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Amin Ramezani,1– 4 Amir Sharafkhaneh,1,4 Ahmed S BaHammam,5,6 Samuel T Kuna,7 Javad Razjouyan1– 4 1VA’s Health Services Research and Development Service (HSR&D), Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA; 2Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, 20420, USA; 3VA Quality Scholars Coordinating Center, Iquest, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA; 4Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA; 5Department of Medicine, College of Medicine, University Sleep Disorders Center, King Saud University, Riyadh, Saudi Arabia; 6The Strategic Technologies Program of the National Plan for Sciences and Technology and Innovation in the Kingdom of Saudi Arabia, Riyadh, Saudi Arabia; 7Department of Medicine, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USACorrespondence: Amir Sharafkhaneh, VA’s Health Services Research and Development Service (HSR&D), Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA, Email amirs@bcm.eduPurpose: The COVID-19 pandemic affected the utilization of various healthcare services differentially. Sleep testing services utilization (STU), including Home Sleep Apnea Testing (HSAT) and Polysomnography (PSG), were uniquely affected. We assessed the effects of the pandemic on STU and its recovery using the Veterans Health Administration (VHA) data.Patients and Methods: A retrospective cohort study from the VHA between 01/2019 and 10/2023 of veterans with age ≥ 50. We extracted STU data using Current Procedural Terminology codes for five periods based on STU and vaccination status: pre-pandemic (Pre-Pan), pandemic sleep test moratorium (Pan-Mor), and pandemic pre-vaccination (Pan-Pre-Vax), vaccination (Pan-Vax), and postvaccination (Pan-Post-Vax). We compared STU between intervals (Pre-Pan as the reference).Results: Among 261,371 veterans (63.7± 9.6 years, BMI 31.9± 6.0 kg/m², 80% male), PSG utilization decreased significantly during Pan-Mor (− 56%), Pan-Pre-Vax (− 61%), Pan-Vax (− 42%), and Pan-Post-Vax (− 36%) periods all compared to Pre-Pan. HSAT utilization decreased significantly during the Pan-Mor (− 59%) and Pan-Pre-Vax (− 9%) phases compared to the Pre-Pan and subsequently increased during Pan-Vax (+6%) and Pan-Post-Vax (− 1%) periods. Over 70% of STU transitioned to HSAT, and its usage surged five months after the vaccine Introduction.Conclusion: Sleep testing services utilization recovered differentially during the pandemic (PSG vs HSAT), including a surge in HSAT utilization post-vaccination.Keywords: home sleep apnea testing, polysomnography, COVID-19, pandemic, current procedural terminology

To design a potent agent for positron emission tomography/magnetic resonance imaging (PET/MRI) imaging and targeted magnetic hyperthermia-radioisotope cancer therapy radiolabeled surface modified superparamagnetic iron oxide nanoparticles (SPIONs) were used as nanocarriers. Folic acid was conjugated for increasing selective cellular binding and internalization through receptor-mediated endocytosis. SPIONs were synthesized by the thermal decomposition of tris (acetylacetonato) iron (III) to achieve narrow and uniform nanoparticles. To increase the biocompatibility of SPIONs, they were coated with (3-aminopropyl) triethoxysilane (APTES), and then conjugated with synthesized folic acid-polyethylene glycol (FA-PEG) through amine group of (3-aminopropyl) triethoxysilane. Finally, the particles were labeled with 64Cu (t1/2 = 12.7 h) using 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid mono (N-hydroxy succinimide ester) DOTA-NHS chelator. After the characterization of SPIONs, their cellular internalization was evaluated in folate receptor (FR) overexpressing KB (established from a HeLa cell contamination) and mouse fibroblast cell (MFB) lines. Eventually, active and passive targeting effects of complex were assessed in KB tumor-bearing Balb/C mice through biodistribution studies. Synthesized bare SPIONs had low toxicity effect on healthy cells, but surface modification increased their biocompatibility. Moreover, KB cells viability was reduced when using folate conjugated SPIONs due to FR-mediated endocytosis, while having little effect on healthy cells (MFB). Moreover, this radiotracer had tolerable in vivo characteristics and tumor uptake. In the receptor blocked case, tumor uptake was decreased, indicating FR-specific uptake in tumor tissue while enhanced permeability and retention effect was major mechanism for tumor uptake.

Rationale and Objectives: The relationship between Sleep Apnea (SA) and mortality remains a topic of debate. We explored the relationship between the severity of SA and mortality and the effect of age on this association., Methods: Using a Veterans' database, we extracted the apnea-hypopnea index (AHI) from physician interpretation of sleep studies, by developing a Natural Language Processing (NLP) pipeline (with 944 manually annotated notes) which achieved more than 85% accuracy. We categorized the participants into no-SA (n-SA, AHI< 5), mild to moderate SA (m-SA, 5 ≤AHI<30), and severe SA (s-SA, AHI≥30). We propensity-matched the m-SA and s-SA categories with n-SA based on age, sex, race, ethnicity, body mass index (BMI) and 38 components of the Elixhauser Comorbidity index. Using logistic regression, we estimated the odds ratio (OR) for all-cause mortality using m-SA as a reference. Also, we stratified the findings based on age: Young ≤40; Middle-aged >40 and<65; and Older ≥65 adults., Measurement and Main Results: We extracted AHI on 179,121 propensity-matched participants (age, 45.85±14.1; BMI 30.15±5.37 kg/m2; 79.09% male; 64.5% White). All-cause mortality rates among 3 AHI categories showed a U-shaped curve (11.55%, 7.07%, and 8.15% in the n-SA, m-SA, and s-SA, respectively) regardless of age groups. Compared to m-SA, the odds of all-cause mortality in n-SA (OR, 1.72; 95%CI:1.65,1.79) and s-SA (OR, 1.17;95%CI:1.12,1.22) were higher. Stratifying by age yielded consistent findings., Conclusions: All-cause mortality showed a U-shaped association with AHI. Further investigations to understand the underlying mechanisms of this phenomenon are warranted.

Study Objectives: Excessive daytime sleepiness is prevalent and overwhelmingly stems from disturbed sleep. We hypothesized that age modulates the association between excessive daytime sleepiness and increased all-cause mortality., Methods: We utilized the Veterans' Health Administration data from 1999-2022. We enrolled participants with sleep related International Classification of Diseases 9/10 codes or sleep services. A natural language processing pipeline was developed and validated to extract the Epworth Sleepiness Scale (ESS) as a self-reported tool to measure excessive daytime sleepiness from physician progress notes. The natural language processing's accuracy was assessed through manual annotation of 470 notes. Participants were categorized into normal-ESS (ESS 0-10) and high-ESS (ESS 11-24). We created 3 age groups: < 50 years, 50 to < 65 years, and ≥ 65 years. The adjusted odds ratio of mortality was calculated for age, body mass index, sex, race, ethnicity, and the Charlson Comorbidity Index, using normal-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 body mass index of 32.6 (± 6.2), and 90.5% male. The adjusted odds ratio across all ages was 17% higher (1.15, 1.19) in the high-ESS category. The adjusted odds ratio s only became statistically significant for individuals aged ≥ 50 years in the high-ESS compared to the normal-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., Citation: Maghsoudi A, Azarian M, Sharafkhaneh A, et al. Age modulates the predictive value of self-reported sleepiness for all-cause mortality risk: insights from a comprehensive national database of veterans. J Clin Sleep Med . 2024;20(11):1785-1792., (© 2024 American Academy of Sleep Medicine.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

We investigated the accuracy of International Classification of Diseases (ICD) codes for the identification of veterans with rapid eye movement sleep behavior disorder. The charts of 139 randomly sampled veterans with ≥ 1 ICD-9 and ICD-10 code(s) for rapid eye movement sleep behavior disorder were reviewed for documentation of a suspected, previous, or current diagnosis; clinical symptoms; and/or empiric treatments for this disorder. Notably, 71 (51.1%) patients with rapid eye movement sleep behavior disorder electronic diagnoses had not undergone polysomnography, and 29 (20.9%) had polysomnography reports without commentary on rapid eye movement sleep without atonia. Sleep centers are therefore encouraged to include a brief sentence in polysomnography report templates commenting on the presence/absence of rapid eye movement sleep without atonia., Citation: Jones MB, Schenck CH, Azarian M, Jorge RE, Sharafkhaneh A, Razjouyan J. Validation of electronic diagnostic codes for rapid eye movement sleep behavior disorder. J Clin Sleep Med. 2024;20(8);1387-1389., (© 2024 American Academy of Sleep Medicine.)

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 32 nd 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/m 2 ). 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., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Background: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes., Research Question: To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification., Study Design and Methods: A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates., Results: Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality., Interpretation: We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population., Competing Interests: Declaration of competing interest None of the authors disclose any financial or personal relationship with other people or organizations that could inappropriately influence this work., (Published by Elsevier Ltd.)

Objectives: Patient priorities care (PPC) is an evidence-based approach designed to help patients achieve what matters most to them by identifying their health priorities and working with clinicians to align the care they provide to the patient's priorities. This study examined the impact of the PPC approach on long-term service and support (LTSS) use among veterans., Design: Quasi-experimental study examining differences in LTSS use between veterans exposed to PPC and propensity-matched controls not exposed to PPC adjusting for covariates., Setting and Participants: Fifty-six social workers in 5 Veterans Health Administration (VHA) sites trained in PPC in 2018, 143 veterans who used the PPC approach, and 286 matched veterans who did not use the PPC approach., Methods: Veterans with health priorities identified through the PPC approach were the intervention group (n = 143). The usual care group included propensity-matched veterans evaluated by the same social workers in the same period who did not participate in PPC (n = 286). The visit with the social worker was the index date. We examined LTSS use, emergency department (ED), and urgent care visits, 12 months before and after this date for both groups. Electronic medical record notes were extracted with a validated natural language processing algorithm (84% sensitivity, 95% specificity, and 92% accuracy)., Results: Most participants were white men, mean age was 76, and 30% were frail. LTSS use was 48% higher in the PPC group compared with the usual care group [odds ratio (OR), 1.48; 95% CI, 1.00-2.18; P = .05]. Among those who lived >2 years after the index date, new LTSS use was higher (OR, 1.69; 95% CI, 1.04-2.76; P = .036). Among nonfrail individuals, LTSS use was also higher in the PPC group (OR, 1.70; 95% CI, 1.06-2.74; P = .028). PPC was not associated with higher ED or urgent care use., Conclusions and Implications: PPC results in higher LTSS use but not ED or urgent care in these veterans. LTSS use was higher for nonfrail veterans and those living longer. The PPC approach helps identify health priorities, including unmet needs for safe and independent living that LTSS can support., Competing Interests: Disclosure The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Performance status (PS), an essential indicator of patients' functional abilities, is often documented in clinical notes of patients with cancer. The use of natural language processing (NLP) in extracting PS from electronic medical records (EMRs) has shown promise in enhancing clinical decision-making, patient monitoring, and research studies. We designed and validated a multi-institute NLP pipeline to automatically extract performance status from free-text patient notes., Patients and Methods: We collected data from 19,481 patients in Harris Health System (HHS) and 333,862 patients from veteran affair's corporate data warehouse (VA-CDW) and randomly selected 400 patients from each data source to train and validate (50%) and test (50%) the proposed pipeline. We designed an NLP pipeline using an expert-derived rule-based approach in conjunction with extensive post-processing to solidify its proficiency. To demonstrate the pipeline's application, we tested the compliance of PS documentation suggested by the American Society of Clinical Oncology (ASCO) Quality Metric and investigated the potential disparity in PS reporting for stage IV non-small cell lung cancer (NSCLC). We used a logistic regression test, considering patients in terms of race/ethnicity, conversing language, marital status, and gender., Results: The test results on the HHS cohort showed 92% accuracy, and on VA data demonstrated 98.5% accuracy. For stage IV NSCLC patients, the proposed pipeline achieved an accuracy of 98.5%. Furthermore, our analysis revealed a documentation rate of over 85% for PS among NSCLC patients, surpassing the ASCO Quality Metrics. No disparities were observed in the documentation of PS., Conclusion: Our proposed NLP pipeline shows promising results in extracting PS from free-text notes from various health institutions. It may be used in longitudinal cancer data registries.

Objectives: The objective of this study was to evaluate the interaction between obesity and obstructive sleep apnea on acute MI in hospital mortality., Methods: This retrospective cohort study utilized Veterans Health Administration data from years 1999-2020. Participants were categorized according to their body mass index (BMI) to non-obese (BMI <30) and obese (BMI ≥30) groups. Clinical obstructive sleep apnea (SA) diagnosis was confirmed using ICD9/10 codes and the study subgroups included non-obese with no obstructive sleep apnea (nOB-nSA), non-Obese with obstructive sleep apnea (nOB-SA), obese with no obstructive sleep apnea (OB-nSA), and obese with obstructive sleep apnea (OB-SA). The primary outcome was odds ratio of in-hospital mortality during the hospitalization with acute MI as the principal diagnosis adjusted for age, gender, race, ethnicity, and Charlson comorbidity index (CCI) with the nOB-nSA group as the comparison group., Results: Among 72,036 veterans with acute-MI hospitalization, individuals with obesity and obstructive sleep apnea (OB-SA) had the lowest in-hospital mortality rate (1.0%) compared to those without obesity and obstructive sleep apnea (nOB-nSA, 2.8%), with obesity but without obstructive sleep apnea (OB-nSA, 2.4%), and with obesity and obstructive sleep apnea (nOB-SA, 1.4%). The adjusted odds ratio for mortality, compared to nOB-nSA, was 9% higher but not significant in OB-nSA (aOR, 1.09, 95%CI: 0.95, 1.25), 46% lower in OB-nSA (aOR, 0.54, 95%CI: 0.45, 0.66), and 52% lower in OB-SA (aOR, 0.48: 95%CI: 0.41, 0.57)., Conclusion: Our data suggest that the association between obesity and improved survival in acute MI is largely driven by the presence of sleep apnea., Competing Interests: Declaration of competing interest Authors declare none., (Published by Elsevier B.V.)

Introduction: Investigating an effective controller to shift hippocampal epileptic periodicity to normal chaotic behavior will be new hope for epilepsy treatment. Astrocytes nourish and protect neurons and maintain synaptic transmission and network activity. Therefore, this study explored the ameliorating effect of the astrocyte computational model on epileptic periodicity., Methods: Modified Morris-Lecar equations were used to model the hippocampal CA3 network. Network inhibitory parameters were employed to generate oscillation-induced epileptiform periodicity. The astrocyte controller was based on a functional dynamic mathematical model of brain astrocytic cells., Results: Results demonstrated that the synchronization of two neural networks shifted the brain's chaotic state to periodicity. Applying an astrocytic controller to the synchronized networks returned the system to the desynchronized chaotic state., Conclusion: It is concluded that astrocytes are probably a good model for controlling epileptic periodicity. However, more research is needed to delineate this effect., Highlights: Modeling of CA3 neurons reproduced synchronized periodic epileptiform discharges.An astrocyte mathematical model modulated neuronal network excitability.The astrocyte controller desynchronized neural network periodic oscillations.Application of the astrocyte model restored a chaotic desynchronized state.Results suggest astrocytes may control hypersynchronous epileptiform activity., Plain Language Summary: This study looked at whether a mathematical model of brain cells called astrocytes could help control seizure activity. Seizures happen when groups of brain cells become overly active and synchronized. Normally, brain cell activity is chaotic and unsynchronized. The researchers modeled a small network of hippocampus brain cells using equations. We adjusted the model to create seizure-like periodic synchronized activity. Then we added a mathematical astrocyte model to try to disrupt this unwanted synchronization. Astrocytes are a type of glial cell in the brain. They help nourish neurons and regulate brain cell communication. The researchers modeled astrocyte activity using equations based on calcium levels. Calcium levels affect how astrocytes communicate with brain cells. When the researchers added the astrocyte model to the seizure-like network activity, it was able to restore chaotic unsynchronized activity. The astrocyte model accomplished this by affecting the excitability of the neuronal network. These results suggest astrocytes could potentially be used to control seizure activity. More research is needed to further test this astrocyte model. Currently, many seizure patients do not respond fully to medication. Astrocyte-based treatments could potentially provide an alternative approach. The findings are notable because they demonstrate a biologically-based method to restore normal chaotic brain activity. Most previous efforts have used electrical stimulation. An astrocyte-based approach could modulate communication between brain cells in a more natural way., Competing Interests: The authors declared no conflict of interest., (Copyright© 2023 Iranian Neuroscience Society.)

Background: While the longer-term Obstructive Sleep apnea (OSA)-related intermittent hypoxia (IH) leads to various comorbidities, it has become increasingly evident that OSA confers protective advantages during and after acute myocardial infarction (AMI). We hypothesized in patients who were admitted with acute MI, the presence of OSA is associated with lower in-hospital mortality compared to those without a prior diagnosis of OSA., Methods: In this nationwide retrospective study utilizing Veterans Health Administration records, we included patients hospitalized for MI with a history of sleep disorders from 1999 to 2020. We divided patients into two cohorts: those with OSA and those without OSA. The primary outcome was in-hospital mortality during AMI hospitalization. We analyzed the data using logistic regression and calculated the odds ratio of in-hospital mortality., Results: Out of more than four million veterans with any sleep diagnosis, 76,359 patients were hospitalized with a diagnosis of AMI. We observed 30,116 with OSA (age, 64 ± 10 years; BMI, 33 ± 7 kg/m 2 ) and 43,480 without OSA (age, 68 ± 12 years; BMI, 29 ± 6 kg/m 2 ). The aOR of in-patient mortality (n = 333 (1.1%)) was lower in those with OSA (aOR, 0.43; 95% CI, 0.38 to 0.49) compared to without-OSA (n = 1,102, 2.5%). However, the OSA cohort had a higher proportion of the prolonged length of stay (28.1%)., Conclusions: Presence of OSA is associated with lower in-hospital mortality among patients admitted for AMI, after adjusting for various demographic and co-morbidity factors. This study highlights the complex relationship between OSA and cardiovascular health and highlights the need for further research in this area., Competing Interests: Conflict of interest The authors report NO conflict of interests relevant to this work., (Published by Elsevier Ltd.)

Frailty is associated with adverse outcomes in heart failure (HF). A parsimonious frailty index (FI) that predicts outcomes of older, multimorbid patients with HF could be a useful resource for clinicians. A retrospective study of veterans hospitalized from October 2015 to October 2018 with HF, aged ≥50 years, and discharged home developed a 10-item parsimonious FI using machine learning from diagnostic codes, laboratory results, vital signs, and ejection fraction (EF) from outpatient encounters. An unsupervised clustering technique identified 5 FI strata: severely frail, moderately frail, mildly frail, prefrail, and robust. We report hazard ratios (HRs) of mortality, adjusting for age, gender, race, and EF and odds ratios (ORs) for 30-day and 1-year emergency department visits and all-cause hospitalizations after discharge. We identified 37,431 veterans (age, 73 ± 10 years; co-morbidity index, 5 ± 3; 43.5% with EF ≤40%). All frailty groups had a higher mortality than the robust group: severely frail (HR 2.63, 95% confidence interval [CI] 2.42 to 2.86), moderately frail (HR 2.04, 95% CI 1.87 to 2.22), mildly frail (HR 1.60, 95% CI 1.47 to 1.74), and prefrail (HR 1.18, 95% CI: 1.07 to 1.29). The associations between frailty and mortality remained unchanged in the stratified analysis by age or EF. The combined (severely, moderately, and mildly) frail group had higher odds of 30-day emergency visits (OR 1.62, 95% CI 1.43 to 1.83), all-cause readmission (OR, 1.75, 95% CI 1.52 to 2.02), 1-year emergency visits (OR 1.70, 95% CI 1.53 to 1.89), rehospitalization (OR 2.18, 95% CI 1.97 to 2.41) than the robust group. In conclusion, a 10-item FI is associated with postdischarge outcomes among patients discharged home after a hospitalization for HF. A parsimonious FI may aid clinical prediction at the point of care., Competing Interests: Disclosures The study is funded partially by the Center for Innovations in Quality, Effectiveness and Safety (VA HSRD CIN 13-413), Michael E. DeBakey VA Medical Center, Houston, Texas, USA and CSP 2010A. Dr. Razjouyan is supported by NIH-NHLBI award 1K25HL152006-01. Dr. Horstman is supported by VA HSR&D CDA-2 award IK2-HX003163. Dr. Orkaby is supported by VA CSR&D CDA-2 award IK2-CX001800. Dr. Virani is supported by research grants from The Department of Veterans Affairs Health Services Research and Development Services (IIR 16-072 and IIR 19-069). Dr. Goyal is supported by American Heart Association grant 20CDA35310455, and National Institute on Aging grant K76AG064428. Dr. Goyal receives personal fees for medicolegal consulting related to heart failure and receives consulting fees from Sensorum Health. The remaining authors have no conflicts of interest to declare., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Rationale: Central sleep apnea (CSA) is associated with high mortality. Current knowledge stems from studies with limited sample size (fewer than 100 subjects) and in homogeneous populations such as heart failure (HF). Objectives: To address this knowledge gap, we compared the mortality pattern and time to death between the CSA and obstructive sleep apnea (OSA) patients in the large Veterans Health Administration patient population using the big data analytic approach. Methods: This is a retrospective study using national Veterans Health Administration electronic medical records from October 1, 1999, through September 30, 2020. We grouped the patients with underlying sleep disorders into CSA and OSA, using the International Classification of Diseases, Ninth and Tenth Revision codes. We applied Cox regression analysis to compare the mortality rate and hazard ratio (HR) among the two groups and adjusted HR by gender, race, body mass index (BMI), age, and Charlson Comorbidity Index. In CSA groups, a machine-learning algorithm was used to determine the most important predictor of time to death. Further subgroup analysis was also performed in patients that had comorbid HF. Results: Evaluation of patients resulted in 2,961 grouped as CSA and 1,487,353 grouped as OSA. Patients with CSA were older (61.8 ± 15.6 yr) than those with OSA (56.7 ± 13.9 yr). A higher proportion of patients with CSA (25.1%) died during the study period compared with the OSA cohort (14.9%). The adjusted HR was 1.53 (95% confidence interval [CI], 1.43-4.65). Presence of HF history of cerebrovascular disease, hemiplegia, and having a BMI less than 18.5 were among the highest predictors of mortality in CSA. The subgroup analysis revealed that the presence of HF was associated with increased mortality both in CSA (HR, 7.4; 95% CI, 6.67-8.21) and OSA (HR, 4.3; 95% CI, 4.26-4.34) groups. Conclusions: Clinically diagnosed CSA was associated with a shorter time to death from the index diagnostic date. Almost one-fifth of patients with CSA died within 5 years of diagnosis. The presence of HF, history of cerebrovascular disease and hemiplegia, male sex, and being underweight were among the highest predictors of mortality in CSA. CSA was associated with higher mortality than OSA, independent of associated comorbidity.

Importance: The underlying biological risk factors for severe outcome due to SAR-CoV-2 infection are not well defined., Objective: To determine the association between glucose-6-phosphate dehydrogenase (G6PD) deficiency and severity of COVID-19., Design, Setting, and Participants: This retrospective cohort study included analysis of 24 700 veterans with G6PD enzyme testing prior to January 1, 2020, obtained through the US Veterans Health Administration national databases. These veterans were cross-referenced with the Veterans Administration COVID-19 Shared Data Resource for SARS-CoV-2 testing from February 15, 2020, to January 1, 2021. The final study population consisted of 4811 veterans who tested positive for SARS-CoV-2. Statistical analysis was performed from June to December 2021., Exposures: G6PD deficiency., Main Outcomes and Measures: COVID-19 severe illness, as defined by the Centers for Disease Control and Prevention: hospitalization, need for mechanical ventilation and/or intensive care unit admission, or in-hospital mortality after a positive SARS-CoV-2 test., Results: Among 4811 veterans in the Veterans Health Administration who had historical G6PD enzyme activity test results and SARS-CoV-2 positivity included in this study, 3868 (80.4%) were male, 1553 (32.3%) were Black, and 1855 (39%) were White; 1228 (25.5%) were 65 years or older and 3583 (74.5%) were younger than 65 years. There were no significant differences in age, body mass index, or Charlson Comorbidity Index were present between the veterans with G6PD deficiency and without G6PD deficiency. Among these veterans with SARS-CoV-2 infection, G6PD deficiency was more prevalent in Black male veterans (309 of 454 [68.1%]) compared with other racial and ethnic groups. Black male veterans less than 65 years of age with G6PD deficiency had approximately 1.5-fold increased likelihood of developing severe outcomes from SARS-CoV-2 infection compared with Black male veterans without G6PD deficiency (OR, 1.47; 95% CI, 1.03-2.09). In the small subset of White male veterans with G6PD deficiency, we observed an approximately 3.6-fold increased likelihood of developing severe outcomes from SARS-CoV-2 infection compared with White male veterans aged 65 years or older without G6PD deficiency (OR, 3.58; 95% CI, 1.64-7.80). This difference between veterans with and without G6PD deficiency was not observed in younger White male veterans or older Black male veterans, nor in smaller subsets of other male veterans or in female veterans of any age., Conclusions and Relevance: In this cohort study of COVID-19-positive veterans, Black male veterans less than 65 years of age and White male veterans 65 years of age or older with G6PD deficiency had an increased likelihood of developing severe COVID-19 compared with veterans without G6PD deficiency. These data indicate a need to consider the potential for G6PD deficiency prior to treatment of patients with SARS-CoV-2 infection as part of clinical strategies to mitigate severe outcomes.

Primary sclerosing cholangitis (PSC) is a rare autoimmune bile duct disease that is strongly associated with immune-mediated disorders. In this study, we implemented multitrait joint analyses to genome-wide association summary statistics of PSC and numerous clinical and epidemiological traits to estimate the genetic contribution of each trait and genetic correlations between traits and to identify new lead PSC risk-associated loci. We identified seven new loci that have not been previously reported and one new independent lead variant in the previously reported locus. Functional annotation and fine-mapping nominated several potential susceptibility genes such as MANBA and IRF5. Network-based in silico drug efficacy screening provided candidate agents for further study of pharmacological effect in PSC., (© 2023. The Author(s).)

Mortality due to COVID-19 has been correlated with laboratory markers of inflammation, such as C-reactive protein (CRP). The lower mortality during Omicron variant infections could be explained by variant-specific immune responses or host factors, such as vaccination status. We hypothesized that infections due to Omicron variant cause less inflammation compared to Alpha and Delta, correlating with lower mortality. This was a retrospective cohort study of veterans hospitalized for COVID-19 at the Veterans Health Administration. We compared inflammatory markers among patients hospitalized during Omicron infection with those of Alpha and Delta. We reported the adjusted odds ratio (aOR) of the first laboratory results during hospitalization and in-hospital mortality, stratified by vaccination status. Of 2,075,564 Veterans tested for COVID-19, 29,075 Veterans met the criteria: Alpha (45.1%), Delta (23.9%), Omicron (31.0%). Odds of abnormal CRP in Delta (aOR = 1.85, 95% CI:1.64-2.09) and Alpha (aOR = 1.94, 95% CI:1.75-2.15) were significantly higher compared to Omicron. The same trend was observed for Ferritin, Alanine aminotransferase, Aspartate aminotransferase, Lactate dehydrogenase, and Albumin. The mortality in Delta (aOR = 1.92, 95% CI:1.73-2.12) and Alpha (aOR = 1.68, 95% CI:1.47-1.91) were higher than Omicron. The results remained significant after stratifying the outcomes based on vaccination status. Veterans infected with Omicron showed milder inflammatory responses and lower mortality than other variants.

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing. Physiological wellbeing is represented as a latent variable in an empirical Bayes model with heart rate variability measures-SDNN and normalized-HF as the observed outcomes and with exogenous factors including sound level as inputs. We find that an individual's physiological wellbeing is optimal when sound level in the workplace is at 50 dBA. At lower (<50dBA) and higher (>50dBA) amplitude ranges, a 10 dBA increase in sound level is related to a 5.4% increase and 1.9% decrease in physiological wellbeing respectively. Age, body-mass-index, high blood pressure, anxiety, and computer use intensive work are person-level factors contributing to heterogeneity in the sound-wellbeing association., (© 2023. The Author(s).)

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., (©Arash Maghsoudi, Sara Nowakowski, Ritwick Agrawal, Amir Sharafkhaneh, Mark E Kunik, Aanand D Naik, Hua Xu, Javad Razjouyan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.12.2022.)

Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000−30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care.

The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.

Importance: Type 2 diabetes is a prevalent and morbid condition. Poor engagement with self-management can contribute to diabetes-associated distress and hinder diabetes control., Objective: To evaluate the implementation and effectiveness of Empowering Patients in Chronic Care (EPICC), an evidence-based intervention to improve diabetes-associated distress and hemoglobin A1c (HbA1c) levels after the intervention and after 6-month maintenance., Design, Setting, and Participants: This hybrid (implementation-effectiveness) randomized clinical trial was performed in Veterans Affairs clinics across Illinois, Indiana, and Texas from July 1, 2015, to June 30, 2017. Participants included adults with uncontrolled type 2 diabetes (HbA1c level >8.0%) who received primary care during the prior year in participating clinics. Data collection was completed on November 30, 2018, and data analysis was completed on June 30, 2020. All analyses were based on intention to treat., Interventions: Participants in EPICC attended 6 group sessions based on a collaborative goal-setting theory led by health care professionals. Clinicians conducted individual motivational interviewing sessions after each group. Usual care was enhanced (EUC) with diabetes education., Main Outcomes and Measures: The primary outcome consisted of changes in HbA1c levels after the intervention and during maintenance. Secondary outcomes included the Diabetes Distress Scale (DDS), Morisky Medication Adherence Scale, and Lorig Self-efficacy Scale. Secondary implementation outcomes included reach, adoption, and implementation (number of sessions attended per patient)., Results: A total of 280 participants with type 2 diabetes (mean [SD] age, 67.2 [8.4] years; 264 men [94.3]; 134 non-Hispanic White individuals [47.9%]) were equally randomized to EPICC or EUC. Participants receiving EPICC had significant postintervention improvements in HbA1c levels (F1, 252 = 9.12, Cohen d = 0.36 [95% CI, 0.12-0.59]; P = .003) and DDS (F1, 245 = 9.06, Cohen d = 0.37 [95% CI, 0.13-0.60]; P = .003) compared with EUC. During maintenance, differences between the EUC and EPICC groups remained significant for DDS score (F1, 245 = 8.94, Cohen d = 0.36 [95% CI, 0.12-0.59]; P = .003) but not for HbA1c levels (F1, 252 = 0.29, Cohen d = 0.06 [95% CI, -0.17 to 0.30]; P = .60). Improvements in DDS scores were modest. There were no differences between EPICC and EUC in improvements after intervention or maintenance for either adherence or self-efficacy. Among all 4002 eligible patients, 280 (7.0%) enrolled in the study (reach). Each clinic conducted all planned EPICC sessions and cohorts (100% adoption). The EPICC group participants attended a mean (SD) of 4.34 (1.98) sessions, with 54 (38.6%) receiving all 6 sessions., Conclusions and Relevance: A patient-empowerment approach using longitudinal collaborative goal setting and motivational interviewing is feasible in primary care. Improvements in HbA1c levels after the intervention were not sustained after maintenance. Modest improvements in diabetes-associated distress after the intervention were sustained after maintenance. Innovations to expand reach (eg, telemedicine-enabled shared appointments) and sustainability are needed., Trial Registration: ClinicalTrials.gov Identifier: NCT01876485.

Importance: Recent reports indicate differences in COVID-19-related care and outcomes between Black and White Americans., Objective: We examine the COVID-19-related healthcare utilization and mortality by race and ethnicity of patients tested for SARS-CoV-2 in the Veterans Health Administration (VHA)., Design: A retrospective cohort study., Setting: We used the VHA COVID-19 shared data resources between February 1 and June 30, 2020., Participants: Veterans tested for SARS-CoV-2 virus by VHA., Exposure(s): Three racial-ethnicity groups of Black, Hispanic, and White (as reference) veterans., Main Outcome(s) and Measure(s): Main outcomes are testing rate, positivity rate, hospitalization rate, ICU admission rate, and in-hospital mortality. Controlling for sex, age, and Elixhauser comorbidity index, we report adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) from logistic regression models., Results: Of the 8,667,996 active veteran enrollees, 252,702 were tested by VHA from February to June, 2020, with 20,500 positive results and 4,790 hospitalizations. The testing rate was 4.4% among Black and 4.7% among Hispanic veterans compared to White veterans, 2.8%. The testing positivity rate was similarly elevated among Black (12.2%) and Hispanic (11.6%) veterans compared to White veterans (6.0%). The aORs of hospitalization in Black veterans (1.88; 95% CI 1.74, 2.03) and Hispanic veterans (1.41; 95% CI 1.25, 1.60) were higher compared to White veterans. No significant differences by race and ethnicity were observed in OR or aOR of ICU admission and in-hospital death among hospitalized patients., Conclusions and Relevance: On a national level, the VHA was more likely to test and hospitalize Black and Hispanic veterans compared to White veterans, but there were no significant differences in ICU admission or in-hospital mortality among those hospitalized. This pattern of differences may relate to social determinants of health, factors affecting access to non-VHA care, or preferences for VHA care affecting initial care seeking, but not in-hospital outcomes., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Competing Interests: Competing interests: None declared.

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., (Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco 2021.)

SARS-CoV-2 infection prominently affects the respiratory system, and patients hospitalized with COVID-19 are at an increased risk of developing respiratory conditions. We examined the risk of new respiratory conditions of COVID-19 among hospitalized patients in the national Veterans Health Administration between 15 February 2020 and 16 June 2021. The study cohort included all COVID-19-tested, hospitalized individuals who survived the index admission and did not have any previously diagnosed chronic respiratory conditions (asthma, bronchitis, chronic lung disease, chronic obstructive pulmonary disease (COPD), emphysema, or venous thromboembolism) before SARS-CoV-2 testing. Of 373,048 patients hospitalized after SARS-CoV-2 testing, 18,686 positive and 37,372 negative patients met the inclusion/exclusion criteria and were matched by age, sex, and race using propensity score matching. The results showed that the SARS-CoV-2 positive group had a greater risk of developing asthma (adjusted odds ratio (aOR) = 1.37), bronchitis (aOR = 2.81), chronic lung disease (aOR = 2.14), COPD (aOR = 1.56), emphysema (aOR = 1.52), and venous thromboembolism (aOR = 1.92) within 60 days after the index COVID date of testing. These findings could inform that the clinical care team considers a risk of new respiratory conditions and address these conditions in the post-hospitalization management of the patient, which could potentially lead to reduce the risk of complications and optimize recovery.

Objectives and Background: Social demands of the school-year and summer environment may affect children's sleep patterns and circadian rhythms during these periods. The current study examined differences in children's sleep and circadian-related behaviors during the school-year and summer and explored the association between sleep and circadian parameters and change in body mass index (BMI) during these time periods., Methods: This was a prospective observational study with 119 children ages 5 to 8 years with three sequential BMI assessments: early school-year (fall), late school-year (spring), and beginning of the following school-year in Houston, Texas, USA. Sleep midpoint, sleep duration, variability of sleep midpoint, physical activity, and light exposure were estimated using wrist-worn accelerometry during the school-year (fall) and summer. To examine the effect of sleep parameters, physical activity level, and light exposure on change in BMI, growth curve modeling was conducted controlling for age, race, sex, and chronotype., Results: Children's sleep midpoint shifted later by an average of 1.5 h during summer compared to the school-year. After controlling for covariates, later sleep midpoints predicted larger increases in BMI during summer, (γ = .0004, p = .03), but not during the school-year. Sleep duration, sleep midpoint variability, physical activity levels, and sedentary behavior were not associated with change in BMI during the school-year or summer. Females tended to increase their BMI at a faster rate during summer compared to males, γ = .06, p = .049. Greater amounts of outdoor light exposure (γ = -.01, p = .02) predicted smaller increases in school-year BMI., Conclusions: Obesity prevention interventions may need to target different behaviors depending on whether children are in or out of school. Promotion of outdoor time during the school-year and earlier sleep times during the summer may be effective obesity prevention strategies during these respective times., (© 2021. The Author(s).)

Background: Traditional assessment (e.g., checklists, videotaping) for surgical proficiency may lead to subjectivity and does not predict performance in the clinical setting. Hand motion analysis is evolving as an objective tool for grading technical dexterity; however, most devices accompany with technical limitations or discomfort. We purpose the use of flexible wearable sensors to evaluate the kinematics of surgical proficiency., Methods: Surgeons were recruited and performed a vascular anastomosis task in a single institution. A modified objective structured assessment of technical skills (mOSATS) was used for technical qualification. Flexible wearable sensors (BioStamp RCTM, mc10 Inc., Lexington, MA) were placed on the dorsum of the dominant hand (DH) and nondominant hand (nDH) to measure kinematic parameters: path length (T path ), mean (V mean ) and peak (V peak ) velocity, number of hand movements (N move ), ratio of DH to nDH movements (rMov), and time of task (tTask) and further compared with the mOSATS score., Results: Participants were categorized as experts (n = 12) and novices (n = 8) based on a cutoff mean mOSATS score. Significant differences for tTask (P = 0.02), rMov (P = 0.07), DH T path (P = 0.04), V mean (P = 0.07), V peak (P = 0.04), and nDH N move (P = 0.02) were in favor of the experts. Overall, mOSATS had significant correlation with tTask (r = -0.69, P = 0.001), N move of DH (r = -0.44, P = 0.047) and nDH (r = -0.66, P = 0.001), and rMov (r = 0.52, P = 0.017)., Conclusions: Hand motion analysis evaluated by flexible wearable sensors is feasible and informative. Experts utilize coordinated two-handed motion, whereas novices perform one-handed tasks in a hastily jerky manner. These tendencies create opportunity for improvement in surgical proficiency among trainees., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient's electronic health record (EHR)., Objective: Our goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption., Methods: This is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient's free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review., Results: Of 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P<.001). The NLP model in the training stage had an accuracy of 0.98 (95% CI 0.98-0.99), a recall of 0.98 (95% CI 0.98-0.99), and precision of 0.98 (95% CI 0.97-1.00). The NLP model in the validation stage had an accuracy of 0.92 (95% CI 0.90-0.94), recall of 0.84 (95% CI 0.79-0.89), and precision of 0.84 (95% CI 0.77-0.91). In contrast, an approach using simple search terms for PPC only had a precision of 0.757., Conclusions: An automated NLP model can reliably measure with high precision, recall, and accuracy when clinicians document patient priorities as a key step in the adoption of PPC., (©Javad Razjouyan, Jennifer Freytag, Lilian Dindo, Lea Kiefer, Edward Odom, Jaime Halaszynski, Jennifer W Silva, Aanand D Naik. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 19.02.2021.)

Background: Many individuals with diabetes live in low- or middle-income settings. Glycemic control is challenging, particularly in resource-limited areas that face numerous healthcare barriers., Objective: To compare HbA1c outcomes for individuals randomized to TIME, a Telehealth-supported, Integrated care with CHWs (Community Health Workers), and MEdication-access program (intervention) versus usual care (wait-list control)., Design: Randomized clinical trial., Participants: Low-income Latino(a) adults with type 2 diabetes., Interventions: TIME consisted of (1) CHW-participant telehealth communication via mobile health (mHealth) for 12 months, (2) CHW-led monthly group visits for 6 months, and (3) weekly CHW-physician diabetes training and support via telehealth (video conferencing)., Main Measures: Investigators compared TIME versus control participant baseline to month 6 changes of HbA1c (primary outcome), blood pressure, body mass index (BMI), weight, and adherence to seven American Diabetes Association (ADA) standards of care. CHW assistance in identifying barriers to healthcare in the intervention group were measured at the end of mHealth communication (12 months)., Key Results: A total of 89 individuals participated. TIME individuals compared to control participants had significant HbA1c decreases (9.02 to 7.59% (- 1.43%) vs. 8.71 to 8.26% (- 0.45%), respectively, p = 0.002), blood pressure changes (systolic: - 6.89 mmHg vs. 0.03 mmHg, p = 0.023; diastolic: - 3.36 mmHg vs. 0.2 mmHg, respectively, p = 0.046), and ADA guideline adherence (p < 0.001) from baseline to month 6. At month 6, more TIME than control participants achieved > 0.50% HbA1c reductions (88.57% vs. 43.75%, p < 0.001). BMI and weight changes were not significant between groups. Many (54.6%) TIME participants experienced > 1 barrier to care, of whom 91.7% had medication issues. CHWs identified the majority (87.5%) of barriers., Conclusions: TIME participants resulted in improved outcomes including HbA1c. CHWs are uniquely positioned to identify barriers to care particularly related to medications that may have gone unrecognized otherwise. Larger trials are needed to determine the scalability and sustainability of the intervention., Clinical Trial: NCT03394456, accessed at https://clinicaltrials.gov/ct2/show/NCT03394456.

Introduction: This study sought to evaluate the impact of changes made to the process of continually screening hospitalized patients for decompensation., Methods: Patients admitted to hospital wards were screened using a cloud-based early warning score (modified National Early Warning Score [mNEWS]). Patient with mNEWS ≥7 triggered a structured response. Outcomes of this quality improvement study during the intervention period from February through August 2018 (1741 patients) were compared with a control population (1,610 patients) during the same months of 2017., Results: The intervention group improved the time to the first lactate order within 24 hours of mNEWS ≥7 (p < .001), the primary outcome, compared with the control group. There was no significant improvement in time to intensive care unit (ICU) transfer, ICU length of stay (LOS), or hospital mortality. Among patients with a lactate ordered within 24 hours, there was a 47% reduction of in-hospital mortality (odds ratio 0.53, 95% confidence interval 0.3-0.89, p = .02) and a 4.7 day reduction in hospital LOS (p < .001) for intervention versus control cohorts., Conclusions: Cloud-based electronic surveillance can result in earlier detection of clinical decompensation. This intervention resulted in lower hospital LOS and mortality among patients with early detection of and intervention for clinical decompensation., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 National Association for Healthcare Quality.)

Purpose: This study aimed to determine if sequential deployment of a nurse-led Rapid Response Team (RRT) and an intensivist-led Medical Emergency Team (MET) for critically ill patients in the Emergency Department (ED) and acute care wards improved hospital-wide cardiac arrest rates., Methods: In this single-center, retrospective observational cohort study, we compared the cardiac arrest rates per 1000 patient-days during two time periods. Our hospital instituted a nurse-led RRT in 2012 and added an intensivist-led MET in 2014. We compared the cardiac arrest rates during the nurse-led RRT period and the combined RRT-MET period. With the sequential approach, nurse-led RRT evaluated and managed rapid response calls in acute care wards and if required escalated care and co-managed with an intensivist-led MET. We specifically compared the rates of pulseless electrical activity (PEA) in the two periods. We also looked at the cardiac arrest rates in the ED as RRT-MET co-managed patients with the ED team., Results: Hospital-wide cardiac arrests decreased from 2.2 events per 1000 patient-days in the nurse-led RRT period to 0.8 events per 1000 patient-days in the combined RRT and MET period (p-value = 0.001). Hospital-wide PEA arrests and shockable rhythms both decreased significantly. PEA rhythms significantly decreased in acute care wards and the ED., Conclusion: Implementing an intensivist-led MET-RRT significantly decreased the overall cardiac arrest rate relative to the rate under a nurse-led RRT model. Additional MET capabilities and early initiation of advanced, time-sensitive therapies likely had the most impact., Competing Interests: The authors have declared that no competing interests exist.

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., Competing Interests: The authors declare no conflict of interest.