Sleep最新文献

Study objectives: This study assessed the utilization of potentially inappropriate medications (PIM) including oral sedative-hypnotic and atypical antipsychotic (OSHAA), healthcare resource utilization (HCRU), and costs among elderly individuals with insomnia and in the subpopulation with Alzheimer's Disease (AD) who also had a diagnosis of insomnia.

Methods: Using claims database containing International Classification of Diseases, 10th Revision (ICD-10) codes, the cohort included individuals aged ≥ 65 with incident insomnia (EI, N=152,969) and AD insomnia subpopulation (ADI, N=4,888). Proportion of patients utilizing atypical antipsychotics or oral sedative-hypnotic medications, namely z-drugs, benzodiazepines, doxepin, Dual Orexin Receptor Antagonists (DORAs), and melatonin agonists, were assessed. Inappropriate OSHAA utilization was defined as per the American Geriatrics Society (AGS) Beers criteria. Multivariable models were utilized to compare HCRU and costs between PIM-OSHAA and no PIM-OSHAA groups.

Results: Among the EI cohort, z-drugs (13.39%) were the most commonly utilized OSHAA, and in the ADI cohort, it was AAPs (29.97%). PIM-OSHAA was utilized by 20% of the EI and 35% of the ADI cohorts. Patients with PIM-OSHAA use among the EI cohort had a higher annualized adjusted mean HCRU (pharmacy visits: 31.21 vs. 23.68; ambulatory & outpatient visits: 18.55 vs. 16.85) and costs, primarily due to medical costs (mean total cost: $36,676.08 vs. $31,346.54) compared to those without.

Conclusions: Substantial utilization of PIM-OSHAA was observed in EI and ADI cohorts. PIM-OSHAA use was associated with higher HCRU and costs. These findings underscore the importance of appropriate treatment strategies for insomnia in the elderly population especially in those with concurrent AD.

Study objectives: Although heart rate variability (HRV), a marker of cardiac autonomic modulation (CAM), is known to predict cardiovascular morbidity, the circadian timing of sleep (CTS) is also involved in autonomic modulation. We examined whether circadian misalignment is associated with blunted HRV in adolescents as a function of entrainment to school or on-breaks.

Methods: We evaluated 360 subjects from the Penn State Child Cohort (median 16y) who had at least 3-night at-home actigraphy (ACT), in-lab 9-h polysomnography (PSG) and 24-h Holter-monitoring heart rate variability (HRV) data. ACT-measured metrics of circadian misalignment included sleep midpoint (SM), sleep irregularity (SI), and social jetlag (SJL). Five 24-h, daytime and nighttime frequency- and time-domain HRV indices were the primary outcomes. Linear regression models adjusted for sex, race/ethnicity, age, body mass index, apnea/hypopnea index, sleep duration and its variability. These associations were also examined as a function of being in-school or on-break.

Results: While on-break, a later SM on weekends was significantly associated with all five nighttime HRV indices. While in-school, greater SI on weekdays was significantly associated with three daytime and three nighttime HRV indices. Greater SJL was not associated with any HRV index. Longitudinal analyses confirmed the association of adolescent SM, SI and SJL with change in nighttime HRV since childhood.

Conclusions: An irregular sleep phase during days of entrainment to social demands and a delayed sleep phase during ad-libitum days are associated with blunted HRV in adolescents. Circadian misalignment contributes to increased cardiovascular risk via an altered CAM in youth.

Study objectives: Cancer-related fatigue is one of the most common symptoms in cancer survivors. Cognitive behavioural therapy for insomnia (CBT-I) can improve fatigue, but mechanisms are unclear. This secondary analysis of a randomized controlled trial evaluated whether CBT-I led to a significant improvement in fatigue, accounting for change in comorbid symptoms of insomnia, perceived cognitive impairment (PCI), anxiety, and depression. The parent study evaluated the impacts of CBT-I on PCI and insomnia.

Methods: Cancer survivors with insomnia and PCI were randomized to CBT-I or a sleep-self monitoring waitlist control. Fatigue was measured using the Multidimensional Fatigue Symptom Inventory - Short Form at pre-, mid-, and post-treatment. Significant improvement in fatigue was defined as a reduction >10.79 points. Insomnia, PCI, anxiety and depression symptoms were assessed. A linear mixed model evaluated whether CBT-I improved fatigue after adjusting for comorbidities. Mediation analyses examined whether change in comorbidities accounted for the effect of CBT-I on fatigue.

Results: The sample consisted of 132 cancer survivors (77% female, Mage=60.12 years, 41% breast cancer). There was a significant group-by-time interaction on fatigue, p<.001, with the CBT-I group experiencing a 20.6-point reduction in fatigue compared to 3.7-points in the control. Improvements in fatigue were fully accounted for by improvements in the comorbidities with change in insomnia accounting for 45.3% of the effect observed in fatigue.

Conclusions: CBT-I resulted in significant improvement in fatigue, and these effects were largely accounted for by change in insomnia. CBT-I is a robust intervention with efficacy for improving fatigue among cancer survivors.

Study objectives: Multilevel upper airway surgery is effective for some patients with obstructive sleep apnea (OSA), but prediction the response to surgery remains a challenge. The underlying endotypes of OSA include upper airway collapsibility, muscle compensation, loop gain, and the arousal threshold. This study aimed to explore the effect of surgery on polysomnography (PSG)-derived OSA endotypes and establish a surgical response prediction model.

Methods: Our study included 54 Chinese patients with OSA who underwent multilevel upper airway surgery. Participants underwent PSG before and after surgery with a median follow-up time of 6.5 months. Using AHIBaseline/AHIpost-surgery ≥ 2 and AHIpost-surgery < 10 events/h as criteria, participants were classified as surgery responders and non-responders. The surgical success rate was 26%. These endotypic traits were derived from a standard PSG data by validated methods.

Results: The surgery altered both anatomical and non-anatomical endotypic traits, including increased Vpassive (baseline VS post-surgery: 51.5[18.7-84.2] VS 86.8 [67.4-93.7] %Veupnea, p<0.001), decreased loop gain (baseline VS post-surgery: 0.7 [0.7-0.8] VS 0.6[0.5-0.6]; p<0.001), and a higher arousal threshold (baseline VS post-surgery: 202.9[183.7-222.0] VS 160.7[143.9-177.4] %Veupnea; p<0.001). However, it did not significantly affect muscle compensation. Fully adjusted logistic regression analyses indicated that a favorable response to surgery was independently associated with a lower LG (OR [CI 95%], 0.1[0.0-0.5], p= 0.032). In patients with improved muscle compensation or a more collapsible airway (lower Vpassive), a lower loop gain was more strongly indicative of success. However, when muscle compensation was lower or collapsibility was less severe (higher Vpassive), a lower loop gain was less predictive of success.

Conclusions: This study demonstrated that multilevel upper airway surgery altered both anatomical and non-anatomical endotypes in Chinese patients with OSA. An endotype based regression model may meaningfully predict surgical success.

Study objectives: Sleep disturbances are prevalent during acute hospitalization in medically ill older patients, with undesirable outcomes. Sleep medication use is common, but its effectiveness is questionable. This study explored the trajectory of sleep parameters from home to hospital and assessed the impact of sleep medication use, considering covariates such as physical symptom burden.

Methods: A prospective multicenter study was conducted in four Israeli hospitals. Cognitively intact older patients (n=683), with an admission interview and at least one follow-up, were recruited. Total sleep time (TST), sleep efficiency (SE), sleep quality (SQ), number of awakenings (NOA), sleep medication use, sleep medication burden (quantity and dosage), and physical symptom burden were recorded daily. Personal and illness-related covariates were included in a repeated-measures mixed model design.

Results: Participants (male: 54%, aged 77.31±6.60) showed shorter TST (329.73±111.94 vs. 377.03±101.06 minutes), lower SE (71.49±19.28% vs. 76.14±15.53%), and higher probability for lower SQ, in the hospital compared to home. Sleep medication use was not correlated with any sleep parameters; sleep medication burden was associated with NOA. Physical symptom burden showed significant main effects on SE, SQ, and NOA, and a significant interaction was found with time-points on TST, such that higher burden was more strongly associated with shorter TST at first in-hospital follow-up than at admission, with no differences between all subsequent in-hospital time points. Conclusions: Sleep declined during acute hospitalization compared to the home, with sleep medications showing minimal effect. Managing symptom burden should be prioritized when addressing sleep disturbances in older patients during hospitalization.

Study objectives: This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilizes PPG (red, infrared, and green channels) and accelerometer sensors to assess sleep apnea by the AI system.

Methods: We prospectively enrolled 240 participants suspected of obstructive sleep apnea (OSA) at a tertiary medical center for internal validation and 112 participants independently at another center for external validation. All participants underwent simultaneous polysomnography (PSG) and TripTraQ HSAT. We compared TipTraQ-derived total sleep time (TQ-TST) and TipTraQ-derived Respiratory Events Index (TQ-REI) with expert-determined total sleep time (TST) and apnea-hypopnea index (AHI), based on AASM standards with the 1B hypopnea rule. Temporal event localization analysis for respiratory event prediction was conducted at both event and hourly levels.

Results: In the external validation, the Spearman correlation coefficients for TQ-TST vs. TST and TQ-REI vs. AHI were 0.81 and 0.95. respectively. The root mean square error were 0.53 hours for TQ-TST vs. TST and 7.53 events/hour for TQ-REI vs. AHI. For apnea/hypopnea prediction with a 10s grace period, the true positive, false positive and false negative rates in temporal event localization analysis were 0.76, 0.24, and 0.23, respectively. The four-way OSA severity classification achieved a Cohen's kappa of 0.7.

Conclusions: TQ-TST and TQ-REI predict TST and AHI with comparable performance to existing devices of the same type, and respiratory event prediction is validated through temporal event localization analysis.