Delirium risk prediction models for intensive care unit patients: A systematic review

Nursing diagnoses as factors associated with delirium among intensive care unit patients with sepsis: A retrospective correlational study

AIMS

To examine whether nursing diagnoses were associated with delirium in patients with sepsis.

BACKGROUND

Nursing diagnosis is a nurse's clinical judgement about clients' current or potential health conditions. Delirium is regarded as an important nurse-sensitive outcome. Nonetheless, nursing diagnoses associated with delirium have not yet been identified.

DESIGN

Retrospective correlational study.

METHODS

This study was carried out from December 2021 to January 2023. We analysed electronic health records of patients with sepsis admitted to the intensive care units (ICUs) of a tertiary hospital in Seoul, South Korea. Delirium was defined based on the Intensive Care Delirium Screening Checklist score. Nursing diagnoses established within 24 h of admission to the ICU were included and were based on the North American Nursing Diagnosis Association diagnostic classification. The data were analysed using logistic regression. Demographics, comorbidities, procedures and physiological measures were adjusted. Regression model was evaluated via receiver operating characteristic curve, Nagelkerke R2, accuracy and F1 score.

RESULTS

The prevalence of delirium in patients with sepsis was 51.8%. Ineffective breathing patterns, decreased cardiac output and impaired skin integrity were significant nursing diagnoses related to delirium. Age ≥ 65 years, Acute Physiology and Chronic Health Evaluation II score, mechanical ventilation, continuous renal replacement therapy, physical restraint and comatose state were also associated with delirium in patients with sepsis. The area under the receiver operating characteristic curve was 0.806.

CONCLUSION

Ineffective breathing patterns, decreased cardiac output and impaired skin integrity could manifest as prodromal symptoms of delirium among patients with sepsis.

IMPACT

The prodromal symptoms of delirium revealed through nursing diagnoses can be efficiently used to identify high-risk groups for delirium. The use of nursing diagnosis system should be recommended in clinical practice caring for sepsis patients.

REPORTING METHODS

STROBE checklist.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public involvement.

Machine learning for the prediction of delirium in elderly intensive care unit patients

PURPOSE

This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage.

METHODS

Patients admitted to ICU for at least 24 h and using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (76,943 ICU stays from 2008 to 2019) were considered. Patients with a positive delirium test in the first 24 h and under 65 years of age were excluded. Two prediction models, machine learning extreme gradient boosting (XGBoost) and logistic regression (LR) model, were developed and validated to predict the onset of delirium.

RESULTS

Of the 18,760 patients included in the analysis, 3463(18.5%) were delirium positive. A total of 22 significant predictors were selected by LASSO regression. The XGBoost model demonstrated superior performance over the LR model, with the Area Under the Receiver Operating Characteristic (AUC) values of 0.853 (95% confidence interval [CI] 0.846-0.861) and 0.831 (95% CI 0.815-0.847) in the training and testing datasets, respectively. Moreover, the XGBoost model outperformed the LR model in both calibration and clinical utility. The top five predictors associated with the onset of delirium were sequential organ failure assessment (SOFA), infection, minimum platelets, maximum systolic blood pressure (SBP), and maximum temperature.

CONCLUSION

The XGBoost model demonstrated good predictive performance for delirium among elderly ICU patients, thus assisting clinicians in identifying high-risk patients at the early stage and implementing targeted interventions to improve outcome.

Dynamic Nomogram for Subsyndromal Delirium in Adult Intensive Care Unit: A Prospective Cohort Study

Purpose

To develop a dynamic nomogram of subsyndromal delirium (SSD) in intensive care unit (ICU) patients and internally validate its efficacy in predicting SSD.

Patients and Methods

Patients who met the inclusion and exclusion criteria in the ICU of a tertiary hospital in Zhejiang from September 2021 to June 2022 were selected as the research objects. The patient data were randomly divided into the training set and validation set according to the ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen the predictors of SSD, and R software was used to construct a dynamic nomogram. Receiver operating characteristic (ROC) curve, calibration band and decision curve were used to evaluate the discrimination, calibration and clinical effectiveness of the model.

Results

A total of 1000 eligible patients were included, including 700 in the training set and 300 in the validation set. Age, drinking history, C reactive protein level, APACHE II, indwelling urinary catheter, mechanical ventilation, cerebrovascular disease, respiratory failure, constraint, dexmedetomidine, and propofol were predictors of SSD in ICU patients. The ROC curve values of the training set was 0.902 (95% confidence interval: 0.879-0.925), the best cutoff value was 0.264, the specificity was 78.4%, and the sensitivity was 88.0%. The ROC curve values of the validation set was 0.888 (95% confidence interval: 0.850-0.930), the best cutoff value was 0.543, the specificity was 94.9%, and the sensitivity was 70.9%. The calibration band showed good calibration in the training and validation set. Decision curve analysis showed that the net benefit in the model was significantly high.

Conclusion

The dynamic nomogram has good predictive performance, so it is a precise and effective tool for medical staff to predict and manage SSD in the early stage.

A protocol of an international validation study to assess the clinical accuracy of the eDIS-ICU delirium screening tool

BACKGROUND

Delirium is a common, yet underdiagnosed neuropsychiatric complication of intensive care unit (ICU) admission, associated with significant mortality and morbidity. Delirium can be difficult to diagnose, with gold standard assessments by a trained specialist being impractical and rarely performed. To address this, various tools have been developed, enabling bedside clinicians to assess for delirium efficiently and accurately. However, the performance of these tools varies depending on factors including the assessor's training. To address the shortcomings of current tools, electronic tools have been developed.

AIMS AND OBJECTIVES

The aims of this validation study are to assess the feasibility, acceptability, and generalisability of a recently developed and pilot-tested electronic delirium screening tool (eDIS-ICU) and compare diagnostic concordance, sensitivity, and specificity between eDIS-ICU, Confusion Assessment Method for the ICU (CAM-ICU), and the Diagnostic and Statistical Manual of Mental Disorders - 5th edition (DSM-V) gold standard in diverse ICU settings.

METHODS

Seven hundred participants will be recruited across five sites in three countries. Participants will complete three assessments (eDIS-ICU, CAM-ICU, and DSM-V) twice within one 24-h period. At each time point, assessments will be completed within one hour. Assessments will be administered by three different people at any given time point, with the assessment order and assessor for eDIS-ICU and CAM-ICU randomly allocated. Assessors will be blinded to previous and concurrent assessment results.

RESULTS

The primary outcome is comparing diagnostic sensitivity of eDIS-ICU and CAM-ICU against the DSM-V.

RELEVANCE TO CLINICAL PRACTICE

This protocol describes a definitive validation study of an electronic diagnostic tool to assess for delirium in the ICU. Delirium remains a common and difficult challenge in the ICU and is linked with multiple neurocognitive sequelae. Various challenges to routine assessment mean many cases are still unrecognised or misdiagnosed. An improved ability for bedside clinicians to screen for delirium accurately and efficiently will support earlier diagnosis, identification of underlying cause(s) and timely treatments, and ultimately improved patient outcomes.

CLINICAL TRIAL REGISTRATION NUMBER

This study was prospectively registered on the Australian New Zealand Clinical Trials Registry (ANZCTR) on 8th February 2022 (ACTRN12622000220763).

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review

PURPOSE

The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

METHODS

APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis.

RESULTS

Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability.

CONCLUSIONS

Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

[Dealing with coercion in intensive care medicine : Recommendations from the Ethics Section of the German Interdisciplinary Association for Intensive Care and Emergency Medicine (DIVI) in collaboration with the Ethics Section of the German Society for Internal Intensive Care and Emergency Medicine (DGIIN)]

The treatment situation in intensive care is characterised by a specific asymmetry in the relationship between patients and the team: Patients are particularly dependent on their environment and often show impaired consciousness and capacity to consent. This facilitates the use of coercion or enables and/or provokes it. The aim of this recommendation is to show ways to recognise patients with their wishes and needs and to integrate them into treatment concepts in the intensive care unit in order to reduce and avoid coercion whenever possible. The recommendation shows the variety of possible forms of coercion and discusses the moral standards to be considered in the ethical weighing process as well as legal conditions for justifying its use. It becomes obvious that treatment measures which may involve the use of coercion always require a careful and self-critical review of the measures in relation to the indication and the therapeutic goal. The recommendation's intention therefore is not to disapprove the use of coercion by interprofessional teams. Instead, it aims to contribute to a sensitive perception of coercion and to a critical and caring approach to formal and especially informal (indirect) coercion.

Delirium in Intensive Care

Purpose of Review

Delirium in the intensive care unit (ICU) has become increasingly acknowledged as a significant problem for critically ill patients affecting both the actual course of illness as well as outcomes. In this review, we focus on the current evidence and the gaps in knowledge.

Recent Findings

This review highlights several areas in which the evidence is weak and further research is needed in both pharmacological and non-pharmacological treatment. A better understanding of subtypes and their different response to therapy is needed and further studies in aetiology are warranted. Larger studies are needed to explore risk factors for developing delirium and for examining long-term consequences. Finally, a stronger focus on experienced delirium and considering the perspectives of both patients and their families is encouraged.

Summary

With the growing number of studies and a better framework for research leading to stronger evidence, the outcomes for patients suffering from delirium will most definitely improve in the years to come.

Screening for delirium in the intensive care unit using eDIS-ICU - A purpose-designed app: A pilot study

INTRODUCTION

Delirium, a common complication of an intensive care unit (ICU) admission, is inconsistently diagnosed by clinicians. Current screening tools require specialist expertise and/or training. Some are time-consuming to administer, and reliability in routine clinical practice is questionable. An innovative app designed to enable efficient and sensitive screening for delirium without specialist training (eDIS-ICU) has recently been described. This pilot study compared the eDIS-ICU against the reference standard expert assessment using DSM-V (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) criteria and the Confusion Assessment Method for the ICU (CAM-ICU).

METHODS

In this prospective, single-centre pilot study, a convenience sample of 29 ICU patients were recruited at a tertiary referral hospital between November 2018 and August 2019. After obtaining written consent, demographic and clinical data were collected, and the patients were screened for delirium using eDIS-ICU and CAM-ICU by two clinician researchers in random order. The patients were also assessed for presence of delirium independently by an expert clinician using a structured interview to diagnose as per DSM-V criteria. The results of screening and diagnosis were tabulated to allow comparison of screening tools against diagnosis; sensitivity and specificity of the tools were calculated.

RESULTS

Seven participants were diagnosed with delirium as per DSM-V criteria. The eDIS-ICU tool correctly identified six of these participants compared with two identified by CAM-ICU. The sensitivity of the eDIS-ICU tool was 86% (95% confidence interval [CI] = 81.5-100.0) compared with 29% (95% CI = 5.1-69.7) for CAM-ICU (p < 0.05), and the specificity was 73% (95% CI = 81.5-100.0) versus 96% (95% CI = 75.1-99.8), respectively.

CONCLUSION

The simple and novel eDIS-ICU delirium screening tool was noninferior to the CAM-ICU in detecting delirium as per DSM-V criteria. A definitive validation study is warranted.

Stratified delirium risk using prescription medication data in a state-wide cohort

OBJECTIVE

Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list.

METHOD

Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort.

RESULTS

The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender.

CONCLUSION

Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.