. Latent class analysis was used to identify patient clusters based on baseline anthropometric data and comorbidities.
RESULTS: Of 812 patients, 421 (52%) were in Cluster (C) 1 (older, high rate of cardiovascular comorbidities [including stroke], high body mass index [BMI]), 239 (29%) were in C2 (elderly/middle-aged, average-low cardiovascular comorbidity rate, high stroke rate, lower BMI, low diabetes rate), and 152 (19%) were in Cluster 3 (younger, low cardiovascular comorbidity rate, higher BMI, high depression rate). Patients from C3 versus C1/C2 had the highest apnea-hypopnea index at baseline (55 vs. 48/39.5 events/h), were the most symptomatic, had the worst quality of life (QoL), and had the greatest symptomatic and QoL improvement on ASV. They remained the most symptomatic cluster after treatment.
CONCLUSIONS: The identification of subgroups of ASV users with differing responses to therapy and residual symptom burden could facilitate a more personalized approach to ASV prescription and therapy management.
CLINICAL TRIAL REGISTRATION: https://www.
CLINICALTRIALS: gov; NCT03032029.