A novel deep learning model based on transformer and cross modality attention for classification of sleep stages.

The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspective on sleep patterns, can have a great impact on the efficiency of the classification models. In the context of neural networks and deep learning models, transformers are very effective, especially when dealing with time series data, and have shown remarkable compatibility with sequential data analysis as physiological channels. On the other hand, cross-modality attention by integrating information from multiple views of the data enables to capture relationships among different modalities, allowing models to selectively focus on relevant information from each modality. In this paper, we introduce a novel deep-learning model based on transformer encoder-decoder and cross-modal attention for sleep stage classification. The proposed model processes information from various physiological channels with different modalities using the Sleep Heart Health Study Dataset (SHHS) data and leverages transformer encoders for feature extraction and cross-modal attention for effective integration to feed into the transformer decoder. The combination of these elements increased the accuracy of the model up to 91.33% in classifying five classes of sleep stages. Empirical evaluations demonstrated the model's superior performance compared to standalone approaches and other state-of-the-art techniques, showcasing the potential of combining transformer and cross-modal attention for improved sleep stage classification.

Co-morbid Insomnia and Sleep Apnea (COMISA) in a large sample of Iranian: prevalence and associations in a sleep clinic population.

BACKGROUND: Sleep apnea (SA) and insomnia (INS) are prevalent sleep disorders among referrals to sleep clinics. People with comorbid insomnia and sleep apnea (COMISA) suffer both disorders simultaneously. The epidemiology of COMISA is not well known in the Middle East including Iran. We hypothesized that COMISA is prevalent in metropolitan sleep clinic cohorts in Iran. METHOD: The records of patients aged < 95 years referred to sleep disorders centers in four large metropolitan areas including Kermanshah, Tabriz, Shiraz, and Ahvaz were examined. Polysomnography (PSG) was performed in all these patients in specialized centers and the results were scored by a trained technician and interpreted by a sleep specialist. SA was defined as an Apnea-Hypopnea Index (AHI ≥ 5), INS was defined by psychiatrists according to self-report and clinical interviews, and COMISA was defined if both disorders were present. Participants with neither condition were included in as comparator group. One-way ANOVAs, correlation, and linear/logistic regression analyses were used. RESULTS: This study included 1807 patients (Mean age 49.3, SE ± 13.7; 38.8% Female). Comparator, INS, SA and COMISA made up 7.2%, 16%, 50.2% and 26.6% of the sample, respectively. Logistic regression analyses showed that male gender, older age, and increasing neck circumference, but not BMI, were associated with COMISA. Epworth Sleepiness Scale scores were lower in INS (5.39 ± 5.78) compared to the other three groups. CONCLUSION: COMISA is a prevalent condition in metropolitan sleep centers in Iran among participants referred to sleep centers. The data showed that male gender and age were associated significantly with COMISA.

Dream Enactment Behaviors Associated With Posttraumatic Stress Disorder.

The investigators aimed to draw attention to current debates surrounding the etiologies of dream enactment behaviors in patients with posttraumatic stress disorder (PTSD). The phenomenological overlap between PTSD-related nocturnal symptoms, rapid eye movement sleep behavior disorder (RBD), and trauma-associated sleep disorder (TASD) is discussed. Strategies used to diagnose and manage dream enactment behaviors, whether due to RBD or another confounding sleep disorder, are considered. Finally, the need for further research on the pathophysiological overlap and integrated treatment of PTSD, RBD, and, possibly, TASD is highlighted.

Smoking Status and Factors associated with COVID-19 In-Hospital Mortality among US Veterans.

INTRODUCTION: The role of smoking in risk of death among patients with COVID-19 remains unclear. We examined the association between in-hospital mortality from COVID-19 and smoking status and other factors in the United States Veterans Health Administration (VHA). METHODS: This is an observational, retrospective cohort study using the VHA COVID-19 shared data resources for February 1 to September 11, 2020. Veterans admitted to the hospital who tested positive for SARS-CoV-2 and hospitalized by VHA were grouped into Never (as reference, NS), Former (FS), and Current smokers (CS). The main outcome was in-hospital mortality. Control factors were the most important variables (among all available) determined through a cascade of machine learning. We reported adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) from logistic regression models, imputing missing smoking status in our primary analysis. RESULTS: Out of 8 667 996 VHA enrollees, 505 143 were tested for SARS-CoV-2 (NS = 191 143; FS = 240 336; CS = 117 706; Unknown = 45 533). The aOR of in-hospital mortality was 1.16 (95%CI 1.01, 1.32) for FS vs. NS and 0.97 (95%CI 0.78, 1.22; p > .05) for CS vs. NS with imputed smoking status. Among other factors, famotidine and nonsteroidal anti-inflammatory drugs (NSAID) use before hospitalization were associated with lower risk while diabetes with complications, kidney disease, obesity, and advanced age were associated with higher risk of in-hospital mortality. CONCLUSIONS: In patients admitted to the hospital with SARS-CoV-2 infection, our data demonstrate that FS are at higher risk of in-hospital mortality than NS. However, this pattern was not seen among CS highlighting the need for more granular analysis with high-quality smoking status data to further clarify our understanding of smoking risk and COVID-19-related mortality. Presence of comorbidities and advanced age were also associated with increased risk of in-hospital mortality. IMPLICATIONS: Veterans who were former smokers were at higher risk of in-hospital mortality compared to never smokers. Current smokers and never smokers were at similar risk of in-hospital mortality. The use of famotidine and nonsteroidal anti-inflammatory drugs (NSAIDs) before hospitalization were associated with lower risk while uncontrolled diabetes mellitus, advanced age, kidney disease, and obesity were associated with higher risk of in-hospital mortality.

Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease.

INTRODUCTION: An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients. METHODS: Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m2) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m2) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed. RESULTS: Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64-1.26, p < 0.010) and all FM metrics (d = 0.45-1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35-0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82-2.12, p < 0.050). CONCLUSION: This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.

Obstructive sleep apnea in diabetic patients is associated with higher healthcare utilization.

BACKGROUND: Obstructive sleep apnea (OSA) is a frequent comorbid condition in patients with type 2 diabetic (T2DM). Concomitant OSA is associated with a detrimental impact on metabolic control. Both OSA and T2DM independently lead to increased cardiovascular disease and mortality. The impact of OSA on the acceleration of organ dysfunction leading to increased healthcare utilization is unknown. METHODS: This is a retrospective case-control cohort study, a secondary analysis utilizing a nationwide dataset. Patients who underwent elective surgical procedures from 2009 to 2014 were identified. Among these patients, we compared patients with obstructive sleep apnea and those without obstructive sleep apnea. Exact 1:1 matching was performed based on similar characteristics such as age, sex, geographic location, surgical facility environment, performing surgeon, and severity of illness during hospitalization. The subgroup of patients with T2DM with or without OSA was analyzed for post-discharge hospital admissions, intensive care unit (ICU) admissions, emergency room (ER) visits, and outpatient visits. RESULTS: Among 47,719 matched patients of the initial study, this subgroup included 4,567 patients with diabetes and OSA and 3,842 patients with diabetes but no OSA. In the presence of comorbid OSA, patients with T2DM had higher odds of increased healthcare utilization among all the outcomes: inpatient visits increased with an odds ratio of 2.50 (confidence interval (CI) 2.28-2.74) and ICU admissions 1.96 (CI 1.73-2.25) ER 1.93 **(CI 1.75-2.12) and outpatient visits 2.18 (CI 2.00-2.38). Future healthcare utilization per 100 patient-years was also increased significantly among all outcomes (p < 0.0001). CONCLUSIONS: In patients with diabetes undergoing elective surgery, the presence of OSA was associated with higher future healthcare utilization.

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study.

BACKGROUND: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. OBJECTIVE: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. METHODS: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. RESULTS: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. CONCLUSIONS: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Telerehabilitation: Future of Phase II Cardiac Rehabilitation: Review of Preliminary Outcomes.

In this brief communication, we reported Telehealth Home-Based Cardiac Rehabilitation (CR) program structure and preliminary outcomes from patients that completed a 12-week program after coronary artery bypass graft surgery (CABG). We aim to advocate the use of Telerehabilitation as a Phase II CR in patients immediately after the CABG. This approach was innovative and encouraging because the patients were still in subacute phase. The program can serve as a continuation of care for the patients after being discharged from a hospital while regaining their functional ability at home. Our preliminary outcomes demonstrated improvements in resting heart rate, activity level, nutrition status, self-efficacy for managing cardiac diseases, muscle strength, endurance and depression. There were no adverse events during the virtual sessions. Patient satisfaction score was high.

Toward Remote Assessment of Physical Frailty Using Sensor-based Sit-to-stand Test.

BACKGROUND: Traditional physical frailty (PF) screening tools are resource intensive and unsuitable for remote assessment. In this study, we used five times sit-to-stand test (5×STS) with wearable sensors to determine PF and three key frailty phenotypes (slowness, weakness, and exhaustion) objectively. MATERIALS AND METHODS: Older adults (n = 102, age: 76.54 ± 7.72 y, 72% women) performed 5×STS while wearing sensors attached to the trunk and bilateral thigh and shank. Duration of 5×STS was recorded using a stopwatch. Seventeen sensor-derived variables were analyzed to determine the ability of 5×STS to distinguish PF, slowness, weakness, and exhaustion. Binary logistic regression was used, and its area under curve was calculated. RESULTS: A strong correlation was observed between sensor-based and manually-recorded 5xSTS durations (r = 0.93, P < 0.0001). Sensor-derived variables indicators of slowness (5×STS duration, hip angular velocity range, and knee angular velocity range), weakness (hip power range and knee power range), and exhaustion (coefficient of variation (CV) of hip angular velocity range, CV of vertical velocity range, and CV of vertical power range) were different between the robust group and prefrail/frail group (P < 0.05) with medium to large effect sizes (Cohen's d = 0.50-1.09). The results suggested that sensor-derived variables enable identifying PF, slowness, weakness, and exhaustion with an area under curve of 0.861, 0.865, 0.720, and 0.723, respectively. CONCLUSIONS: Our study suggests that sensor-based 5×STS can provide digital biomarkers of PF, slowness, weakness, and exhaustion. The simplicity, ease of administration in front of a camera, and safety of 5xSTS may facilitate a remote assessment of PF, slowness, weakness, and exhaustion via telemedicine.

Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test.

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health.

This study offers a new perspective on the role of relative humidity in strategies to improve the health and wellbeing of office workers. A lack of studies of sufficient participant size and diversity relating relative humidity (RH) to measured health outcomes has been a driving factor in relaxing thermal comfort standards for RH and removing a lower limit for dry air. We examined the association between RH and objectively measured stress responses, physical activity (PA), and sleep quality. A diverse group of office workers (n = 134) from four well-functioning federal buildings wore chest-mounted heart rate variability monitors for three consecutive days, while at the same time, RH and temperature (T) were measured in their workplaces. Those who spent the majority of their time at the office in conditions of 30%-60% RH experienced 25% less stress at the office than those who spent the majority of their time in drier conditions. Further, a correlational study of our stress response suggests optimal values for RH may exist within an even narrower range around 45%. Finally, we found an indirect effect of objectively measured poorer sleep quality, mediated by stress responses, for those outside this range.

Serum testosterone/cortisol ratio in people with obstructive sleep apnea.

OBJECTIVES: Obstructive sleep apnea (OSA) is a major health problem that has been associated with endocrine dysfunction in the hypothalamic-pituitary-gonadal (HPG) and hypothalamic-pituitary-adrenal (HPA) axes. This study investigated cortisol, testosterone, and the testosterone/cortisol ratio in patients with OSA compared to normal sleepers. METHODS: Thirty-nine OSA patients diagnosed by overnight polysomnography (PSG) were divided into three groups, including ten mild OSA patients, 16 patients with moderate OSA, and 13 patients with severe OSA according to the apnea-hypopnea index (AHI). In addition, 13 normal sleepers with normal PSG findings were recruited as the control group. Serum levels of cortisol, testosterone, and sex hormone-binding globulin (SHBG) were measured using enzyme-linked immunosorbent assay (ELISA). RESULTS: There were no significant differences between the normal sleepers and the three subtypes of OSA in terms of total and free testosterone levels (P > .1). The results showed significantly higher levels of cortisol in the severe OSA group compared to the normal sleepers and the two other subtypes of OSA (P < .01). In addition, the testosterone/cortisol (T/C) ratio was significantly lower among the severe OSA compared to the moderate OSA patients (P = .01). A significant correlation was observed between minimal SpO2 and AHI (r=-0.69, P < .01), cortisol and AHI (r = .47, P < .01) and cortisol and minimal SpO2 (r = -.26, P = .06). CONCLUSION: According to the findings, OSA is linked to HPA axis activity in severe OSA patients but not among the mild and moderate subtypes of the disorder.

Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study.

Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog- individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog- (p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.

Accuracy of electronic health record data for the diagnosis of chronic obstructive pulmonary disease in persons living with HIV and uninfected persons.

PURPOSE: No prior studies have addressed the performance of electronic health record (EHR) data to diagnose chronic obstructive pulmonary disease (COPD) in people living with HIV (PLWH), in whom COPD could be more likely to be underdiagnosed or misdiagnosed, given the higher frequency of respiratory symptoms and smoking compared with HIV-uninfected (uninfected) persons. METHODS: We determined whether EHR data could improve accuracy of ICD-9 codes to define COPD when compared with spirometry in PLWH vs uninfected, and quantified level of discrimination using the area under the receiver-operating curve (AUC). The development cohort consisted of 350 participants who completed research spirometry in the Examinations of HIV Associated Lung Emphysema (EXHALE) study, a pulmonary substudy of the Veterans Aging Cohort Study. Results were externally validated in 294 PLWH who performed spirometry for clinical indications from the University of Washington (UW) site of the Centers for AIDS Research Network of Integrated Clinical Systems cohort. RESULTS: ICD-9 codes performed similarly by HIV status, but alone were poor at discriminating cases from non-cases of COPD when compared with spirometry (AUC 0.633 in EXHALE; 0.651 in the UW cohort). However, algorithms that combined ICD-9 codes with other clinical variables available in the EHR-age, smoking, and COPD inhalers-improved discrimination and performed similarly in EXHALE (AUC 0.771) and UW (AUC 0.734). CONCLUSIONS: These data support that EHR data in combination with ICD-9 codes have moderately good accuracy to identify COPD when spirometry data are not available, and perform similarly in PLWH and uninfected individuals.

A weight-independent association between atypical antipsychotic medications and obstructive sleep apnea.

BACKGROUND: With increasing use of atypical antipsychotic (AAP) agents, the concern has been raised about the association between AAP agents and medical complications. Obstructive sleep apnea (OSA) is a common breathing disorder that adversely affects health and quality of life. Because the major risk factors for OSA are weight gain and obesity by altering the upper airway anatomy, an association between AAP and development of OSA is predictable. However, we hypothesized that AAP may promote OSA not only by weight gain but also because of its potential effects on upper airway muscle function. In the present study, we evaluated the possible association between AAP use and the severity of OSA. METHODS: A sample of patients using AAP for treatment of paradoxical insomnia was evaluated before and at least 8 weeks after AAP use. Patients were divided based on type of AAP use to olanzapine, risperidone, and quetiapine groups. Patients used olanzapine (5-10 mg), risperidone (2-4 mg), or quetiapine (100-200 mg) 2 h before bedtime. Before and after treatment, respiratory variables were recorded using polysomnography. BMI, neck circumference (NC), and waist circumference (WC) were measured before and after treatment period. RESULTS: There was no significant difference between pre- and post-treatment apnea index (0.2 ± 0.6 vs. 2.6 ± 4.3; p = 0.094) in olanzapine group. However, significant differences in hypopnea index (5.1 ± 5 vs. 30 ± 10.8; p < 0.0001) and AHI (5.3 ± 4.9 vs. 32.6 ± 9.6; p < 0.0001) were observed. Similar results were found in quetiapine and risperidone groups, except that in quetiapine group, apnea index was significantly increased after treatment period (0.7 ± 1.2 in pre-treatment vs. 3.1 ± 2.4 in post-treatment; p = 0.007). There were no significant changes in BMI, NC, and WC during treatment period in all three groups. CONCLUSION: While AAP medications are known cause of weight gain as a main risk factor of OSA, our finding demonstrated a weight-independent association between AAP medications and worsening respiration during sleep.

Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study.

Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%), frail (22%), or non-frail (27%). A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP), physical activity behavior (PAB), and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity), and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies.

Obesity modulates diaphragm curvature in subjects with and without COPD.

Obesity is a common comorbidity of chronic obstructive pulmonary disease (COPD) and has been associated with worse outcomes. However, it is unknown whether the interaction between obesity and COPD modulates diaphragm shape and consequently its function. The body mass index (BMI) has been used as a correlate of obesity. We tested the hypothesis that the shape of the diaphragm muscle and size of the ring of its insertion in non-COPD and COPD subjects are modulated by BMI. We recruited 48 COPD patients with postbronchiodilator forced expiratory volume in 1 s (FEV1)-to-forced vital capacity (FVC) < 0.7 and 29 age-matched smoker/exsmoker control (non-COPD) subjects, who underwent chest computed tomography (CT) at lung volumes ranging from functional residual capacity (FRC) to total lung capacity (TLC). We then computed maximum principal diaphragm curvature in the midcostal region of the left hemidiaphragm at the end of inspiration during quiet breathing (EI) and at TLC. The radius of maximum curvature of diaphragm muscle increased with BMI in both COPD and non-COPD subjects. The size of diaphragm ring of insertion on the chest wall also increased significantly with increasing BMI. Surprisingly, COPD severity did not appear to cause significant alteration in diaphragm shape except in normal-weight subjects at TLC. Our data uncovered important factors such as BMI, the size of the diaphragm ring of insertion, and disease severity that modulate the structure of the ventilatory pump in non-COPD and COPD subjects.

Admission for COPD Exacerbation Is Associated with the Clinical Diagnosis of Pulmonary Hypertension: Results from a Retrospective Longitudinal Study of a Veteran Population.

Patients with chronic obstructive pulmonary disease and pulmonary hypertension (PH-COPD) have an increased risk of hospitalizations and death compared to COPD alone. Identifying PH in COPD is challenging because performing right heart catheterization, the gold standard for PH diagnosis, is invasive and not routinely performed. Clinical characterization of COPD patients at risk who are progressing toward PH will aid therapeutic development at earlier stages of progressively fatal PH-COPD. We studied the records of 5,45,086 patients in a large Veterans Affairs healthcare network (2000-2012) with a primary discharge diagnosis of COPD based on encounters' ICD-9 codes and further stratified into those who received an additional ICD-9 code for a PH diagnosis. Patients with PH-COPD were assigned to one of the four subgroups: those with (a) no history of exacerbation or hospital admissions, (b) history of exacerbations but no hospital admissions, (c) hospital admissions unrelated to COPD and (d) history of COPD exacerbation-related hospital admissions. We also examined the COPD and COPD-PH cohorts for associated comorbidities such as cardiac disease and the presence of obstructive sleep apnea (OSA). A regression analysis revealed that patients with COPD exacerbation-related hospital admissions had 7 × higher risk of having a concomitant clinical diagnosis of PH compared to non-hospitalized patients. COPD-PH patients had higher rates of cardiac comorbidities (89% vs. 66%) and OSA (34% vs. 16%) compared to COPD alone. We conclude that COPD patients hospitalized for COPD exacerbations are at a higher risk for developing PH, and hospitalized COPD patients with cardiac comorbidities and/or OSA should be screened as at-risk population for developing PH.

Mortality in Patients Admitted for Concurrent COPD Exacerbation and Pneumonia.

It is unclear whether concurrent pneumonia and chronic obstructive pulmonary disease (COPD) have a higher mortality than either condition alone. Further, it is unknown how this interaction changes over time. We explored the effect of pneumonia and COPD on inpatient, 30-day and overall mortality. We used a Veterans Health Affairs database to compare patients who were hospitalized for a COPD exacerbation without pneumonia (AECOPD), patients hospitalized for pneumonia without COPD (PNA) and patients hospitalized for pneumonia who had a concurrent diagnosis of COPD (PCOPD). We studied records of 15,065 patients with the following primary discharge diagnoses: (a) AECOPD cohort (7,154 individuals); (b) PNA cohort (4,433 individuals); and (c) PCOPD (3,478 individuals), comparing inpatient, 30-day and overall mortality in the three study cohorts. We observed a stepwise increase in inpatient mortality for AECOPD, PNA and PCOPD (4.8%, 9.5% and 13.2%, respectively). These differences persisted at 30 days post-discharge (AECOPD = 6.7%, PNA = 12.4% and PCOPD = 14.6%; p < 0.0001), but not throughout the study period (median follow-up: 37 months). With time, the death rate rose disproportionally in patients who had been admitted for AECOPD (AECOPD = 64.5%; PNA = 57.4% and PCOPD 66.2%; p < 0.001). In multivariate analysis, PCOPD predicted the greatest inpatient mortality (p < 0.001). The data showed a progression in inpatient and 30-day mortality from AECOPD to PNA to PCOPD. Pneumonia and COPD differentially affected inpatient, 30-day and overall mortality with pneumonia affecting predominantly inpatient and 30-day mortality while COPD affecting the overall mortality.

Prevalence, predictors and economic consequences of no-shows.

BACKGROUND: Patients not attending to clinic appointments (no-show) significantly affects delivery, cost of care and resource planning. We aimed to evaluate the prevalence, predictors and economic consequences of patient no-shows. METHOD: This is a retrospective cohort study using administrative databases for fiscal years 1997-2008. We searched administrative databases for no-show frequency and cost at a large medical center. In addition, we estimated no-show rates and costs in another 10 regional hospitals. We studied no-show rates in primary care and various subspecialty settings over a 12-year period, the monthly and seasonal trends of no-shows, the effects of implementing a reminder system and the economic effects of missed appointments. RESULTS: The mean no-show rate was 18.8% (2.4%) in 10 main clinics with highest occurring in subspecialist clinics. No-show rate in the women clinic was higher and the no-show rate in geriatric clinic was lower compared to general primary care clinic (PCP). The no-show rate remained at a high level despite its reduction by a centralized phone reminder (from 16.3% down to 15.8%). The average cost of no-show per patient was $196 in 2008. CONCLUSIONS: Our data indicates that no-show imposed a major burden on this health care system. Further, implementation of a reminder system only modestly reduced the no-show rate.