Biological rhythms in COVID-19 vaccine effectiveness in an observational cohort study of 1.5 million patients

BACKGROUNDCircadian rhythms are evident in basic immune processes, but it is unclear if rhythms exist in clinical endpoints like vaccine protection. Here, we examined associations between COVID-19 vaccination timing and effectiveness.METHODSWe retrospectively analyzed a large Israeli cohort with timestamped COVID-19 vaccinations (n = 1,515,754 patients over 12 years old, 99.2% receiving BNT162b2). Endpoints included COVID-19 breakthrough infection and COVID-19-associated emergency department visits and hospitalizations. Our main comparison was among patients vaccinated during morning (800-1159 hours), afternoon (1200-1559 hours), or evening hours (1600-1959 hours). We employed Cox regression to adjust for differences in age, sex, and comorbidities.RESULTSBreakthrough infections differed based on vaccination time, with lowest the rates associated with late morning to early afternoon and highest rates associated with evening vaccination. Vaccination timing remained significant after adjustment for patient age, sex, and comorbidities. Results were consistent in patients who received the basic 2-dose series and who received booster doses. The relationship between COVID-19 immunization time and breakthrough infections was sinusoidal, consistent with a biological rhythm that modifies vaccine effectiveness by 8.6%-25%. The benefits of daytime vaccination were concentrated in younger (\u3c20 years old) and older patients (\u3e50 years old). COVID-19-related hospitalizations varied significantly with the timing of the second booster dose, an intervention reserved for older and immunosuppressed patients (HR = 0.64, morning vs. evening; 95% CI, 0.43-0.97; P = 0.038).CONCLUSIONWe report a significant association between the time of COVID-19 vaccination and its effectiveness. This has implications for mass vaccination programs.FUNDINGNIH

Changes in mental health among U.S. military veterans during the COVID-19 pandemic: A network analysis

Increases of symptoms of posttraumatic stress disorder (PTSD), anxiety and depression have been observed among individuals exposed to potentially traumatic events in the first months of the COVID-19 pandemic. Similarly, associations among different aspects of mental health, such as symptoms of PTSD and suicidal ideation, have also been documented. However, studies including an assessment prior to the onset and during the height of the pandemic are lacking. We investigated changes in symptoms of PTSD, depression, anxiety, suicidal ideation, and posttraumatic growth in a population-based sample of 1232 U.S. military veterans who experienced a potentially traumatic event during the first year of the pandemic. Symptoms were assessed prior to (fall/winter 2019) and one year into the pandemic (fall/winter 2020). We compared changes in symptom interrelations using network analysis, and assessed their associations with pandemic-related PTSD and posttraumatic growth symptoms. A subtle increase in psychopathological symptoms and a decrease in posttraumatic growth was observed one year into the pandemic. The peripandemic network was more densely connected, and pandemic-related PTSD symptoms were positively associated with age, anxiety, worst-event PTSD symptoms, and pandemic-related posttraumatic growth. Our findings highlight the resilience of veterans exposed to a potentially traumatic event during the first year of a pandemic. Similarly, the networks did not fundamentally change from prepandemic to one year into the pandemic. Despite this relative stability on a group level, individual reactions to potentially traumatic events could have varied substantially. Clinicians should individualize their assessments but be aware of the general resilience of most veterans

Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice

Mapping the availability of translated versions of posttraumatic stress disorder screening questionnaires for adults: A scoping review

Background: The most used questionnaires for PTSD screening in adults were developed in English. Although many of these questionnaires were translated into other languages, the procedures used to translate them and to evaluate their reliability and validity have not been consistently documented. This comprehensive scoping review aimed to compile the currently available translated and evaluated questionnaires used for PTSD screening, and highlight important gaps in the literature. Objective: This review aimed to map the availability of translated and evaluated screening questionnaires for posttraumatic stress disorder (PTSD) for adults. Methods: All peer-reviewed studies in which a PTSD screening questionnaire for adults was translated, and which reported at least one result of a qualitative and /or quantitative evaluation procedure were included. The literature was searched using Embase, MEDLINE, and APA PsycInfo, citation searches and contributions from study team members. There were no restrictions regarding the target languages of the translations. Data on the translation procedure, the qualitative evaluation, the quantitative evaluation (dimensionality of the questionnaire, reliability, and performance), and open access were extracted. Results: A total of 866 studies were screened, of which 126 were included. Collectively, 128 translations of 12 different questionnaires were found. Out of these, 105 (83.3%) studies used a forward and backward translation procedure, 120 (95.2%) assessed the reliability of the translated questionnaire, 60 (47.6%) the dimensionality, 49 (38.9%) the performance, and 42 (33.3%) used qualitative evaluation procedures. Thirty-four questionnaires (27.0%) were either freely available or accessible on request. Conclusions: The analyses conducted and the description of the methods and results varied substantially, making a quality assessment impractical. Translations into languages spoken in middle- or low-income countries were underrepresented. In addition, only a small proportion of all translated questionnaires were available. Given the need for freely accessible translations, an online repository was developed

Structural neuroimaging of hippocampus and amygdala subregions in posttraumatic stress disorder: A scoping review

Numerous studies have explored the relationship between posttraumatic stress disorder (PTSD) and the hippo-campus and the amygdala because both regions are implicated in the disorder’s pathogenesis and pathophysiology. Nevertheless, those key limbic regions consist of functionally and cytoarchitecturally distinct substructures that may play different roles in the etiology of PTSD. Spurred by the availability of automatic segmentation software, structural neuroimaging studies of human hippocampal and amygdala subregions have proliferated in recent years. Here, we present a preregistered scoping review of the existing structural neuroimaging studies of the hippocampus and amygdala subregions in adults diagnosed with PTSD. A total of 3513 studies assessing subregion volumes were identified, 1689 of which were screened, and 21 studies were eligible for this review (total N = 2876 individuals). Most studies examined hippocampal subregions and reported decreased CA1, CA3, dentate gyrus, and subiculum volumes in PTSD. Fewer studies investigated amygdala subregions and reported altered lateral, basal, and central nuclei volumes in PTSD. This review further highlights the conceptual and methodological limitations of the current literature and identifies future directions to increase understanding of the distinct roles of hippocampal and amygdalar subregions in posttraumatic psychopathology

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Network Approach Analysis on large VA cohort

Running network analysis on large VA cohort and analyzing PCL scores to assess network of PTSD symptoms and PCL with depression symptoms (measured using PHQ9

Evaluating a novel 8-factor dimensional model of PTSD in U.S. military veterans: Results from the National Health and Resilience in Veterans Study

Background: Accumulating data suggest that the symptom structure of posttraumatic stress disorder (PTSD) may be more nuanced than proposed by prevailing nosological models. Emerging theory further suggests that an 8-factor model with separate internally- (e.g., flashbacks) and externally- (e.g., trauma cue-related emotional reactivity) generated intrusive symptoms may best represent PTSD symptoms. To date, however, scarce research has evaluated the fit of this model and whether index traumas are differentially associated with it in populations at high risk for trauma exposure, such as military veterans. Methods: Data were analyzed from a nationally representative sample of 3,847 trauma-exposed U.S. military veterans who participated in the National Health and Resilience in Veterans Study. Confirmatory factor analyses were conducted to evaluate PTSD symptom structure. Results: The 8-factor model fit the data significantly better than the 7-factor hybrid and 4-factor DSM-5 models. Combat exposure and harming others were more strongly associated with internally-generated intrusions, while interpersonal violence and disaster/accident showed stronger significant associations with externally-generated intrusions. Limitations: The 8-factor model requires validation in non-veteran and more diverse populations, as well as with clinician-administered interviews. Conclusions: Findings support an 8-factor model of PTSD symptoms that separates out internally- and externally-generated intrusions. They also provide preliminary evidence that certain index traumas may lead to differential expression of these intrusive symptoms. Results support that PTSD symptoms may be better characterized by a more nuanced phenotypic structure, which is differentially linked to index traumas

High-threshold-low-tolerance: a model for the emotionality paradox in PTSD

Post-traumatic stress disorder (PTSD) is known as a disorder with volatile mood swings and overexpression of emotions. However, PTSD is also associated with symptoms of emotional numbing, which involve reduced emotional reactivity. To address these contrasting symptoms, we employed a computational approach to investigate whether individuals with PTSD exhibit a faster transition between emotional states and whether the severity of emotional numbing symptoms influences this transition. One thousand forty-five trauma-exposed were assessed for PTSD and rated valenced images through an online platform. Using hierarchical Bayesian modeling, we fitted a five-parameter logistic regression model to the data. We compared the function slope between individuals with probable PTSD (pPTSD) and the control group. Additionally, we examined the association between the slope and the severity of emotional numbing. Our findings revealed that individuals with pPTSD exhibited a higher slope, indicating a faster transition between emotional states. Furthermore, emotional numbing significantly contributed to the slope, with greater severity of numbing associated with a higher slope. This rapid transition between neutral and negative emotional states may have a fundamental role in the symptomatology of PTSD. Moreover, these insights inform targeted therapeutic approaches, emphasizing emotion regulation and mindfulness to manage such abrupt emotional shifts