Age modifies the association between severe sleep apnea and all-cause mortality.

PURPOSE: While sleep apnea (SA) gets more prevalent with advancing age, the impact of age on the association between SA and health outcomes is not well known. We assessed the association between the severity of SA and all-cause mortality in different age groups using large longitudinal data. METHOD: We applied a Natural Language Processing pipeline to extract the apnea-hypopnea index (AHI) from the physicians' interpretation of sleep studies performed at the Veteran Health Administration (FY 1999-2022). We categorized the participants as no SA (n-SA, AHI< 5) and severe SA (s-SA, AHI≥30). We grouped the cohort based on age: Young≤40; Middle-aged:40-65; and Older adults≥65; and calculated the odds ratio (aOR) of mortality adjusted for age, sex, race, ethnicity, BMI, and Charlson-Comorbidity Index (CCI) using n-SA as the reference. RESULTS: We identified 146,148 participants (age 52.23 ± 15.02; BMI 32.11 ± 6.05; male 86.7 %; White 66 %). Prevalence of s-SA increased with age. All-cause mortality was lower in s-SA compared to n-SA in the entire cohort (aOR,0.56; 95%CI: 0.54,0.58). Comparing s-SA to n-SA, the all-cause mortality rates (Young 1.86 % vs 1.49 %; Middle-aged 12.07 % vs 13.34 %; and Older adults 26.35 % vs 40.18 %) and the aOR diminished as the age increased (Young: 1.11, 95%CI: 0.93-1.32; Middle-aged: 0.64, 95%CI: 0.61-0.67; and Older adults: 0.44, 95%CI: 0.41-0.46). CONCLUSION: The prevalence of severe SA increased while the odds of all-cause mortality compared to n-SA diminished with age. SA may exert less harmful effects on the aged population. A causality analysis is warranted to assess the relationship between SA, aging, and all-cause mortality.

Age modulates the predictive value of self-reported sleepiness for all-cause mortality risk: insights from a comprehensive national database of veterans.

STUDY OBJECTIVES: Excessive daytime sleepiness (EDS) is prevalent and overwhelmingly stems from disturbed sleep. We hypothesized that age modulates the association between EDS and increased all-cause mortality. METHODS: We utilized the Veterans' Health Administration data from 1999-2022. We enrolled participants with sleep related ICD9/10 codes or sleep services. A natural language processing (NLP) pipeline was developed and validated to extract the Epworth Sleepiness Scale (ESS) as a self-reported tool to measure EDS from physician progress notes. The NLP's accuracy was assessed through manual annotation of 470 notes. Participants were categorized into Normal-ESS, n-ESS, (ESS 0-10) and high-ESS, h-ESS, (ESS 11-24). We created three age groups: < 50 years; 50 to < 65 years; and ≥ 65 years. The adjusted odds ratio (aOR) of mortality was calculated for age, BMI, sex, race, ethnicity, and the Charlson Comorbidity Index (CCI), using n-ESS as the reference. Subsequently, we conducted age stratified analysis. RESULTS: The first ESS records were extracted from 423,087 veterans with a mean age of 54.8 (±14.6), mean BMI of 32.6 (±6.2), and 90.5% male. The aOR across all ages was 17% higher (1.15,1.19) in the h-ESS category. The aORs only became statistically significant for individuals aged ≥ 50 years in the h-ESS compared to the n-ESS category (< 50 years: 1.02 [0.96,1.08], 50 to < 65 years 1.13[1.10,1.16]; ≥ 65 years: 1.25 [1.21-1.28]). CONCLUSIONS: High ESS, predicted increased mortality only in participants aged 50 and older. Further research is required to identify this differential behavior in relation to age.

Assessing the Acceptability and Effectiveness of a Novel Therapeutic Footwear in Reducing Foot Pain and Improving Function among Older Adults: A Crossover Randomized Controlled Trial.

INTRODUCTION: Nearly, a quarter of older adults suffer from frequent foot pain, impacting their quality of life. While proper footwear can alleviate this, design issues often hinder regular use. This study evaluated novel therapeutic footwear, designed for aesthetics and custom fit, to reduce foot pain. We hypothesized that older adults would experience less foot pain and favor the new footwear over their own. METHODS: This 12-week crossover randomized controlled trial evaluated the effectiveness of OrthoFeet therapeutic footwear on reducing foot pain in older adults (n = 50, age = 65 ± 5, 18% male) with moderate to severe pain. Participants were assigned to either the AB or BA sequence. In AB, they wore OrthoFeet shoes for 6 weeks and then their own shoes for another 6 weeks; BA followed the reverse order. Pain and function were measured using the Foot Function Index. Acceptability was assessed through a technology acceptance model (TAM) questionnaire. Data collected at baseline, six, and 12 weeks were analyzed using t tests, χ2 tests, and generalized linear model. RESULTS: Compared to participants' own shoes, OrthoFeet shoes significantly reduced foot pain and disability. Notable improvements were observed in "foot pain at its worst," "foot pain at the end of the day," "overall pain score," and "overall Foot Function Index score," all showing statistically significant reductions (p < 0.050). Participants reported high adherence to wearing the OrthoFeet shoes, averaging 8 h per day and 5.8 days per week. TAM scores favored OrthoFeet shoes over participants' own shoes in terms of ease of use, perceived benefit, and intention to recommend. Significant differences were noted in components representing perceived joint pain relief (p < 0.001, χ2 = 21.228) and the intention of use as determined by the likelihood of recommending the shoes to a friend with a similar condition (p < 0.001, χ2 = 29.465). Additionally, a majority of participants valued the appearance of the shoes, with 66% prioritizing shoe appearance and 96% finding the study shoes more stylish than their previous ones. CONCLUSION: This study underscores the significance of design and custom fit in promoting continuous wear for effective foot pain reduction in older adults. More research is needed on the intervention's long-term impacts.

What is the additive value of nutritional deficiency to VA-FI in the risk assessment for heart failure patients?

OBJECTIVES: To assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure. METHODS: A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (>0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32nd deficit with the total number of deficits divided by 32. Frailty levels used the same cut-offs as the VA-FI. We compared categories based on VA-FI to those based on VA-FI-Nutrition and estimated the hazard ratio (HR) for post-discharge all-cause mortality over the study period as the primary outcome and other adverse events as secondary outcomes among patients with reduced or preserved ejection fraction in each VA-FI and VA-FI-Nutrition frailty groups. RESULTS: We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m2). In general, VA-FI-Nutrition reclassified 1959 (18.6%) Veterans to a higher frailty level. The VA-FI identified 1,880 (5%) as robust, 8,644 (23%) as prefrail, and 27,077 (72%) as frail. The VA-FI-Nutrition reclassified 382 (20.3%) from robust to prefrail and 1577 (18.2%) from prefrail to frail creating the modified-prefrail and modified-frail categories based on the VA-FI-Nutrition. We observed shorter time-to-death among Veterans reclassified to a higher frailty status vs. those who remained in their original group (Median of 2.8 years (IQR:0.5,6.8) in modified-prefrail vs. 6.3 (IQR:1.8,6.8) years in robust, and 2.2 (IQR:0.7,5.7) years in modified-frail vs. 3.9 (IQR:1.4,6.8) years in prefrail). The adjusted HR in the reclassified groups was also significantly higher in the VA-FI-Nutrition frailty categories with a 38% increase in overall all-cause mortality among modified-prefrail and a 50% increase among modified-frails. Similar trends of increasing adverse events were also observed among reclassified groups for other clinical outcomes. CONCLUSION: Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF.

Validation of electronic diagnostic codes for rapid eye movement sleep behavior disorder.

We investigated the accuracy of International Classification of Diseases (ICD) codes for the identification of Veterans with rapid eye movement (REM) sleep behavior disorder (RBD). The charts of 139 randomly sampled Veterans with ≥1 ICD-9 and ICD-10 code(s) for RBD were reviewed for documentation of a suspected, previous, or current diagnosis; clinical symptoms; and/or empiric treatments for this disorder. Notably, 71 (51.1%) of patients with RBD electronic diagnoses lacked polysomnography (PSG), and 29 (20.9%) had PSG reports without commentary on REM sleep without atonia (RSWA). Sleep centers are therefore encouraged to include a brief sentence in PSG report templates commenting on the presence/absence of RSWA.

Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer.

This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies.

The association between OSA and glycemic control in diabetes.

Background: Obstructive sleep apnea (OSA) is the most common sleep-realted respiratory disorder. It is frequently comorbid with cardiovascular, cerebrovascular, and metabolic diseases and is commonly observed in populations with these comorbidities. Investigators aimed to assess the effect of OSA on glycemic control in patients with diabetes. Methods: In this cross-sectional study, 266 adult patients with diabetes mellitus (DM) attending the outpatient endocrinology clinic at the Guilan University of Medical Sciences were enrolled. Patients completed a checklist that included demographic characteristics, factors, and laboratory results in addition to Berlin and STOP-BANG questionnaires to evaluate the risk of OSA. Data were analyzed by independent t-test, Mann-Whitney U test, and Chi-squared or Fisher's exact tests using the Statistical Package for the Social Sciences (SPSS) version 17. Results: A total of 266 patients with DM were enrolled in this study (34.6% males, mean age 47.00 ± 19.04 years). Based on the Berlin Questionnaire, 38.6% of all participants were at high risk of developing OSA. Based on the STOP-BANG Questionnaire (SBQ), 45.1% were at moderate and high risks. Additionally, this questionnaire showed a significant difference between low and moderate-to-severe groups regarding sex, age, body mass index (BMI), neck size, other chronic diseases, types of DM, use of insulin, Berlin Questionnaire, fasting blood sugar (FBS), and mean HbA1c. Conclusions: Based on the SBQ, our results indicated a significant relationship between OSA and glycemic control according to mean HbA1c and FBS. Therefore, by controlling the OSA, we may find a way to acheieve better glycemic control in diabetic patients.

Association Between Poor Sleep Quality and Glycemic Control in Adult Patients with Diabetes Referred to Endocrinology Clinic of Guilan: A Cross-sectional Study.

Background: Diabetes is a prevalent chronic medical comorbid condition worldwide. Diabetes mellitus is associated with various sleep disorders. Objectives: We aimed to determine the prevalence of poor sleep and the main factors of sleep interruptions in patients with diabetes mellitus. We further evaluated the association of sleep interruptions with glycemic control in this cohort. Methods: We conducted a cross-sectional study on 266 patients with type 1 and type 2 diabetes recruited from a university outpatient endocrinology clinic. Patients completed a checklist including demographic and disease-related characteristics in addition to the Pittsburgh Sleep Quality Index (PSQI) to evaluate sleep quality. Using the PSQI cutoff score of 5, we created two subgroups of good sleepers (GS) and poor sleepers (PS). Results: Our results showed that good sleeper and poor sleeper patients with diabetes were significantly different regarding sex, employment status, BMI, presence of diabetes-related complications, HbA1c, and 2-hour postprandial blood sugar (2HPPBS) (all significant at P < 0.05). The most prevalent factors of sleep interruptions were "waking up to use a bathroom", "feeling hot", "pain", "having coughs or snores", and "bad dreams". Among the subjective factors of sleep interruption, problems with sleep initiation, maintenance, or early morning awakenings in addition to having pain or respiratory problems such as coughing or snoring had the most significant associations with HbA1c. Conclusions: Our study showed significant subjective sleep disturbances (both quality and quantity) in patients with diabetes mellitus (both type I and II) and its association with diabetes control. We further identified the main factors that led to sleep interruptions in this cohort.