Incidence, characteristics, and outcomes of delirium in patients with noninvasive ventilation: a prospective observational study

Bringing Delirium to Light: Impact of CAM-ICU Tool to Improve Care Coordination

BACKGROUND

Delirium is a serious complication in patients in the critical care unit (CCU) that may lead to prolonged hospitalization if left undetected. The CCU at our hospital does not have a framework for determining delirium that could affect patient outcomes and discharge planning.

PRIMARY PRACTICE SETTING

CCU in a community hospital.

METHOD

A posttest-only design was used for this study. We established a framework for the early assessment of delirium, educated and trained nurses to detect delirium, collaborated with the informatics department, intensivist, nursing, respiratory therapy and worked with case management to deploy the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). We used a one-tailed independent t test to determine the impact of CAM-ICU on length of stay (LOS). Cross-tabulation and chi-square tests were used to examine the impact of CAM-ICU tool on home care utilization between the intervention and comparison groups.

RESULTS

There was a 3.12% reduction in LOS after implementing the CAM-ICU tool. Also, a reduction in home care service utilization demonstrated statistical significance ( p = .001) between the intervention group (62.5%; n = 177) and the comparison group (37.5%; n = 106).

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

Case managers are essential in improving care transitions. Case managers need to become competent in understanding the implications of the CAM-ICU tool because of their relevant role in the multidisciplinary rounds as advocates to improve care transitions across the continuum of care. Case managers need to have an understanding on how to escalate when changes in the Richmond Agitation-Sedation Scale scores occur during the multidisciplinary rounds because it can affect care coordination throughout the hospital.

CONCLUSIONS

Implementing the CAM-ICU decreased LOS, and reduced health care utilization. The early identification of patients with delirium can affect the outcomes of critically ill patients and entails multidisciplinary collaboration.

A Novel Scale to Assess Humidification during Noninvasive Ventilation: A Prospective Observational Study

Objective

To develop a novel scale to assess humidification during noninvasive ventilation (NIV).

Methods

This study was performed in an ICU of a teaching hospital. Three ICU practitioners with more than 10 years of clinical experience developed an oral humidification scale with a range of 1-4 points. Each studied the current literature on humidification and examined 50 images of mouths of NIV patients with different levels of humidification. Then, through discussion, a consensus scale was developed. Next, 10 practitioners and 33 NIV patients were recruited to validate the scale. Finally, the patients rated the dryness of their mouths using the 1-4 visual scale just after the practitioners' assessment. Talking and discussion were forbidden during the assessment, and the scorers were blinded to each other.

Results

We performed 36 assessments in 33 NIV patients. Three patients were assessed twice each more than 2 days apart. The interitem correlation coefficients between the 10 practitioners ranged from 0.748 to 0.917. Fleiss's kappa statistic was 0.516, indicating moderate agreement among practitioners. Of the 33 patients, 5 (15%) were unable to make an assessment using the 1-4 visual scale. Among the remainder, 55.7% provided scores that matched those given by the practitioners; 13.7% of scores were 1 point higher than that rated by the practitioners, and 20.7% were 1 point lower. Only 10% were beyond a 1-point difference. The kappa coefficient was 0.483 between patients and practitioners.

Conclusions

The oral humidification scale showed moderate agreement between practitioners. It was also highly accurate in reflecting the level of humidification assessed by patients.

Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes

BACKGROUND

Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.

METHODS

We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both.

RESULTS

We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1.35-2.51, p < 0.001) compared to non-agitated phenotype patients but not compared to combined phenotype patients (OR 1.27, 0.86-1.89, p = 0.229). Moreover, agitated phenotype patients were more likely to die than other phenotypes patients (OR 1.57, 1.10-2.25, p = 0.012 vs non-agitated phenotype; OR 4.61, 2.14-9.90, p < 0.001 vs. combined phenotype). This association was strongest in patients receiving mechanical ventilation when compared with the combined phenotype (OR 7.03, 2.07-23.79, p = 0.002). A similar increased risk was also seen for patients with the non-agitated phenotype compared with the combined phenotype (OR 6.10, 1.80-20.64, p = 0.004).

CONCLUSIONS

NLP-Dx-BD screening enabled identification of three behavioural disturbance phenotypes with different characteristics, prevalence, trajectory, treatment, and outcome. Such phenotype identification appears relevant to prognostication and trial design.

Association of Delirium and Depression with Respiratory and Outcome Measures in COVID-19 Inpatients

Delirium (DEL) and depression (DEP) may impair the course and severity of acute respiratory illness. The impact of such syndromes on respiratory and outcome parameters in inpatients with COVID-19 needs clarification. To clarify the relationship between DEL and DEP and respiratory outcome measures, we enrolled 100 inpatients from COVID-19 units of the Fondazione Policlinico Universitario Agostino Gemelli IRCCS of Rome. Participants were divided into those with DEL, DEP, or absence of either delirium or depression (CONT). Delirium severity was assessed with the Neelson and Champagne Confusion Scale (NEECHAM). Psychopathology was assessed with the Hamilton Rating Scale for Depression (HAM-D), the Hamilton Rating Scale for Anxiety (HAM-A), and the Brief Psychiatric Rating Scale (BPRS). Dependent variables include: (a) respiratory parameters, i.e., partial pressure of oxygen in arterial blood (PaO2), oxygen saturation (SpO2), ratio between arterial partial pressure of oxygen (PaO2), and fraction of inspired oxygen (PaO2/FiO2); (b) outcome parameters, i.e., duration of hospitalization and number of pharmacological treatments used during the hospitalization. We investigated between-group differences and the relationships between severity of delirium/depression and the dependent variables. Duration of hospitalization was longer for DEL than for either DEP or CONT and for DEP compared to CONT. NEECHAM and HAM-D scores predicted lower PaO2 and PaO2/FiO2 levels in the DEL and DEP groups, respectively. In DEP, BPRS scores positively correlated with duration of hospitalization. Delirium impacted the course of COVID-19 more severely than depression. The mechanisms by which delirium and depression worsen respiratory parameters differ.

Noninvasive positive pressure in acute exacerbations of chronic obstructive pulmonary disease

PURPOSE OF REVIEW

Noninvasive positive pressure ventilation (NIV) is standard of care for patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). We review the most current evidence and highlight areas of uncertainty and ongoing research. We highlight key concepts for the clinician caring for patients with AECOPD which require NIV.

RECENT FINDINGS

Implementation of NIV in AECOPD is not uniform in spite of the evidence and guidelines. Initiation of NIV should be done early and following protocols. Low-intensity NIV remains the standard of care, although research and guidelines are evaluating higher intensity NIV. Scores to predict NIV failure continue to be refined to allow early identification and interventions. Several areas of uncertainty remain, among them are interventions to improve tolerance, length of support and titration and nutritional support during NIV.

SUMMARY

The use of NIV in AECOPD is the standard of care as it has demonstrated benefits in several patient-centered outcomes. Current developments and research is related to the implementation and adjustment of NIV.

Predisposing and Precipitating Factors Associated With Delirium: A Systematic Review

IMPORTANCE

Despite discrete etiologies leading to delirium, it is treated as a common end point in hospital and in clinical trials, and delirium research may be hampered by the attempt to treat all instances of delirium similarly, leaving delirium management as an unmet need. An individualized approach based on unique patterns of delirium pathophysiology, as reflected in predisposing factors and precipitants, may be necessary, but there exists no accepted method of grouping delirium into distinct etiologic subgroups.

OBJECTIVE

To conduct a systematic review to identify potential predisposing and precipitating factors associated with delirium in adult patients agnostic to setting.

EVIDENCE REVIEW

A literature search was performed of PubMed, Embase, Web of Science, and PsycINFO from database inception to December 2021 using search Medical Subject Headings (MeSH) terms consciousness disorders, confusion, causality, and disease susceptibility, with constraints of cohort or case-control studies. Two reviewers selected studies that met the following criteria for inclusion: published in English, prospective cohort or case-control study, at least 50 participants, delirium assessment in person by a physician or trained research personnel using a reference standard, and results including a multivariable model to identify independent factors associated with delirium.

FINDINGS

A total of 315 studies were included with a mean (SD) Newcastle-Ottawa Scale score of 8.3 (0.8) out of 9. Across 101 144 patients (50 006 [50.0%] male and 49 766 [49.1%] female patients) represented (24 015 with delirium), studies reported 33 predisposing and 112 precipitating factors associated with delirium. There was a diversity of factors associated with delirium, with substantial physiological heterogeneity.

CONCLUSIONS AND RELEVANCE

In this systematic review, a comprehensive list of potential predisposing and precipitating factors associated with delirium was found across all clinical settings. These findings may be used to inform more precise study of delirium's heterogeneous pathophysiology and treatment.

[Factors Associated With Delirium In Patients Hospitalized During The COVID-19 Pandemic]

OBJECTIVES

To determine the risk factors associated with delirium in patients admitted to a COVID-19 Hospitalization of a general hospital during the first year of the COVID-19 pandemic.

METHODS

An observational, analytical, case-control study was conducted. We analyzed 50 medical records of patients who presented delirium and 50 who did not present delirium in a COVID Hospitalization area during the period from March 2020 to March 2021. The Odd Ratio of sociodemographic and clinical factors for presenting delirium was evaluated. The logistic regression statistical test was performed using STATA 17.0 software. The protocol was approved by the Research Ethics Committee of the Villa El Salvador Emergency Hospital.

RESULTS

Ventilatory support (p=0.04), especially the use of a reservoir mask (OR=0.34, CI 0.12-0.88) and the presence of systemic complications (p=0.03) such as metabolic acidosis (OR=12.9, CI 1.45-115.58) were statistically significant variables associated with a higher risk of presenting delirium.

CONCLUSIONS

The type of ventilatory support and the presence of systemic complications were significant risk factors for delirium in patients hospitalized in a COVID area. Further studies are required in this field to obtain more evidence in this regard.

Predicting hospital mortality and length of stay: A prospective cohort study comparing the Intensive Care Delirium Screening Checklist versus Confusion Assessment Method for the Intensive Care Unit

OBJECTIVE

The objective of this study was to compare two tools, the Intensive Care Delirium Screening Checklist (ICDSC) and Confusion Assessment Method for the intensive care unit (ICU) (CAM-ICU), for their predictive validity for outcomes related to delirium, hospital mortality, and length of stay (LOS).

METHODS

The prospective study conducted in six medical ICUs at a tertiary care hospital in Taiwan enrolled consecutive patients (≥20 years) without delirium at ICU admission. Delirium was screened daily using the ICDSC and CAM-ICU in random order. Arousal was assessed by the Richmond Agitation-Sedation Scale (RASS). Participants with any one positive result were classified as ICDSC- or CAM-ICU-delirium groups.

RESULTS

Delirium incidence evaluated by the ICDSC and CAM-ICU were 69.1% (67/97) and 50.5% (49/97), respectively. Although the ICDSC identified 18 more cases as delirious, substantial concordance (κ = 0.63; p < 0.001) was found between tools. Independent of age, Acute Physiology and Chronic Health Evaluation II score, and Charlson Comorbidity Index, both ICDSC- and CAM-ICU-rated delirium significantly predicted hospital mortality (adjusted odds ratio: 4.93; 95% confidence interval [CI]:1.56 to 15.63 vs. 2.79; 95% CI: 1.12 to 6.97, respectively), and only the ICDSC significantly predicted hospital LOS with a mean of 17.59 additional days compared with the no-delirium group. Irrespective of delirium status, a sensitivity analysis of normal-to-increased arousal (RASS≥0) test results did not alter the predictive ability of ICDSC- or CAM-ICU-delirium for hospital mortality (adjusted odds ratio: 2.97; 95% CI: 1.06 to 8.37 vs. 3.82; 95% CI: 1.35 to 10.82, respectively). With reduced arousal (RASS<0), neither tool significantly predicted mortality or LOS.

CONCLUSIONS

The ICDSC identified more delirium cases and may have higher predictive validity for mortality and LOS than the CAM-ICU. However, arousal substantially affected performance. Future studies may want to consider patients' arousal when deciding which tool to use to maximise the effects of delirium identification on patient mortality.

Incidence and Risk Factors of Ventilator-Associated Pneumonia among Patients with Delirium in the Intensive Care Unit: A Prospective Observational Study

INTRODUCTION

The incidence and risk factors for ventilator-related pneumonia (VAP) in patients with delirium are deficient, and there is a lack of in-depth knowledge of the impact of VAP on outcomes in this population. We investigated the incidence, risk factors, and outcomes of VAP in patients with delirium.

MATERIALS AND METHODS

This prospective observational study was performed in a surgical ICU at Be'sat Hospital in Hamadan, Iran, between 2018 and 2019. A total of 108 patients with delirium were identified using the Confusion Assessment Method (CAM) for the ICU and Intensive Care Delirium Screening Checklist (ICDSC) and enrolled in this study. The association between VAP and delirium, risk factors, and outcomes (ICU length of stay and ICU mortality) for VAP were investigated using the Cox proportional hazards model and logistic and simple linear regression analyses with a 95% confidence interval.

RESULTS

Of 108 delirium patients, 86 patients (79.6%) underwent mechanical ventilation (MV) and 16 patients (18.6%) experienced VAP during ICU stay. The median onset of VAP was 6.5 (IQR 4.2-7.7) days after intubation. Delirium patients with VAP stayed longer in the ICU (21.68 ± 4.26 vs.12.93 ± 1.71, P < 0.001) and also had higher ICU mortality (31.25% vs. 0%, P < 0.001) than subjects without VAP. According to multivariate cox regression, the expected HR for VAP was 53.5% lower for patients with early-onset delirium than in patients with late-onset delirium (HR: 0.465, 95% CI: 0.241-0.894, P=0.022). However, the expected hazard for VAP was 1.854 times and 4.604 times higher in patients with longer ICU stay (HR: 1.854, 95% CI: 1.689-3.059, P=0.032) and in patients with a prolonged MV duration (HR: 4.604, 95%CI: 1.567-6.708, P=0.023).

CONCLUSION

According to the results, there seems to be an inverse relationship between early onset of delirium and VAP. This finding cannot be conclusively cited, and more studies in this filed should be conducted with a larger sample size. Furthermore, VAP in delirium patients is associated with increases in poor outcomes (higher ICU mortality) and the use of medical resources (longer stay in the ICU and MV duration).

Phenotypes and subphenotypes of delirium: a review of current categorisations and suggestions for progression

Delirium is a clinical syndrome occurring in heterogeneous patient populations. It affects 45-87% of critical care patients and is often associated with adverse outcomes including acquired dementia, institutionalisation, and death. Despite an exponential increase in delirium research in recent years, the pathophysiological mechanisms resulting in the clinical presentation of delirium are still hypotheses. Efforts have been made to categorise the delirium spectrum into clinically meaningful subgroups (subphenotypes), using psychomotor subtypes such as hypoactive, hyperactive, and mixed, for example, and also inflammatory and non-inflammatory delirium. Delirium remains, however, a constellation of symptoms resulting from a variety of risk factors and precipitants with currently no successful targeted pharmacological treatment. Identifying specific clinical and biological subphenotypes will greatly improve understanding of the relationship between the clinical symptoms and the putative pathways and thus risk factors, precipitants, natural history, and biological mechanism. This will facilitate risk factor mitigation, identification of potential methods for interventional studies, and informed patient and family counselling. Here, we review evidence to date and propose a framework to identify subphenotypes. Endotype identification may be done by clustering symptoms with their biological mechanism, which will facilitate research of targeted treatments. In order to achieve identification of delirium subphenotypes, the following steps must be taken: (1) robust records of symptoms must be kept at a clinical level. (2) Global collaboration must facilitate large, heterogeneous research cohorts. (3) Patients must be clustered for identification, validation, and mapping of subphenotype stability.