Prediction of ICU Delirium: Validation of Current Delirium Predictive Models in Routine Clinical Practice

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.

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

AIMS AND OBJECTIVES

To investigate whether a low Braden Skin Score (BSS), reflecting an increased risk of pressure injury, could predict the risk of delirium in older patients in the intensive care unit (ICU).

BACKGROUND

Delirium, a common acute encephalopathy syndrome in older ICU patients, is associated with prolonged hospital stay, long-term cognitive impairment and increased mortality. However, few studies have explored the relationship between BSS and delirium.

DESIGN

Multicenter cohort study.

METHODS

The study included 24,123 older adults from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 1090 older adults from the eICU Collaborative Research Database (eICU-CRD), all of whom had a record of BSS on admission to the ICU. We used structured query language to extract relevant data from the electronic health records. Delirium, the primary outcome, was primarily diagnosed by the Confusion Assessment Method for the ICU or the Intensive Care Delirium Screening Checklist. Logistic regression models were used to validate the association between BSS and outcome. A STROBE checklist was the reporting guide for this study.

RESULTS

The median age within the MIMIC-IV and eICU-CRD databases was approximately 77 and 75 years, respectively, with 11,195 (46.4%) and 524 (48.1%) being female. The median BSS at enrollment in both databases was 15 (interquartile range: 13, 17). Multivariate logistic regression showed a negative association between BSS on ICU admission and the prevalence of delirium. Similar patterns were found in the eICU-CRD database.

CONCLUSIONS

This study found a significant negative relationship between ICU admission BSS and the prevalence of delirium in older patients.

RELEVANCE TO CLINICAL PRACTICE

The BSS, which is simple and accessible, may reflect the health and frailty of older patients. It is recommended that BSS assessment be included as an essential component of delirium management strategies for older patients in the ICU.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a retrospective cohort study, and no patients or the public were involved in the design and conduct of the study.

Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study

Background and objective

Delirium is the most common neuropsychological complication among older adults admitted to the intensive care unit (ICU) and is often associated with a poor prognosis. This study aimed to construct and validate an interpretable machine learning (ML) for early delirium prediction in older ICU patients.

Methods

This was a retrospective observational cohort study and patient data were extracted from the Medical Information Mart for Intensive Care-IV database. Feature variables associated with delirium, including predisposing factors, disease-related factors, and iatrogenic and environmental factors, were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, decision trees, support vector machines, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, F1 score, calibration plot, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to improve the interpretability of the final model.

Results

Nine thousand seven hundred forty-eight adults aged 65 years or older were included for analysis. Twenty-six features were selected to construct ML prediction models. Among the models compared, the XGBoost model demonstrated the best performance including the highest AUC (0.836), accuracy (0.765), sensitivity (0.713), recall (0.713), and F1 score (0.725) in the training set. It also exhibited excellent discrimination with AUC of 0.810, good calibration, and had the highest net benefit in the validation cohort. The SHAP summary analysis showed that Glasgow Coma Scale, mechanical ventilation, and sedation were the top three risk features for outcome prediction. The SHAP dependency plot and SHAP force analysis interpreted the model at both the factor level and individual level, respectively.

Conclusion

ML is a reliable tool for predicting the risk of critical delirium in elderly patients. By combining XGBoost and SHAP, it can provide clear explanations for personalized risk prediction and more intuitive understanding of the effect of key features in the model. The establishment of such a model would facilitate the early risk assessment and prompt intervention for delirium.

Dynamic Nomogram for Predicting the Risk of Perioperative Neurocognitive Disorders in Adults

BACKGROUND

Simple and rapid tools for screening high-risk patients for perioperative neurocognitive disorders (PNDs) are urgently needed to improve patient outcomes. We developed an online tool with machine-learning algorithms using routine variables based on multicenter data.

METHODS

The entire dataset was composed of 49,768 surgical patients from 3 representative academic hospitals in China. Surgical patients older than 45 years, those undergoing general anesthesia, and those without a history of PND were enrolled. When the patient's discharge diagnosis was PND, the patient was in the PND group. Patients in the non-PND group were randomly extracted from the big data platform according to the surgical type, age, and source of data in the PND group with a ratio of 3:1. After data preprocessing and feature selection, general linear model (GLM), artificial neural network (ANN), and naive Bayes (NB) were used for model development and evaluation. Model performance was evaluated by the area under the receiver operating characteristic curve (ROCAUC), the area under the precision-recall curve (PRAUC), the Brier score, the index of prediction accuracy (IPA), sensitivity, specificity, etc. The model was also externally validated on the multiparameter intelligent monitoring in intensive care (MIMIC) Ⅳ database. Afterward, we developed an online visualization tool to preoperatively predict patients' risk of developing PND based on the models with the best performance.

RESULTS

A total of 1051 patients (242 PND and 809 non-PND) and 2884 patients (6.2% patients with PND) were analyzed on multicenter data (model development, test [internal validation], external validation-1) and MIMIC Ⅳ dataset (external validation-2). The model performance based on GLM was much better than that based on ANN and NB. The best-performing GLM model on validation-1 dataset achieved ROCAUC (0.874; 95% confidence interval [CI], 0.833-0.915), PRAUC (0.685; 95% CI, 0.584-0.786), sensitivity (72.6%; 95% CI, 61.4%-81.5%), specificity (84.4%; 95% CI, 79.3%-88.4%), Brier score (0.131), and IPA (44.7%), and of which the ROCAUC (0.761, 95% CI, 0.712-0.809), the PRAUC (0.475, 95% CI, 0.370-0.581), Brier score (0.053), and IPA (76.8%) on validation-2 dataset. Afterward, we developed an online tool (https://pnd-predictive-model-dynnom.shinyapps.io/ DynNomapp/) with 10 routine variables for preoperatively screening high-risk patients.

CONCLUSIONS

We developed a simple and rapid online tool to preoperatively screen patients' risk of PND using GLM based on multicenter data, which may help medical staff's decision-making regarding perioperative management strategies to improve patient outcomes.

Sedation for Patients with Sepsis: Towards a Personalised Approach

This article looks at the challenges of sedoanalgesia for sepsis patients, and argues for a personalised approach. Sedation is a necessary part of treatment for patients in intensive care to reduce stress and anxiety and improve long-term prognoses. Sepsis patients present particular difficulties as they are at increased risk of a wide range of complications, such as multiple organ failure, neurological dysfunction, septic shock, ARDS, abdominal compartment syndrome, vasoplegic syndrome, and myocardial dysfunction. The development of any one of these complications can cause the patient's rapid deterioration, and each has distinct implications in terms of appropriate and safe forms of sedation. In this way, the present article reviews the sedative and analgesic drugs commonly used in the ICU and, placing special emphasis on their strategic administration in sepsis patients, develops a set of proposals for sedoanalgesia aimed at improving outcomes for this group of patients. These proposals represent a move away from simplistic approaches like avoiding benzodiazepines to more "objective-guided sedation" that accounts for a patient's principal pathology, as well as any comorbidities, and takes full advantage of the therapeutic arsenal currently available to achieve personalised, patient-centred treatment goals.

Biomarkers of alcohol abuse potentially predict delirium, delirium duration and mortality in critically ill patients

Carbohydrate-deficient transferrin (CDT) and the γ-glutamyltransferase-CDT derived Anttila-Index are established biomarkers for sustained heavy alcohol consumption and their potential role to predict delirium and mortality in critically ill patients is not clear. In our prospective observational study, we included 343 consecutive patients admitted to our ICU, assessed the occurrence of delirium and investigated its association with biomarkers of alcohol abuse measured on the day of ICU admission. 35% of patients developed delirium during ICU stay. We found significantly higher CDT levels (p = 0.011) and Anttila-Index (p = 0.001) in patients with delirium. CDT above 1.7% (OR 2.06), CDT per percent increase (OR 1.26, AUROC 0.75), and Anttila-Index per unit increase (OR 1.28, AUROC 0.74) were associated with delirium development in adjusted regression models. Anttila-Index and CDT also correlated with delirium duration exceeding 5 days. Additionally, Anttila-Index above 4, Anttila-Index per unit increase, and CDT per percent increase were independently associated with hospital mortality.

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department

INTRODUCTION

Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting.

OBJECTIVE

Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units.

METHODS

This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses.

RESULTS

A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837-0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%-54.0%) a positive predictive value of 43.5% (95% CI 43.2%-43.9%), and a negative predictive value of 93.1% (95% CI 93.1%-93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835-0.838) and 0.831 (95% CI, 0.830-0.833) respectively.

CONCLUSION

This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.

Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment

STUDY OBJECTIVE

To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission.

DESIGN

Retrospective cohort study.

SETTING

Single university teaching hospital in Taipei, Taiwan.

PATIENTS

6238 critically ill patients from August 2020 to August 2021.

MEASUREMENTS

Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance.

MAIN RESULTS

Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835-0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780-0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911-0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792-0.886) was the highest for the 24H LR model.

CONCLUSION

Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission.

Comparison of Prognostic Accuracy of 3 Delirium Prediction Models

BACKGROUND

Delirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed.

OBJECTIVES

To compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation.

METHODS

This retrospective study involved adult patients admitted to the intensive care unit during a 2-year period. Delirium was assessed by using the Confusion Assessment Method for the Intensive Care Unit or any administered dose of haloperidol or quetiapine. Model discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC); values were compared using the DeLong test.

RESULTS

The study enrolled 1353 patients. The AUC values were calculated as 0.716 (95% CI, 0.688-0.745), 0.681 (95% CI, 0.650-0.712), and 0.660 (95% CI, 0.629-0.691) for the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, respectively. The difference in model discrimination was statistically significant for comparison of the PRE-DELIRIC with the E-PRE-DELIRIC (AUC difference, 0.035; P = .02) and Lanzhou models (AUC difference, 0.056; P < .001). In the PRE-DELIRIC model, the AUC was 0.711 (95% CI, 0.680-0.743) for patients receiving mechanical ventilation and 0.664 (95% CI, 0.586-0.742) for those not receiving it (difference, 0.047; P = .27).

CONCLUSION

Statistically significant differences in prognostic accuracy were found between delirium prediction models. The PRE-DELIRIC model was the best-performing model and can be used in patients receiving or not receiving mechanical ventilation.

External validation and comparison of two delirium prediction models in patients admitted to the cardiac intensive care unit

Background

No data is available on delirium prediction models in the cardiac intensive care unit (CICU), although preexisting delirium prediction models [PREdiction of DELIRium in ICu patients (PRE-DELIRIC) and Early PREdiction of DELIRium in ICu patients (E-PRE-DELIRIC)] were developed and validated based on a population admitted to the general intensive care unit (ICU). Therefore, we externally validated the usefulness of the PRE-DELIRIC and E-PRE-DELIRIC models and compared their predictive performance in patients admitted to the CICU.

Methods

A total of 2,724 patients admitted to the CICU were enrolled between September 2012 and December 2018. Delirium was defined as at least one positive Confusion Assessment Method for the ICU (CAM-ICU) which was screened at least once every 8 h. The PRE-DELIRIC value was calculated within 24 h of CICU admission, and the E-PRE-DELIRIC value was calculated at CICU admission. The predictive performance of the models was evaluated by using the area under the receiver operating characteristic (AUROC) curve, and the calibration slope was assessed graphically by plotting.

Results

Delirium occurred in 677 patients (24.8%) when the patients were assessed thrice daily until 7 days of the CICU stay. The AUROC curve for the prediction of delirium was significantly greater for PRE-DELIRIC values [0.84, 95% confidence interval (CI): 0.82–0.86] than for E-PRE-DELIRIC values (0.79, 95% CI: 0.77–0.80) [z score of −6.24 (p < 0.001)]. Net reclassification improvement for the prediction of delirium increased by 0.27 (95% CI: 0.21–0.32, p < 0.001). Calibration was acceptable in the PRE-DELIRIC model (Hosmer-Lemeshow p = 0.170) but not in the E-PRE-DELIRIC model (Hosmer-Lemeshow p < 0.001).

Conclusion

Although both models have good predictive performance for the development of delirium, even in critically ill cardiac patients, the performance of the PRE-DELIRIC model might be superior to that of the E-PRE-DELIRIC model. Further studies are required to confirm our results and design a specific delirium prediction model for CICU patients.

A Scoping Review of the Incidence, Predictors, and Outcomes of Delirium Among Critically Ill Stroke Patients

ABSTRACT

BACKGROUND: Delirium is a common, often iatrogenically induced syndrome that may impede the physical, cognitive, and psychological recovery of critically ill adults. The effect delirium has on outcomes of intensive care unit patients having acute neurologic injury remains unclear because previous studies frequently exclude this vulnerable population. The aim of this scoping review was to describe the incidence, predictors, and outcomes of delirium among adults admitted to an intensive care unit experiencing an acute ischemic stroke, intracerebral hemorrhage, or aneurysmal subarachnoid hemorrhage. METHODS: PubMed, CINAHL, Web of Science, EMBASE, and Scopus were searched with the terms (1) stroke, (2) critical care, and (3) delirium. Inclusion criteria were original peer-reviewed research reporting the incidence, outcomes, or predictors of delirium after acute stroke among critically ill adults. Editorials, reviews, posters, conference proceedings, abstracts, and studies in which stroke was not the primary reason for admission were excluded. Title and abstract screening, full-text review, and data extraction were performed by 2 authors, with disagreements adjudicated by a third author. RESULTS: The initial search yielded 1051 results. Eighteen studies met eligibility criteria and were included in the review. Stroke type was not mutually exclusive and included persons given a diagnosis of acute ischemic stroke (11), intracerebral hemorrhage (12), aneurysmal subarachnoid hemorrhage (8), and other (1) strokes. Incidence of delirium among stroke patients ranged from 12% to 75%. Predictors of delirium included older age, preexisting dementia, higher severity of illness, and physical restraint use. Outcomes associated with delirium included higher mortality, longer length of stay, worse cognition and quality of life, and lower functional status. CONCLUSIONS: Current findings are limited by heterogenous populations, assessments, and measurement parameters. Detection and management of delirium among critically ill stroke patients requires an approach with specific considerations to the complexities of acute neurological injury and concomitant critical illness.

Diuretics: a contemporary pharmacological classification?

Diuretics are drugs that increase the flow of urine. They are commonly used to treat edema, hypertension, and heart failure. Typically, the pharmacological group consists of five classes: thiazide diuretics, loop diuretics, potassium-sparing diuretics, osmotic diuretics, and carbonic anhydrase inhibitors. This traditional classification and the nomenclature of diuretics have not changed over the last decades, which means that it was not adapted to current pharmacological research. Modern approaches in the field of pharmacological nomenclature suggest the introduction of mechanism-based drug class designations, which is not yet reflected in the group of diuretics. Moreover, included drug classes have lost their relevance as diuretic agents. Carbonic anhydrase inhibitors, for example, are mainly used in the treatment of glaucoma. Newer agents such as vasopressin-2 receptor antagonists or SGLT2 inhibitors possess diuretic properties but are not included in the pharmacological group. This review discusses the currentness of the pharmacological classification of diuretics. We elaborate changes in the field of nomenclature, the contemporary medical use of classical diuretics, and new diuretic agents.

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.

Predicting brain function status changes in critically ill patients via Machine learning

OBJECTIVE

In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hospital resources. We aim to develop a machine learning model to predict next-day brain function status changes.

MATERIALS AND METHODS

Using multicenter prospective adult cohorts involving medical and surgical ICU patients from 2 civilian and 3 Veteran Affairs hospitals, we trained and externally validated a light gradient boosting machine to predict brain function status changes. We compared the performances of the boosting model against state-of-the-art models-an ABD predictive model and its variants. We applied Shapley additive explanations to identify influential factors to develop a compact model.

RESULTS

There were 1026 critically ill patients without evidence of prior major dementia, or structural brain diseases, from whom 12 295 daily transitions (ABD: 5847 days; ABD-free: 6448 days) were observed. The boosting model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.824 (95% confidence interval [CI], 0.821-0.827), compared with the state-of-the-art models of 0.697 (95% CI, 0.693-0.701) with P < .001. Using 13 identified top influential factors, the compact model achieved 99.4% of the boosting model on AUROC. The boosting and the compact models demonstrated high generalizability in external validation by achieving an AUROC of 0.812 (95% CI, 0.812-0.813).

CONCLUSION

The inputs of the compact model are based on several simple questions that clinicians can quickly answer in practice, which demonstrates the model has direct prospective deployment potential into clinical practice, aiding in critical hospital resource allocation.

Delirium in critical illness: clinical manifestations, outcomes, and management

Delirium is the most common manifestation of brain dysfunction in critically ill patients. In the intensive care unit (ICU), duration of delirium is independently predictive of excess death, length of stay, cost of care, and acquired dementia. There are numerous neurotransmitter/functional and/or injury-causing hypotheses rather than a unifying mechanism for delirium. Without using a validated delirium instrument, delirium can be misdiagnosed (under, but also overdiagnosed and trivialized), supporting the recommendation to use a monitoring instrument routinely. The best-validated ICU bedside instruments are CAM-ICU and ICDSC, both of which also detect subsyndromal delirium. Both tools have some inherent limitations in the neurologically injured patients, yet still provide valuable information about delirium once the sequelae of the primary injury settle into a new post-injury baseline. Now it is known that antipsychotics and other psychoactive medications do not reliably improve brain function in critically ill delirious patients. ICU teams should systematically screen for predisposing and precipitating factors. These include exacerbations of cardiac/respiratory failure or sepsis, metabolic disturbances (hypoglycemia, dysnatremia, uremia and ammonemia) receipt of psychoactive medications, and sensory deprivation through prolonged immobilization, uncorrected vision and hearing deficits, poor sleep hygiene, and isolation from loved ones so common during COVID-19 pandemic. The ABCDEF (A2F) bundle is a means to facilitate implementation of the 2018 Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS) Guidelines. In over 25,000 patients across nearly 100 institutions, the A2F bundle has been shown in a dose-response fashion (i.e., greater bundle compliance) to yield improved survival, length of stay, coma and delirium duration, cost, and less ICU bounce-backs and discharge to nursing homes.

Validation of E-PRE-DELIRIC in cardiac surgical ICU delirium: A retrospective cohort study

BACKGROUND

The early prediction model for delirium in intensive care units (ICUs)-E-PRE-DELIRIC-has been created to predict delirium development during the length of stay in ICUs. However, there have been few early predictive models for delirium in the cardiac surgical ICU (CSICU), and the predictive ability of the E-PRE-DELIRIC among patients following cardiac surgeries is still unknown.

AIMS AND OBJECTIVES

To validate the performance of E-PRE-DELIRIC in CSICU.

DESIGN

A retrospective cohort study.

METHODS

Data were retrospectively extracted from the electronic records for patients admitted in CSICU from January 2018 to December 2018 in a tertiary teaching hospital in China. Adult patients were included following the criteria of the E-PRE-DELIRIC model. Predictors, including age, history of cognitive impairment, history of alcohol abuse, urgent admission, use of corticosteroids, respiratory failure, blood urea nitrogen, and mean arterial pressure, at the time of ICU admission were retrieved, and delirium was assessed twice a day using the Confusion Assessment Method for the ICU. The performance of the E-PRE-DELIRIC model was evaluated by area under receiver operator characteristic curve, precision-recall curve (AUPRC), Hosmer-Lemeshow (HL) test, and calibration belt.

RESULTS

Of the 725 patients included, 120 (16.6%) developed delirium. The AUROC was 0.54 (95% confidence interval [CI], 0.48-0.59), and the AUPRC was 0.18 (95% CI, 0.12-0.20). The HL test showed a significant difference between predicted probability and delirium occurrence (χ² = 17.326, P = .027), and the overestimation chance of the E-PRE-DELIRIC score was 0.24 to 0.43.

CONCLUSION

The E-PRE-DELIRIC model has poor-to-fair predictive value in this study; thus, its application among the CSICU patients is limited. Development of reliable and validated tools for early prediction of delirium in CSICU is required.

RELEVANCE TO CLINICAL PRACTICE

Early prediction of delirium risk at CSICU admission is of vital importance and could provide timely information to caregivers. However, the E-PRE-DELIRIC model should be applied cautiously in the CSICU because of the significant probability of over-estimating the risk of developing delirium.

Delirium and Associated Length of Stay and Costs in Critically Ill Patients

Purpose

Delirium frequently affects critically ill patients in the intensive care unit (ICU). The purpose of this study is to evaluate the impact of delirium on ICU and hospital length of stay (LOS) and perform a cost analysis.

Materials and Methods

Prospective studies and randomized controlled trials of patients in the ICU with delirium published between January 1, 2015, and December 31, 2020, were evaluated. Outcome variables including ICU and hospital LOS were obtained, and ICU and hospital costs were derived from the respective LOS.

Results

Forty-one studies met inclusion criteria. The mean difference of ICU LOS between patients with and without delirium was significant at 4.77 days (p < 0.001); for hospital LOS, this was significant at 6.67 days (p < 0.001). Cost data were extractable for 27 studies in which both ICU and hospital LOS were available. The mean difference of ICU costs between patients with and without delirium was significant at $3,921 (p < 0.001); for hospital costs, the mean difference was $5,936 (p < 0.001).

Conclusion

ICU and hospital LOS and associated costs were significantly higher for patients with delirium, compared to those without delirium. Further research is necessary to elucidate other determinants of increased costs and cost-reducing strategies for critically ill patients with delirium. This can provide insight into the required resources for the prevention of delirium, which may contribute to decreasing healthcare expenditure while optimizing the quality of care.

Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019

Background

The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Objectives

To develop an incident delirium predictive model among coronavirus disease 2019 patients.

Methods

We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.

Results

Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71–0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.

Conclusion

Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

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.

Utility of a prediction model for delirium in intensive care unit patients (PRE-DELIRIC) in mechanically ventilated patients with sepsis

Aim

Delirium frequently develops in patients with sepsis during their intensive care unit (ICU) stay, which is associated with increased morbidity and mortality. A prediction model for delirium in patients in ICU, PRE‐DELIRIC, has been utilized in overall ICU patients, but its utility is uncertain among patients with sepsis. This study aims to examine the utility of PRE‐DELIRIC to predict delirium in mechanically ventilated patients with sepsis.

Methods

This is a post hoc analysis of a randomized clinical trial in eight Japanese ICUs, which aimed to evaluate the sedative strategy with/without dexmedetomidine in adult mechanically ventilated patients with sepsis. The Confusion Assessment Method for the ICU was used every day to assess for delirium throughout their ICU stay. We excluded patients who were delirious on the first day of ICU, those who were under sustained coma throughout their ICU stay, and those who stayed in the ICU less than 24 h. The discriminative ability of PRE‐DELIRIC was evaluated by measuring the area under the receiver operating characteristic curve (AUROC).

Results

Of the 201 patients enrolled in the trial, we analyzed 158 patients. The mean age was 69.4 ± 14.0 years, and 99 patients (63%) were men. Delirium occurred at least once during the ICU stay of 63 patients (40%). The AUROC of PRE‐DELIRIC was 0.60 (95% confidence interval, 0.50–0.69). Subgroup analyses indicated that PRE‐DELIRIC was useful in those with Sequential Organ Failure Assessment score >8 with AUROC of 0.65 (95% confidence interval, 0.51–0.77).

Conclusions

The PRE‐DELIRIC model could not predict delirium in mechanically ventilated patients with sepsis.

Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity

Background

Risk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.

Methods

This retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.

Results

A total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.

Conclusions

Our results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients’ risk for ICU delirium. Classification of patients’ risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

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

OBJECTIVE

To systematically review the delirium risk prediction models for intensive care unit (ICU) patients.

METHODS

A systematic review was conducted. The Cochrane Library, PubMed, Ovid and Web of Science were searched to collect studies on delirium risk prediction models for ICU patients from database establishment to 31 March 2019. Two reviewers independently screened the literature according to the pre-determined inclusion and exclusion criteria, extracted the data and evaluated the risk of bias of the included studies using the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist. A descriptive analysis was used to describe and summarise the data.

RESULTS

A total of six models were included. All studies reported the area under the receiver operating characteristic curve (AUROC) of the prediction models in the derivation and (or) validation datasets as over 0.7 (from 0.75 to 0.9). Five models reported calibration metrics. Decreased cognitive reserve and the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score were the most commonly reported predisposing and precipitating factors, respectively, of ICU delirium among all models. The small sample size, lack of external validation and the absence of or unreported blinding method increased the risk of bias.

CONCLUSION

According to the discrimination and calibration statistics reported in the original studies, six prediction models may have moderate power in predicting ICU delirium. However, this finding should be interpreted with caution due to the risk of bias in the included studies. More clinical studies should be carried out to validate whether these tools have satisfactory predictive performance in delirium risk prediction for ICU patients.

ICU nurses feel unprepared to care for patients with mental illness: A survey of nurses' attitudes, knowledge, and skills

OBJECTIVES

To examine the knowledge, skills, and attitudes of a cohort of Australian nurses towards caring for patients with mental illness in the intensive care unit.

RESEARCH DESIGN

A questionnaire was developed and distributed via internal email to all nurses working in the study intensive care unit. Responses were anonymous.

SETTING

A metropolitan intensive care unit located in Melbourne, Australia.

MAIN OUTCOME MEASURES

Intenisve care nurses completed a 76-question self-administered questionnaire.

RESULTS

Forty intensive care nurses completed the survey, a response rate of 35.7% (n = 40/112). Respondents were predominantly female (82.5%) and held a post-graduate qualification (62.5%). ICU nurses felt that they needed further training and education to care for patients with mental illness in the intensive care unit. While respondents were empathetic to this patient group, negative stereotypes and stigma were reported by some participants. The pressures of the environment were perceived barriers to delivering optimal person-centred care for patients with mental illness.

CONCLUSION

This sample of nurses felt they require education and support in order to care for patients with mental illness in the intenisve care unit. Further education may also help to reduce negative perceptions of this patient group.