Tina Moghadam Fard; Atiye Sarabi-Jamab; Amirhossein Mohammadi; Nafiseh Sami; Mahdieh Saeidi; Behnam Shariati; Leila Kamalzadeh; Soode Tajik Esmaeeli; Zeynab Yassin; Fatemeh Sadat Mirfazeli

doi:10.4266/acc.004944

Articles

Page Path: HOME > Acute Crit Care > Volume 40(3); 2025 > Article

Original Article Neurology COVID-19 delirium versus non–COVID-19 delirium in Iran: a computational approach: Acute and Critical Care 2025;40(3):462-472.
DOI: https://doi.org/10.4266/acc.004944
Published online: July 21, 2025

Tina Moghadam Fard^1,2

, Atiye Sarabi-Jamab³

, Amirhossein Mohammadi⁴

, Nafiseh Sami⁴

, Mahdieh Saeidi⁵

, Behnam Shariati⁶

, Leila Kamalzadeh⁷

, Soode Tajik Esmaeeli⁸

, Zeynab Yassin⁹

, Fatemeh Sadat Mirfazeli^2,8,10

¹Cellular and Molecular Research Center, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran

²National Brain Center, Iran University of Medical Sciences, Tehran, Iran

³Faculty of Governance, University of Tehran, Tehran, Iran

⁴School of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran

⁵Research Center for Addiction and Risky Behaviors, Iran University of Medical Sciences, Tehran, Iran

⁶Mental Health Research Center, Psychosocial Health Research Institute (PHRI), Department of Psychiatry, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

⁷Geriatric Mental Health Research Center, Department of Psychiatry, School of Medicine, Iran University of Medical Psychiatry, Tehran, Iran

⁸Mental Health Research Center, Psychosocial Health Research Institute, Department of Psychiatry, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

⁹Antimicrobial Resistance Research Center, Institute of Immunology and Infectious Diseases, Iran University Of Medical Sciences, Tehran, Iran

¹⁰Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran

Corresponding author: Fatemeh Sadat Mirfazeli National Brain Center, Iran University of Medical Sciences, Tehran 1449614535, Iran Tel: +98-91-2424-5389, Email: f_m_tums@yahoo.com

• Received: December 21, 2024 • Revised: May 7, 2025 • Accepted: May 8, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

2,904 Views
53 Download

prev next

Full Article

Download PDF

Abstract
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
KEY MESSAGES
NOTES
SUPPLEMENTARY MATERIALS
REFERENCES

Abstract

Background
Delirium is an acute condition marked by disturbances in cognition, awareness, and attention, commonly observed in hospitalized patients due to factors such as illness severity and medication. It is particularly prevalent in intensive care unit settings, affecting up to 80% of ventilated patients. This study investigates whether coronavirus disease 2019 (COVID-19) delirium aligns with expectations of non–COVID-19 delirium incidence in other hospitalized patients and identifies unique or common factors contributing to delirium in these groups.
Methods
An observational cross-sectional study was conducted on 107 hospitalized patients diagnosed with delirium, comprising 56 COVID-19 patients and 51 non–COVID-19 patients. Data were collected through detailed medical record reviews and structured interviews with patients and their caregivers to evaluate factors associated with delirium.
Results
The findings revealed a higher prevalence of medication-related stressors in COVID-19 delirium compared to non–COVID-19 delirium. This suggests that overmedication may play a critical role in the development of delirium, regardless of the underlying critical illness condition.
Conclusions
This study highlights the significant association between medication stressors and COVID-19 delirium. These findings emphasize the importance of minimizing unnecessary medications and closely monitoring pharmacological treatments to reduce delirium incidence and improve outcomes in hospitalized populations.
Key Words: COVID-19; delirium; polypharmacy

INTRODUCTION

Delirium is an acute, fluctuating condition characterized by disturbances in cognition, awareness, and attention which is arising from another medical condition and not attributable to an existing neurocognitive disorder [1]. Patients may develop delirium in hospital settings due to the severity of their illness and factors such as sedation, immobilization, cerebral metabolic insufficiency, neuroinflammation, imbalances in neurotransmitter levels, and disruptions in network connectivity [2]. Delirium is the most frequent form of brain dysfunction associated with critical illness and occurs in approximately 60% to 80% of patients undergoing mechanical ventilation and in 20% to 50% of those who are hospitalized but not ventilated [2,3]. This condition poses a significant clinical challenge for healthcare providers, and its high prevalence underscores the importance of managing it in clinical settings [4]. Delirium significantly affects health outcomes, particularly in adults and the elderly, where it is linked to higher mortality rates during and after hospitalization [5]. Delirium not only increases mortality but also brings additional challenges. It causes significant psychological and physical burden on effected individuals, and additionally, places a considerable burden on healthcare systems, amplifying existing pressures and complicating the management of patients conditions [6]. This condition is multifactorial and it is influenced by both predisposing factors such as elderly, cognitive impairments, frailty, medical and psychiatric conditions, alcohol consumption, poor nutrition, and sensory impairments, and precipitating factors like acute medical illness, surgery, dehydration or drug effects. These elements combine to determine an individual's susceptibility to delirium [7,8].

Delirium is one of the neuropsychiatric manifestations observed in patients with coronavirus disease 2019 (COVID-19) [9], a viral disease that, as of April 7, 2024, has resulted in over 704 million confirmed cases and more than 7 million fatalities worldwide [10]. Common manifestations of this disease include fever, cough, altered sense of taste and olfactory disturbances, dyspnea, and gastrointestinal symptoms. Additionally, various other symptoms such as cutaneous manifestations, cardiovascular involvement, and neurological manifestations including coma, encephalopathy, and delirium have been reported [11,12]. In a recent review study, it was found that delirium manifested in approximately 30% of patients diagnosed with COVID-19 [13]. Studies have shown that the incidence of delirium in COVID-19 patients admitted to the intensive care unit (ICU) is higher than in non–COVID-19 patients within the same ICU setting [12,14-16]. Older age, medical and psychiatric comorbidities, as well as the use of certain medications, are identified as key risk factors for delirium in COVID-19 patients [17]. In COVID-19 pandemic, the strong desire to actively combat COVID-19 has resulted in the overmedication and extensive use of drugs that lack proven effectiveness for its treatment [18]. Polypharmacy can lead to a higher medication burden and is associated with an increased risk of adverse drug reactions, decreased quality of life, drug-drug interactions, and medication errors [19,20]. Furthermore, polypharmacy itself has been recognized as a risk factor for the development of delirium [21], further complicating the management of COVID-19 patients.

This neuropsychiatric condition in COVID-19 patients was correlated with higher mortality rates, tripling the risk of death compared to those not experiencing delirium [13]. Given that delirium in COVID-19 patients is independently linked to higher mortality rates [22] identifying stressors and implementing strategies to minimize its occurrence are crucial. Given the higher prevalence of COVID-19 delirium and the associated increase in mortality and morbidity, investigating the contributing factors and stressors of COVID-19 delirium during the pandemic is crucial. Our primary goal in this study is to examine delirium and its associated stressors in the context of an unknown, emerging disease compared to established well-known diseases.

MATERIALS AND METHODS

Ethical Considerations

Ethical approval for this study was obtained from the Ethics Committee of Iran University of Medical Sciences (No. IR.IUMS.REC.1399.1040). Informed consent was obtained from all participants or, if they were unable, from a first-degree relative (legally authorized representative) after they received a comprehensive explanation of the study's objectives, the methods to be used, and their rights as participants. Participant privacy was strictly protected by anonymizing the data to prevent any identification of the individuals involved (Figure 1).

The text of this article has been reviewed by artificial intelligence (AI; GPT-4o) for grammar, fluency, and language enhancement after its completion. The authors reviewed and edited the content as necessary after using this tool and take full responsibility for the published article's content.

Study Design and Participants

This observational cross-sectional study focused on patients with delirium. Randomly selected hospitalized patients at a university-based hospital; from 2021 to 2022, who required psychiatric consultation for signs of delirium and whose diagnosis was confirmed by a psychiatrist were included. Delirium diagnosis was made based on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria [1], using medical history and mental status tests. Patients were randomly selected from those seeking consultation for delirium to mitigate selection bias. A random sampling method was employed to minimize selection bias, with numbers assigned to eligible patients and a random number generator used for selection. Patients were categorized into two groups: COVID-19 delirium and non–COVID-19 delirium. The diagnosis of COVID-19 was based on symptoms, PCR (Polymerase chain reaction) test results, and chest computed tomography findings.

Inclusion criteria included patients were hospitalized and diagnosed with delirium based on DSM-5 criteria. Exclusion criteria were as follows. (1) Patients with an uncertain diagnosis of delirium. (2) Patients with multiple diagnoses, including both COVID-19 and non–COVID-19 conditions leading to hospitalization. Additionally, patients who contracted COVID-19 during their hospitalization were also excluded. (3) Patients with at least two stressor information missing, or those we could not contact to complete their data for any reason, were excluded.

Data Collection

Data were collected on patient age, sex, education level, delirium signs, medications being taken at the time of delirium onset, substance abuse history, medical history, and psychiatric history from their medical records. Total mortality included both in-hospital mortality and mortality after a 6-month follow-up. Patients were followed up during hospitalization and after 6 months. Any patients with incomplete data were excluded from the study. Standardized protocols were utilized to ensure consistency and minimize information bias across all subjects.

Data was categorized as follow. (1) Age: 60 years or older was considered a stressor. (2) Education: lack of education was considered a stressor. (3) Medications: use of 5 or more medications was considered a stressor. (4) Substance abuse: presence of a substance abuse history in the medical records was considered a stressor. (5) Psychiatric history: the presence of at least one psychiatric condition such as personality disorders, anxiety disorders, psychosis, mood disorders, or other disorders based on DSM-5 was considered a stressor. (6) Medical history: having at least one medical condition was classified as a stressor, whereas patients reporting no medical conditions were categorized as not having a medical stressor.

Statistical Analysis

The aim of this study was to investigate the differences between COVID-19 and non–COVID-19 delirium and to determine the differentiating criteria between the two groups. The analysis was performed using R (version 4.4.1) with R Studio (version 2024.04.2-764). Data manipulation was conducted with the "dplyr" and "rlang" packages, while "ggplot2" was used for data visualizations. For numerical data, we used the mean and standard deviation to describe normally distributed data, and proportions to summarize categorical data. The chi-square test was employed to determine whether the frequencies in each category were statistically different from each other. Stressors and In-hospital mortality rates were analyzed using a chi-square test, and differences in age between the groups were evaluated using the Wilcoxon rank-sum test. To identify the predictor stressor that differentiates COVID-19 delirium from non–COVID-19 delirium, multivariate analyses using generalized linear model regression analysis was performed. In this study, a decision tree approach as a computational method to analyze the retrospective data was employed. Unlike traditional statistical methods that might rely on predefined assumptions and manual analyses, a decision tree is a machine learning algorithm that automatically identifies patterns and complex relationships within the data. We utilized the R software and the rpart package to construct and analyze the decision tree model. This involved steps such as data loading, model building using stressors to predict whether a case of delirium was COVID-19 delirium or non–COVID-19 delirium, and visualizing the resulting tree. The use of a decision tree allowed us to explore potential non-linear relationships and interactions between variables and to develop a data-driven predictive/classification model, which distinguishes our approach from a typical retrospective study that might primarily rely on descriptive or inferential statistical analyses. The relationship between the stressor and the dependent variable was simultaneously examined using these models, while the influence of potential confounding factors was controlled. By adjusting for multiple predictor variables, including confounders, the specific effect of the stressor was isolated. We applied the rpart() function, specifying the model formula, dataset, and method parameters. The importance of each variable in the model were assessed. Medications were categorized into the following groups: corticosteroids, antibiotics/antivirals, cardiovascular medications, non-steroidal anti-inflammatory drugs, sedatives, psychotropic medications, anticholinergics, and others. A generalized linear model was then used to assess the relationships between these medication categories and COVID-19 versus non–COVID-19 delirium.

RESULTS

A total of 119 delirium patients were randomly selected. Twelve patients were excluded due to incomplete information, lack of consent, or unwillingness to participate and 107 patients were included in the study. Among them, 56 patients were hospitalized due to COVID-19, with a mean age of 64.8 years (standard deviation [SD], 16.7 years), and the remaining 51 patients were hospitalized due to other causes, with a mean age of 61.7 years (SD, 21.9 years). These other causes included multiple traumas, sepsis, encephalitis, suicide attempts, acute coronary syndrome, seizures, and postoperative conditions. Information on drug consumption, education, and medical history was incomplete for a subset of patients. However, these individuals were not excluded from the study because their remaining data were considered sufficient for analysis. Patients with at least two stressor information missing, or those we could not contact to complete their data for any reason, were excluded. Demographic information of the participants is presented in Table 1. Age comparisons between the two groups indicated no significant difference (W=1,468, P=0.805) (Figure 2). The number of medications was compared between the two groups, and the results showed a significantly higher number of medications among COVID-19 patients compared to non–COVID-19 patients (mean, 9.63; SD, 4.13 in COVID-19 patients vs. median, 3; range, 0–11 in non–COVID-19 patients; P=5.15 ×10⁻¹²) (Figure 3).

The presence of medication stressors was compared between the two groups and the result revealed a significantly higher number of individuals with medication stressors among COVID-19 patients compared to non–COVID-19 patients (92.3% in COVID-19 patients vs. 19.1% in non–COVID-19 patients P=1.91 ×10⁻¹³) (Figure 4). Substance stressor was also significantly more prevalent among non–COVID-19 patients (8.9% in COVID-19 vs. 37.2% in non–COVID-19 patients, P=0.0004) (Figure 5). No significant difference in educational, psychiatric, or medical stressors was found between the two groups (P-value in order=0.54, 0.18, and 0.45) (Figure 6). The observed total mortality rate was higher in the non–COVID-19 patient group than in the COVID-19 patient group, with rates of 35.2% and 21.4%, respectively. However, this difference did not reach statistical significance (P=0.11) (Figure 7).

A (generalized) linear model regression analysis was performed to identify the predictor stressor that differentiates COVID-19 delirium from non–COVID-19 delirium. The analysis aimed to ascertain which stressor could be used to predict whether a case of delirium was COVID-19 delirium or not. The variables included as potential stressors in the analysis were age stressor, medication stressor, substance stressor, psychiatric stressor, and medical stressor. It was found through the regression analysis that the presence of substance and medication stressors were significantly associated with the delirium classification as either COVID-19 delirium or non–COVID-19 delirium among patients. As previously indicated, it was observed that the prevalence of substance stressor was significantly higher in non–COVID-19 patients.

Conversely, the presence of patients with medication stressor was significantly more frequent in COVID-19 patients (Table 2). Analysis of the decision tree indicates that the presence of the medication stressor is the most significant factor in differentiating COVID-19 delirium and non–COVID-19 delirium (Figure 8).

Furthermore, the significance of each predictor in the model can be assessed using the variable. The result showed that the medication stressor emerged as the most influential predictor (variable importance=19.796), following the substance stressor (4.525), the age stressor (3.394), and the medical stressor (2.262), are arranged in decreasing order of importance, respectively (Table 3). To differentiate between medication categories, a generalized linear model was used to assess the relationship between various medications and COVID-19 vs. non–COVID-19 delirium. The results showed a significant positive relationship between corticosteroids and antibacterial/antiviral medications and COVID-19 delirium, highlighting the potential role of these medications in the development of delirium in COVID-19 patients (Table 4). A graphical abstract summarizing the study design, participant selection, data collection, and main outcomes (Supplementary Material 1).

DISCUSSION

This study evaluated 107 hospitalized delirium patients—56 with COVID-19 and 51 without. The results indicate a significantly higher prevalence of substance stressors among the non–COVID-19 patients. In contrast, medication stressors were notably more common among the COVID-19 patients. Importantly, the presence of medication stressors emerged as the most significant differentiating factor between COVID-19 delirium and non–COVID-19 delirium, serving as the most influential predictor of the condition's occurrence. Between different medication categories, corticosteroids and antibacterial/antiviral medications were significantly associated with COVID-19 delirium.

Delirium is associated with increased mortality rates and longer hospital stays in adults and the elderly, while in children, it does not raise mortality but still lengthens hospitalization [6,8,23]. This condition imposes significant psychological, physical, and systemic burdens, complicating patient management and intensifying pressures on healthcare systems [6,8,23]. A recent meta-analysis of nine studies showed that delirium was observed in 27% of COVID-19 patients and was independently associated with a higher mortality rate among critically ill COVID-19 patients who were hospitalized [15].

Considering the complications associated with delirium in patients, an important question arises: how can we reduce its incidence? Among the factors that increase the risk of delirium are various medications [7]. Do critically ill patients experience delirium primarily because of their severe medical conditions, or is it due to the overmedication often prescribed for critically ill patients? The results of our study indicated that medication stressors could distinguish between COVID-19 delirium and non–COVID-19 delirium. Although both groups experienced critical conditions, medication stressor was still a factor that could separate these two groups. This suggests that overmedication in COVID-19 patients, independent of their critical condition, can be a factor that flares up delirium.

When the COVID-19 pandemic emerged, the novel disease induced widespread fear, affecting everyone, including medical staff, who were urgently seeking effective treatments. Consequently, a variety of drugs such as antibiotics, corticosteroids, anticoagulants, immune boosters, antiretrovirals, anti-inflammatories, and etc. were prescribed, often irrationally, before their effectiveness was definitively established. This led to overmedication in many patients [18,24]. Overmedication among older adults often leads to several adverse effects, including increased risk of medication errors and drug interactions, falls, urinary incontinence, weight loss, frailty, cognitive impairment, and episodes of delirium. Consequently, this practice complicates the management of patient care and contributes to rising healthcare expenditures [21]. A study involving 602 patients, investigated the outcomes of various treatment strategies for delirium in individuals averaging 71.6 years of age. The findings revealed that a more severe and persistent form of delirium was associated with a higher frequency of psychotropic drug use [25]. Numerous drugs administered to critically ill patients are linked to the onset of delirium. These include analgesics, anticholinergics, antidepressants, antipsychotics, corticosteroids, sedatives, dopaminergic agents, prokinetics, antihistamines, and cardiac medications. The mechanisms through which these drugs may induce delirium include GABAminergic activity, anticholinergic effects, disruptions in serotonin function, elevated dopaminergic or glucocorticoid activity, and other yet unidentified processes [26].

Given the significant role of medication stressors in the development of delirium, it is essential to focus on non-pharmacological interventions to reduce its incidence. Key nonpharmacological interventions for delirium prevention and management include minimizing unnecessary transfers between wards or rooms, assessing patients' delirium risk, providing cognitive stimulation, ensuring adequate hydration, promoting mobility, managing pain, addressing sensory impairments, and maintaining proper sleep hygiene. Specific interventions may involve reorientation techniques (e.g., providing clocks and calendars), encouraging regular mobility and active exercises, resolving reversible sensory impairments (e.g., ensuring availability and proper use of hearing aids and glasses), and fostering restful sleep by minimizing nighttime disruptions [27]. Multi-component interventions have been shown to be more effective in preventing delirium compared to single-component interventions. The most effective combination of interventions includes proper sleep hygiene and cognitive stimulation, combined with pain control, mobilization, and patient assessment [28]. Implementing these interventions can help mitigate delirium risk and improve overall patient outcomes, especially in vulnerable populations such as those with COVID-19.

In summary, our findings emphasize the critical need for vigilant medication management in hospitalized patients, particularly those with COVID-19 who face a higher burden of medication stressors. By minimizing unnecessary medications and implementing close monitoring of pharmacological treatments, healthcare providers can potentially reduce the incidence of delirium and improve outcomes in this vulnerable population.

This study highlights the critical role of medication stressors in differentiating between COVID-19 delirium and non–COVID-19 delirium. Our findings indicate that patients experiencing COVID-19 delirium exhibit a significantly higher burden of medication stressors compared to those with non–COVID-19 delirium. This distinction underscores the importance of careful medication management in hospitalized patients, particularly in the context of COVID-19. Given the established link between delirium and increased mortality and morbidity, it is imperative to adopt strategies aimed at minimizing the number of medications prescribed. Implementing thorough monitoring and control of pharmacological treatments can potentially reduce the incidence of delirium in this vulnerable population.

This study's strengths include the application of advanced statistical techniques, such as generalized linear models and decision trees, offers a nuanced understanding of the distinctions between COVID-19 and non–COVID-19 delirium, providing actionable insights. However, this study has several limitations. As a single-center study with a limited sample size, the findings should be interpreted with caution, as they may not fully represent broader patient populations. Additionally, the cross-sectional design does not allow for causal conclusions, and the exclusion of patients with incomplete data could introduce some degree of selection bias.

Future studies should aim to validate these findings using larger, multi-center cohorts to enhance generalizability. Longitudinal designs would be particularly valuable to better assess the progression and causal relationships of delirium in COVID-19 patients over time. Additionally, incorporating diverse patient populations across various healthcare settings would provide a more comprehensive understanding of the contributing factors and ensure findings are applicable to a wider range of clinical scenarios.

KEY MESSAGES

▪ Coronavirus disease 2019 (COVID-19) patients with delirium consumed more medication than non–COVID-19 patients.

▪ Medication consumption differentiates COVID-19 delirium from non–COVID-19 delirium.

▪ Minimizing prescribed drugs is suggested in hospitalized patients.

NOTES

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

None.

ACKNOWLEDGMENTS

We wish to express our deepest gratitude to all the patients and their families who participated in this study. Your willingness to contribute has been invaluable in advancing our understanding of COVID-19 and non–COVID-19 delirium. We also thank the medical staff and colleagues for their support and collaboration.

AUTHOR CONTRIBUTIONS

Conceptualization: BS, LK, STE, ZY, FSM. Methodology: ASJ, FSM. Validation: BS, FSM. Formal analysis: ASJ. Data curation: TMF, AM, NS, MS. Visualization: TMF, ASJ. Project administration: FSM. Writing - original draft: TMF, ASJ. Writing - review & editing: TMF, BS, LK, STE, ZY, FSM. All authors read and agreed to the published version of the manuscript.

SUPPLEMENTARY MATERIALS

Supplementary materials can be found via https://doi.org/10.4266/acc.004944.

Supplementary Material 1.

COVID-19 delirium versus non-COVID-19 delirium, A Computational Approach

acc-004944-Supplementary-Material-1.pdf

Figure 1.

Study flowchart illustrating the selection process for participants included in the study. The flowchart details the inclusion and exclusion criteria, random sampling process, and final distribution of patients into coronavirus disease 2019 (COVID-19) delirium and non–COVID-19 delirium groups. DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Figure 2.

Age comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. The figure shows the distribution of patients' ages in both groups, with no statistically significant difference observed. (A) Age distribution of COVID-19 and non–COVID-19 delirium groups. Median ages and interquartile ranges are similar, with no significant difference observed. (B) Age distribution by sex. Both males and females show comparable median ages and ranges across the groups, with no notable differences.

Figure 3.

Comparison of the number of medications between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. COVID-19 patients had a significantly higher number of medications.

Figure 4.

Medication stressor comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. The proportion of individuals with medication stressors was significantly higher in COVID-19 patients (92.3%) compared to non–COVID-19 patients (19.1%) (P=1.91 ×10⁻¹³).

Figure 5.

Substance stressor comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. Non–COVID-19 patients had a significantly higher prevalence of substance stressors (37.2%) compared to COVID-19 patients (8.9%) (P=0.0004).

Figure 6.

Comparison of educational, psychiatric, and medical stressors between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. No statistically significant differences were found between the two groups for these stressors. (A) Educational stressors were reported by 16% of COVID-19 patients and 21.2% of non-COVID-19 patients. (B) Psychiatric stressors were reported by 50% of COVID-19 patients and 37.2% of non-COVID-19 patients. (C) Medical stressors were reported by 92.7% of COVID-19 patients and 72.5% of non-COVID-19 patients.

Figure 7.

Total mortality comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. No statistically significant differences were found between the two groups.

Figure 8.

Decision tree analysis for differentiating coronavirus disease 2019 (COVID-19)-associated delirium from non–COVID-19-associated delirium. The decision tree identifies the presence of a medication stressor as the most significant factor distinguishing the two groups. In each split, the left branch represents cases that meet the condition (Yes), and the right branch represents those that do not meet the condition (No). Node numbers are automatically assigned by the analysis software and may not be sequential.

Table 1.

Demography information of COVID-19 and non–COVID-19 patients

Variable	COVID-19 patient	Non–COVID-19 patient
Number	56	51
Age (yr)	65±17	62±22
Sex (male)	34/56 (60.7)	28/51 (54.9)
Age stressor	29/56 (51.7)	28/51 (54.9)
Education stressor	9/56 (16)	7/33 (21.2)
Medication stressor	48/52 (92.3)	9/47 (19.1)
Substance stressor	5/56 (8.9)	19/51 (37.2)
Psychiatry stressor	28/56 (50.0)	19/51 (37.2)
Medical stressor (%)	82.2	87.7
Total mortality (%)	21.4	35.2

Values are presented as mean±standard deviation or number (%) unless otherwise indicated.

COVID-19: coronavirus disease 2019.

Table 2.

Results of a generalized linear model regression analysis identifying the predictor stressor that distinguishes COVID-19 delirium from non–COVID-19 delirium

Effect	Coefficient	SE	t-value	P-value	95% CI	OR
Intercept	0.397	0.104	3.804	3.17 ×10⁻⁴	1.212–1.824	1.49
Age stressor	–0.157	0.084	–1.876	0.065	0.726–1.007	0.85
Medication stressor	0.752	0.084	8.930	6.59 ×10⁻¹³	1.799–2.503	2.12
Substance stressor	–0.285	0.094	–3.016	0.004	0.625–0.905	0.75
Psychiatry stressor	–0.096	0.086	–1.120	0.267	0.768–1.075	0.91
Medical stressor	–0.120	0.088	–1.364	0.177	0.747–1.054	0.89

COVID-19: coronavirus disease 2019; SE: standard error; OR: odds ratio.

Table 3.

Predictor importance in decision tree model

Stressor	Importance of the attribute to differentiate COVID-19 delirium from non–COVID-19 delirium
Medication stressor	19.796
Substance stressor	4.525
Age stressor	3.394
Medical stressor	2.262

The medication stressor is the most significant factor.

COVID-19: coronavirus disease 2019.

Table 4.

Logistic regression analysis of different medication categories for COVID-19 vs. non–COVID-19 delirium

Predictor	Estimate	t-value	P-value	95% CI	OR
Intercept	0.163	2.288	0.025	1.020–1.358	1.177
Medication stressor	0.444	4.921	4.38 ×10⁻⁶	1.306–1.860	1.559
Corticosteroid	0.292	3.477	8.13 ×10⁻⁴	1.132–1.584	1.339
Cardiovascular medication	–0.055	–0.908	0.366	0.836–1.070	0.946
NSAID	–0.052	–0.852	0.397	0.835–1.079	0.949
Anticholinergic	0.056	0.557	0.579	0.870–1.287	1.058
Sedatives	–0.023	–0.365	0.716	0.863–1.106	0.977
Antibiotic/antiviral	0.281	3.830	2.49 ×10⁻⁴	1.141–1.535	1.324
Psychotropic medication	–0.032	–0.453	0.652	0.843–1.113	0.969
Other medications	–0.105	–1.554	0.124	0.780–1.039	0.900
Substance stressor	–0.172	–2.434	0.017	0.732–0.968	0.842

COVID-19: coronavirus disease 2019; OR: odds ratio; NSAID: non-steroidal anti-inflammatory drug.

REFERENCES

1. European Delirium Association; American Delirium Society. The DSM-5 criteria, level of arousal and delirium diagnosis: inclusiveness is safer. BMC Med 2014;12:141.Article PubMed
2. Wilcox ME, Burry L, Englesakis M, Coman B, Daou M, van Haren FM, et al. Intensive care unit interventions to promote sleep and circadian biology in reducing incident delirium: a scoping review. Thorax 2024;79:988-97.Article PubMed PMC
3. Palakshappa JA, Hough CL. How we prevent and treat delirium in the ICU. Chest 2021;160:1326-34.Article PubMed
4. Bellelli G, Nobili A, Annoni G, Morandi A, Djade CD, Meagher DJ, et al. Under-detection of delirium and impact of neurocognitive deficits on in-hospital mortality among acute geriatric and medical wards. Eur J Intern Med 2015;26:696-704.Article PubMed
5. Witlox J, Eurelings LS, de Jonghe JF, Kalisvaart KJ, Eikelenboom P, van Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA 2010;304:443-51.Article PubMed
6. Gunther ML, Morandi A, Ely EW. Pathophysiology of delirium in the intensive care unit. Crit Care Clin 2008;24:45-65.Article PubMed PMC PDF
7. Wilson JE, Mart MF, Cunningham C, Shehabi Y, Girard TD, MacLullich AM, et al. Delirium. Nat Rev Dis Primers 2020;6:90.Article PubMed PDF
8. Tachibana M, Inada T. Poor prognostic impact of delirium: especially on mortality and institutionalisation. Psychogeriatrics 2023;23:187-95.Article PubMed
9. Jarcuskova D, Kozarova M, Bali-Hudak M, Lackova A, Gazda J. Characteristics of delirium among COVID-19 patients. Bratisl Lek Listy 2024;125:50-4.
10. Worldometer. COVID-19 coronavirus pandemic [Internet]. Worldometer; 2024 [cited 2025 Apr 10]. Available from: https://www.worldometers.info/coronavirus/#google_vignettePubMed
11. Ochani R, Asad A, Yasmin F, Shaikh S, Khalid H, Batra S, et al. COVID-19 pandemic: from origins to outcomes: a comprehensive review of viral pathogenesis, clinical manifestations, diagnostic evaluation, and management. Infez Med 2021;29:20-36.Article PubMed PMC
12. Rego LL, Salluh JI, Souza-Dantas VC, Silva JR, Póvoa P, Serafim RB. Delirium severity and outcomes of critically ill COVID-19 patients. Crit Care Sci 2023;35:394-401.Article PubMed PDF
13. Shao SC, Lai CC, Chen YH, Chen YC, Hung MJ, Liao SC. Prevalence, incidence and mortality of delirium in patients with COVID-19: a systematic review and meta-analysis. Age Ageing 2021;50:1445-53.Article PubMed PMC PDF
14. Martinotti G, Bonanni L, Barlati S, Miuli A, Sepede G, Prestia D, et al. Delirium in COVID-19 patients: a multicentric observational study in Italy. Neurol Sci 2021;42:3981-8.Article PubMed PMC
15. Pranata R, Huang I, Lim MA, Yonas E, Vania R, Kuswardhani RA. Delirium and mortality in coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Arch Gerontol Geriatr 2021;95:104388.Article PubMed PMC
16. Pun BT, Badenes R, Heras La Calle G, Orun OM, Chen W, Raman R, et al. Prevalence and risk factors for delirium in critically ill patients with COVID-19 (COVID-D): a multicentre cohort study. Lancet Respir Med 2021;9:239-50.Article PubMed
17. Guo Y, Lin J, Wu T, Zhou T, Mu Y. Risk factors for delirium among hospitalized adults with COVID-19: a systematic review and meta-analysis of cohort studies. Int J Nurs Stud 2023;148:104602.Article PubMed PMC PDF
18. Martinez-Rivera RN, Taype-Rondan A. Overmedication in COVID-19 context: a report from Peru. J Clin Pharmacol 2020;60:1155-6.Article PubMed
19. Pesante-Pinto JL. Clinical pharmacology and the risks of polypharmacy in the geriatric patient. Phys Med Rehabil Clin N Am 2017;28:739-46.Article PubMed
20. Iqbal A, Matthews F, Hanratty B, Todd A. How should a physician assess medication burden and polypharmacy? Expert Opin Pharmacother 2022;23:1-4.
21. Glaser I. Polypharmacy and delirium in the elderly. Praxis (Bern 1994) 2023;112:335-9.Article PubMed PMC
22. Garcez FB, Aliberti MJ, Poco PC, Hiratsuka M, Takahashi SF, Coelho VA, et al. Delirium and adverse outcomes in hospitalized patients with COVID-19. J Am Geriatr Soc 2020;68:2440-6.Article PubMed
23. Burry L, Hutton B, Williamson DR, Mehta S, Adhikari NK, Cheng W, et al. Pharmacological interventions for the treatment of delirium in critically ill adults. Cochrane Database Syst Rev 2019;9:CD011749.Article PubMed
24. Little MO. The burden of overmedication: what are the real issues? J Am Med Dir Assoc 2016;17:97-8.Article PubMed
25. Zipser CM, Knoepfel S, Hayoz P, Schubert M, Ernst J, von Känel R, et al. Clinical management of delirium: the response depends on the subtypes: an observational cohort study in 602 patients. Palliat Support Care 2020;18:4-11.Article PubMed
26. Garpestad E, Devlin JW. Polypharmacy and delirium in critically ill older adults: recognition and prevention. Clin Geriatr Med 2017;33:189-203.Article PubMed
27. National Institute for Health and Care Excellence (NICE). Delirium: prevention, diagnosis and management in hospital and long-term care [Internet]. NICE; 2023 [cited 2025 Apr 10]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK553009/
28. Matsuura Y, Ohno Y, Toyoshima M, Ueno T. Effects of non-pharmacologic prevention on delirium in critically ill patients: a network meta-analysis. Nurs Crit Care 2023;28:727-37.Article PubMed PDF

Figure & Data

References

Citations

Citations to this article as recorded by

PubReader
ePub Link

Cite

CITE: export Copy Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

COVID-19 delirium versus non–COVID-19 delirium in Iran: a computational approach

Acute Crit Care. 2025;40(3):462-472. Published online July 21, 2025

DOI: https://doi.org/10.4266/acc.004944

XML Download

Figure

COVID-19 delirium versus non–COVID-19 delirium in Iran: a computational approach

Figure 1. Study flowchart illustrating the selection process for participants included in the study. The flowchart details the inclusion and exclusion criteria, random sampling process, and final distribution of patients into coronavirus disease 2019 (COVID-19) delirium and non–COVID-19 delirium groups. DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Figure 2. Age comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. The figure shows the distribution of patients' ages in both groups, with no statistically significant difference observed. (A) Age distribution of COVID-19 and non–COVID-19 delirium groups. Median ages and interquartile ranges are similar, with no significant difference observed. (B) Age distribution by sex. Both males and females show comparable median ages and ranges across the groups, with no notable differences.

Figure 3. Comparison of the number of medications between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. COVID-19 patients had a significantly higher number of medications.

Figure 4. Medication stressor comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. The proportion of individuals with medication stressors was significantly higher in COVID-19 patients (92.3%) compared to non–COVID-19 patients (19.1%) (P=1.91 ×10⁻¹³).

Figure 5. Substance stressor comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. Non–COVID-19 patients had a significantly higher prevalence of substance stressors (37.2%) compared to COVID-19 patients (8.9%) (P=0.0004).

Figure 6. Comparison of educational, psychiatric, and medical stressors between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. No statistically significant differences were found between the two groups for these stressors. (A) Educational stressors were reported by 16% of COVID-19 patients and 21.2% of non-COVID-19 patients. (B) Psychiatric stressors were reported by 50% of COVID-19 patients and 37.2% of non-COVID-19 patients. (C) Medical stressors were reported by 92.7% of COVID-19 patients and 72.5% of non-COVID-19 patients.

Figure 7. Total mortality comparison between coronavirus disease 2019 (COVID-19) and non–COVID-19 delirium patients. No statistically significant differences were found between the two groups.

Figure 8. Decision tree analysis for differentiating coronavirus disease 2019 (COVID-19)-associated delirium from non–COVID-19-associated delirium. The decision tree identifies the presence of a medication stressor as the most significant factor distinguishing the two groups. In each split, the left branch represents cases that meet the condition (Yes), and the right branch represents those that do not meet the condition (No). Node numbers are automatically assigned by the analysis software and may not be sequential.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

COVID-19 delirium versus non–COVID-19 delirium in Iran: a computational approach

Variable	COVID-19 patient	Non–COVID-19 patient
Number	56	51
Age (yr)	65±17	62±22
Sex (male)	34/56 (60.7)	28/51 (54.9)
Age stressor	29/56 (51.7)	28/51 (54.9)
Education stressor	9/56 (16)	7/33 (21.2)
Medication stressor	48/52 (92.3)	9/47 (19.1)
Substance stressor	5/56 (8.9)	19/51 (37.2)
Psychiatry stressor	28/56 (50.0)	19/51 (37.2)
Medical stressor (%)	82.2	87.7
Total mortality (%)	21.4	35.2

Effect	Coefficient	SE	t-value	P-value	95% CI	OR
Intercept	0.397	0.104	3.804	3.17 ×10⁻⁴	1.212–1.824	1.49
Age stressor	–0.157	0.084	–1.876	0.065	0.726–1.007	0.85
Medication stressor	0.752	0.084	8.930	6.59 ×10⁻¹³	1.799–2.503	2.12
Substance stressor	–0.285	0.094	–3.016	0.004	0.625–0.905	0.75
Psychiatry stressor	–0.096	0.086	–1.120	0.267	0.768–1.075	0.91
Medical stressor	–0.120	0.088	–1.364	0.177	0.747–1.054	0.89

Stressor	Importance of the attribute to differentiate COVID-19 delirium from non–COVID-19 delirium
Medication stressor	19.796
Substance stressor	4.525
Age stressor	3.394
Medical stressor	2.262

Predictor	Estimate	t-value	P-value	95% CI	OR
Intercept	0.163	2.288	0.025	1.020–1.358	1.177
Medication stressor	0.444	4.921	4.38 ×10⁻⁶	1.306–1.860	1.559
Corticosteroid	0.292	3.477	8.13 ×10⁻⁴	1.132–1.584	1.339
Cardiovascular medication	–0.055	–0.908	0.366	0.836–1.070	0.946
NSAID	–0.052	–0.852	0.397	0.835–1.079	0.949
Anticholinergic	0.056	0.557	0.579	0.870–1.287	1.058
Sedatives	–0.023	–0.365	0.716	0.863–1.106	0.977
Antibiotic/antiviral	0.281	3.830	2.49 ×10⁻⁴	1.141–1.535	1.324
Psychotropic medication	–0.032	–0.453	0.652	0.843–1.113	0.969
Other medications	–0.105	–1.554	0.124	0.780–1.039	0.900
Substance stressor	–0.172	–2.434	0.017	0.732–0.968	0.842

Table 1. Demography information of COVID-19 and non–COVID-19 patients

Values are presented as mean±standard deviation or number (%) unless otherwise indicated.

COVID-19: coronavirus disease 2019.

Table 2. Results of a generalized linear model regression analysis identifying the predictor stressor that distinguishes COVID-19 delirium from non–COVID-19 delirium

COVID-19: coronavirus disease 2019; SE: standard error; OR: odds ratio.

Table 3. Predictor importance in decision tree model

The medication stressor is the most significant factor.

COVID-19: coronavirus disease 2019.

Table 4. Logistic regression analysis of different medication categories for COVID-19 vs. non–COVID-19 delirium

COVID-19: coronavirus disease 2019; OR: odds ratio; NSAID: non-steroidal anti-inflammatory drug.