Medical cost in the last one month of life of Taiwanese oral cancer decedents from 2009 to 2011 by hierarchical generalized linear model using a random-intercept model.

<p>*Medical cost of aggressive care in the last one month of life US dollars 2,611±3,329.</p><p>**95% CI, 95% confidence interval.</p><p>Medical cost in the last one month of life of Taiwanese oral cancer decedents from 2009 to 2011 by hierarchical generalized linear model using a random-intercept model.</p

Factors associated with increased or decreased EOL expenditure in oral cancer decedents.

<p>Factors associated with increased or decreased EOL expenditure in oral cancer decedents.</p

Distribution of wait time and duration of radiotherapy for nasopharyngeal cancer patients from 2008 to 2011 by univariate analysis (n = 3,605).

†<p>CCRT, Concurrent chemoradiotherapy.</p>‡<p>RT, Radiotherapy.</p><p>SD, standard deviation;</p><p>Distribution of wait time and duration of radiotherapy for nasopharyngeal cancer patients from 2008 to 2011 by univariate analysis (n = 3,605).</p

Distribution of wait time and duration of radiotherapy for nasopharyngeal cancer patients from 2008 to 2011 by multivariate analysis using a random-intercept model (n = 3605).

†<p>CCRT, Concurrent chemoradiotherapy.</p>‡<p>RT, Radiotherapy.</p><p>CI, confidence interval.</p><p>Distribution of wait time and duration of radiotherapy for nasopharyngeal cancer patients from 2008 to 2011 by multivariate analysis using a random-intercept model (n = 3605).</p

Data_Sheet_1_Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study.docx

ObjectivesObstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD.MethodsPatients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared.ResultsWe included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea–hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product.ConclusionsRecurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.</p

sj-docx-1-dhj-10.1177_20552076231205744 - Supplemental material for Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles

Supplemental material, sj-docx-1-dhj-10.1177_20552076231205744 for Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles by Chih-Fan Kuo, Cheng-Yu Tsai, Wun-Hao Cheng, Wen-Hua Hs, Arnab Majumdar, Marc Stettler, Kang-Yun Lee, Yi-Chun Kuan, Po-Hao Feng, Chien-Hua Tseng, Kuan-Yuan Chen and Jiunn-Horng Kang, Hsin-Chien Lee, Cheng-Jung Wu, Wen-Te Liu in DIGITAL HEALTH</p