Drug-induced sleep endoscopy (DISE) is used for directly visualizing sites of obstruction among patients with obstructive sleep apnea (OSA). Owing to the scarcity of data, there is still no consensus on the anesthetic regimen for conducting pediatric DISE.

This paper presents a 5-year-old patient who underwent DISE using an opioid-sparing regimen with dexmedetomidine and propofol infusion.

Simultaneous dexmedetomidine and propofol infusion is a promising opioid-sparing regimen for pediatric DISE.

INTRODUCTION

Cardiovascular diseases (CVD) are a leading global health concern. Modifiable behavioral risk factors are increasingly recognized in young adults, especially among medical students who often live independently. This study investigated the association between living alone and modifiable cardiovascular risk factors—sleep quality, sodium intake, physical activity, and body mass index (BMI)—among medical students at UERMMMCI during the 2022-2023 academic year.

Researchers conducted an analytical cross-sectional study among 220 medical students. Validated tools were used: Pittsburgh Sleep Quality Index (PSQI), Scored Sodium Questionnaire, International Physical Activity Questionnaire (IPAQ), and BMI classification. Researchers performed statistical analyses using Chi-square tests and calculated relative risks (RR) with 95% confidence intervals.

A significant positive association was found between living alone and poor sleep quality (RR 2.132 p = 0.047). No significant associations were observed between living alone and sodium intake (RR 0.96 p = 0.6868), physical activity (RR 1.18 p = 0.2239), or BMI (RR 1.03 p = 0.7367).

Among the studied cardiovascular risk factors, only poor sleep quality was significantly more prevalent among students living alone. These findings highlight the importance of interventions targeting sleep hygiene in this demographic.

Objective:To determine the sensitivity, specificity and positive predictive value of the STOP BANG questionnaire in diagnosing Obstructive Sleep Apnea (OSA) in adults admitted for polysomnography at the Rizal Medical Center from January 2019 to June 2024

Methods:
Design:Review of Records
Setting:Tertiary Government Training Hospital
Participants:166 adult patients

Results:A total of 166 patient records were included with an average age of 35.6 ± 12 years, BMI of 29.3 ± 6.44 and 67% were male. The STOP-BANG questionnaire had a sensitivity of 77% to screen for all (AHI ≥ 5), mild (AHI = 5-14.9), moderate (AHI = 15-19.9), and severe OSA (AHI > 30), respectively. The specificity was 62% and the area under the curve was 0.717 for all, mild, moderate and severe OSA

Conclusion:A STOP-BANG score of 3/8 can predict the presence of OSA with a sensitivity of 77% and specificity of 62% with AUC of 0.717. The increase in score does not predict the severity. Further research can be done to identify other co-morbidities associated with OSA.

Humans ; Sleep Apnea, Central/therapy* ; Hypoventilation/congenital* ; Homeodomain Proteins

Objective: Our study aimed to identify and describe pulmonary complications and its associated risk factors in children with suspected or confirmed obstructive sleep apnea (OSA) who underwent tonsillectomy or adenotonsillectomy in a tertiary government hospital. Methods: We conducted a retrospective cohort study. Medical charts of pediatric patients with suspected or confirmed OSA who were admitted for tonsillectomy or adenotonsillectomy from January 1, 2016 to December 31, 2020 were retrieved and reviewed. Information of the individual patients including the demographic data, clinical profile, polysomnography results, and presence of postoperative pulmonary complications were recorded. Descriptive statistics was utilized to present continuous data while frequency and percentage for categorical data. Fisher exact test was used to compare the demographic profile of patients with postoperative pulmonary complications from those without. Results: A total of 90 patient records were analyzed. The mean age of the patient population was 7.87 years, 55.6% were male, 17.8% of patients were classified as obese. Thirty-four children had preoperative polysomnography and of these, 47.1% were classified as severe. Only two (2.2%) patients had postoperative pulmonary complications, which were bronchospasm and desaturation, respectively. There were no statistically significant differences noted in comparing the clinicodemographic profile of patients with postoperative pulmonary complications from those without complications. Conclusion Our results showed that most pediatric patients with suspected or confirmed OSA who underwent adenotonsillectomy did not have pulmonary complications.
Sleep Apnea, Obstructive ; Tonsillectomy

BACKGROUND AND OBJECTIVE

Due to their academic load, medical students are highly susceptible to stress. Stress is one of the factors that can alter sleep quality which may consequently affect the cognitive performance of medical students. There has been a lack of published local literature that looks into the association between stress and sleep quality, especially during the COVID-19 pandemic. With this, the general objective of this study is to determine the effect of stress on the sleep quality of medical students from the University of the Philippines Manila - College of Medicine (UPCM).

A cross-sectional study was conducted using a stratified random sample of 273 males and females of Learning Unit (LU) III (1st year) to VII (5th year) medical students from a college of medicine based in the Philippines, UPCM, during the second semester of the academic year 2021-2022. A self-administered questionnaire was distributed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI), and stress level using the Kessler Psychological Distress Scale (K10). Kruskal-Wallis was used to test statistical differences between stress scores and the sleep quality of students from different year levels. Spearman's Rho was used to determine the correlation between stress and sleep, and a binary logistic regression was employed to study the association of stress with sleep while accounting for confounding variables namely caffeine intake, year level, daytime nap, duty hours, clinical rotation, sex, and age.

A high prevalence of stress (79.71%) and poor sleep quality (59.73%) among LU III to LU VII UPCM students were found, with a statistically positive correlation (⍴=0.44) 95CI [0.33-0.55] (p-value < 0.001). Both the stress scores and sleep quality indices were not statistically significantly different across LUs. Gathered data and interpreted results showed that medical students suffering from stress are more likely to have poor sleep quality, which can lead to low academic performance and high susceptibility to chronic diseases, compared to those medical students with low levels of stress. Only being an LU IV [OR=1.38 95CI (0.036-4.625)] and LU V [OR=2.13 95CI (0.296-6.936)] student had increased odds of having poor sleep quality compared to LU III students. Caffeine intake, daytime nap, duty hours, clinical rotation, sex, and age were not associated with poor sleep quality.

This study documents a statistically significant association between stress and poor sleep quality among LU III to LU VII UPCM students. A larger study covering multiple medical schools in the Philippines may be of merit for future investigations to generate nationwide data. Additional recommendations include: a) conducting a cross-sectional or a longitudinal study to detect changes in the characteristics of the population, b) observing the differences in the contributing factors at multiple points throughout the year, c) investigating the effect of dwelling set-up on sleep quality may also be investigated and d) determining if sleep quality affects the level of perceived stress of medical students.

OBJECTIVE

Our study aimed to identify and describe pulmonary complications and its associated risk factors in children with suspected or confirmed obstructive sleep apnea (OSA) who underwent tonsillectomy or adenotonsillectomy in a tertiary government hospital.

We conducted a retrospective cohort study. Medical charts of pediatric patients with suspected or confirmed OSA who were admitted for tonsillectomy or adenotonsillectomy from January 1, 2016 to December 31, 2020 were retrieved and reviewed. Information of the individual patients including the demographic data, clinical profile, polysomnography results, and presence of postoperative pulmonary complications were recorded. Descriptive statistics was utilized to present continuous data while frequency and percentage for categorical data. Fisher exact test was used to compare the demographic profile of patients with postoperative pulmonary complications from those without.

A total of 90 patient records were analyzed. The mean age of the patient population was 7.87 years, 55.6% were male, 17.8% of patients were classified as obese. Thirty-four children had preoperative polysomnography and of these, 47.1% were classified as severe. Only two (2.2%) patients had postoperative pulmonary complications, which were bronchospasm and desaturation, respectively. There were no statistically significant differences noted in comparing the clinicodemographic profile of patients with postoperative pulmonary complications from those without complications.

Our results showed that most pediatric patients with suspected or confirmed OSA who underwent adenotonsillectomy did not have pulmonary complications.

Chaihu Jia Longgu Muli Decoction was firstly recorded in Treatise on Cold Damage(ZHANG Zhong-jing, Eastern Han dynasty). According to this medical classic, it is originally used in the treatment of the Shaoyang and Yangming syndrome. Based on the modern pathophysiological mechanism, this study interpreted the classic provisions of Chaihu Jia Longgu Muli Decoction. Original records of "chest fullness" "annoyance" "shock" "difficult urination" "delirium" "heavy body and failing to turn over" all have profound pathophysiological basis, involving disorders in cardiovascular, respiratory, nervous, and mental systems. This formula is widely used, which can be applied to treat epilepsy, cerebral arteriosclerosis, cerebral infarction, and other cerebrovascular diseases, hypertension, arrhythmia, and other cardiovascular diseases, insomnia, constipation, anxiety, depression, cardiac neurosis and other acute and chronic diseases as well as diseases in psychosomatic medicine. The clinical indications include Bupleuri Radix-targeted syndrome such as fullness and discomfort in chest and hypochondrium, bitter taste mouth, dry throat, and dizziness, the insomnia, anxiety, depression, susceptibility to fright, upset, dreamfulness and other psychiatric symptoms, red tongue, thick and yellow tongue coating, and wiry hard and powerful pulse. This formula was found to be used in combination with other formulas, such as Gualou Xiebai Decoction, Wendan Decoction, Zhizhu Pills, Juzhijiang Decoction, Suanzaoren Decoction, and Banxia Baizhu Tianma Decoction.
Humans ; Sleep Initiation and Maintenance Disorders/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/drug therapy* ; Syndrome ; Arrhythmias, Cardiac/drug therapy* ; Medicine, Chinese Traditional

The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
Humans ; Random Forest ; Bayes Theorem ; Sleep Stages ; Sleep ; Electroencephalography/methods*

The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.
Humans ; Sleep Stages ; Algorithms ; Sleep ; Wavelet Analysis ; Electroencephalography/methods* ; Machine Learning