Associations Between COVID-19, Delirium, and 1-Year Mortality: Exploring Influences on Delirium Incidence in COVID-19 Patients.

BACKGROUND: This study investigated the relationship between coronavirus disease 2019 (COVID-19), delirium, and 1-year mortality. Factors associated with delirium in COVID-19 patients were identified, along with the influence of psychotropic medications on delirium. METHODS: The study used the South Korean National Health Insurance Service database. Adult COVID-19 patients diagnosed between October 2020 and December 2021 were included, with a propensity score-matched control group. Time-dependent Cox regression assessed associations among COVID-19, delirium, and mortality. Logistic regression analyzed the impact of psychotropic medications on delirium incidence. RESULTS: The study included 832,602 individuals, with 416,301 COVID-19 patients. COVID-19 (hazard ratio [HR], 3.03; 95% confidence interval [CI], 2.92-3.13) and delirium (HR, 2.33; 95% CI, 2.06-2.63) were independent risk factors for 1-year mortality. Comorbidities, insurance type, and residence were also related to mortality. Among COVID-19 patients, antipsychotic use was associated with lower delirium incidence (odds ratio [OR], 0.38; 95% CI, 0.30-0.47), while mood stabilizers (OR, 1.77; 95% CI, 1.40-2.21) and benzodiazepines (OR, 8.62; 95% CI, 7.46-9.97) were linked to higher delirium incidence. CONCLUSION: COVID-19 and delirium are risk factors for 1-year mortality. Some factors associated with delirium in COVID-19 patients are modifiable and can be targeted in preventive and therapeutic interventions.

Psychosocial Factors Associated with dizziness and chronic dizziness: a nationwide cross-sectional study.

BACKGROUND: Dizziness is a common symptom in adults, and chronic dizziness, such as persistent postural-perceptual dizziness, is also frequently reported and affects the quality of life of patients. This study aimed to identify psychosocial factors related to dizziness and chronic dizziness in a large-scale nationwide cohort. METHODS: This population-based cross-sectional study used the database of the Eighth Korea National Health and Nutrition Examination Survey in 2020. Data from 4,147 adults over 40 years old were analyzed, and 1,102 adults who experienced dizziness were included in the dizziness cohort. Demographic data, medical conditions, comorbidities, functional status variables, nutritional variables and psychological variables were collected. The pattern of depressive symptoms according to the severity of dizziness was analyzed by network analysis. RESULTS: The prevalence rate of dizziness was 24.6% in the general population, and chronic dizziness (≥ 3 months) developed in 210 of 1,102 (17.1%) individuals who experienced dizziness. Multiple logistic regression analysis revealed that female sex, stress, and depression were associated with dizziness. Chronic dizziness was related to tympanic abnormalities, diabetes, short sleep duration, and higher levels of stress and depression. Psychomotor retardation/agitation was a central symptom of depression in patients with chronic dizziness. CONCLUSIONS: This study found sex differences in factors associated with dizziness and identified psychosocial factors linked to chronic dizziness. Focusing on somatic factors rather than depressive symptoms may benefit patients with chronic dizziness.

Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).

Objective: Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. Methods: The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Results: Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). Conclusions: We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.

A prospective study of remote delirium screening using the modified K-4AT for COVID-19 inpatients.

Background: Delirium is a neuropsychiatric condition strongly associated with poor clinical outcomes such as high mortality and long hospitalization. In the patients with Coronavirus disease 2019 (COVID-19), delirium is common and it is considered as one of the risk factors for mortality. For those admitted to negative-pressure isolation units, a reliable, validated and contact-free delirium screening tool is required. Materials and methods: We prospectively recruited eligible patients from multiple medical centers in South Korea. Delirium was evaluated using the Confusion Assessment Method (CAM) and 4'A's Test (4AT). The attentional component of the 4AT was modified such that respondents are required to count days, rather than months, backward in Korean. Blinded medical staff evaluated all patients and determined whether their symptoms met the delirium criteria of the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). An independent population of COVID-19 patients was used to validate the 4AT as a remote delirium screening tool. We calculated the area under the receiver operating characteristic curve (AUC). Results: Out of 286 general inpatients, 28 (9.8%) inpatients had delirium. In this population, the patients with delirium were significantly older (p = 0.018) than the patients without delirium, and higher proportion of males were included in the delirium group (p < 0.001). The AUC of the 4AT was 0.992 [95% confidence interval (CI) 0.983-1.000] and the optimal cutoff was at 3. Of the independent COVID-19 patients, 13 of 108 (12.0%) had delirium. Demographically, the COVID-19 patients who had delirium only differed in employment status (p = 0.047) from the COVID-19 patients who did not have delirium. The AUC for remote screening using the 4AT was 0.996 (0.989-1.000). The optimal cutoff of this population was also at 3. Conclusion: The modified K-4AT had acceptable reliability and validity when used to screen inpatients for delirium. More importantly, the 4AT efficiently screened for delirium during remote evaluations of COVID-19 patients, and the optimal cutoff was 3. The protocol presented herein can be used for remote screening of delirium using the 4AT.