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Translational Psychiatry Jun 2024The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in... (Randomized Controlled Trial)
Randomized Controlled Trial
The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in arousal-related sleep metrics between 36 individuals with treatment-resistant major depression (TRD) and 25 healthy volunteers (HVs). It also sought to determine whether ketamine normalizes arousal in individuals with TRD and whether ketamine's effects on arousal mediate its antidepressant and anti-SI effects. This was a secondary analysis of a biomarker-focused, randomized, double-blind, crossover trial of ketamine (0.5 mg/kg) compared to saline placebo. Polysomnography (PSG) studies were conducted one day before and one day after ketamine/placebo infusions. Sleep arousal was measured using spectral power functions over time including alpha (quiet wakefulness), beta (alert wakefulness), and delta (deep sleep) power, as well as macroarchitecture variables, including wakefulness after sleep onset (WASO), total sleep time (TST), rapid eye movement (REM) latency, and Post-Sleep Onset Sleep Efficiency (PSOSE). At baseline, diagnostic differences in sleep macroarchitecture included lower TST (p = 0.006) and shorter REM latency (p = 0.04) in the TRD versus HV group. Ketamine's temporal dynamic effects (relative to placebo) in TRD included increased delta power earlier in the night and increased alpha and delta power later in the night. However, there were no significant diagnostic differences in temporal patterns of alpha, beta, or delta power, no ketamine effects on sleep macroarchitecture arousal metrics, and no mediation effects of sleep variables on ketamine's antidepressant or anti-SI effects. These results highlight the role of sleep-related variables as part of the systemic neurobiological changes initiated after ketamine administration. Clinical Trials Identifier: NCT00088699.
Topics: Humans; Ketamine; Male; Depressive Disorder, Treatment-Resistant; Female; Adult; Double-Blind Method; Cross-Over Studies; Polysomnography; Arousal; Middle Aged; Sleep; Depressive Disorder, Major; Wakefulness; Suicidal Ideation; Antidepressive Agents; Young Adult
PubMed: 38834540
DOI: 10.1038/s41398-024-02956-2 -
Psychiatry Research Aug 2024Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general...
Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general population-based cohort is not well known. This study investigated associations between both polysomnography (PSG)-based or subjective sleep features and incident MDD. Participants representative of the general population who had never had MDD completed sleep questionnaires (n = 2000) and/or underwent PSG (n = 717). Over 8 years' follow-up, participants completed psychiatric interviews enabling the diagnosis of MDD. Survival Cox models were used to analyze associations between sleep features and MDD incidence. A higher Epworth Sleepiness Scale and presence of insomnia symptoms were significantly associated with a higher incidence of MDD (hazard ratio [HR] [95 % confidence interval (CI)]: 1.062 [1.022-1.103], p = 0.002 and 1.437 [1.064-1.940], p = 0.018, respectively). Higher density of rapid eye movements in rapid eye movement (REM) sleep was associated with a higher incidence of MDD in men (HR 1.270 [95 % CI 1.064-1.516], p = 0.008). In women, higher delta power spectral density was associated with a lower MDD incidence (HR 0.674 [95 % CI 0.463-0.981], p = 0.039). This study confirmed the associations between subjective and objective sleep features and the incidence of MDD in a large community dwelling cohort.
Topics: Humans; Male; Depressive Disorder, Major; Female; Adult; Middle Aged; Incidence; Sleep Wake Disorders; Polysomnography; Cohort Studies; Sleep Initiation and Maintenance Disorders; Proportional Hazards Models; Surveys and Questionnaires; Risk Factors
PubMed: 38833937
DOI: 10.1016/j.psychres.2024.115934 -
Nature and Science of Sleep 2024Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous...
PURPOSE
Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy.
PATIENTS AND METHODS
Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed.
RESULTS
Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, =0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm, <0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, =0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], =0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm, <0.001) and better response to treatment (41.2% and 82.6%, =0.004).
CONCLUSION
Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.
PubMed: 38831958
DOI: 10.2147/NSS.S455705 -
Nature and Science of Sleep 2024This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in...
PURPOSE
This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses.
PATIENTS AND METHODS
A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician.
RESULTS
U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement.
CONCLUSION
Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.
PubMed: 38827394
DOI: 10.2147/NSS.S455649 -
AMIA Joint Summits on Translational... 2024Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the...
Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.
PubMed: 38827094
DOI: No ID Found -
International Heart Journal 2024This study aimed to clarify (1) the association among the atrial fibrillation (AF) type, sleep-disordered breathing (SDB), heart failure (HF), and left atrial (LA)...
Bidirectional Association Among the Type of Atrial Fibrillation, Sleep-Disordered Breathing Severity, Heart Failure Progression, and Left Atrial Enlargement, in Patients with Atrial Fibrillation.
This study aimed to clarify (1) the association among the atrial fibrillation (AF) type, sleep-disordered breathing (SDB), heart failure (HF), and left atrial (LA) enlargement, (2) the independent predictors of LA enlargement, and (3) the effects of ablation on those conditions in patients with AF. The study's endpoint was LA enlargement (LA volume index [LAVI] ≥ 78 mL/m).Of 423 patients with nonvalvular AF, 236 were enrolled. We evaluated the role of the clinical parameters such as the AF type, SDB severity, and HF in LA enlargement. Among them, 141 patients exhibiting a 3% oxygen desaturation index (ODI) of ≥ 10 events/hour underwent polysomnography to evaluate the SDB severity measured by the apnea-hypopnea index (AHI). The LA enlargement and HF were characterized by the LA diameter/LAVI, an increase in the B-type natriuretic peptide level, and a lower left ventricular ejection fraction.This study showed that non-paroxysmal AF (NPAF) rather than paroxysmal AF (PAF), the SDB severity, LA enlargement, and HF progression had bidirectional associations and exacerbated each other, which generated a vicious cycle that contributed to the LA enlargement. NPAF (OR = 4.55, P < 0.001), an AHI of ≥ 25.10 events/hour (OR = 1.55, P = 0.003), and a 3% ODI of ≥ 15.43 events/hour (OR = 1.52, P = 0.003) were independent predictors of an acceleration of the LA enlargement. AF ablation improved the HF and LA enlargement.To break this vicious cycle, AF ablation may be the basis for suppressing the LA enlargement and HF progression subsequently eliminating the substrates for AF and SDB in patients with AF.
Topics: Humans; Atrial Fibrillation; Male; Female; Sleep Apnea Syndromes; Heart Failure; Disease Progression; Middle Aged; Aged; Heart Atria; Severity of Illness Index; Catheter Ablation; Polysomnography; Atrial Remodeling; Echocardiography
PubMed: 38825490
DOI: 10.1536/ihj.23-493 -
NPJ Digital Medicine Jun 2024Apnea and hypopnea are common sleep disorders characterized by the obstruction of the airways. Polysomnography (PSG) is a sleep study typically used to compute the...
Apnea and hypopnea are common sleep disorders characterized by the obstruction of the airways. Polysomnography (PSG) is a sleep study typically used to compute the Apnea-Hypopnea Index (AHI), the number of times a person has apnea or certain types of hypopnea per hour of sleep, and diagnose the severity of the sleep disorder. Early detection and treatment of apnea can significantly reduce morbidity and mortality. However, long-term PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To address these issues, we propose a method, named DRIVEN, to estimate AHI at home from wearable devices and detect when apnea, hypopnea, and periods of wakefulness occur throughout the night. The method can therefore assist physicians in diagnosing the severity of apneas. Patients can wear a single sensor or a combination of sensors that can be easily measured at home: abdominal movement, thoracic movement, or pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% of all test patients into one of the four AHI classes, with 99.3% either correctly classified or placed one class away from the true one. This is a reasonable trade-off between the model's performance and the patient's comfort. We use publicly available data from three large sleep studies with a total of 14,370 recordings. DRIVEN consists of a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. It can be implemented for automatic estimation of AHI in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.
PubMed: 38824175
DOI: 10.1038/s41746-024-01139-z -
Frontiers in Big Data 2024To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely...
OBJECTIVE
To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.
METHODS
We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.
RESULTS
Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.
CONCLUSION
Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.
PubMed: 38817683
DOI: 10.3389/fdata.2024.1353469 -
Chest May 2024Stroke is the second-leading cause of death worldwide. Obstructive sleep apnea (OSA) is an independent risk factor for stroke and is associated with multiple vascular... (Review)
Review
TOPIC IMPORTANCE
Stroke is the second-leading cause of death worldwide. Obstructive sleep apnea (OSA) is an independent risk factor for stroke and is associated with multiple vascular risk factors. Post-stroke OSA is prevalent and closely linked with various stroke subtypes including cardioembolic stroke and cerebral small vessel disease. Observational studies have demonstrated that untreated post-stroke OSA is associated with an increased risk of recurrent stroke, mortality, poorer functional recovery and longer hospitalizations.
REVIEW FINDINGS
Post-stroke OSA tends to be underdiagnosed and under-treated, possibly because stroke patients with OSA present atypically compared to the general population with OSA. Objective testing, such as the use of ambulatory sleep testing or in-laboratory polysomnography, is recommended for diagnosing OSA. The gold standard for treating OSA is continuous positive airway pressure (CPAP) therapy. Randomized controlled trials (RCTs) have shown that treatment of post-stroke OSA using CPAP improves non-vascular outcomes such as cognition and neurological recovery. However, RCTs that have evaluated the effect of CPAP on recurrent stroke risk and mortality have been largely negative.
SUMMARY
There is a need for high quality RCTs in post-stroke OSA that may provide evidence to support the utility of CPAP (and/or other treatment modalities) in reducing recurrent vascular events and mortality. This may be achieved by examining treatment strategies that have yet to be trialed in post-stroke OSA, tailoring interventions according to post-stroke OSA endotypes and phenotypes, selecting high risk populations, and using metrics that reflect the physiological abnormalities that underlie the harmful effects of OSA on cardiovascular outcomes.
PubMed: 38815623
DOI: 10.1016/j.chest.2024.04.028 -
Turkish Journal of Medical Sciences 2024Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder in children. Determination of risk factors for the development of OSA is essential for early...
BACKGROUND/AIM
Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder in children. Determination of risk factors for the development of OSA is essential for early diagnosis and treatment of the disease and decreases the risk of negative consequences. This study aimed to investigate the predictive values of Mallampati score, tonsillar size, and BMI z-score in the presence and severity of OSA in children.
MATERIALS AND METHODS
This prospective cross-sectional study included 114 children with OSA symptoms. All children were assessed by BMI z-score, Mallampati score, and tonsillar size and underwent overnight polysomnography. They were consecutively selected and assigned to 4 groups as follows: Group 1 included normal-weight with a low Mallampati score; Group 2 involved normal-weight with a high Mallampati score; Group 3 included obese with a low Mallampati score; and Group 4 involved obese with a high Mallampati score.
RESULTS
Of the 114 included children, 58 were female and 56 were male, with a mean age of 13.1 ± 2.9 years. OSA frequency and apnea-hypopnea index were significantly higher in group 4 compared with other groups (p = 0.003 and p < 0.0001, respectively), whereas average and minimum spO were significantly lower (for both, p = 0.001). Mallampati score and BMI z-score were found to be significant for predicting OSA (odds ratio = 4.147, 95% CI: 1.440-11.944; p = 0.008 and odds ratio = 1.760, 95% CI: 1.039-2.980; p = 0.035, respectively). Among OSA patients, the Mallampati score, tonsillar size, and BMI z-score were found to be significant for predicting OSA severity (odds ratio = 4.520, 95% CI: 1.332-15.335, p = 0.015, odds ratio = 9.177, 95% CI: 2.513-33.514, p = 0.001, and odds ratio = 2.820, 95% CI: 1.444-5.508; p = 0.002, respectively).
CONCLUSION
The coexistence of the Mallampati score and BMI z-score significantly increases the presence of OSA in children. Mallampati score, tonsillar size, and BMI z-score are promising parameters for predicting OSA severity.
Topics: Humans; Sleep Apnea, Obstructive; Male; Female; Palatine Tonsil; Cross-Sectional Studies; Body Mass Index; Prospective Studies; Child; Adolescent; Severity of Illness Index; Polysomnography; Predictive Value of Tests; Risk Factors
PubMed: 38812649
DOI: 10.55730/1300-0144.5791