Vaccines and the Risk of Hospitalization for Multiple Sclerosis Flare-Ups.

Importance: Scientific literature is sparse about the association of vaccination with the onset of multiple sclerosis (MS) flare-ups. Immunization by vaccines of the entire population is crucially important for public health. Objective: To evaluate the risk of hospitalization for severe MS flare-ups after vaccination in patients with MS. Design, Setting, Participants: This cohort study included patients diagnosed with MS between January 1, 2007, and December 31, 2017, who were included in the System of National Health Databases, a national health claims database in France. In a nested case-crossover analysis, cases were defined by vaccine exposure prior to the onset of hospitalization due to an MS flare-up, and flare-up rates were compared with those that occurred prior to vaccine exposure in up to 4 control time windows immediately preceding the at-risk time window (ie, the MS flare-up) for each patient. Data were analyzed from January 2022 to December 2022. Exposure: Receipt of at least 1 vaccination, including the diphtheria, tetanus, poliomyelitis, pertussis, or Haemophilus influenzae (DTPPHi) vaccine, influenza vaccine, and pneumococcal vaccine, during follow-up. Main Outcomes and Measures: The primary outcome was the risk of hospitalization for an MS flare-up after receipt of a vaccine. Adjusted odds ratios (AORs) and 95% CIs were derived using conditional logistic regression to measure the risk of hospitalization for an MS flare-up associated with vaccination. Results: A total of 106 523 patients constituted the MS cohort (mean [SD] age, 43.9 [13.8] years; 76 471 females [71.8%]; 33 864 patients [31.8%] had incident MS and 72 659 patients [68.2%] had prevalent MS) and were followed up for a mean (SD) of 8.8 (3.1) years. Of these patients, 35 265 (33.1%) were hospitalized for MS flare-ups during the follow-up period for a total of 54 036 MS-related hospitalizations. The AORs of hospitalization for an MS flare-up and vaccine exposure in the 60 days prior to the flare-up were 1.00 (95% CI, 0.92-1.09) for all vaccines, 0.95 (95% CI, 0.82-1.11) for the DTPPHi, 0.98 (95% CI, 0.88-1.09) for the influenza vaccine, and 1.20 (95% CI, 0.94-1.55) for the pneumococcal vaccine. Conclusions and Relevance: A nationwide study of the French population found no association between vaccination and the risk of hospitalization due to MS flare-ups. However, considering the number of vaccine subtypes available, further studies are needed to confirm these results.

Long-term impact of COVID-19 among maintenance haemodialysis patients.

BACKGROUND: Maintenance haemodialysis (MHD) patients have a high risk of initial mortality from coronavirus disease 2019 (COVID-19). However, long-term consequences of this disease in the MHD population are poorly described. We report the clinical presentation, outcome and long-term follow-up of MHD patients affected by COVID-19 in a multicentric cohort from the Paris, France area. METHODS: We conducted a retrospective analysis of clinical presentation and long-term follow-up of MHD patients affected by COVID-19 in 19 MHD centres in the Paris, France area. RESULTS: In this cohort of 248 patients with an initial mortality rate of 18%, age, comorbidities, dyspnoea and previous immunosuppressive treatment were associated with death at <30 days. Among the 203 surviving patients following the acute phase, long-term follow-up (median 180 days) was available for 189 (93%) patients. Major adverse events occurred in 30 (16%) patients during follow-up, including 12 deaths (6%) after a median of 78 days from onset of symptoms. Overall, cardiovascular events, infections and gastrointestinal bleeding were the main major adverse events. Post-COVID-19 cachexia was observed in 25/189 (13%) patients. Lower initial albuminaemia was significantly associated with this cachexia. No reinfection with severe acute respiratory syndrome coronavirus 2 was observed. CONCLUSIONS: This work demonstrates the long-term consequences of COVID-19 in MHD patients, highlighting both initial and long-term severity of the disease, including severe cachexia.

Media use, attention, mental health and academic performance among 8 to 12 year old children.

The rise in digital media consumption, especially among children, raises the societal question of its impact on cognition, mental health and academic achievement. Here, we investigate three different ways of measuring technology use--total hours of media consumed, hours of video game play and number of media used concurrently--in 118 eight-to-twelve year-old children. At stake is the question of whether different technology uses have different effects, which could explain some of the past mixed findings. We collected data about children's media uses as well as (i) attentional and behavioral control abilities, (ii) psychological distress, psychosocial functioning, and sleep, and (iii) academic achievement and motivation. While attentional control abilities were assessed using both cognitive tests and questionnaires, mental health and sleep were all questionnaire-based. Finally, academic performance was based on self-reported grades, with motivational variables being measured through the grit and the growth-mindset questionnaires. We present partial correlation analyses and construct a psychological network to assess the structural associations between different forms of media consumption and the three categories of measures. We observe that children consume large amounts of media and media multitask substantially. Partial correlation analyses show that media multitasking specifically was mostly correlated with negative mental health, while playing video games was associated with faster responding and better mental health. No significant partial correlations were observed for total hours on media. Psychological network analysis complement these first results by indicating that all three ways of consuming technology are only indirectly related to self-reported grades. Thus, technology uses appear to only indirectly relate to academic performance, while more directly affecting mental health. This work emphasizes the need to differentiate among technology uses if one is to understand how every day digital consumption impacts human behavior.

Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures.

PURPOSE: Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians', which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients' and witnesses' descriptions of seizures may assist in the early, accurate diagnosis of patients. METHODS: Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients. RESULTS: The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37-62%). The overall accuracy was not significantly higher than the naïve assumption that all patients have ES (accuracy DF 71%, LR 70%, naïve 68%, p > 0.05). CONCLUSIONS: Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient's seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with "probable" PNES that, in the right clinical context, may warrant further diagnostic assessment.

An objective score to identify psychogenic seizures based on age of onset and history.

OBJECTIVE: Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic. METHODS: Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews. RESULTS: The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES. DISCUSSION: Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.

Identifying psychogenic seizures through comorbidities and medication history.

OBJECTIVE: Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost. METHODS: Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database. RESULTS: Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone. SIGNIFICANCE: An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.