Delirium: the invisible syndrome

Gnerre, Paola; Regina, Micaela La; Bozzano, Chiara; Pomero, Fulvio; Re, Roberta; Meschi, Michele; Montemurro, Domenico; Marchetti, Annalisa; Lillo, Mariangela Di; Tirotta, Daniela

doi:10.4081/itjm.2016.626

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Healthcare staff may significantly underestimate the incidence of delirium in different healthcare settings (Awad, 2019). Therefore, healthcare providers must improve their knowledge and ability to recognize delirium early, manage modifiable risk factors, and provide better care during the acute phase (Gnerre et al, 2016). Enhancing nurses' knowledge and skills concerning delirium and its recognition is necessary to implement delirium screening successfully.…”

Section: Discussionmentioning

confidence: 99%

Nurses’ Knowledge and Ability to Diagnose Delirium in Intensive Care Units of Iraq Teaching Hospitals

Ghezeljeh,

Jaber Muhaibes,

Haghani

et al. 2023

JCCNC

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Background: Timely diagnosis and effective treatment of delirium, a clinical syndrome, significantly impact the outcomes of patients in intensive care units (ICUs). Despite its significance and prevalence, delirium continues to be underdiagnosed. This study aims to determine the knowledge and capability of ICU nurses in Iraq in recognizing delirium syndrome. Methods: This descriptive cross-sectional study was conducted on 154 nurses working in the ICUs of hospitals in Babylon, Al-Diwaniyah, and Karbala cities, Iraq, in 2022. The subjects were recruited by the census. The study data were collected using a demographic questionnaire, case vignettes, and the delirium knowledge questionnaire (DKQ) to assess participants’ characteristics, delirium recognition, and delirium knowledge. The data were analyzed using an independent t-test and Pearson correlation coefficient in SPSS software, version 22. The significance level was set at P<0.05. Results: The Mean±SD score of nurses’ overall knowledge was 46.73±14.34. The Mean±SD score for their understanding of delirium’s incidence, symptoms, signs, and effects was 43.57±17.70, whereas the Mean±SD score for knowledge of its risk factors and causes was 49.7±18.74. Their overall ability to recognize delirium had a Mean±SD score of 1.44±1.13. There was a significant relationship between knowledge of delirium, participation in a delirium training course (P=0.008), and work experience in ICUs (P=0.046). Conclusion: Given the results of our study, it is crucial to develop plans for increasing the knowledge and recognition ability of ICU nurses in Iraq regarding delirium.

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Section: Discussionmentioning

confidence: 99%

Nurses’ Knowledge and Ability to Diagnose Delirium in Intensive Care Units of Iraq Teaching Hospitals

Ghezeljeh,

Jaber Muhaibes,

Haghani

et al. 2023

JCCNC

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“…A person with dementia has severe impairments in memory, language, problem solving, and other thinking abilities [ 4 ]. In most cases, delirium is defined as a state of acute disturbance of consciousness accompanied by a change in cognition during the day [ 5 , 6 ], whereas MCI is characterized by loss of memory and other cognitive abilities in individuals [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

et al. 2022

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Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

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Nurses’ experiences in the management of delirium among older persons in acute care ward settings: A qualitative systematic review and meta-aggregation

Min

Trina

Ignacio

2022

International Journal of Nursing Studies

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Delirium: the invisible syndrome

Cited by 4 publications

References 8 publications

Nurses’ Knowledge and Ability to Diagnose Delirium in Intensive Care Units of Iraq Teaching Hospitals

Nurses’ Knowledge and Ability to Diagnose Delirium in Intensive Care Units of Iraq Teaching Hospitals

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

Nurses’ experiences in the management of delirium among older persons in acute care ward settings: A qualitative systematic review and meta-aggregation

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