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Fragilidade , Insuficiência Cardíaca , Veteranos , Humanos , Insuficiência Cardíaca/epidemiologia , Insuficiência Cardíaca/diagnóstico , Veteranos/estatística & dados numéricos , Estudos Retrospectivos , Fragilidade/diagnóstico , Idoso , Masculino , Feminino , Idoso Fragilizado/estatística & dados numéricos , Idoso de 80 Anos ou mais , Avaliação Geriátrica/métodos , Estados Unidos/epidemiologiaRESUMO
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.
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Síndromes da Apneia do Sono , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Síndromes da Apneia do Sono/mortalidade , Síndromes da Apneia do Sono/epidemiologia , Fatores Etários , Idoso , Adulto , Prevalência , Índice de Gravidade de Doença , Estados Unidos/epidemiologia , Estudos Longitudinais , Mortalidade/tendências , Fatores de RiscoRESUMO
Purpose: 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.
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STUDY OBJECTIVES: Excessive daytime sleepiness (EDS) is prevalent and overwhelmingly stems from disturbed sleep. We hypothesized that age modulates the association between EDS and increased all-cause mortality. METHODS: We utilized the Veterans' Health Administration data from 1999-2022. We enrolled participants with sleep related ICD9/10 codes or sleep services. A natural language processing (NLP) pipeline was developed and validated to extract the Epworth Sleepiness Scale (ESS) as a self-reported tool to measure EDS from physician progress notes. The NLP's accuracy was assessed through manual annotation of 470 notes. Participants were categorized into Normal-ESS, n-ESS, (ESS 0-10) and high-ESS, h-ESS, (ESS 11-24). We created three age groups: < 50 years; 50 to < 65 years; and ≥ 65 years. The adjusted odds ratio (aOR) of mortality was calculated for age, BMI, sex, race, ethnicity, and the Charlson Comorbidity Index (CCI), using n-ESS as the reference. Subsequently, we conducted age stratified analysis. RESULTS: The first ESS records were extracted from 423,087 veterans with a mean age of 54.8 (±14.6), mean BMI of 32.6 (±6.2), and 90.5% male. The aOR across all ages was 17% higher (1.15,1.19) in the h-ESS category. The aORs only became statistically significant for individuals aged ≥ 50 years in the h-ESS compared to the n-ESS category (< 50 years: 1.02 [0.96,1.08], 50 to < 65 years 1.13[1.10,1.16]; ≥ 65 years: 1.25 [1.21-1.28]). CONCLUSIONS: High ESS, predicted increased mortality only in participants aged 50 and older. Further research is required to identify this differential behavior in relation to age.
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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.
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Aprendizado de Máquina , Fenótipo , Polissonografia , Doença Pulmonar Obstrutiva Crônica , Transtornos do Sono-Vigília , Humanos , Doença Pulmonar Obstrutiva Crônica/fisiopatologia , Doença Pulmonar Obstrutiva Crônica/mortalidade , Doença Pulmonar Obstrutiva Crônica/complicações , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Análise por Conglomerados , Masculino , Feminino , Idoso , Estudos Longitudinais , Pessoa de Meia-Idade , Transtornos do Sono-Vigília/epidemiologia , Transtornos do Sono-Vigília/fisiopatologia , Polissonografia/métodos , Sono/fisiologia , Comorbidade , Qualidade de Vida , Aprendizado de Máquina não Supervisionado , Fatores Etários , Estudos de CoortesRESUMO
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.
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Fragilidade , Insuficiência Cardíaca , Desnutrição , Avaliação Nutricional , Veteranos , Humanos , Masculino , Idoso , Estudos Retrospectivos , Feminino , Desnutrição/diagnóstico , Desnutrição/epidemiologia , Medição de Risco/métodos , Veteranos/estatística & dados numéricos , Fragilidade/complicações , Pessoa de Meia-Idade , Estados Unidos/epidemiologia , Hospitalização/estatística & dados numéricos , Prognóstico , Avaliação Geriátrica/métodos , Avaliação Geriátrica/estatística & dados numéricos , Estado Nutricional , United States Department of Veterans Affairs/estatística & dados numéricos , Idoso de 80 Anos ou maisRESUMO
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.
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Classificação Internacional de Doenças , Polissonografia , Transtorno do Comportamento do Sono REM , Veteranos , Humanos , Transtorno do Comportamento do Sono REM/diagnóstico , Transtorno do Comportamento do Sono REM/fisiopatologia , Masculino , Polissonografia/métodos , Feminino , Veteranos/estatística & dados numéricos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Idoso , AdultoRESUMO
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.
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Pontuação de Propensão , Humanos , Masculino , Feminino , Estados Unidos , Idoso , Pessoa de Meia-Idade , Estudos de Coortes , Veteranos , Prioridades em Saúde , United States Department of Veterans Affairs , Assistência de Longa DuraçãoRESUMO
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.
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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.
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Infarto do Miocárdio , Síndromes da Apneia do Sono , Apneia Obstrutiva do Sono , Humanos , Índice de Massa Corporal , Hipóxia , Obesidade/complicações , Paradoxo da Obesidade , Estudos RetrospectivosRESUMO
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.
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Infarto do Miocárdio , Apneia Obstrutiva do Sono , Veteranos , Humanos , Pessoa de Meia-Idade , Idoso , Idoso de 80 Anos ou mais , Estudos Retrospectivos , Infarto do Miocárdio/complicações , Infarto do Miocárdio/epidemiologia , Apneia Obstrutiva do Sono/complicações , Apneia Obstrutiva do Sono/epidemiologia , Apneia Obstrutiva do Sono/diagnóstico , Comorbidade , Síndrome , Fatores de RiscoRESUMO
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 24â¯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.
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COVID-19 , Deficiência de Glucosefosfato Desidrogenase , Veteranos , Humanos , Masculino , Feminino , Idoso , COVID-19/epidemiologia , SARS-CoV-2 , Estudos de Coortes , Estudos Retrospectivos , Teste para COVID-19 , Deficiência de Glucosefosfato Desidrogenase/complicações , Deficiência de Glucosefosfato Desidrogenase/epidemiologiaRESUMO
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.
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Colangite Esclerosante , Estudo de Associação Genômica Ampla , Humanos , Fenótipo , Fatores Reguladores de Interferon/genética , Polimorfismo de Nucleotídeo ÚnicoRESUMO
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.
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COVID-19 , Veteranos , Humanos , SARS-CoV-2 , Estudos Retrospectivos , Biomarcadores , Proteína C-Reativa , InflamaçãoRESUMO
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.
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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.
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Fragilidade , Insuficiência Cardíaca , Idoso , Humanos , Adulto , Pessoa de Meia-Idade , Idoso de 80 Anos ou mais , Fragilidade/diagnóstico , Idoso Fragilizado , Estudos Retrospectivos , Assistência ao Convalescente , Alta do Paciente , Hospitalização , Aprendizado de Máquina , Avaliação Geriátrica/métodosRESUMO
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.
Assuntos
Insuficiência Cardíaca , Apneia do Sono Tipo Central , Apneia Obstrutiva do Sono , Veteranos , Humanos , Masculino , Estudos Retrospectivos , Hemiplegia/complicações , Apneia Obstrutiva do Sono/complicaçõesRESUMO
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.
Assuntos
COVID-19 , Distúrbios do Início e da Manutenção do Sono , Mídias Sociais , Humanos , COVID-19/epidemiologia , Estudos Retrospectivos , Análise de Sentimentos , Distúrbios do Início e da Manutenção do Sono/epidemiologia , PandemiasRESUMO
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.