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.

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

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.

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.

Patient Priorities Care Increases Long-Term Service and Support Use: Propensity Match Cohort Study.

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.

Astrocytic-based Controller Shifts Epileptic Activity to the Chaotic State.

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.

Obesity paradox or hypoxia preconditioning: How obstructive sleep apnea modifies the Obesity-MI relationship.

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.

Association of Glucose-6-Phosphate Dehydrogenase Deficiency With Outcomes in US Veterans With COVID-19.

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.

Obstructive sleep apnea modulates clinical outcomes post-acute myocardial infarction: A large longitudinal veterans' dataset report.

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/m2) and 43,480 without OSA (age, 68 ± 12 years; BMI, 29 ± 6 kg/m2). 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.

Inflammatory Biomarkers Differ among Hospitalized Veterans Infected with Alpha, Delta, and Omicron SARS-CoV-2 Variants.

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.

Multitrait genome-wide analyses identify new susceptibility loci and candidate drugs to primary sclerosing cholangitis.

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.

Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.

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.

Mortality Patterns Associated with Central Sleep Apnea among Veterans: A Large, Retrospective, Longitudinal Report.

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.

Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning.

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.

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.

Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports.

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.

Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans.

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.

Comparison of Collaborative Goal Setting With Enhanced Education for Managing Diabetes-Associated Distress and Hemoglobin A1c Levels: A Randomized Clinical Trial.

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.

Elevated Risk of Chronic Respiratory Conditions within 60 Days of COVID-19 Hospitalization in Veterans.

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.

Differences in COVID-19-Related Testing and Healthcare Utilization by Race and Ethnicity in the Veterans Health Administration.

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.