Delirium Assessment Research Articles

Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit

Septic patients in the intensive care unit (ICU) often develop sepsis-associated delirium (SAD), which is strongly associated with poor prognosis. The aim of this study is to develop a machine learning-based model for the early prediction of SAD. Patient data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were divided into a training set and an internal validation set, while the eICU-CRD data served as an external validation set. Feature variables were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, support vector machines, decision trees, random forests, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. The performance of the models was evaluated in the validation set. The model was also applied to a group of patients who were not assessed or could not be assessed for delirium. The MIMIC-IV and eICU-CRD databases included 14,620 and 1723 patients, respectively, with a median time to diagnosis of SAD of 24 and 30 h. Compared with Non-SAD patients, SAD patients had higher 28-days ICU mortality rates and longer ICU stays. Among the models compared, the XGBoost model had the best performance and was selected as the final model (internal validation area under the receiver operating characteristic curves (AUROC) = 0.793, external validation AUROC = 0.701). The XGBoost model outperformed other models in predicting SAD. The establishment of this predictive model allows for earlier prediction of SAD compared to traditional delirium assessments and is applicable to patients who are difficult to assess with traditional methods.

Lived Experience of Violence Perpetrated by Treated Patients and Their Visitors in Intensive Care Units: A Qualitative Study of Nurses.

Patient and visitor violence (PVV) is a widespread problem for health professionals. Nurses working in intensive care units (ICUs) face a relatively high risk of experiencing PVV, which significantly impacts both the health of nurses and the institution as a whole. The subjective perceptions of ICU nurses regarding PVV are inadequately explored in the literature. The purpose of this study was to explore the perspectives, experiences, and perceptions of PVV in ICU nurses and to better understand the precipitating factors of violence. A phenomenological qualitative design and purposive sampling were used. A semistructured interview guide was used to conduct in-depth interviews with 12 ICU nurses with PVV experiences. Giorgi's method of analysis was used to discover and identify the essential categories of experience. Five main experience categories were identified: family and patient factors as flashpoints, managing suppressed emotions by weathering the emotional storm, spiritual awakening after violence, and strategies for surviving further violence. The participants' experiences with PVV included a range of caring and mental health difficulties. In ICU settings, patient progress is often unpredictable, resulting in discrepancies between patient/family expectations and reality. Because feelings of frustration and powerlessness can eventually cause exhaustion in ICU nurses, implementing effective emotional management, stress adjustment, psychological counseling, team support, and violence intervention programs are crucial. This study provides new information on the process by which nurses can progress from inner trauma to self-recovery, moving from a negative affectivity disposition to a better understanding of threat appraisals and coping response options. Nurses should increase their awareness of the complexity of the phenomenon and of the interplay among the factors underlying PVV. The results of this study suggest that routine confusion and delirium assessments to rule out patients with ICU delirium in ICUs are important to preventing PVV. This study considers some of the implications of the research findings for nursing managers. Interventions, training programs, and/or management action should be used to ensure psychological and mental support is extended to all witnesses of PVV events and not only to those targeted by violence.

Delirium in hospitalized COVID-19 patients: a prospective, multicenter, cohort study.

Delirium is a prevalent symptom of acute brain dysfunction caused by Coronavirus disease 2019 (COVID-19). However, the understanding of delirium in COVID-19 patients is currently limited. This study aimed to investigate the prevalence of delirium and its risk factors in hospitalized COVID-19 patients for early identification and management of delirium. This cohort study included hospitalized patients with SARS-CoV-2 infection at seven tertiary hospitals from January to February 2023. Delirium was assessed at a single time point using the 3-Minute Diagnostic Assessment for Delirium by trained research assistants. Demographic data, clinical characteristics, in-hospital mortality and other variables were collected from health information system. Multivariate regression analyses were conducted to investigate the risk factors for delirium and the impact of delirium on in-hospital mortality. A total of 4589 COVID-19 patients were included, out of which 651 cases (14.2%) were identified as delirium. In the multivariable analysis, aging (OR 3.58 [95%CI 2.75-4.67], p < 0.001), higher aspartic transaminase/alanine transaminase ratio (OR = 1.11, [95%CI 1.02-1.21], p = 0.018), Mg2+ (OR = 3.04, [95%CI 2.56-3.62], p < 0.001), blood urea nitrogen (OR = 1.01, [95%CI 1.00, 1.02], p = 0.024), and indwelling urethral catheterization (OR = 1.59, [95%CI 1.21, 2.09], p < 0.001) were associated with an increased risk of delirium. After adjusting for age, sex, and CCI, delirium was found to be associated with an increased risk of in-hospital mortality (OR = 2.42, [95%CI 1.59, 3.67], p < 0.001). Delirium was a frequent complication among hospitalized COVID-19 patients and was related to unfavorable outcomes. It is crucial to reduce delirium and its long-term effects by addressing the modifiable risk factors.

The Development and Preliminary Exploratory Validation of the PEdiatric Delirium Scale: Assessing the Feasibility and Accessibility of a Novel Delirium Scale

AbstractDelirium screening and identification in the pediatric intensive care unit (PICU) can be a diagnostic challenge. Primarily, the burden of screening falls on the bedside nurses, who are juggling countless tasks throughout their shift. The nursing staff at the researcher's institution were concerned that the existing screen, Cornell Assessment for Pediatric Delirium (CAPD), detracted from workflow. The PEdiatric Delirium Scale (PEDS) was developed to accurately identify delirium in children of all developmental abilities and improve nursing workflow. This is a single-center, double-blinded, preliminary exploratory validation study that assesses the feasibility and accessibility of PEDS. This study was performed in a busy 24-bed quaternary PICU serving a diverse, noncardiac patient population. Enrolled patients underwent screening for delirium using the CAPD and PEDS. These results were compared to the gold standard psychiatric evaluation to determine the validity of the novel screen. Finally, the surveyed nurses reviewed their experience with CAPD and PEDS. The primary outcome was to explore the validation of PEDS in the PICU. Using the Youden index, an overall sensitivity of 79% for the detection of delirium (95% confidence interval [CI]: 0.61–0.91) and a specificity of 68% (95% CI: 0.64–0.73) were achieved with an optimal cut-point of 4, on a scale of 0 to 10. PEDS demonstrated a higher predictive value compared to CAPD. Elicited nursing feedback favored PEDS over CAPD, with 86% of respondents citing a shorter time to perform the screen. PEDS is a streamlined tool that can be used to detect pediatric delirium regardless of developmental abilities. Nursing surveys revealed improved workflow when comparing PEDS to CAPD.

Machine learning-based prediction of delirium 24 h after pediatric intensive care unit admission in critically ill children: A prospective cohort study

BackgroundAccurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium among critically ill children are not well researched. ObjectiveTo develop and validate machine learning-based models for predicting delirium among critically ill children 24 h after pediatric intensive care unit (PICU) admission. DesignA prospective cohort study. SettingA large academic medical center with a 57-bed PICU in southwestern China from November 2019 to February 2022. ParticipantsOne thousand five hundred and seventy-six critically ill children requiring PICU stay over 24 h. MethodsFive machine learning algorithms were employed. Delirium was screened by bedside nurses twice a day using the Cornell Assessment of Pediatric Delirium. Twenty-four clinical features from medical and nursing records during hospitalization were used to inform the models. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC). ResultsOf the 1576 enrolled patients, 929 (58.9 %) were boys, and the age ranged from 28 days to 15 years with a median age of 12 months (IQR 3 to 60 months). Among them, 1126 patients were assigned to the training cohort, and 450 were assigned to the validation cohort. The AUCs ranged from 0.763 to 0.805 for the five models, among which the eXtreme Gradient Boosting (XGB) model performed best, achieving an AUC of 0.805 (95 % CI, 0.759–0.851), with 0.798 (95 % CI, 0.758–0.834) accuracy, 0.902 sensitivity, 0.839 positive predictive value, 0.640 F1-score and a Brier score of 0.144. Almost all models showed lower predictive performance in children younger than 24 months than in older children. The logistic regression model also performed well, with an AUC of 0.789 (95 % CI, 0.739, 0.838), just under that of the XGB model, and was subsequently transformed into a nomogram. ConclusionsMachine learning-based models can be established and potentially help identify critically ill children who are at high risk of delirium 24 h after PICU admission. The nomogram may be a beneficial management tool for delirium for PICU practitioners at present.

Modifiable risk factors for post-operative delirium in older adults undergoing major non-cardiac elective surgery: a multi-centre, trainee delivered observational cohort feasibility study and trainee survey

BackgroundPost-operative delirium (POD) is an acute brain failure which may occur following major surgery, with serious implications for participants and caregivers. Evidence regarding optimal anaesthetic management for older participants at higher risk of POD is conflicting. We conducted a feasibility study of our protocol in 5 centres to guide sample size estimation and inform future recruitment strategies for a larger cohort study.MethodsParticipants aged over 65 and scheduled for major surgery were recruited. They were assessed pre-operatively for delirium, cognitive impairment, depression, comorbidity, activity levels and alcohol use. Details of management during surgery, all medications and complications were recorded by a trainee-led research team. Participants were assessed for delirium in the immediate recovery period and then on post-operative days 1–4 using the 4 question attention test (4AT) with complications assessed at day 4 using the post-operative morbidity survey (POMS). Primary outcomes were the incident rates of POD. Secondary outcomes were number of eligible patients, recruitment rates and retention rates throughout the study, time required for data collection, preoperative risk factors assessment and daily postoperative delirium assessments. Also to assess the added value of employing the regional trainee research network (INCARNNET) to deliver the study. Specifically, what proportion of patient consent, data collection and post-operative testing is performed by anaesthesia trainees from this group, especially the success of weekend delirium assessment by trainees? A survey was completed at the end of the study by the trainees involved regarding their involvement in the study.ResultsNinety-five participants were recruited, of whom 93 completed the study. Overall, POD occurred in 9 patients. Of these, three were detected in recovery and six on post-op days 1–4. Median length of stay was 6 days. Recruitment rates were high in all but one site. 59 (62%) participants were consented by trainees and 189 (63%) of post op delirium assessments were performed by trainees. A total of six patients declined the study (in a follow up survey of trainees). Pre-existing cognitive impairment, depression and problem drinking were detected in 4(4.3%), 3(3.2%) and 5(5.37%) participants, respectively. Co-morbidity was common with 55(59%) in class three or four of the geriatric index of morbidity. Overall, from a total of 641 data points, levels of missing data were as follows, site A = 9.3%, B = 13.5%, C = 15.4%, D = 10.9%, E = 11.1% (data could not be completed retrospectively).ConclusionsA multi-centre observational cohort study of delirium carried out by UK trainee anaesthetists is feasible. Patients are content to undergo day of surgery consent and multiple short questionnaires pre-operatively. Proposed data, especially pharmacological, should be carefully considered for their relevance to modifiable mechanisms that can lead to POD. Future research to enable prognostic modelling of POD should involve large scale cohort studies of enriched populations to capture a higher POD incidence. POD remains a common complication in older persons undergoing major surgery in the UK and studies of interventions are urgently needed.Trial registrationAll methods were carried out in accordance with relevant guidelines and regulations. The study was retrospectively registered with ISRCTN94663125 on 07/02/2018.

Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.

Delirium is a critically underdiagnosed syndrome of altered mental status affecting more than 50% of older adults admitted to hospital. Few studies have incorporated speech and language disturbance in delirium detection. We sought to describe speech and language disturbances in delirium, and provide a proof of concept for detecting delirium using computational speech and language features. Participants underwent delirium assessment and completed language tasks. Speech and language disturbances were rated using standardized clinical scales. Recordings and transcripts were processed using an automated pipeline to extract acoustic and textual features. We used binomial, elastic net, machine learning models to predict delirium status. We included 33 older adults admitted to hospital, of whom 10 met criteria for delirium. The group with delirium scored higher on total language disturbances and incoherence, and lower on category fluency. Both groups scored lower on category fluency than the normative population. Cognitive dysfunction as a continuous measure was correlated with higher total language disturbance, incoherence, loss of goal and lower category fluency. Including computational language features in the model predicting delirium status increased accuracy to 78%. This was a proof-of-concept study with limited sample size, without a set-aside cross-validation sample. Subsequent studies are needed before establishing a generalizable model for detecting delirium. Language impairments were elevated among patients with delirium and may also be used to identify subthreshold cognitive disturbances. Computational speech and language features are promising as accurate, noninvasive and efficient biomarkers of delirium.

Opioids increase the risk of delirium in critically ill patients: A propensity score analysis.

Medications are biologically plausible and potentially modifiable risk factors for delirium. Therapies for delirium might involve more specific strategies such as avoiding the use of delirium-inducing drugs to reduce the incidence of delirium. The association between opioid exposure within 24 hours prior to delirium assessment and the risk of delirium was studied. Using three large databases, the MIMIC III v1.4, MIMIC-IV v0.4 and eICU Collaborative Research, we performed a multicenter, observational cohort study with two cohorts to estimate the relative risks of outcomes among patients administered opioids within 24 hours prior to delirium assessment. Propensity score matching was performed to generate a balanced 1:1 matched cohort and to identify potential prognostic factors. The outcomes included mortality, length of intensive care unit (ICU) stay, length of hospitalization, and odds of being discharged home. Propensity matching successfully balanced the covariates for the 9,529 patients in each group. Opioid use was associated with a significantly higher risk for delirium than not using opioids (p<0.001). Additionally, treatment with opioids was associated with higher mortality and a longer ICU stay (p<0.001) than treatment without opioids. However, patients treated with opioids were more likely to be discharged home (p<0.001). Opioids may be an independent risk factor for delirium in critically ill patients.

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.

Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge. We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms. Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit. We found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

Alcohol consumption may be associated with postoperative delirium in the elderly: the PNDABLE study

ObjectivesThis study aimed to reveal the relationship between alcohol consumption and Postoperative delirium (POD) in the elderly.MethodsWe selected 252 patients from the Perioperative Neurocognitive Disorder And Biomarker Lifestyle (PNDABLE ) study. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aβ40, Aβ42, P-tau, and tau protein). Mini-Mental State Examination (MMSE) was used to assess the preoperative mental status of patients. POD was diagnosed using the Confusion Assessment Method (CAM) and assessed for severity using the Memorial Delirium Assessment Scale (MDAS). Logistic regression analysis was utilized to explore the association of alcohol consumption with POD. Linear regression analysis was used to study the relationship between alcohol consumption and CSF biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we constructed the receiver operating characteristic (ROC) curve and the nomogram model to evaluate the efficacy of alcohol consumption and CSF biomarkers in predicting POD. ResultThe incidence of POD was 17.5%. Logistic regression showed that alcohol consumption (OR = 1.016, 95%CI 1.009–1.024, P < 0.001) is a risk factor for POD. What’s more, Aβ42 is a protective factor for POD (OR = 0.993, 95%CI 0.989–0.997, P < 0.05), and P-Tau was a risk factor for POD (OR = 1.093, 95%CI 1.022–1.168, P < 0.05). Linear regression analysis revealed that alcohol consumption was negatively associated with CSF Aβ42 (β = -0.638, P < 0.001) in POD patients. Mediation analyses showed that alcohol consumption is likely to partially mediate POD through Aβ42 (proportion:14.21%). ROC curve showed that alcohol consumption (AUC = 0.904; P < 0.001) exhibited a relatively better discriminatory ability in POD prediction compared to Aβ42 (AUC = 0.798; P < 0.001). The calibration curve indicated a good nomogram prediction (P = 0.797).ConclusionAlcohol consumption is a risk factor for POD (particularly for those with > 24 g a day on average) in the elderly, and contributes to POD through the mediation of Aβ42.

Preliminary evaluation of a smartphone application (DelApp) for identification of delirium in sub-Saharan Africa.

In sub-Saharan Africa, there are no validated screening tools for delirium in older adults, despite the known vulnerability of older people to delirium and the associated adverse outcomes. This study aimed to assess the effectiveness of a brief smartphone-based assessment of arousal and attention (DelApp) in the identification of delirium amongst older adults admitted to the medical department of a tertiary referral hospital in Northern Tanzania. Consecutive admissions were screened using the DelApp during a larger study of delirium prevalence and risk factors. All participants subsequently underwent detailed clinical assessment for delirium by a research doctor. Delirium and dementia were identified against DSM-5 criteria by consensus. Complete data for 66 individuals were collected of whom 15 (22.7%) had delirium, 24.5% had dementia without delirium, and 10.6% had delirium superimposed on dementia. Sensitivity and specificity of the DelApp for delirium were 0.87 and 0.62, respectively (AUROC 0.77) and 0.88 and 0.73 (AUROC 0.85) for major cognitive impairment (dementia and delirium combined). Lower DelApp score was associated with age, significant visual impairment (<6/60 acuity), illness severity, reduced arousal and DSM-5 delirium on univariable analysis, but on multivariable logistic regression only arousal remained significant. In this setting, the DelApp performed well in identifying delirium and major cognitive impairment but did not differentiate delirium and dementia. Performance is likely to have been affected by confounders including uncorrected visual impairment and reduced level of arousal without delirium. Negative predictive value was nevertheless high, indicating excellent 'rule out' value in this setting.

Associations of postoperative delirium with activities of daily living in older people after major surgery: A prospective cohort study.

To assess the association of postoperative delirium developed in the post-anaesthetic care unit (PACU) with older patients' ability to perform activities of daily living (ADL) during the first five postoperative days. Previous studies have focused on the association between postoperative delirium and long-term function decline, however the association between postoperative delirium and the ability to perform ADL, particularly in the immediate postoperative period, needs further investigation. A prospective cohort study. A total of 271 older patients who underwent elective or emergency surgery at a tertiary care hospital in Victoria, Australia, participated in the study. Data were collected between July 2021 and December 2021. Delirium was assessed using the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). The Katz Index of Independence in Activities of Daily Living (KATZ ADL) scale was used to measure ADL. ADL was assessed preoperatively and daily during the first five postoperative days. The STROBE checklist was used to report this study. Results showed that 44 (16.2%) patients developed new episode of delirium. Postoperative delirium was independently associated with decline in ADL (RR = 2.83, 95% CI = 2.71-2.97; p < 0.001). Postoperative delirium was associated with a decline in ADL among older people during the first five postoperative days. Screening for delirium in the PACU is essential to identify delirium during the early stages of postoperative period and implement a timely comprehensive plan. Delirium assessment of older patients in the PACU, and for at least the first five postoperative days, is strongly recommended. We also recommend engagement of patients in a focused physical and cognitive daily activity plan, particularly for older patients undergoing major surgery. Patients and nurses helped in data collection at a tertiary care hospital.

Stuck in the Dark: No Association between Light, Sound, and Delirium in the PICU

AbstractThe purpose of this study was to evaluate the association between sound and light variables and development of delirium in pediatric intensive care unit (PICU) patients within the subsequent 3 days. Light and sound were continuously measured, and patient-specific data were collected daily. Outcomes included presence of delirium, determined by Cornell Assessment of Pediatric Delirium (CAPD) score ≥ 9 on at least 1 day, and development of delirium in the subsequent 3 days. One hundred and forty-two patients were enrolled, totaling 896 study days. Delirium prevalence was 31%. Neurologic diagnosis, younger age, longer PICU stay, restraints, benzodiazepines, opiate infusion, and dexmedetomidine were associated with delirium. Nighttime sound peaks (&gt;70 and &gt;80 dB) were not associated with delirium (p = 0.94 and 0.36, respectively). Light intensity during midday hours (12:00, 1:00, 2:00 pm) was not associated with the development of delirium (p=0.93, 0.81, 0.83, respectively), nor was maximum light at night, difference between maximum daytime and nighttime light, or between maximum and minimum daytime light (p = 0.15, 0.46, and 0.96, respectively). When adjusting for covariables, none of the sound or light variables was associated with delirium. Despite increasing awareness of pediatric delirium, data surrounding modifiable risk factors and concrete treatment strategies are lacking. We found no significant association between light or sound and development of delirium in PICU patients. However, there was little difference between daytime and nighttime light and sound, which may have impacted findings. As the etiology of delirium is multifactorial and potentially different for individual patients, targeted light/dark cycles warrant further study as they relate to delirium and critical illness.

A single preoperative low-dose dexamethasone may reduce the incidence and severity of postoperative delirium in the geriatric intertrochanteric fracture patients with internal fixation surgery: an exploratory analysis of a randomized, placebo-controlled trial

ObjectivePostoperative delirium (POD) is a common complication along with poor prognosis in geriatric intertrochanteric fracture (ITF) patients. However, the prevention and treatment of POD remain unclear. Previous studies have confirmed that POD is essentially a consequence of neuro-inflammatory responses. Dexamethasone is a glucocorticoid with comprehensive anti-inflammatory effects, while a high dose of dexamethasone correlates with many side effects or even adverse consequences. Thus, this prospective study aims to discuss whether a single preoperative low-dose dexamethasone can reduce the impact of POD on geriatric ITF patients with internal fixation surgery.MethodsBetween June 2020 and October 2022, there were 219 consecutive ITF patients assessed in our department. Of the 219 ITF patients, 160 cases who met the inclusion and exclusion criteria were finally enrolled and randomly allocated to the dexamethasone group and the placebo group (80 geriatric ITF patients in each group) in this prospective study. The patients in the dexamethasone group received intravenous 10 mg (2 ml) dexamethasone while the patients in the placebo group received intravenous 2 ml saline in 30 min before being sent to the operating room, respectively. The baseline characteristics, surgical information, incidence and severity of POD as the efficacy-related outcomes, and infection events and hyperglycemia as safety-related outcomes (adverse events), were collected and analyzed between the two groups. The severity of POD was evaluated by Memorial Delirium Assessment Scale (MDAS) score.ResultsThere were no differences in baseline characteristics and surgical information between the dexamethasone group and the placebo group. The dexamethasone group had a lower incidence of POD than the placebo group within the first 5 days after surgery [(9/80, 11.3% vs. 21/80, 26.3%, RR = 0.83, 95% CI 0.71–0.97, P = 0.015]. The dexamethasone group had lower MDAS scores (Mean ± SD) than the placebo group [13.2 ± 1.0 (range 11 to 15) vs. 15.48 ± 2.9 (range 9 to 20), P = 0.011, effect size = 0.514]. There were no differences in infection events and hyperglycemia between the two groups.ConclusionsA single preoperative low-dose dexamethasone may reduce the incidence and severity of POD in geriatric ITF patients with internal fixation surgery.Trial registration: ChiCTR2200055281.

Delirium in hip fracture patients admitted from home during the COVID-19 pandemic is associated with higher mortality, longer total length of stay, need for post-acute inpatient rehabilitation, and readmission to acute services.

Delirium is associated with adverse outcomes following hip fracture, but the prevalence and significance of delirium for the prognosis and ongoing rehabilitation needs of patients admitted from home is less well studied. Here, we analyzed relationships between delirium in patients admitted from home with 1) mortality; 2) total length of hospital stay; 3) need for post-acute inpatient rehabilitation; and 4) hospital readmission within 180 days. This observational study used routine clinical data in a consecutive sample of hip fracture patients aged ≥ 50 years admitted to a single large trauma centre during the COVID-19 pandemic between 1 March 2020 and 30 November 2021. Delirium was prospectively assessed as part of routine care by the 4 A's Test (4AT), with most assessments performed in the emergency department. Associations were determined using logistic regression adjusted for age, sex, Scottish Index of Multiple Deprivation quintile, COVID-19 infection within 30 days, and American Society of Anesthesiologists grade. A total of 1,821 patients were admitted, with 1,383 (mean age 79.5 years; 72.1% female) directly from home. Overall, 87 patients (4.8%) were excluded due to missing 4AT scores. Delirium prevalence in the whole cohort was 26.5% (460/1,734): 14.1% (189/1,340) in the subgroup of patients admitted from home, and 68.8% (271/394) in the remaining patients (comprising care home residents and inpatients when fracture occurred). In patients admitted from home, delirium was associated with a 20-day longer total length of stay (p < 0.001). In multivariable analyses, delirium was associated with higher mortality at 180 days (odds ratio (OR) 1.69 (95% confidence interval (CI) 1.13 to 2.54); p = 0.013), requirement for post-acute inpatient rehabilitation (OR 2.80 (95% CI 1.97 to 3.96); p < 0.001), and readmission to hospital within 180 days (OR 1.79 (95% CI 1.02 to 3.15); p = 0.041). Delirium affects one in seven patients with a hip fracture admitted directly from home, and is associated with adverse outcomes in these patients. Delirium assessment and effective management should be a mandatory part of standard hip fracture care.

Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage

ObjectiveDelirium is associated with worse outcomes in patients with stroke and neurocritical illness, but delirium detection in these patients can be challenging with existing screening tools. To address this gap, we aimed to develop and evaluate machine learning models that detect episodes of post-stroke delirium based on data from wearable activity monitors in conjunction with stroke-related clinical features.DesignProspective observational cohort study.SettingNeurocritical Care and Stroke Units at an academic medical center.PatientsWe recruited 39 patients with moderate-to-severe acute intracerebral hemorrhage (ICH) and hemiparesis over a 1-year period [mean (SD) age 71.3 (12.20), 54% male, median (IQR) initial NIH Stroke Scale 14.5 (6), median (IQR) ICH score 2 (1)].Measurements and main resultsEach patient received daily assessments for delirium by an attending neurologist, while activity data were recorded throughout each patient's hospitalization using wrist-worn actigraph devices (on both paretic and non-paretic arms). We compared the predictive accuracy of Random Forest, SVM and XGBoost machine learning methods in classifying daily delirium status using clinical information alone and combined with actigraph data. Among our study cohort, 85% of patients (n = 33) had at least one delirium episode, while 71% of monitoring days (n = 209) were rated as days with delirium. Clinical information alone had a low accuracy in detecting delirium on a day-to-day basis [accuracy mean (SD) 62% (18%), F1 score mean (SD) 50% (17%)]. Prediction performance improved significantly (p &amp;lt; 0.001) with the addition of actigraph data [accuracy mean (SD) 74% (10%), F1 score 65% (10%)]. Among actigraphy features, night-time actigraph data were especially relevant for classification accuracy.ConclusionsWe found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.

Delirium in older patients given propofol or sevoflurane anaesthesia for major cancer surgery: a multicentre randomised trial

BackgroundDelirium is a common and disturbing postoperative complication that might be ameliorated by propofol-based anaesthesia. We therefore tested the primary hypothesis that there is less delirium after propofol-based than after sevoflurane-based anaesthesia within 7 days of major cancer surgery. MethodsThis multicentre randomised trial was conducted in 14 tertiary care hospitals in China. Patients aged 65–90 yr undergoing major cancer surgery were randomised to either propofol-based anaesthesia or to sevoflurane-based anaesthesia. The primary endpoint was the incidence of delirium within 7 postoperative days. ResultsA total of 1228 subjects were enrolled and randomised, with 1195 subjects included in the modified intention-to-treat analysis (mean age 71 yr; 422 [35%] women); one subject died before delirium assessment. Delirium occurred in 8.4% (50/597) of subjects given propofol-based anaesthesia vs 12.4% (74/597) of subjects given sevoflurane-based anaesthesia (relative risk 0.68 [95% confidence interval {CI}: 0.48–0.95]; P=0.023; adjusted relative risk 0.59 [95% CI: 0.39–0.90]; P=0.014). Delirium reduction mainly occurred on the first day after surgery, with a prevalence of 5.4% (32/597) with propofol anaesthesia vs 10.7% (64/597) with sevoflurane anaesthesia (relative risk 0.50 [95% CI: 0.33–0.75]; P=0.001). Secondary endpoints, including ICU admission, postoperative duration of hospitalisation, major complications within 30 days, cognitive function at 30 days and 3 yr, and safety outcomes, did not differ significantly between groups. ConclusionsDelirium was a third less common after propofol than sevoflurane anaesthesia in older patients having major cancer surgery. Clinicians might therefore reasonably select propofol-based anaesthesia in patients at high risk of postoperative delirium. Clinical trial registrationChinese Clinical Trial Registry (ChiCTR-IPR-15006209) and ClinicalTrials.gov (NCT02662257).

Italian Pediatric Intensive Care Units need to improve the assessment of delirium, like many other Countries.

Italian Pediatric Intensive Care Units need to improve the assessment of delirium, like many other Countries.

Accuracy of Delirium Screening Tools in Older People with Cancer-A Systematic Review.

The increase in life expectancy worldwide has led to a larger population of older people, which in turn entails a rising prevalence of cancer. One of the main complications of cancer is delirium, especially in advanced stages. To determine which delirium screening instrument is the most accurate in older people with cancer. A systematic review was designed. A literature search was performed in MEDLINE, EBSCO and SCOPUS; additional records were identified by handsearching. Selection criteria were studies involving people with cancer and a mean sample age of 60 years or older, assessing delirium, and reporting the metric properties of the assessment instrument. Studies with post-surgical patients and substance abuse delirium were excluded. From 2001 to 2021, 14 eligible studies evaluated 13 different assessment tools, reporting an incidence of delirium ranging from 14.3% to 68.3%. The Delirium Observation Screening Scale (DOSS) showed the best metric properties, followed by the Nursing Delirium Screening Scale (NuDESC), Confusion Assessment Method (CAM) and Memorial Delirium Assessment Scale (MDAS). Only two studies were considered to be at low risk of bias using the QUADAS-2 Tool. No study exclusively examined this population group. Screening tools for delirium are heterogeneous for older people with cancer, and there is a need to analyze metric properties exclusively in the older population. Registered on PROSPERO ID: CRD42022303530.

68 Using the Cornell Assessment of Pediatric Delirium Score to Identify Delirium in Pediatric Burn Patients

Abstract Introduction Delirium rates in pediatric critical care range from 18-40% and its development is associated with baseline cognitive dysfunction, primary diagnosis, and mechanical ventilation. The Cornell Assessment of Pediatric Delirium (CAPD) is a tool used to detect delirium in children of all ages, but has not been validated in burn patients. Our study aimed to use CAPD score to determine the frequency of delirium in pediatric burn patients and assess the association of delirium with burn demographics. Methods We conducted a retrospective review of patients aged 2.5 weeks to 18 years who were admitted to our ABA-verified pediatric burn center from 2018-2021 and who underwent delirium screening with the CAPD tool (CAPD ≥9 indicates a diagnosis of delirium). Injury mechanism, patient demographics, hospitalization details, and CAPD score were collected and χ2, Fisher’s exact test, and univariate analyses performed. Results 389 patients with documented CAPD scores were included in our cohort, with median age of 2 years at the time of burn injury. Delirium was identified in 42 (10.8%) patients. Delirious patients were generally older compared to non-delirious patients (4 years (IQR 2, 11) vs 2 years (IQR 1, 6), P &amp;lt; 0.0005) and had higher TBSA burned (21.63% (IQR 9, 42) vs 3.5% (IQR 1.75, 6), P &amp;lt; 0.0001). Odds of developing delirium with increasing age was 1.08 (95% CI: 1.02-1.15, P = 0.0068) and 1.16 (95% CI: 1.11-1.20, P = &amp;lt; 0.0001) for each percent increase in TBSA burned. Delirium diagnosis did not vary significantly with gender or race/ethnicity. Though it did not reach statistical significance, percent probability of developing delirium was highest among fire burns (26.53% (95% CI: 17.79-35.27)), and lowest among chemical burns (1.49% (95%CI: 0-4.40), P &amp;gt; 0.05). Patients who underwent 6 or more Anesthesia Events (AE) were 74.43 times more likely to develop delirium than patients who underwent 0 AEs (P &amp;lt; 0.001). Compared to non-delirious patients, patients with delirium were 81.02 times more likely to have an ICU admission (95% CI: 38.79-169.22, P &amp;lt; 0.0001) and 11.37 times more likely to have a longer hospital admission (95% CI: 7.74-16.72, P &amp;lt; 0.0001). Conclusions Screening with the CAPD tool identified delirium in pediatric burn patients with known risk factors for delirium including higher TBSA burned, greater number of AEs, and longer ICU course or hospital admission. Delirium increased with age in our study group. There are few validated tools for measuring delirium in pediatric burn patients, and further studies are needed determine whether CAPD accurately captures delirium in younger burn patients. Applicability of Research to Practice Knowing associations between clinical burn characteristics and delirium can help predict which patients are at highest risk. Early identification of these patients can enable extra precautions to prevent delirium and to treat it early if it develops.