Sleep Quality Patterns in Patients With Heart Failure

Patients with heart failure experience varied sleep quality patterns, according to study results published in BMJ Open.

Researchers examined cross-sectional data from the Motivational Interviewing to Improve Self-Care in Heart Failure Patients trial (ClinicalTrials.gov Identifier: NCT02894502) to identify sleep quality patterns in patients with heart failure, and the effect of demographic and clinical characteristics on these patterns.

The study included 510 patients diagnosed with heart failure and poor self-care. Participants were assessed at baseline and then at 3, 6, 9, and 12 months. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index, which measures domains including sleep duration, latency, disturbances, efficiency, daytime dysfunction, overall quality, and use of sleep medications.

In addition to sleep assessments, the researchers collected data on comorbidities using the Charlson Comorbidity Index, physical symptom burden using the Heart Failure Somatic Perception Scale, cognitive function using the Montreal Cognitive Assessment, and psychological symptoms using the Hospital Anxiety and Depression Scale.

The mean (SD) age of participants was 72.37 (12.28) years, 42% were women, and the mean (SD) BMI was 27.72 (4.71). Most patients (61.4%) were classified as New York Heart Association class II. Over half (53.3%) had mild comorbidity severity. Overall, patients reported a low symptom burden and rated their health status as fair to good. Mean (SD) disease duration was 66.7 (76.66) months.

Using Pittsburgh Sleep Quality Index scores, the researchers identified 3 clusters of sleep quality patterns. Cluster 1, comprising 46.1% of patients (n=235), included individuals who frequently experienced sleep disturbances, daytime dysfunction, and difficulty falling asleep. Despite these issues, they generally maintained adequate sleep duration and efficiency and reported minimal use of sleep medications. Cluster 2 included 25.3% of patients (n=129) and was marked by a high likelihood of sleep problems, although these individuals were also less likely to use sleep medications. Cluster 3, which comprised 28.6% of patients (n=146), included those who had a low overall likelihood of sleep problems but were still prone to disturbances and poor perceived sleep quality.

Statistically significant differences in demographic and clinical characteristics were observed among the clusters. Cluster 3 consisted of the youngest patients (mean [SD] age, 68.24 [13.02] years), most of whom were women (71.2%). These individuals had the highest ejection fraction, least cognitive impairment, lowest comorbidity severity, and the lowest burden of physical symptoms, anxiety, and sleep issues (all P <.001). Conversely, Cluster 2 included the oldest patients (mean [SD] age, 75.37 [9.80] years) with the worst cognitive performance, highest levels of depressive symptoms and symptom burden, and the poorest quality of life (all P <.001). Cluster 1 patients displayed characteristics that fell between those of the other 2 groups.

Multinomial regression analysis showed independent predictors of cluster membership. Compared to Cluster 3, patients in Cluster 1 were more likely to be older (odds ratio [OR], 1.04; P =.003), formally educated (OR, 2.14; P =.011), and to report poorer physical (OR, 0.95; P =.007) and mental (OR, 0.96; P =.020) quality of life, elevated anxiety symptoms (OR, 1.12; P =.020), and a greater burden of physical symptoms (OR, 1.06; P <.001). Patients in Cluster 2 were more likely to be older (OR, 1.04; P =.024), have more severe comorbidities (OR, 1.24; P =.010), lower ejection fraction (OR, 0.95; P <.001), and higher overall symptom burden (OR, 1.06; P <.001) than those in Cluster 3.

Study limitations include an exclusively Italian population, a lack of data on the development of sleep disturbances over time, and the potential impact of pharmacological treatments.

“Aligning care with the distinct needs of each sleep disturbance profile could significantly improve both patient well-being and health outcomes in this population,” the authors concluded.