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Enregistrement W4224316864 · doi:10.1111/jgs.17814

Delirium after <scp>COVID</scp> ‐19 vaccination in nursing home residents: A case series

2022· letter· en· W4224316864 sur OpenAlexaboutno aff
W Mak, Abena A. Prempeh, Eva M. Schmitt, Tamara G. Fong, Edward R. Marcantonio, Sharon K. Inouye, Kenneth S. Boockvar

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2022
Typeletter
Langueen
DomaineMedicine
ThématiqueIntensive Care Unit Cognitive Disorders
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Aging
Mots-clésMedicineVaccinationDeliriumAdverse effectCoronavirus disease 2019 (COVID-19)Family medicinePsychiatryDiseaseInternal medicineImmunologyInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Older adults in nursing homes (NH) are particularly vulnerable to severe illness and death due to COVID-19 infection. Vaccination is associated with reduced risk of infection,1 and vaccinated individuals who develop COVID-19 infection are less likely to experience severe symptoms or death.2 The rate of adverse events after vaccination has been minimal to none.3 However, there have been reports of delirium in older adults after COVID-19 vaccination.4, 5 The objective of this case series was to describe the frequency of delirium and its severity among NH residents after COVID-19 vaccination. This study was conducted at a 514-bed NH in 2021 during 1–2 day initiatives to provide COVID-19 vaccinations to residents. It was ancillary to a larger study, the Better Assessment of Illness (BASIL) II study, designed to improve the assessment of delirium in older adults. Institutional Review Board approval was obtained at the NH and affiliated medical center. Participants were NH residents who were ≥70 years old, English-speaking, and expected to stay in the NH for at least 3 months. Residents who were non-verbal, blind/deaf, non-English-speaking, had active alcohol abuse, or were COVID-19+, were excluded. Participants or their legal surrogates provided informed consent. After enrollment participants were followed for conditions that could precipitate a change in health status. For the current study, COVID-19 vaccination was considered a precipitating condition, and 1 day after vaccination participants were screened for the presence of items in the Confusion Assessment Method-Severity (CAM-S)6 (see Table 1). Those who endorsed any of these items and a random sample of those who endorsed none were selected for structured assessment. Structured assessments were conducted in-person and included the Severe Illness Battery-8 (SIB-8),7 the Montreal Cognitive Assessment (MoCA),8 the Confusion Assessment Method (CAM),9 and CAM-S severity score.6 Presence of delirium was determined using the Diagnostic and Statistical Manual of Mental Disorders (5th edition; DSM-5),10 based on testing at baseline and post-vaccination. Subsyndromal delirium was defined as new onset of delirium symptoms without fulfilling DSM-5 criteria. If delirium symptoms were present after vaccination, a repeat assessment was conducted 2 weeks later. Demographic information, prior history of delirium, and baseline major neurocognitive disorder (dementia) or minor neurocognitive disorder (mild cognitive impairment) using DSM-5 criteria were recorded. Descriptive statistics were used to characterize baseline characteristics and post-vaccination delirium outcomes. Paired t tests were used to test the change in cognitive function scores across time points. Fisher's exact tests were conducted to test whether post-vaccination delirium was associated with having dementia at baseline or a prior history of delirium. Forty participants were included; for 39 participants it was the third vaccination; for 1, the second vaccination. The average age was 82 ± 7 (SD) years; 55% were female, 43% were non-White, and 13% indicated Latino/Hispanic ethnicity. At baseline 65% had major neurocognitive disorder, and 35% had minor neurocognitive disorder. Seven (18%) had a prior history of delirium. The day following COVID-19 vaccination, three (7.5%) had delirium and 1 (2.5%) had subsyndromal delirium (Table 1), for a total of 10% (95% confidence interval 3%–24%). The day following vaccination, these four cases had elevated mean CAM-S scores (8.8 ± 1.5 vs 4.5 ± 1.9; p = 0.003) and reduced MOCA scores (12 ± 4.1 vs 15 ± 4.2; p = 0.014) relative to baseline. None had competing causes of delirium, and all delirium resolved and cognitive scores returned to baseline at 2 weeks (Figure 1). The SIB-8 scores followed the same pattern. In stratified analyses, 3 of 26 (12%) with versus 1 of 14 (7%) without dementia experienced delirium (p = 0.56), and 0 of 7 (0%) with and 4 of 33 (12%) without a prior history of delirium experienced delirium (p = 0.45). In a general population, delirium is uncommon after COVID-19 vaccination,3 but cases of older adults with delirium after COVID-19 vaccination have been reported.4, 5 Vaccination may cause systemic inflammation that adversely affects brain function. In the current study, delirium or subsyndromal delirium of mild to moderate severity was identified in 10% of NH residents the day after vaccination, with no potential competing explanation. Strengths of this study are the inclusion of older adults with physical and cognitive impairment, underrepresented minorities, baseline assessments to facilitate determination of a change in cognition, and rigorous cognitive testing. In this study, delirium after COVID-19 vaccination resolved without complications, which contrasts with complications of COVID-19 infection itself. Thus, the risk–benefit ratio strongly supports vaccination in this population. Nevertheless, because of the heightened risk of delirium and its potential complications in NH residents, clinicians and staff should monitor for delirium after COVID-19 vaccination. The authors have no conflicts of interest. All authors meet the criteria for authorship stated in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals: study concept and design: Kenneth Boockvar, Wingyun Mak, Sharon Inouye; acquisition of data: Wingyun Mak, Abena Prempeh, Kenneth S. Boockvar; analysis and interpretation of data: all authors; drafting of the manuscript: Wingyun Mak; critical revision of the manuscript for important intellectual content and final approval: all authors. This work was supported by funding from the National Institute on Aging under award numbers R01AG044518 (Sharon K. Inouye), R33AG071744 (Sharon K. Inouye), and K24AG035075 (Edward R. Marcantonio). The sponsors had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: Étude de cas
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0040,002
Communication savante0,0010,002
Science ouverte0,0020,002
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,285
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeÉtude de cas
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2022
Routes d'admission1
Résumé présentoui

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