Distribution of delirium motor subtypes in the intensive care unit: a systematic scoping review

Delirium in psychiatric settings: risk factors and assessment tools in patients with psychiatric illness: a scoping review

BACKGROUND

Delirium is a common disorder affecting patients' psychiatric illness, characterized by a high rate of underdiagnosis, misdiagnosis, and high risks. However, previous studies frequently excluded patients with psychiatric illness, leading to limited knowledge about risk factors and optimal assessment tools for delirium in psychiatric settings.

OBJECTIVES

The scoping review was carried out to (1) identify the risk factors associated with delirium in patients with psychiatric illness; (2) synthesize the performance of assessment tools for detecting delirium in patients with psychiatric illness in psychiatric settings.

DESIGN

Scoping review.

DATA SOURCES

PubMed, Web of Science, and Embase were searched to identify primary studies on delirium in psychiatric settings from inception to Dec 2023 inclusive. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

A final set of 36 articles meeting the inclusion criteria, two main themes were extracted: risk factors associated with delirium in patients with psychiatric illness and assessment tools for detecting delirium in psychiatric settings. The risk factors associated with delirium primarily included advanced age, physical comorbid, types of psychiatric illness, antipsychotics, anticholinergic drug, Electroconvulsive therapy, and the combination of lithium and Electroconvulsive therapy. Delirium Rating Scale-Revised-98, Memorial Delirium Assessment Scale, and Delirium Diagnostic Tool-Provisional might be valuable for delirium assessment in patients with psychiatric illness in psychiatric settings.

CONCLUSIONS

Delirium diagnosis in psychiatric settings is complex due to the overlapping clinical manifestations between psychiatric illness and delirium, as well as their potential co-occurrence. It is imperative to understand the risk factors and assessment methods related to delirium in this population to address diagnostic delays, establish effective prevention and screening strategies. Future research should focus on designing, implementing, and evaluating interventions that target modifiable risk factors, to prevent and manage delirium in patients with psychiatric illness.

Occurrence and risk factors for post-stroke delirium: A systematic review and meta-analysis

OBJECTIVES

Delirium is a significant health concern in acute stroke patients. We aim to systematically summarize existing evidence to conduct a meta-analysis to quantify the occurrence and risk factors for delirium after acute stroke.

METHOD

PubMed, EMBASE and MEDLINE were searched from inception to Feb. 2023 for prospective observational studies that reported the incidence or prevalence of post-stroke delirium and/or evaluated potential risk factors. The search strategy was created using controlled vocabulary terms and text words for stroke and delirium. We performed a meta-analysis of the estimates for occurrence and risk factors using random-effects models. Meta-regression and subgroup meta-analyses were conducted to explore the sources of heterogeneity. Study quality and quality of evidence were assessed using the customized Newcastle-Ottawa Scale and GRADE, respectively.

RESULTS

Forty-nine studies that enrolled 12383 patients were included. The pooled occurrence rate of post-stroke delirium was 24.4 % (95 %CI, 20.4 %-28.9 %, I2=96.2 %). The pooled occurrence of hyperactive, hypoactive, and mixed delirium was 8.5 %, 5.7 % and 5.0 %, respectively. Study location, delirium assessment method and stroke type independently affected the heterogeneity of the pooled estimate of delirium. Statistically significant risk factors were older age, low education level, cigarette smoking, alcohol drinking, atrial fibrillation, lower ADL level, higher pre-stroke mRS score, premorbid cognitive impairment or dementia, aphasia, total anterior circulation impairment, higher National Institute of Health Stroke Scale score and infection.

CONCLUSIONS

Delirium affected 1 in 4 acute stroke patients, although reported rates may depend on assessment method and stroke type. Timely prevention, recognition and intervention require prioritizing patients with dominant risk factors.

National survey on perioperative cognitive dysfunction

BACKGROUND

Perioperative cognitive dysfunction (PCD) is a very prevalent clinical syndrome due to the progressive aging of the surgical population.The aim of our study is to evaluate the clinical practice of Spanish anesthesiologists surveyed regarding this entity.

MATERIAL AND METHODS

Prospective online survey conducted by the Neurosciences Section and distributed by SEDAR.

RESULTS

544 responses were obtained, with a participation rate of 17%. 54.4% of respondents never make a preoperative assessment of cognitive impairment, only 7.5% always do it. 79.6% lack an intraoperative management protocol for the patient at risk of PCD. In the anesthetic planning, only 23.3% of the patients was kept in mind. Eighty-nine percent considered regional anesthesia with or without sedation preferable to general anesthesia for the prevention of PCD. 88.8% considered benzodiazepines to present a high risk of PCD. 71.7% considered that anesthetic depth monitoring could prevent postoperative cognitive deficit. Routine evaluation of postoperative delirium is low, only 14%. More than 80% recognize that PCD is underdiagnosed.

CONCLUSIONS

Among Spanish anesthesiologists surveyed, PCD is still a little known and underappreciated entity. It is necessary to raise awareness of the need to detect risk factors for PCD, as well as postoperative assessment and diagnosis. Therefore, the development of guidelines and protocols and the implementation of continuing education programs in which anesthesiologists should be key members of multidisciplinary teams in charge of perioperative care are suggested.

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, 64 years; 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.

Interventions to prevent and treat delirium: An umbrella review of randomized controlled trials

Delirium is a common condition across different settings and populations. The interventions for preventing and managing this condition are still poorly known. The aim of this umbrella review is to synthesize and grade all preventative and therapeutic interventions for delirium. We searched five databases from database inception up to March 15th, 2023 and we included meta-analyses of randomized controlled trials (RCTs) to decrease the risk of/the severity of delirium. From 1959 records after deduplication, we included 59 systematic reviews with meta-analyses, providing 110 meta-analytic estimates across populations, interventions, outcomes, settings, and age groups (485 unique RCTs, 172,045 participants). In surgery setting, for preventing delirium, high GRADE evidence supported dexmedetomidine (RR=0.53; 95%CI: 0.46-0.67, k=13, N=3988) and comprehensive geriatric assessment (OR=0.46; 95%CI=0.32-0.67, k=3, N=496) in older adults, dexmedetomidine in adults (RR=0.33, 95%CI=0.24-0.45, k=7, N=1974), A2-adrenergic agonists after induction of anesthesia (OR= 0.28, 95%CI= 0.19-0.40, k=10, N=669) in children. High certainty evidence did not support melatonergic agents in older adults for delirium prevention. Moderate certainty supported the effect of dexmedetomidine in adults and children (k=4), various non-pharmacological interventions in adults and older people (k=4), second-generation antipsychotics in adults and mixed age groups (k=3), EEG-guided anesthesia in adults (k=2), mixed pharmacological interventions (k=1), five other specific pharmacological interventions in children (k=1 each). In conclusion, our work indicates that effective treatments to prevent delirium differ across populations, settings, and age groups. Results inform future guidelines to prevent or treat delirium, accounting for safety and costs of interventions. More research is needed in non-surgical settings.

Risk Factors for Postoperative Delirium in Patients Undergoing Major Head and Neck Cancer Surgery: A Meta-analysis

BACKGROUND

Postoperative delirium (POD) is a common and serious complication after extensive surgery. Understanding the independent and potential modifiable risk factors leading to POD in patients with head and neck cancer (HNC) can provide information for future intervention trials aimed at reducing this risk.

OBJECTIVE

To systematically analyze influencing factors of POD in patients with HNC and identify high-risk individuals for delirium.

METHODS

PubMed, EMBASE, Scopus, OVID, and Cochrane Library were searched for publications prior to June 2023. Comparative studies in which POD risk factors were investigated were identified following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Newcastle-Ottawa Scale was used to evaluate the study quality. Pooled odds ratios or mean differences for individual risk factors were estimated using the Mantel-Haenszel and inverse-variance methods.

RESULTS

This review included 17 studies with a total of 4188 patients undergoing HNC surgery. The pooled prevalence of POD was 15.44%. Based on pooled analysis, 8 significant risk factors were identified including age older than 70 years, male sex, history of smoking, history of psychiatric disorder, American Society of Anesthesiologists score, albumin level, postoperative insomnia, and fluid intake.

CONCLUSION

In the present study, 8 factors that correlated with POD were identified: 6 preoperative, 1 intraoperative, and 1 postoperative.

IMPLICATIONS FOR PRACTICE

The influencing factors for POD in patients with HNC were identified that can provide a reference for improving the psychological state of the patient population and further development of effective treatment interventions.

Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations

Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients.

Clinical Features of Delirium among Patients in the Intensive Care Unit According to Motor Subtype Classification: A Retrospective Longitudinal Study

PURPOSE

Delirium in the intensive care unit (ICU) poses a significant safety and socioeconomic burden to patients and caregivers. However, invasive interventions for managing delirium have severe drawbacks. To reduce unnecessary interventions during ICU hospitalization, we aimed to investigate the features of delirium among ICU patients according to the occurrence of hypoactive symptoms, which are not expected to require invasive intervention.

MATERIALS AND METHODS

Psychiatrists assessed all patients with delirium in the ICU during hospitalization. Patients were grouped into two groups: a "non-hypoactive" group that experienced the non-hypoactive motor subtype once or more or a "hypoactive only" group that only experienced the hypoactive motor subtype. Clinical variables routinely gathered for clinical management were collected from electronic medical records. Group comparisons and logistic regression analyses were conducted.

RESULTS

The non-hypoactive group had longer and more severe delirium episodes than the hypoactive only group. Although the non-hypoactive group was prescribed more antipsychotics and required restraints longer, the hypoactive only group also received both interventions. In multivariable logistic regression analysis, BUN [odds ratio (OR): 0.993, pH OR: 0.202], sodium (OR: 1.022), RASS score (OR: 1.308) and whether restraints were applied [OR: 1.579 (95% confidence interval 1.194-2.089), p<0.001] were significant predictors of hypoactive only group classification.

CONCLUSION

Managing and predicting delirium patients based on whether patients experienced non-hypoactive delirium may be clinically important. Variables obtained during the initial 48 hours can be used to determine which patients are likely to require invasive interventions.

Long-term mortality of critically ill patients with cancer and delirium who survived to discharge: a retrospective cohort study

PURPOSE

Delirium is common in critically ill patients and has been associated with lower short-term survival; however, its association with long-term survival has been scarcely evaluated and few studies have shown divergent results.

METHODS

We conducted a retrospective cohort study of adult patients with cancer admitted to the intensive care unit (ICU) and discharged from hospital from January 2015 to December 2018. We considered delirium present if the Confusion Assessment Method for Intensive Care Unit (CAM-ICU) result was positive. We assessed the association between delirium during ICU stay and long-term mortality (up to three years after discharge). We also assessed the association between delirium type (hypoactive, hyperactive, and mixed) with long-term mortality.

RESULTS

We included 3,079 patients. Of these, 430 (14%) were considered delirious at some point during their ICU stay. Delirium was associated with one-year mortality after hospital discharge (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.36 to 1.83) after adjustment for potential confounders, but not with one to three year-mortality (HR, 0.92; 95% CI, 0.61 to 1.39). Hypoactive and mixed delirium were associated with one-year mortality (HR, 1.77; 95% CI, 1.46 to 2.14 and HR, 1.56; 95% CI, 1.21 to 2.00, respectively), but none of the delirium motor types was associated with one to three-year mortality.

CONCLUSIONS

We observed that delirium during ICU stay was associated with increased one-year mortality, but was not with mortality after one year. This association was observed in hypoactive and mixed delirium types but not with hyperactive delirium.

The role of hormones in the pathogenesis and treatment mechanisms of delirium in ICU: The past, the present, and the future

Delirium is an acute brain dysfunction. As one of the common psychiatric disorders in ICU, it can seriously affect the prognosis of patients. Hormones are important messenger substances found in the human body that help to regulate and maintain the function and metabolism of various tissues and organs. They are also one of the most commonly used drugs in clinical practice. Recent evidences suggest that aberrant swings in cortisol and non-cortisol hormones might induce severe cognitive impairment, eventually leading to delirium. However, the role of hormones in the pathogenesis of delirium still remains controversial. This article reviews the recent research on risk factors of delirium and the association between several types of hormones and cognitive dysfunction. These mechanisms are expected to offer novel ideas and clinical relevance for the treatment and prevention of delirium.

Haloperidol for the treatment of delirium in critically ill patients: an updated systematic review with meta-analysis and trial sequential analysis

BACKGROUND

Haloperidol is frequently used in critically ill patients with delirium, but evidence for its effects has been sparse and inconclusive. By including recent trials, we updated a systematic review assessing effects of haloperidol on mortality and serious adverse events in critically ill patients with delirium.

METHODS

This is an updated systematic review with meta-analysis and trial sequential analysis of randomised clinical trials investigating haloperidol versus placebo or any comparator in critically ill patients with delirium. We adhered to the Cochrane handbook, the PRISMA guidelines and the grading of recommendations assessment, development and evaluation statements. The primary outcomes were all-cause mortality and proportion of patients with one or more serious adverse events or reactions (SAEs/SARs). Secondary outcomes were days alive without delirium or coma, delirium severity, cognitive function and health-related quality of life.

RESULTS

We included 11 RCTs with 15 comparisons (n = 2200); five were placebo-controlled. The relative risk for mortality with haloperidol versus placebo was 0.89; 96.7% CI 0.77 to 1.03; I2 = 0% (moderate-certainty evidence) and for proportion of patients experiencing SAEs/SARs 0.94; 96.7% CI 0.81 to 1.10; I2 = 18% (low-certainty evidence). We found no difference in days alive without delirium or coma (moderate-certainty evidence). We found sparse data for other secondary outcomes and other comparators than placebo.

CONCLUSIONS

Haloperidol may reduce mortality and likely result in little to no change in the occurrence of SAEs/SARs compared with placebo in critically ill patients with delirium. However, the results were not statistically significant and more trial data are needed to provide higher certainty for the effects of haloperidol in these patients.

TRIAL REGISTRATION

CRD42017081133, date of registration 28 November 2017.

Automated phenotyping of postoperative delirium-like behaviour in mice reveals the therapeutic efficacy of dexmedetomidine

Postoperative delirium (POD) is a complicated and harmful clinical syndrome. Traditional behaviour analysis mostly focuses on static parameters. However, animal behaviour is a bottom-up and hierarchical organizational structure composed of time-varying posture dynamics. Spontaneous and task-driven behaviours are used to conduct comprehensive profiling of behavioural data of various aspects of model animals. A machine-learning based method is used to assess the effect of dexmedetomidine. Fourteen statistically different spontaneous behaviours are used to distinguish the non-POD group from the POD group. In the task-driven behaviour, the non-POD group has greater deep versus shallow investigation preference, with no significant preference in the POD group. Hyperactive and hypoactive subtypes can be distinguished through pose evaluation. Dexmedetomidine at a dose of 25 μg kg-1 reduces the severity and incidence of POD. Here we propose a multi-scaled clustering analysis framework that includes pose, behaviour and action sequence evaluation. This may represent the hierarchical dynamics of delirium-like behaviours.

Haloperidol vs. placebo for the treatment of delirium in ICU patients: a pre-planned, secondary Bayesian analysis of the AID–ICU trial

PURPOSE

The AID-ICU trial was a randomised, blinded, placebo-controlled trial investigating effects of haloperidol versus placebo in acutely admitted, adult patients admitted in intensive care unit (ICU) with delirium. This pre-planned Bayesian analysis facilitates probabilistic interpretation of the AID-ICU trial results.

METHODS

We used adjusted Bayesian linear and logistic regression models with weakly informative priors to analyse all primary and secondary outcomes reported up to day 90, and with sensitivity analyses using other priors. The probabilities for any benefit/harm, clinically important benefit/harm, and no clinically important differences with haloperidol treatment according to pre-defined thresholds are presented for all outcomes.

RESULTS

The mean difference for days alive and out of hospital to day 90 (primary outcome) was 2.9 days (95% credible interval (CrI) - 1.1 to 6.9) with probabilities of 92% for any benefit and 82% for clinically important benefit. The risk difference for mortality was - 6.8 percentage points (95% CrI - 12.8 to - 0.8) with probabilities of 99% for any benefit and 94% for clinically important benefit. The adjusted risk difference for serious adverse reactions was 0.3 percentage points (95% CrI - 1.3 to 1.9) with 98% probability of no clinically important difference. Results were consistent across sensitivity analyses using different priors, with more than 83% probability of benefit and less than 17% probability of harm with haloperidol treatment.

CONCLUSIONS

We found high probabilities of benefits and low probabilities of harm with haloperidol treatment compared with placebo in acutely admitted, adult ICU patients with delirium for the primary and most secondary outcomes.

Sepsis-Associated Delirium: A Narrative Review

Delirium is characterized by an acutely altered mental status accompanied by reductions in cognitive function and attention. Delirium in septic patients, termed sepsis-associated delirium (SAD), differs in several specific aspects from the other types of delirium that are typically encountered in intensive care units. Since sepsis and delirium are both closely associated with increased morbidity and mortality, it is important to not only prevent but also promptly diagnose and treat SAD. We herein reviewed the etiology, pathogenesis, risk factors, prevention, diagnosis, treatment, and prognosis of SAD, including coronavirus disease 2019 (COVID-19)-related delirium. Delirium by itself not only worsens long-term prognosis, but it is also regarded as an important factor affecting the outcome of post-intensive care syndrome. In COVID-19 patients, the difficulties associated with adequately implementing the ABCDEF bundle (Assess, prevent, and manage pain; Both spontaneous awakening and breathing trials: Choice of analgesia and sedation; Delirium assess, prevent, and manage; Early mobility and exercise; Family engagement/empowerment) and the need for social isolation are issues that require the development of conventional care for SAD.

Levetiracetam-associated behavioral adverse events in neurocritical care patients

STUDY OBJECTIVES

The objective of this study was to identify the incidence of levetiracetam-associated BAEs in NCC patients.

DESIGN

Single-center retrospective cohort analysis.

DATA SOURCE

Patient charts.

PATIENTS

965 adult ICU patients with a neurological injury receiving levetiracetam that were admitted to an intensive care unit.

MEASUREMENTS AND MAIN RESULTS

There were 965 patients included; 52% males with a median GCS of 13. Injury types included TBI (43.1%), ICH (21.8%), SAH (20.5%), and CI (14.6%). BAEs were identified in 46% of patients. Of these, 60% had documentation of agitation/restlessness, delirium, or anxiety while receiving levetiracetam, only 25% had a positive CAM-ICU, 13% had restraints ordered, and 42% received antipsychotics. Patients with TBI had the highest incidence of BAEs (52.4%). The median time to initiation of levetiracetam after hospital admission was 6.4 hours and BAEs occurred after 1.3 days of levetiracetam initiation.

CONCLUSIONS

In this study, we found that almost half of our NCC population experienced levetiracetam associated BAEs which were mostly hyperactive in nature. We believe that the incidence of BAEs in our specific patient population cannot solely be attributed to ICU delirium given the lower risk of developing hyperactive delirium in ICU patients as compared to other subtypes. Therefore, monitoring and determination of the benefit versus risk in those experiencing BAEs is highly encouraged.

Delirium in the intensive care unit and its importance in the post-operative context: A review

The burden of delirium in the intensive care setting is a global priority. Delirium affects up to 80% of patients in intensive care units; an episode of delirium is often distressing to patients and their families, and delirium in patients within, or outside of, the intensive care unit (ICU) setting is associated with poor outcomes. In the short term, such poor outcomes include longer stay in intensive care, longer hospital stay, increased risk of other hospital-acquired complications, and increased risk of hospital mortality. Longer term sequelae include cognitive impairment and functional dependency. While medical category of admission may be a risk factor for poor outcomes in critical care populations, outcomes for surgical ICU admissions are also poor, with dependency at hospital discharge exceeding 30% and increased risk of in-hospital mortality, particularly in vulnerable groups, with high-risk procedures, and resource-scarce settings. A practical approach to delirium prevention and management in the ICU setting is likely to require a multi-faceted approach. Given the good evidence for the prevention of delirium among older post-operative outside of the intensive care setting, simple non-pharmacological interventions should be effective among older adults post-operatively who are cared for in the intensive care setting. In response to this, the future ICU environment will have a range of organizational and distinct environmental characteristics that are directly targeted at preventing delirium.

Prognostic significance of delirium subtypes in critically ill medical and surgical patients: a secondary analysis of a prospective multicenter study

BACKGROUND

The prognostic implication of delirium subtypes in critically ill medical and surgical patients is scarcely investigated. The objective was to determine how delirium subtypes are associated with hospital mortality and other clinical outcomes.

METHODS

We performed a secondary analysis on data from a prospective multicenter study aimed at implementation of delirium-oriented measures, conducted between 2012 and 2015 in The Netherlands. We included adults (≥ 18 years) admitted to the medical or surgical intensive care unit (ICU). Exclusion criteria were neurological admission diagnosis, persistent coma or ICU readmissions. Delirium was assessed using the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist, and delirium subtypes (hypoactive, hyperactive, or mixed) were classified using the Richmond Agitation-Sedation Scale. The main outcome was hospital mortality. Secondary outcomes were ICU mortality, ICU length of stay, coma, mechanical ventilation, and use of antipsychotics, sedatives, benzodiazepines and opioids.

RESULTS

Delirium occurred in 381 (24.4%) of 1564 patients (52.5% hypoactive, 39.1% mixed, 7.3% hyperactive). After case-mix adjustment, patients with mixed delirium had higher hospital mortality than non-delirious patients (OR 3.09, 95%CI 1.79-5.33, p = 0.001), whereas hypoactive patients did not (OR 1.34, 95%CI 0.71-2.55, p = 0.37). Similar results were found for ICU mortality. Compared to non-delirious patients, both subtypes had longer ICU stay, more coma, increased mechanical ventilation frequency and duration, and received more antipsychotics, sedatives, benzodiazepines and opioids. Except for coma and benzodiazepine use, the most unfavourable outcomes were observed in patients with mixed delirium.

CONCLUSIONS

Patients with mixed delirium had the most unfavourable outcomes, including higher mortality, compared with no delirium. These differences argue for distinguishing delirium subtypes in clinical practice and future research. Trial registration ClinicalTrials.gov NCT01952899.

Liver diseases as a novel risk factor for delirium in the ICU-Delirium and hepatic encephalopathy are two distinct entities

BACKGROUND

Delirium prevalence is high in critical care settings. We examined the incidence, risk factors, and outcome of delirium in a medical intensive care unit (MICU) with a particular focus on liver diseases. We analyzed this patient population in terms of delirium risk prediction and differentiation between delirium and hepatic encephalopathy.

METHODS

We conducted an observational study and included 164 consecutive patients admitted to an MICU of a university hospital. Patients were assessed for delirium using the Confusion Assessment Method for ICUs and the Richmond Agitation-Sedation Scale (RASS). On admission and at the onset of delirium Sequential Organ Failure Assessment (SOFA) score was determined. A population of patients with liver disease was compared to a population with gastrointestinal diseases. In the population with liver diseases, hepatic encephalopathy was graded according to the West Haven classification. We analyzed the incidence, subtype, predisposing, precipitating, and health-care setting-related factors, treatment, outcome of delirium and the association between delirium and hepatic encephalopathy in patients with liver diseases.

RESULTS

The incidence of delirium was 32.5% (n = 53). Univariable binary regression analyses adjusted by the Holm-Bonferroni method showed that the development of delirium was significantly determined by 10 risk factors: Alcohol abuse (p = 0.016), severity of disease (Simplified Acute Physiology Score (SAPS) II, p = 0.016), liver diseases (p = 0.030) and sepsis (p = 0.016) compared to the control group (gastrointestinal (GI) diseases and others), increased sodium (p = 0.016), creatinine (p = 0.030), urea (p = 0.032) or bilirubin (p = 0.042), decreased hemoglobin (p = 0.016), and mechanical ventilation (p = 0.016). Of note, we identified liver diseases as a novel and relevant risk factor for delirium. Hepatic encephalopathy was not a risk factor for delirium. Delirium and hepatic encephalopathy are both life-threatening but clearly distinct conditions. The median SOFA score for patients with delirium at delirium onset was significantly higher than the SOFA score of all patients at admission (p = 0.008). Patients with delirium had five times longer ICU stays (p = 0.004) and three times higher in-hospital mortality (p = 0.036). Patients with delirium were five times more likely to be transferred to an intensive medical rehabilitation unit for post-intensive care (p = 0.020). Treatment costs per case were more than five times higher in patients with delirium than in patients without delirium (p = 0.004).

CONCLUSIONS

The 10 risk factors identified in this study should be assessed upon admission to ICU for effective detection, prevention, and treatment of delirium. Liver diseases are a novel risk factor for delirium with a level of significance comparable to sepsis as an established risk factor. Of note, in patients with liver diseases delirium and hepatic encephalopathy should be recognized as distinct entities to initiate appropriate treatment. Therefore, we propose a new algorithm for efficient diagnosis, characterization, and treatment of altered mental status in the ICU. This algorithm integrates the 10 risk factor prediction-model for delirium and prompts grading of the severity of hepatic encephalopathy using the West Haven classification if liver disease is present or newly diagnosed.

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study

AIMS

To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients.

METHOD

This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision.

RESULTS

In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%.

CONCLUSIONS

The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.

Association of cholinesterase activities and POD in older adult abdominal surgical patients

Background

Postoperative delirium (POD) is a frequent complication after surgery. Older adult patients undergoing abdominal surgery are at higher risk for developing POD. Studies on the association of cholinesterase activities and POD are rare, but leading hypotheses implicate that the cholinergic pathway might play an important role in neuroinflammation and development of POD. The objective of this study was to figure out if there is an association between the development of POD and acetyl- and butyrylcholinesterase (AChE and BuChE) activities in older adult patients undergoing abdominal surgery.

Methods

The investigation was performed with a subpopulation of BioCog study patients. The BioCog project (http://www.biocog.eu) is a prospective multicenter observational study in older adult surgical patients. Patients ≥ 65 years undergoing elective surgery of at least 60 minutes who scored more than 23 points in the Mini-Mental-State-Examination were included. POD was assessed twice a day on seven consecutive days after the surgery, using the test instruments Nursing Delirium Screening Scale (Nu-Desc) and Confusion Assessment Method (CAM and CAM-ICU) and a patient chart review. Pre- and postoperative blood cholinesterase activities were measured with a photometric rapid-point-of-care-testing. The association between cholinesterase activities and POD was analyzed in a subpopulation of abdominal surgical patients using multivariable logistic regression analysis adjusting for confounders.

Results

One hundred twenty-seven patients were included for analysis (mean age 73 years, 59% female). Fifty-two patients (41%) fulfilled the criteria of POD. These patients were significantly older, had a longer time of surgery and anesthesia and achieved higher comorbidity scores compared to patients without POD. After adjusting for age, duration of surgery and charlson comorbity index, we found an association between pre- and postoperative AChE activity (U/gHb) and the development of POD (Odds ratio (OR), [95% confidence interval (CI)], preoperative 0.95 [0.89–1.00], postoperative 0.94 [0.89–1.00]).

Conclusions

We found an association between POD and AChE activity and provided new information considering patients with abdominal surgery. Future analyses should examine course dynamics of postoperative cholinesterase activities in order to clarify interactions between the cholinergic system and pathophysiological mechanisms leading to POD.

Trial registration

ClinicalTrials.gov: NCT02265263.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01826-y.

Potential Usefulness of Blood Urea Nitrogen to Creatinine Ratio in the Prediction and Early Detection of Delirium Motor Subtype in the Intensive Care Unit

Prediction and early detection of delirium can improve patient outcomes. A high blood urea nitrogen to creatinine ratio (BCR), which reflects dehydration, has been reported as a risk factor for delirium. Additionally, BCR represents skeletal muscle loss in intensive care unit (ICU) patients, which can have critical implications for clinical outcomes. We investigated whether BCR could be used to predict the occurrence and motor subtype of delirium in ICU patients through a retrospective cohort study that included 7167 patients (50 years or older) admitted to the ICU. Patients were assessed daily using the Richmond Agitation-Sedation Scale and the Confusion Assessment Method for ICU and categorized according to the delirium subtype. Participants were split into 10 groups according to BCR at ICU admission and the prevalence of each delirium subtype was compared. Multivariable logistic regression was then used for analysis. A higher BCR at ICU admission was associated with the development of hypoactive delirium. Moreover, BCR > 24.9 was associated with higher rates of hypoactive delirium. Our findings showed that a high BCR at ICU admission was associated with the development of hypoactive delirium, which suggested that BCR could be a potential biomarker for hypoactive delirium in ICU patients.