Risk Factors for Delirium Are Different in the Very Old: A Comparative One-Year Prospective Cohort Study of 5,831 Patients

Delirium detection tools show varying completion rates and positive score rates when used at scale in routine practice in general hospital settings: A systematic review

BACKGROUND

Multiple short delirium detection tools have been validated in research studies and implemented in routine care, but there has been little study of these tools in real-world conditions. This systematic review synthesized literature reporting completion rates and/or delirium positive score rates of detection tools in large clinical populations in general hospital settings.

METHODS

PROSPERO (CRD42022385166). Medline, Embase, PsycINFO, CINAHL, and gray literature were searched from 1980 to December 31, 2022. Included studies or audit reports used a validated delirium detection tool performed directly with the patient as part of routine care in large clinical populations (n ≥ 1000) within a general acute hospital setting. Narrative synthesis was performed.

RESULTS

Twenty-two research studies and four audit reports were included. Tools used alone or in combination were the Confusion Assessment Method (CAM), 4 'A's Test (4AT), Delirium Observation Screening Scale (DOSS), Brief CAM (bCAM), Nursing Delirium Screening Scale (NuDESC), and Intensive Care Delirium Screening Checklist (ICDSC). Populations and settings varied and tools were used at different stages and frequencies in the patient journey, including on admission only; inpatient, daily or more frequently; on admission and as inpatient; inpatient post-operatively. Tool completion rates ranged from 19% to 100%. Admission positive score rates ranged from: CAM 8%-51%; 4AT 13%-20%. Inpatient positive score rates ranged from: CAM 2%-20%, DOSS 6%-42%, and NuDESC 5-13%. Postoperative positive score rates were 21% and 28% (4AT). All but two studies had moderate-high risk of bias.

CONCLUSIONS

This systematic review of delirium detection tool implementation in large acute patient populations found clinically important variability in tool completion rates, and in delirium positive score rates relative to expected delirium prevalence. This study highlights a need for greater reporting and analysis of relevant healthcare systems data. This is vital to advance understanding of effective delirium detection in routine care.

A Cross-Sectional, Retrospective, and Comparative Study between Delirium and Non-Delirium Psychiatric Disorders in a Psychogeriatric Inpatient Population Referred to Consultation-Liaison Psychiatry Unit

Background and objectives: Delirium is the most prevalent psychiatric disorder in inpatient older people. Its presence is associated with higher rates of institutionalization, functional disability and mortality. This study aims to evaluate delirium in a hospitalized psychogeriatric population, focusing on which factors predict the appearance of delirium, the impact it generates and the diagnostic concordance between non-psychiatric physicians and psychiatrists. Material and methods: This is an observational, cross-sectional, retrospective, and comparative study. We obtained data from a sample of 1017 patients (≥65 years) admitted to general hospital and referred from different services to the consultation-liaison psychiatry (CLP) unit. Logistic regression was performed using delirium as the dependent variable. To estimate the concordance of the diagnoses, the Kappa coefficient was used. To assess the impact of delirium, an ordinal regression, Wilcoxon median test and Fisher’s test were performed. Results: Delirium is associated with a higher number of visits, OR 3.04 (95% CI 2.38–3.88), longer length of stay and mortality, OR 2.07 (95% CI, 1.05 to 4.10). The model to predict delirium shows that being >75 years old has an OR of 2.1 (95% CI, 1.59–2.79), physical disability has an OR of 1.66 (95% CI, 1.25–2.20), history of delirium has an OR of 10.56 (95% CI, 5.26–21.18) and no use of benzodiazepines has an OR of 4.24 (95% CI, 2.92–6.14). The concordance between the referring physician’s psychiatric diagnosis and the psychiatrist CLP unit showed a kappa of 0.30. When analysing depression and delirium, the concordance showed Kappa = 0.46. Conclusions: Delirium is a highly prevalent psychiatric disorder, but it is still underdiagnosed, with low diagnostic concordance between non-psychiatric doctors and psychiatrists from CLP units. There are multiple risk factors associated with the appearance of delirium, which must be managed to reduce its appearance.

Quality Improvement in Delirium Health Literacy in Older Adult Patients and Their Caregivers Attending a Geriatric Clinic

Background

Delirium is a common medical condition that is highly prevalent in older adults who are at increased risk for its development with any illness, post-surgery or during hospitalization. The purpose of our study was to evaluate the health literacy of older adult patients and their caregivers about delirium, offer a brief educational intervention, and reevaluate their knowledge post intervention.

Materials and Methods

We conducted a quality improvement project, focused on delirium health literacy in older adult patients ≥60 years and their caregivers. Delirium knowledge of participants was evaluated in a pre-education survey after which they were given a delirium education booklet to read. A post-education delirium survey was conducted within 2-3 weeks of the educational intervention. Chi-square test was used to analyze the knowledge base of older adults.

Results

The study population consisted of a total of 70 older adults who participated in pre-education (n=35) and post-education (n=35) surveys. Older adult patients and their caregivers had significant knowledge gaps about the potential causes or etiologies, risk factors, symptomatology, and prevention of delirium in the pre-education survey. After the educational intervention, in the post-education survey, there were overall improvements in knowledge base of older adults in differentiating delirium with dementia (43% vs 94%, p<0.01) recognizing signs and symptoms (77% vs 94%, p<0.05), complications (76% vs 100%, p<0.01) and identifying the etiological factors associated with delirium.

Conclusion

The quality improvement project demonstrated that older adults and caregivers have significant knowledge deficits about the common condition of delirium. This study also demonstrated that older adults were able to improve their health literacy regarding delirium after the intervention. Appropriate education on delirium for patients and caregivers might help in earlier identification, prevention, and better overall management of delirium.

A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome

Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject’s characteristics and the 4AT (the 4 A’s test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients’ characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance.