Evaluation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for the patients in the intensive care unit

Subsyndromal Delirium in Critically Ill Patients-Cognitive and Functional Long-Term Outcomes

Subsyndromal delirium (SSD) in the Intensive Care Unit (ICU) is associated with an increased morbidity with unknown post-discharge functional and cognitive outcomes. We performed a prospective multicenter study to analyze the mental status of patients during their first 72 h after ICU admission and its trajectory, with follow-ups at 3 and 6 months after hospital discharge. Amongst the 106 included patients, SSD occurred in 24.5% (n = 26) and was associated with the duration of mechanical ventilation (p = 0.003) and the length of the ICU stay (p = 0.002). After the initial 72 h, most of the SSD patients (30.8%) improved and no longer had SSD; 19.2% continued to experience SSD and one patient (3.8%) progressed to delirium. The post-hospital discharge survival rate for the SSD patients was 100% at 3 months and 87.5% at 6 months. At admission, 96.2% of the SSD patients were fully independent in daily living activities, 66.7% at 3-month follow-up, and 100% at 6-month follow-up. Most SSD patients demonstrated a cognitive decline from admission to 3-month follow-up and improved at 6 months (IQCODE-SF: admission 3.13, p < 0.001; 3 months 3.41, p = 0.019; 6 months 3.19, p = 0.194). We concluded that early SSD is associated with worse outcomes, mainly a transitory cognitive decline after hospital discharge at 3 months, with an improvement at 6 months. This highlights the need to prevent and identify this condition during ICU stays.

Survey of Guideline Compliance and Attitude Toward Symptom Management in Japanese Intensive Care Units

Introduction

The Clinical Practice Guideline for the Management of Pain, Agitation, and Delirium in Adult Patients in the Intensive Care Unit (ICU) was revised in 2018 to include sleep disruption and immobility. Inadequate management of these symptoms can lead to negative consequences. A 2019 survey in Japan found that the guideline was recognized but needed to be consistently implemented.

Objective

This study aimed to examine compliance with the guideline for symptom management of pain, agitation, delirium, and sleep in Japanese ICUs.

Methods

This study included all ICUs in Japan and asked one representative from each unit to respond to the web survey from January 2022 to February 2022.

Results

Of a potential 643 units, 125 respondents from the ICU were included in the analysis (19.4% response rate). Compared to the guideline's recommendations, (a) pain assessment was performed in 86.3% of patients who could self-report, and in 72.0% of those who could not self-report; (b) agitation and sedation assessment was performed in 99% of patients; (c) only 66.1% of nurses reported assessing sleep quality on the units, and 9.1% performed the subjective sleep quality assessment; (d) the use of the recommended risk factor of the delirium assessment tool was low (9.6%). Additionally, according to the survey respondents, contrary to the guideline, many units administered medications to prevent and treat delirium, and approximately 30% used multiple non-drug interventions. The data are expressed as numbers and percentages. Some datasets were incomplete due to missing values.

Conclusion

Most units used drugs for delirium prevention and treatment, and only a few used non-drug interventions. There is a need to popularize the assessment of sleep and delirium risk factors and use non-drug interventions to promote patient-centered care in the future.

Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation

OBJECTIVES

Light sedation is recommended over deep sedation for invasive mechanical ventilation to improve clinical outcome but may increase the risk of agitation. This study aimed to develop and prospectively validate an ensemble machine learning model for the prediction of agitation on a daily basis.

DESIGN

Variables collected in the early morning were used to develop an ensemble model by aggregating four machine learning algorithms including support vector machines, C5.0, adaptive boosting with classification trees, and extreme gradient boosting with classification trees, to predict the occurrence of agitation in the subsequent 24 hours.

SETTING

The training dataset was prospectively collected in 95 ICUs from 80 Chinese hospitals on May 11, 2016, and the validation dataset was collected in 20 out of these 95 ICUs on December 16, 2019.

PATIENTS

Invasive mechanical ventilation patients who were maintained under light sedation for 24 hours prior to the study day and who were to be maintained at the same sedation level for the next 24 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 578 invasive mechanical ventilation patients from 95 ICUs in 80 Chinese hospitals, including 459 in the training dataset and 119 in the validation dataset, were enrolled. Agitation was observed in 36% (270/578) of the invasive mechanical ventilation patients. The stepwise regression model showed that higher body temperature (odds ratio for 1°C increase: 5.29; 95% CI, 3.70-7.84; p < 0.001), greater minute ventilation (odds ratio for 1 L/min increase: 1.15; 95% CI, 1.02-1.30; p = 0.019), higher Richmond Agitation-Sedation Scale (odds ratio for 1-point increase: 2.43; 95% CI, 1.92-3.16; p < 0.001), and days on invasive mechanical ventilation (odds ratio for 1-d increase: 0.95; 95% CI, 0.93-0.98; p = 0.001) were independently associated with agitation in the subsequent 24 hours. In the validation dataset, the ensemble model showed good discrimination (area under the receiver operating characteristic curve, 0.918; 95% CI, 0.866-0.969) and calibration (Hosmer-Lemeshow test p = 0.459) in predicting the occurrence of agitation within 24 hours.

CONCLUSIONS

This study developed an ensemble model for the prediction of agitation in invasive mechanical ventilation patients under light sedation. The model showed good calibration and discrimination in an independent dataset.

Tools Are Needed to Promote Sedation Practices for Mechanically Ventilated Patients

Suboptimal sedation practices continue to be frequent, although the updated guidelines for management of pain, agitation, and delirium in mechanically ventilated (MV) patients have been published for several years. Causes of low adherence to the recommended minimal sedation protocol are multifactorial. However, the barriers to translation of these protocols into standard care for MV patients have yet to be analyzed. In our view, it is necessary to develop fresh insights into the interaction between the patients' responses to nociceptive stimuli and individualized regulation of patients' tolerance when using analgesics and sedatives. By better understanding this interaction, development of novel tools to assess patient pain tolerance and to define and predict oversedation or delirium may promote better sedation practices in the future.

ICU Delirium-Prediction Models: A Systematic Review

Supplemental Digital Content is available in the text.

Objective:

Summarize performance and development of ICU delirium-prediction models published within the past 5 years.

Data Sources:

Systematic electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and Cumulative Index to Nursing and Allied Health Literature to identify peer-reviewed studies.

Study Selection:

Eligible studies were published in English during the past 5 years that specifically addressed the development, validation, or recalibration of delirium-prediction models in adult ICU populations.

Data Extraction:

Screened citations were extracted independently by three investigators with a 42% overlap to verify consistency using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.

Data Synthesis:

Eighteen studies featuring 23 distinct prediction models were included. Model performance varied greatly, as assessed by area under the receiver operating characteristic curve (0.62–0.94), specificity (0.50–0.97), and sensitivity (0.45–0.96). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians.

Conclusions:

Although most ICU delirium-prediction models have relatively good performance, they have limited applicability to clinical practice. Most models were static, making predictions based on data collected at a single time-point, failing to account for fluctuating conditions during ICU admission. Further research is needed to create clinically relevant dynamic delirium-prediction models that can adapt to changes in individual patient physiology over time and deliver actionable predictions to clinicians.

Delirium

Delirium, a syndrome characterized by an acute change in attention, awareness and cognition, is caused by a medical condition that cannot be better explained by a pre-existing neurocognitive disorder. Multiple predisposing factors (for example, pre-existing cognitive impairment) and precipitating factors (for example, urinary tract infection) for delirium have been described, with most patients having both types. Because multiple factors are implicated in the aetiology of delirium, there are likely several neurobiological processes that contribute to delirium pathogenesis, including neuroinflammation, brain vascular dysfunction, altered brain metabolism, neurotransmitter imbalance and impaired neuronal network connectivity. The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) is the most commonly used diagnostic system upon which a reference standard diagnosis is made, although many other delirium screening tools have been developed given the impracticality of using the DSM-5 in many settings. Pharmacological treatments for delirium (such as antipsychotic drugs) are not effective, reflecting substantial gaps in our understanding of its pathophysiology. Currently, the best management strategies are multidomain interventions that focus on treating precipitating conditions, medication review, managing distress, mitigating complications and maintaining engagement to environmental issues. The effective implementation of delirium detection, treatment and prevention strategies remains a major challenge for health-care organizations globally.

Platelet-to-lymphocyte ratio as a predictive index for delirium in critically ill patients: A retrospective observational study

Delirium is a neuropsychiatric syndrome commonly encountered in critically ill patients, and systemic inflammation has been strongly implicated to underlie its pathophysiology. This study aimed to investigate the predictive value of the platelet-to-lymphocyte ratio (PLR) for delirium in the intensive care unit (ICU).In this retrospective observational study, we analyzed the clinical and laboratory data of 319 ICU patients from October 2016 to December 2017. Using the Locally Weighted Scatterplot Smoothing technique, a PLR knot was detected at a value of approximately 100. Logistic regression was used to investigate the association between the PLR and delirium.Of the 319 patients included in this study, 29 (9.1%) were diagnosed with delirium. In the delirium group, the duration of mechanical ventilation was significantly longer than that in the no-delirium group (40.2 ± 65.5 vs. 19.9 ± 26.5 hours, respectively; P < .001). A multiple logistic regression analysis showed that PLR > 100 (odds ratio [OR]: 1.003, 95% confidence interval [CI]: 1.001-1.005), age (OR: 2.76, 95% CI: 1.110-6.861), and the ratio of arterial oxygen partial pressure to the inspired oxygen fraction (OR: 0.996, 95% CI: 0.992-0.999) were independent predictors of delirium.In our study, a high PLR value on ICU admission was associated with a higher incidence of delirium. Owing to easy calculability, the PLR could be a useful delirium predictive index in ICUs, thereby enabling early interventions to be implemented.

Single assessment of delirium severity during postacute intensive care of chronically critically ill patients and its associated factors: post hoc analysis of a prospective cohort study in Germany

OBJECTIVES

To assess the delirium severity (DS), its risk factors and association with adverse patient outcomes in chronically critically ill (CCI) patients.

DESIGN

A prospective cohort study.

SETTING

A tertiary care hospital with postacute intensive care units (ICUs) in Germany.

PARTICIPANTS

N=267 CCI patients with critical illness polyneuropathy and/or critical illness myopathy, aged 18-75 years, who had undergone elective tracheotomy for weaning failure.

INTERVENTIONS

None.

MEASURES

Primary outcomes: DS was assessed using the Confusion Assessment Method for the Intensive Care Unit-7 delirium severity score, within 4 weeks (t1) after the transfer to a tertiary care hospital. In post hoc analyses, univariate linear regressions were employed, examining the relationship of DS with clinical, sociodemographic and psychological variables. Secondary outcomes: additionally, correlations of DS with fatigue (using the Multidimensional Fatigue Inventory-20), quality of life (using the Euro-Quality of Life) and institutionalisation/mortality at 3 (t2) and 6 (t3) months follow-up were computed.

RESULTS

Of the N=267 patients analysed, 9.4% showed severe or most severe delirium symptoms. 4.1% had a full-syndromal delirium. DS was significantly associated with the severity of illness (p=0.016, 95% CI -0.1 to -0.3), number of medical comorbidities (p<0.001, 95% CI .1 to .3) and sepsis (p<0.001, 95% CI .3 to 1.0). Patients with a higher DS at postacute ICU (t1), showed a higher mental fatigue at t2 (p=0.008, 95% CI .13 to .37) and an increased risk for institutionalisation/mortality (p=0.043, 95% CI 1.1 to 28.9/p=0.015, 95% CI 1.5 to 43.2).

CONCLUSIONS

Illness severity is positively associated with DS during postacute care in CCI patients. An adequate management of delirium is essential in order to mitigate functional and cognitive long-term sequelae following ICU.

TRIAL REGISTRATION NUMBER

DRKS00003386.