Detection Of Delirium Research Articles

Assessment of ICU nurses' competency towards delirium among critically ill patients.

ICU nurses play a critical role in managing delirium in critically ill patients, yet their competency in this area remains under-explored. To assess ICU nurses' competency including their knowledge, practice, and attitudes toward delirium among critically ill patients. A correlational descriptive study was conducted with 67 nurses in the medical intensive care unit at Suez Canal University Hospitals. Data was collected using an online survey and analyzed using descriptive statistics, ANOVA, and Pearson's correlation. Nurses showed a moderate knowledge level (M = 8.55), a low practice level (M = 6.62), and positive attitudes (M = 21.65) toward ICU delirium. ANOVA results indicated significant differences in practice scores based on educational level, F (2, 64) = 3.361, p = .041, and in knowledge scores based on ICU experience, F(3, 63) = 6.455, p < .001. Nurses with Master of Science in Nursing (MSN) degrees had higher practice scores than those with diplomas or Bachelor of Science in Nursing (BSN) degrees. There were no significant differences in knowledge and attitude scores based on educational level or age. Gender differences were minimal, with male nurses slightly outperforming female nurses. Correlation analysis showed positive relationships between age, education, ICU experience, and ICU nurses' levels of knowledge and practice. ICU nurses demonstrate a knowledge-practice gap in delirium management. Targeted educational interventions, mentorship programs, and a focus on practical skills are essential to enhance delirium care. These findings highlight the urgent need for comprehensive delirium education and training programs for ICU nurses. By improving nurses' knowledge and practice, healthcare organizations can significantly enhance the early detection, prevention, and management of delirium, ultimately leading to improved patient outcomes and reduced length of stay in the ICU.

Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings.

Delirium is a common and acute neuropsychiatric syndrome that requires timely intervention to prevent its associated morbidity and mortality. Yet, its diagnosis and symptoms are often overlooked due to its variable clinical presentation and fluctuating nature. Thus, in this study, we address the barriers to delirium diagnosis by utilizing a machine learning-based predictive algorithm for incident delirium that relies on archived electronic health records (EHRs) data. We used the Medical Information Mart for Intensive Care (MIMIC) database to create a detailed dataset for identifying delirium in intensive care unit (ICU) patients. Our approach involved training machine learning models on this dataset to pinpoint critical clinical features for delirium detection. These features were then refined and applied to non-ICU patients using EHRs from the American University of Beirut Medical Center (AUBMC). Our study assessed machine learning models like Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Classification and Regression Trees (CART), Random Forest (RF), Neural Oblivious Decision Ensembles (NODE), and Logistic Regression (LR), highlighting superior delirium detection in diverse clinical settings. The CatBoost model excelled in ICU environments with an F1 Score of 89.2%, while XGBoost performed best in general hospital settings with a 75.4% F1 Score. Interpretations using Tabular Local Interpretable Model-agnostic Explanations (LIME) revealed critical indicators such as prothrombin time and hematocrit levels, enhancing model transparency and clinical applicability. These clinical insights help differentiate the delirium predictors between ICU patients, who are often sensitive to various factors. The proposed predictive algorithm improves delirium detection rates and streamlines efficiency in hospital electronic systems, thereby enabling prompt interventions to prevent delirium progression and associated complications. The clinical indicators for delirium that we identified in general hospital settings and ICU can greatly help healthcare professionals identify potential causes of delirium and reduce misdiagnosis.

Delirium detection in the emergency department: A diagnostic accuracy meta-analysis of history, physical examination, laboratory tests, and screening instruments.

Geriatric emergency department (ED) guidelines emphasize timely identification of delirium. This article updates previous diagnostic accuracy systematic reviews of history, physical examination, laboratory testing, and ED screening instruments for the diagnosis of delirium as well as test-treatment thresholds for ED delirium screening. We conducted a systematic review to quantify the diagnostic accuracy of approaches to identify delirium. Studies were included if they described adults aged 60 or older evaluated in the ED setting with an index test for delirium compared with an acceptable criterion standard for delirium. Data were extracted and studies were reviewed for risk of bias. When appropriate, we conducted a meta-analysis and estimated delirium screening thresholds. Full-text review was performed on 55 studies and 27 were included in the current analysis. No studies were identified exploring the accuracy of findings on history or laboratory analysis. While two studies reported clinicians accurately rule in delirium, clinician gestalt is inadequate to rule out delirium. We report meta-analysis on three studies that quantified the accuracy of the 4 A's Test (4AT) to rule in (pooled positive likelihood ratio [LR+] 7.5, 95% confidence interval [CI] 2.7-20.7) and rule out (pooled negative likelihood ratio [LR-] 0.18, 95% CI 0.09-0.34) delirium. We also conducted meta-analysis of two studies that quantified the accuracy of the Abbreviated Mental Test-4 (AMT-4) and found that the pooled LR+ (4.3, 95% CI 2.4-7.8) was lower than that observed for the 4AT, but the pooled LR- (0.22, 95% CI 0.05-1) was similar. Based on one study the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) is the superior instrument to rule in delirium. The calculated test threshold is 2% and the treatment threshold is 11%. The quantitative accuracy of history and physical examination to identify ED delirium is virtually unexplored. The 4AT has the largest quantity of ED-based research. Other screening instruments may more accurately rule in or rule out delirium. If the goal is to rule in delirium then the CAM-ICU or brief CAM or modified CAM for the ED are superior instruments, although the accuracy of these screening tools are based on single-center studies. To rule out delirium, the Delirium Triage Screen is superior based on one single-center study.

Usefulness of the 4A's test for detecting delirium in critically ill patients: a multicenter prospective observation study.

The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) is a robust and reliable instrument for discerning delirium within the specific context of the intensive care unit (ICU). Nonetheless, the CAM-ICU is burdened by various limitations, including a protracted learning curve and the need for frequent daily administration. The 4 A's Test (4AT) was formulated to assess delirium in hospitalized patients and may have distinct advantages over the CAM-ICU, particularly regarding practical applicability within the ICU bundle. This study was performed to assess the utility of the 4AT in detecting delirium in critically ill patients. This multicenter prospective observational study involved critically ill patients at four academic tertiary care hospitals in South Korea from June 2021 to September 2022. In total, 274 patients (median age, 64years; 56.9% men) were included, and 75 (27.4%) developed delirium. The 4AT showed good performance in detecting ICU delirium (area under the curve, 0.879; P < 0.001). The 4AT showed a sensitivity of 74.0%, specificity of 95.4%, positive predictive value of 77.5%, negative predictive value of 94.6%, and accuracy of 91.7% for ICU detection of delirium. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CAM-ICU for detecting ICU delirium were 71.3%, 97.1%, 83.8%, 94.1%, and 92.6%, respectively. The 4AT showed acceptable reliability and validity for detecting ICU delirium in critically ill patients. Because the 4AT is simpler and easier to learn, this scale could be a useful alternative to the CAM-ICU for detecting delirium in critically ill patients.

Results From A Snapshot Survey Of Delirium Prevalence, Detection And Care In Hospitals In Ireland On World Delirium Awareness Day

Abstract Background Delirium, an acute encephalopathy characterized by acute onset, fluctuation, and attention deficits, is associated with increased mortality and morbidity, and with precipitation or worsening of dementia. Thus, delirium prevention, timely detection, and appropriate management are crucial. Methods On World Delirium Awareness Day (15th March 2023), a global survey was assessed delirium prevalence and care. A senior clinical staff member on each participating ward reported on delirium prevalence at 8AM and 8PM, and on usual ward practice; data was entered into an online survey by a data collector (typically an on-site clinician, visiting several wards to record data). This study reports data from Irish hospitals. Results In total, 132 wards from 15 hospitals across Ireland participated. Delirium prevalence, using a formal assessment tool, was 15.9% across all wards, highest in geriatric wards (20.5%). However, on wards where 'personal judgment' to detect delirium was utilized, the prevalence rate was 11.5%, implying that delirium may be missed on these wards. Intensive care units (76.2%), geriatric wards (70%), and Emergency Departments/Acute Medical Units (ED/AMUA; 66.7%) had the highest rate of written delirium protocols. Similarly, staff training (51.5% overall), was highest in ED/AMAU (88.9%), followed by general surgical (66.7%), and geriatrics wards (60%). In contrast, only 30.8% (4/13) of non-acute wards had provided staff with delirium training in the preceding year. Having at least one training session in the preceding year was associated with higher use of formal delirium tools (18.8% versus 60.3%). Wards reported staff training/educating as the main care improvement priority; 72.7% of wards identified lack of time to train staff as a key barrier. Conclusion Delirium is prevalent in Irish hospitals and appears under-detected without formal screening. Training staff in delirium-related skills, including the use of simple screening tools for delirium detection and causation, could be paramount to improving patient outcomes.

Detecting Delirium: The Missed Medical Emergency

Abstract Background Delirium is among the most common of medical emergencies with approximately 1 in 5 patients aged 65 and above presenting with or developing delirium, during their acute hospital admission. Delirium is more common in certain patient groups including those with hip fractures, dementia, previous delirium, frailty and other comorbidities. It is associated with poorer outcomes and complications. Delirium also has the potential to cause significant patient and carer stress. Despite the significance of delirium, it is frequently underdiagnosed. It can be prevented and treated if it is identified early. As per the National Dementia Office all acute Hospitals have to complete the Delirium care algorithm for all patients aged 65 years and above, admitted to hospital or who have any known risk factors for delirium. Methods The Delirium Multidisciplinary Care Bundle was rolled out across the hospital with education provided both face to face and via eLearning. Using the Quality Care Metrics (QCM) delirium indicator and a locally developed audit tool, medical and nursing records where reviewed to identify the level of concordance with the Delirium Multidisciplinary Care Bundle. Results The gradual increase in the compliance in use of the care bundle reached up to hundred percent. The bundle use was audited utilising the Quality Care Metrics (QCM) with the delirium indicator showing an improvement of an average of 60-80% hospital-wide. The hospital-wide audit on the compliance of the delirium care bundle was conducted and identified that compliance on the bundle had improved to 60-100%. Conclusion Continuous evaluation, monitoring and improvement plans are in place. This will be further supported by providing multiple methods of education delivery throughout the organisation to meet the needs of all learners. Ultimately continuing to improve delirium detection and management for our patient group.

Comparison of the Validity of the PRE-DELIRIC model and the E-PRE-DELIRIC model for Predicting Delirium in patients after Cardiac Surgery

Purpose: The purpose of this study was to analyze the validity of the PRE-DELIRIC model and E-PRE-DELIRIC model.Methods: Patients who underwent cardiac surgery at a tertiary hospital between January 2019 and December 2022 were included. The presence or absence of delirium was determined based on risk groups, and the sensitivity, specificity, positive predictive power, and negative predictive power were verified using the Youden index. Receiver operating characteristic curves were derived for the PRE-DELIRIC model and E-PRE-DELIRIC model, the area under the curve was calculated, and the 95% confidence interval (CI) was presented.Results: The incidence of delirium was 28.8%. The average scores of the PRE-DELIRIC model and E-PRE-DELIRIC model were 49.72±29.91 and 18.14±0.41, respectively. In the PRE-DELIRIC model, for which a cut-off point of 14.05 was established using the Youden index, the sensitivity, specificity, positive predictive power, and negative predictive power were 64.8%, 83.2%, 61.0%, and 85.4%, respectively. In the E-PRE-DELIRIC model, with a cut-off point of 64.01, the corresponding indicators were 73.1%, 51.5%, 37.8%, and 82.6%, respectively. The areas under the curve for the PRE-DELIRIC model and the E-PRE-DELIRIC model were 0.77 (95% CI, 0.74~0.82) and 0.65 (95% CI, 0.61~0.69), respectively.Conclusion: It may be helpful to use both the E-PRE-DELIRIC and PRE-DELIRIC models. The two prediction models are expected to help improve the quality of nursing for the early detection and prevention of delirium, enabling intensive observations and interventions for patients at high risk of delirium.

Long-Term Impact of Delirium on the Risk of Dementia in Hospitalized Older Patients: A Real-World Multicenter Study

BackgroundThe association between delirium and dementia has been suggested, but mostly in the postoperative setting. This study aims to explore this relationship in a broader inpatient population, leveraging extensive real-world data to provide a more generalized understanding. MethodsIn this retrospective cohort study, electronic health records of 11,970,475 hospitalized patients aged over 60 from nine institutions in South Korea were analyzed. Patients with and without delirium were identified, and propensity score matching (PSM) was used to create comparable groups. A 10-year longitudinal analysis was conducted using the Cox proportional hazards model, which calculated the hazard ratio (HR) and 95% confidence interval (CI). Additionally, a meta-analysis was performed, aggregating results from all nine medical institutions. Lastly, we conducted various subgroup and sensitivity analyses to demonstrate the consistency of our study results across diverse conditions. ResultsAfter 1:1 PSM, a total of 47,306 patients were matched in both the delirium and nondelirium groups. Both groups had a median age group of 75–79 years, with 43.1% being female. The delirium group showed a significantly higher risk of all dementia compared with the nondelirium group (HR: 2.70 [95% CI: 2.27–3.20]). The incidence risk for different types of dementia was also notably higher in the delirium group (all dementia or mild cognitive impairment, HR: 2.46 [95% CI: 2.10–2.88]; Alzheimer's disease, HR: 2.74 [95% CI: 2.40–3.13]; vascular dementia, HR: 2.55 [95% CI: 2.07–3.13]). This pattern was consistent across all subgroup and sensitivity analyses. ConclusionsDelirium significantly increases the risk of onset for all types of dementia. These findings highlight the importance of early detection of delirium and prompt intervention. Further research studies are warranted to investigate the mechanisms linking delirium and dementia.

Epigenetic signals associated with delirium replicated across four independent cohorts

Delirium is risky and indicates poor outcomes for patients. Therefore, it is crucial to create an effective delirium detection method. However, the epigenetic pathophysiology of delirium remains largely unknown. We aimed to discover reliable and replicable epigenetic (DNA methylation: DNAm) markers that are associated with delirium including post-operative delirium (POD) in blood obtained from patients among four independent cohorts. Blood DNA from four independent cohorts (two inpatient cohorts and two surgery cohorts; 16 to 88 patients each) were analyzed using the Illumina EPIC array platform for genome-wide DNAm analysis. We examined DNAm differences in blood between patients with and without delirium including POD. When we compared top CpG sites previously identified from the initial inpatient cohort with three additional cohorts (one inpatient and two surgery cohorts), 11 of the top 13 CpG sites showed statistically significant differences in DNAm values between the delirium group and non-delirium group in the same directions as found in the initial cohort. This study demonstrated the potential value of epigenetic biomarkers as future diagnostic tools. Furthermore, our findings provide additional evidence of the potential role of epigenetics in the pathophysiology of delirium including POD.

The accuracy of bedside attention tests for delirium detection in older outpatients with cognitive impairment

BackgroundTo determine the test accuracy of four observations, three explorations and four short tests commonly used in psychiatric examinations to detect attention disorders related to delirium in older outpatients. MethodWe performed a test accuracy study among 86 older patients referred for cognitive screening to an outpatient clinic of a psychiatric institution. Eleven index tests were applied without knowledge of the reference standard. A geriatrician or psychiatrist blinded to the results of the index tests determined the reference diagnosis with DSM-IV-TR criteria. Observations were lack of adequate/ regular eye-contact, dozing off during conversation, dozing off when not stimulated, and losing the point of story. Explored was lack of concentration during activities, difficulty following conversation, and being easily distracted. Serial 7s, WORLD spelled backward, months of the year backward (MOTYB) and counting back from 20 to 0 were applied. Test accuracy was calculated for the single tests and combinations. ResultsDozing off during conversation scored high specificity (97.9 %) as did dozing off when not stimulated (97.9 %), but both had low sensitivity (< 27.9 %). Test accuracy of the exploration questions was low. Serial 7s and WORLD spelled backwards had high sensitivity (92.7 %, respectively 80.5 %) but low specificity (< 51.1 % %). The best combination was MOTYB with WORLD spelled backward (sensitivity of 63.4 %, specificity of 77.8 %). ConclusionsNo single observation, exploration or short test of attention was found to have both high sensitivity and specificity. Probably, attention deficits in delirium are best detected with a combination of observations and short tests as part of a comprehensive psychiatric examination.

Long-short-view aware multi-agent reinforcement learning for signal snippet distillation in delirium movement detection

Automatic movement analysis utilizing surveillance video is believed to be an important and convenient way for timely delirium detection in an Intensive Care Unit (ICU). However, video-based delirium movement detection (DMD) faces inherent challenges: 1) Irregular movements with large differences in the four limbs of a patient; 2) similar movements in delirium and normal situations, and large movement variations between patients experiencing delirium. To address the challenges 1, this paper proposes a Long-Short-View Aware Multi-Agent Reinforcement Learning (LS-MARL) method to identify the most representative movement snippet for DMD, considering that the global state provided by the long-view is important for guiding the agent's decision-making, but ignored in existing MARL methods. The proposed LS-MARL has two novel designs. First, a novel Teacher Auxiliary Policy (TAP) is developed for direction preperception of the representative movement snippet. Second, a new reward mechanism, Team Intrinsic Reward (TIR), is introduced to quantify the contribution of each agent. Experiments demonstrate that the proposed LS-MARL method outperforms state-of-the-art methods. Furthermore, to handle the second challenge, a new Self-Adjusting Ensemble Learning (SAEL) strategy is built to adaptively integrate an optimal classifier combination from multidomain features, which further improves the performance of classification tasks in the proposed LS-MARL method.

Screening and detection of delirium in the acute palliative care setting: Performance of the Months of the Year Backwards (MOTYB) test

Screening and detection of delirium in the acute palliative care setting: Performance of the Months of the Year Backwards (MOTYB) test

An Initial Audit of Delirium Detection and Management in an Intensive Care Setting

An Initial Audit of Delirium Detection and Management in an Intensive Care Setting

Detection of delirium in older patients-A point prevalence study in surgical and non-surgical hospital wards.

To (i) determine the prevalence of delirium and identify delirium subtypes in surgical and non-surgical patients aged ≥65 years, (ii) determine whether certain precipitating factors affect the prevalence of delirium and (iii) review patients' medical records for description of delirium symptoms and the presence of International Classification of Diseases (ICD-10) coding for delirium in discharge summaries. Despite being a robust predictor of morbidity and mortality in older adults, delirium might be inadequately recognised and under-reported in patients' medical records and discharge summaries. A point prevalence study (24-h) of patients ≥65 years from surgical and non-surgical wards was therefore conducted in a tertiary university hospital. The study was approved by the Data Protection Officer at the university hospital (2018/3454). Patients were assessed for delirium with 4AT and delirium subtypes with the Delirium Motor Subtype Scale. Information about room transfers, need and use of sensory aids and medical equipment was collected onsite. Patients' medical records were reviewed for description of delirium symptoms and of ICD-10 codes. Overall, 123 patients were screened (52% female). Delirium was identified in 27% of them. Prevalence was associated with advanced age (≥85 years). The uncharacterised delirium subtype was most common (36%), followed by hypoactive (30%), hyperactive (24%) and mixed (9%). There were significant associations between positive screening tests and the need and use of sensory aids. Delirium symptoms were described in 58% of the patients who tested positive for delirium and the ICD-10 code for delirium was registered in 12% of these patients' discharge summaries. The high prevalence of delirium and limited use of discharge codes highlight the need to improve the identification of delirium in hospital settings and at discharge. Increased awareness and detection of delirium in hospital settings are vital to improve patient care.

The 4AT, a rapid delirium detection tool for use in hospice inpatient units: Findings from a validation study

Background: Delirium is a serious neuropsychiatric syndrome with adverse outcomes, which is common but often undiagnosed in terminally ill people. The 4 ‘A’s test or 4AT (www.the4AT.com), a brief delirium detection tool, is widely used in general settings, but validation studies in terminally ill people are lacking. Aim: To determine the diagnostic accuracy of the 4AT in detecting delirium in terminally ill people, who are hospice inpatients. Design: A diagnostic test accuracy study in which participants underwent the 4AT and a reference standard based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The reference standard was informed by Delirium Rating Scale Revised-98 and tests assessing arousal and attention. Assessments were conducted in random order by pairs of independent raters, blinded to the results of the other assessment. Setting/participants: Two hospice inpatient units in Scotland, UK. Participants were 148 hospice inpatients aged ⩾18 years. Results: A total of 137 participants completed both assessments. Three participants had an indeterminate reference standard diagnosis and were excluded, yielding a final sample of 134. Mean age was 70.3 (SD = 10.6) years. About 33% (44/134) had reference standard delirium. The 4AT had a sensitivity of 89% (95% CI 79%–98%) and a specificity of 94% (95% CI 90%–99%). The area under the receiver operating characteristic curve was 0.97 (95% CI 0.94–1). Conclusion: The results of this validation study support use of the 4AT as a delirium detection tool in hospice inpatients, and add to the literature evaluating methods of delirium detection in palliative care settings. Trial registry: ISCRTN 97417474.

A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study

ObjectivesTo create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass. Research Methodology/DesignThis prospective, observational study was conducted in the Cardiac Intensive Care Unit at a Children's Hospital, enrolling 501 pediatric patients from February 2022 to January 2023. Perioperative data were systematically collected through the hospital information system. Postoperative delirium was assessed using the Cornell Assessment of Pediatric Delirium (CAPD). For model development, Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify the most relevant predictors. These selected predictors were then incorporated into a multivariable logistic regression model to construct the predictive nomogram. The performance of the model was evaluated by Harrell's concordance index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. External validity of the model was confirmed through the C-index and calibration plots. ResultsFive independent predictors were identified: age, SpO2 levels, lymphocyte count, diuretic use, and midazolam administration, integrated into a predictive nomogram. This nomogram demonstrated strong predictive capacity (AUC 0.816, concordance index 0.815) with good model fit (Hosmer-Lemeshow test p = 0.826) and high accuracy. Decision curve analysis showed a significant net benefit, and external validation confirmed the nomogram's reliability. ConclusionsThe study successfully developed a precise and effective nomogram for identifying pediatric patients at high risk of post-cardiopulmonary bypass delirium, incorporating age, SpO2 levels, lymphocyte counts, diuretic use, and midazolam medication. Implications for Clinical PracticeThis nomogram aids early delirium detection and prevention in critically ill children, improving clinical decisions and treatment optimization. It enables precise monitoring and tailored medication strategies, significantly contributes to reducing the incidence of delirium, thereby enhancing the overall quality of patient care.

Improving the Detection, Assessment, Management and Prevention of Delirium in Hospices (the DAMPen-D study): Feasibility study of a flexible and scalable implementation strategy to deliver guideline-adherent delirium care

Background: Delirium is a complex condition, stressful for all involved. Although highly prevalent in palliative care settings, it remains underdiagnosed and associated with poor outcomes. Guideline-adherent delirium care may improve its detection, assessment and management. Aim: To inform a future definitive study that tests whether an implementation strategy designed to improve guideline-adherent delirium care in palliative care settings improves patient outcomes (reduced proportion of in-patient days with delirium). Design: With Patient Involvement members, we conducted a feasibility study to assess the acceptability of and engagement with the implementation strategy by hospice staff (intervention), and whether clinical record data collection of process (e.g. guideline-adherent delirium care) and clinical outcomes (evidence of delirium using a validated chart-based instrument;) pre- and 12-weeks post-implementation of the intervention would be possible. Setting/participants: In-patient admissions in three English hospices. Results: Between June 2021 and December 2022, clinical record data were extracted from 300 consecutive admissions. Despite data collection during COVID-19, target clinical record data collection (n = 300) was achieved. Approximately two-thirds of patients had a delirium episode during in-patient stay at both timepoints. A 6% absolute reduction in proportion of delirium days in those with a delirium episode was observed. Post-implementation improvements in guideline-adherent metrics include: clinical delirium diagnosis 15%–28%; delirium risk assessment 0%–16%; screening on admission 7%–35%. Conclusions: Collection of data on delirium outcomes and guideline-adherence from clinical records is feasible. The signal of patient benefit supports formal evaluation in a large-scale study.

Spanish Translation and Cultural Adaptation of the Intensive Care Unit Delirium Playbook.

A lack of high-quality provider education hinders the delivery of standard-of-care delirium detection and prevention practices in the intensive care unit (ICU). To fill this gap, we developed and validated an e-learning ICU Delirium Playbook consisting of eight videos and a 44-question knowledge assessment quiz. Given the increasing Spanish-speaking population worldwide, we translated and cross-culturally adapted the playbook from English into Spanish. To translate and culturally adapt the ICU Delirium Playbook into Spanish, the second most common native language worldwide. The translation and cross-cultural adaptation process included double forward and back translations and harmonization by a 14-person interdisciplinary team of ICU nurses and physicians, delirium experts, methodologists, medical interpreters, and bilingual professionals representing many Spanish-speaking global regions. After a preeducation quiz, a nurse focus group completed the playbook videos and posteducation quiz, followed by a semistructured interview. The ICU Delirium Playbook: Spanish Version maintained conceptual equivalence to the English version. Focus group participants posted mean (standard deviation) pre- and post-playbook scores of 63% (10%) and 78% (12%), with a 15% (11%) pre-post improvement (P = 0.01). Participants reported improved perceived competency in performing the Confusion Assessment Method for the ICU and provided positive feedback regarding the playbook. After translation and cultural adaptation, the ICU Delirium Playbook: Spanish Version yielded significant knowledge assessment improvements and positive feedback. The Spanish playbook is now available for public dissemination.

SQiD talks: A qualitative study about starting conversations with the single question in delirium (SQiD)

Objectives The Single Question in Delirium (SQiD) is a widely used delirium detection tool utilising discussion between clinicians and informants. This study aims to understand how the SQiD works in clinical settings. Methods Using qualitative methodology, with a grounded theory framework, informant interviews were analysed to understand better how the SQiD works in clinical contexts. Participants were the adult relatives, carers, or friends (informants) of inpatients in an oncology ward at an acute hospital in Sydney, Australia. The informant was an available person whom staff would ordinarily approach for collateral information. The SQiD was administered and recorded by nursing staff. The recording was transcribed verbatim, checked, and then thematically analysed independently by two researchers. Themes/subthemes were determined and discussed until consensus was reached, then reviewed with a third researcher. Patient demographics, including documented diagnosis of delirium, were extracted from their Electronic Medical Record. Results Of 29 interviews, 15 patients screened positive for delirium, six of whom had a documented diagnosis of delirium. Emergent themes included recognition of “confusion”, operational factors, and the SQiD outcome. The overarching themes were clinician investment and interest in the process, communication techniques, and knowledge of delirium and other cognitive disorders. Conclusions This study indicates that the SQiD’s usefulness might be enhanced by providing clinicians with specific education about delirium, and differentiation between delirium and other neurocognitive disorders. Moreover, education could be accompanied by measures to encourage clinicians to extend SQiD discussions, act on SQiD findings, and embed the SQiD in clinical practice through implementation strategies. Clinician investment was inconsistent and warrants further investigation.

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.

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. 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. 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. 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.