Sleep-Disordered Breathing in Chronic Heart Failure is Highly Variable when Measured Remotely Using a Novel Non-Contact Biomotion Sensor
Year: 2017
Type: article
Study design: Observational Study
Abstract: Sleep-disordered breathing (SDB) is common in heart failure (HF) and considered a potential risk factor for, or a marker of, disease progression.1-3 The diagnosis of SDB is traditionally made during a single polysomnographic study (PSG) which measures the number of apnoeas/hypopnoeas/h and categorizes them as obstructive, central, or mixed. There is substantial night to night variability in the severity of SDB measured by PSG which is less, but still present, in home PSG. In HF, the effects of postural fluid shifts4 and changes in the HF syndrome over time5 may increase the variability of SDB. The lack of technology to measure SDB over prolonged periods has prevented longer term assessment of SDB in HF. We used a non-contact biomotion sensor (SleepMinder™; SM ResMed Sensor Technologies, Dublin, Ireland) previously validated against PSG measured apnoea/hypopnoea index (AHISM) in HF6 to monitor breathing during sleep remotely over periods of months to years in stable HF patients recruited from clinics at two centres: St Vincent's Hospital, Dublin, Ireland and Royal Brompton Hospital, London, UK. The SM algorithm automatically scored the sleep data and transmitted it daily to the central data management unit in Sydney, Australia. The AHISM calculated for periods of algorithm indicated that sleep did not distinguish between obstructive vs. central events. Short-term and long-term trends in SDB were analysed defining SDB as mild (5–14/h), moderate (15–29/h), or severe (≥30/h), and AHISM ≥15/h as clinically significant. Analysis of category changes in AHISM, (SDB present or absent) was performed on average AHISM during consecutive, non-overlapping, 2-week blocks. We compared both total and short-term study monitoring time SDB trends (Table 1). Analysis of long-term or global AHISM variability was based on mean AHISM values and the coefficient of variation calculated as the standard deviation (SD) of the AHISM from the total monitoring period to the average AHISM for the same period used to assess within-subject variability. Between-group comparisons used the Student's t-test; P-values of <0.05 were considered significant. Pearson correlation coefficient was used to assess the correlation between continuous variables and results analysed using MATLAB software version 2014a (Mathworks, Inc., Natick, MA, USA). Eligible patients aged >18 years had class II–IV chronic HF treated with optimal guideline-based medical care. Inclusion criteria were BNP >200 pg/mL or hospitalization owing to HF within 24 months. Patients with unpredictable sleep or already on positive pressure therapy were excluded. Clinicians were blinded to the SM data. The study was conducted in accordance with the Good Clinical Practice (GCP) and the principles of the Declaration of Helsinki, and informed consent was obtained before study enrolment. Patients with poor quality or less than 3 months of data were excluded from the analysis (9/61 patients in Dublin and 2/43 in London) leaving a total of 91 patients (52 patients in Dublin, 39 patients in London) included in the analysis of a total of 37 465 nights over 3–24 months. The two sites enrolled patients of similar age (mean ± SD: Dublin 75 ± 9 years, London 77 ± 9 years), gender (69% male vs. 72%), left ventricular ejection fraction (37 ± 13% vs. 38 ± 13%) and reduced vs. preserved left ventricular systolic function (69% vs. 67%). Dublin patients had a higher prevalence of hypertension (57% vs. 32%, P = 0.01), atrial fibrillation (57% vs. 28%, P = 0.01), BNP levels (170 ± 179 pg/ml, 135 ± 89, P = 0.008), a lower incidence of cardiac resynchronization therapy (5% vs. 40%, P < 0.001) but more episodes of HF decompensation (57% vs. 26%, P < 0.005). Analysis of the total study period or global analysis identified three groups with similar patterns at the two sites (Table 1). The no-SDB group (44% with total AHISM <15/h) included patients who had this consistently but also included others with transient blocks of AHISM >15/h (Figure 1a). Patients with paroxysmal SDB (38%) showed four distinct patterns of transitions between states of SDB and no-SDB over prolonged periods (Figure 1b). Although there were periods of relatively stable AHISM this changed quite suddenly in many and was persistently unstable in others. Patients with chronic or persistent SDB (18%) showed an AHISM ≥15/h across the whole data collection period (Figure 1c). Within this group some patients were relatively stable with an AHISM ≥15/h for the whole period whereas others consistently showed fluctuations in the AHISM but with values mostly ≥15/h. Short-term, 2-week block by block analysis showed that SDB was highly variable across all groups (no, paroxysmal, and persistent SDB) with 48% having changes in AHISM category (up or down) across consecutive blocks and 58% having at least one block of AHISM ≥15/h at some point. Overall, while a minority (30%) had an average AHISM >15/h during the total period of remote monitoring, most patients (58%) had at least one 2-week period of clinically significant SDB while monitored continuously. Using SM technology to remotely monitor the AHI over prolonged periods in HF patients showed that our current understanding of SDB in HF patients is overly simplistic. There is substantial variation in the presence and severity of SDB despite optimal guideline-based management of HF in two specialist HF clinics. The variability in SDB could not be detected by a single or several single PSG studies. This technology has the potential to identify short- and long-term changes in SDB relatively rapidly for potentially earlier intervention in the management of HF as a chronic disease. This work was supported by ResMed Ltd, Australia. Conflict of interest: R.N.K., S.F., F.J., and K.S. are ResMed employees. M.C. is a ResMed employee, was the co-Principal Investigator of the SERVE-HF Study, sponsored by ResMed, and has received research funding, consultancy fees and speaking fees from ResMed. H.O.S.'s salary as a Research Fellow was funded by ResMed through an Educational grant to Imperial College London. I.W. reports personal fees from ResMed, Inc., for consulting outside the submitted work. K.McD. and R.O'H. has nothing to declare.
DOI: 10.1002/ejhf.810
Language: English
Subfield: Physiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2598359377