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Record W4224316864 · doi:10.1111/jgs.17814

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

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

Bibliographic record

VenueJournal of the American Geriatrics Society · 2022
Typeletter
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsMedicineVaccinationDeliriumAdverse effectCoronavirus disease 2019 (COVID-19)Family medicinePsychiatryDiseaseInternal medicineImmunologyInfectious disease (medical specialty)

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.285
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations11
Published2022
Admission routes1
Has abstractyes

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