Clinical Features and Predisposing Factors of Delirium Due to Covid-19 Pneumonia in Intensive Care Units

Ekmekyapar, Tuba; Taşçı, İrem; Ekmekyapar, Muhammed; Şahin, Levent; Delen, Leman Acun

doi:10.22541/au.162745747.78941421/v1

Cited by 3 publications

(3 citation statements)

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“…With a mean age of 81.7±4 years and a huge prevalence of the male sex, the cases described in the present study appear to follow the evidence in the literature, which report a greater risk of developing delirium in males and among the elderly, a well as among patients hospitalized for COVID-19 (75)(76)(77)(78)(79).…”

Section: Discussionsupporting

confidence: 86%

Delirium during COVID‑19: A report of eight cases and a review of the literature

et al. 2022

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Delirium is an acute confusional state, often associated with long-term hospitalization, oxygen supplementation, the male sex and an older age. Since the start of the coronavirus disease 2019 (COVID-19) pandemic, there was an abrupt increase in intensive care unit (ICU) admissions and hospitalization in general, as well as in the need for oxygen therapy and enforced isolation due to the contagion risk. This caused a sudden increase in the episodes of delirium. The diagnosis of delirium, however, remains a difficult task, as it can often be misdiagnosed or confused with underlying dementia, particularly among the elderly. The present study describes present eight cases of patients admitted to hospital due to severe acute respiratory syndrome coronavirus 2 infection, who manifested delirium. Notably, only one of the patients had psychiatric comorbidities prior to hospitalization. The most prevalent sex was the male (7:1) one, the mean age of the patients was 81.7±4 years, and the mean duration of hospitalization was 23.6±6 days. In total, 3 patients had a virological recovery and were discharged, 3 had a clinical recovery and were transferred to a lower intensity COVID-19 facility and 2 patients did not survive. In the eight cases described herein, the mortality rate was 25%. Delirium was found to be commonly associated with a higher mortality rate and a longer hospitalization period. Therefore, it is imperative to develop protocols and tools with which to rapidly assess delirium and treat it accordingly. In addition, it is fundamental to improve the quality of life of hospitalized patients, supporting behavioral therapy and the environmental factors that can affect patients, to prevent delirium as well.

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Section: Discussionsupporting

confidence: 86%

Delirium during COVID‑19: A report of eight cases and a review of the literature

et al. 2022

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“…Unexpectedly, the occurrence of delirium in our COVID-19 cohort was not associated with certain factors previously reported to predispose to a more severe disease course–which might in turn result into a higher risk of delirium. In particular, delirium was not associated with male gender, cardiovascular disease, or metabolic syndrome–factors well associated with a severe disease COVID-19 course [ 36 – 38 ]. This observation is interesting as it suggests that the factors predisposing to a severe disease course of COVID-19 and the factors predisposing to COVID-19-associated delirium partially dissociate.…”

Section: Discussionmentioning

confidence: 99%

Delirium in hospitalized COVID-19 patients: Predictors and implications for patient outcome

et al. 2022

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Introduction Delirium is recognized as a severe complication of coronavirus-disease-2019 (COVID-19). COVID-19-associated delirium has been linked to worse patient outcomes and is considered to be of multifactorial origin. Here we sought to evaluate the incidence and risk factors of delirium in hospitalized COVID-19 patients, along with its impact on clinical outcome. Methods Consecutive adult COVID-19 patients admitted to a tertiary academic referral hospital between March 1st and December 31st, 2020 were included. Potential risk factors for delirium were evaluated, including: age, gender, disease severity (as per the highest WHO grading reached during admission), laboratory parameters for infection and renal function (as per their most extreme values), and presence of comorbidities. To assess the relative strength of risk factors for predicting the occurrence of delirium, we performed a random-forest survival analysis. Results 347 patients with positive COVID-19 PCR test and median age 68.2 [IQR 55.5, 80.5] years were included. Of those, 79 patients (22.8%) developed delirium, 81 (23.3%) were transferred to ICU, 58 (16.7%) died. 163 (73.8%) patients were discharged home, 13 (5.9%) to another hospital, 32 (14.5%) to nursing homes, 13 (5.9%) to rehabilitation with an overall median admission-to-discharge time of 53 [IQR 14, 195] days. The strongest predictors for the occurrence of delirium were blood urea nitrogen (minimal depth value (MD): 3.33), age (MD: 3.75), disease severity (as captured by WHO grading; MD: 3.93), leukocyte count (MD: 4.22), the presence of a neurodegenerative history (MD: 4.43), ferritin (MD: 4.46) and creatinine (MD: 4.59) levels. Conclusion The risk of delirium in COVID-19 can be stratified based on COVID-19 disease severity and–similar to delirium associated with other respiratory infections–the factors advanced age, neurodegenerative disease history, and presence of elevated infection and renal-retention parameters. Screening for these risk factors may facilitate early identification of patients at high-risk for COVID-19-associated delirium.

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“…Although effective preventive interventions for delirium in hospital settings are currently lacking, physicians and healthcare providers can alleviate modifiable risk factors within the APCU by providing exercise programs and family support to reduce the occurrence of delirium 6 . Therefore, early recognition and prevention are essential in patients with risk factors for developing delirium 7 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort

Kim,

Lee,

Woo

et al. 2024

Sci Rep

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This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website (http://ai-wm.khu.ac.kr/Delirium/) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.

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Clinical Features and Predisposing Factors of Delirium Due to Covid-19 Pneumonia in Intensive Care Units

Cited by 3 publications

References 0 publications

Delirium during COVID‑19: A report of eight cases and a review of the literature

Delirium during COVID‑19: A report of eight cases and a review of the literature

Delirium in hospitalized COVID-19 patients: Predictors and implications for patient outcome

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort

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