External Validation of Two Models to Predict Delirium in Critically Ill Adults Using Either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for Delirium Assessment

Adherence to optimal delirium management practices in intensive care units in Brazil: a nationwide survey

BACKGROUND

Effective delirium management is crucial, considering its association with adverse outcomes. Adherence to best practices has the potential to reduce the incidence and prevalence of delirium and improve health outcomes.

OBJECTIVES

The objectives of this project were to describe self-assessed adherence to best practices in delirium management by health care professionals in intensive care units (ICUs) in Brazil, assess the health care professionals' perception of the importance of adequate delirium prevention and treatment in ICUs, and compare the compliance rates with best practices between public and private ICUs.

METHOD

A cross-sectional study was conducted in Brazil using an online questionnaire consisting of three parts, namely, data about the health care professionals and the ICU in which they worked; statements about the 17 best practices; and questions related to perceptions of delirium prevention and management by ICU physicians and nurses. The survey was sent to email addresses registered with the Brazilian Association of Intensive Care Medicine.

RESULTS

The compliance rate exceeded 50% for only eight best practices. These included the identification and management of pressure sores and falls in delirium patients, with compliance rates of 77.8% and 74.1%, respectively.

CONCLUSION

Among ICU professionals in Brazil, adherence to best practices in delirium management is low, particularly for practices involving patient education and involvement of their relatives in their care. These results emphasize the importance of enhancing delirium management in Brazilian health care institutions, regardless of hospital classification.

SPANISH ABSTRACT

http://links.lww.com/IJEBH/A274.

PupillOmetry for preDIction of DeliriUM in ICU (PODIUM): protocol for a prospective multicentre cohort study

INTRODUCTION

Delirium is a severe complication that is associated with short-term adverse events, prolonged hospital stay and neurological sequelae in survivors. Automated pupillometry is an easy-to-use device that allows for accurate objective assessment of the pupillary light responses in comatose patients in the intensive care unit (ICU). Whether automated pupillometry might predict delirium in critically ill patients is not known. We hypothesise that automated pupillometry could predict the occurrence of delirium in critically ill patients without primary brain injury, requiring more than 48 hours of invasive mechanical ventilation in the ICU.

METHODS AND ANALYSIS

The PupillOmetry for preDIction of DeliriUM in ICU (PODIUM) study is a prospective cohort study, which will be conducted in eight French ICUs in the Paris area. We aim to recruit 213 adult patients requiring invasive mechanical ventilation for more than 48 hours. Automated pupillometry (Neurological Pupil Index; NPi-200, Neuroptics) will be assessed two times per day for 7 days. Delirium will be assessed using the Confusion Assessment Method in ICU two times per day over 14 days in non-comatose patients (Richmond Agitation and Sedation Scale ≥-3).The predictive performances of the seven automated pupillometry parameters (ie, pupillary diameter, variation of the pupillary diameter, pupillary constriction speed, pupillary dilatation speed, photomotor reflex latency, NPi and symmetry of pupillary responses) measured to detect the delirium occurrence within 14 days will be the main outcomes. Secondary outcomes will be the predictive performances of the seven automated pupillometry parameters to detect complications related to delirium, ICU length of stay, mortality, functional and cognitive outcomes at 90 days.

ETHICS AND DISSEMINATION

The PODIUM study has been approved by an independent ethics committee, the Comité de Protection des Personnes (CPP) OUEST IV-NANTES (CPP21.02.15.45239 32/21_3) on 06 April 2021). Participant recruitment started on 15 April 2022. Results will be published in international peer-reviewed medical journals and presented at conferences.

TRIAL REGISTRATION NUMBER

NCT05248035; clinicaltrials.gov.

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.

Validation of a delirium predictive model in patients admitted to surgical intensive care units: a multicentre prospective observational cohort study

OBJECTIVE

To internally and externally validate a delirium predictive model for adult patients admitted to intensive care units (ICUs) following surgery.

DESIGN

A prospective, observational, multicentre study.

SETTING

Three university-affiliated teaching hospitals in Thailand.

PARTICIPANTS

Adults aged over 18 years were enrolled if they were admitted to a surgical ICU (SICU) and had the surgery within 7 days before SICU admission.

MAIN OUTCOME MEASURES

Postoperative delirium was assessed using the Thai version of the Confusion Assessment Method for the ICU. The assessments commenced on the first day after the patient's operation and continued for 7 days, or until either discharge from the ICU or the death of the patient. Validation was performed of the previously developed delirium predictive model: age+(5×SOFA)+(15×benzodiazepine use)+(20×DM)+(20×mechanical ventilation)+(20×modified IQCODE>3.42).

RESULTS

In all, 380 SICU patients were recruited. Internal validation on 150 patients with the mean age of 75±7.5 years resulted in an area under a receiver operating characteristic curve (AUROC) of 0.76 (0.683 to 0.837). External validation on 230 patients with the mean age of 57±17.3 years resulted in an AUROC of 0.85 (0.789 to 0.906). The AUROC of all validation cohorts was 0.83 (0.785 to 0.872). The optimum cut-off value to discriminate between a high and low probability of postoperative delirium in SICU patients was 115. This cut-off offered the highest value for Youden's index (0.50), the best AUROC, and the optimum values for sensitivity (78.9%) and specificity (70.9%).

CONCLUSIONS

The model developed by the previous study was able to predict the occurrence of postoperative delirium in critically ill surgical patients admitted to SICUs.

TRIAL REGISTRATION NUMBER

Thai Clinical Trail Registry (TCTR20180105001).

Risk assessment of perioperative neurocognitive disorders, where are we now?

PURPOSE OF REVIEW

Perioperative neurocognitive disorders (PNDs) are among the most frequent complications after surgery and are associated with considerable morbidity and mortality. We analysed the recent literature regarding risk assessment of PND.

RECENT FINDINGS

Certain genetic variants of the cholinergic receptor muscarinic 2 and 4, as well as a marked degree of frailty but not the kind of anaesthesia (general or spinal) are associated with the risk to develop postoperative delirium (POD). Models predict POD with a discriminative power, for example, area under the receiver operating characteristics curve between 0.52 and 0.94.

SUMMARY

Advanced age as well as preexisting cognitive, functional and sensory deficits remain to be the main risk factors for the development of PND. Therefore, aged patients should be routinely examined for both preexisting and new developing deficits, as recommended in international guidelines. Appropriate tests should have a high discrimination rate, be feasible to be administered by staff that do not require excessive training, and only take a short time to be practical for a busy outpatient clinic. Models to predict PND, should be validated appropriately (and externally if possible) and should not contain a too large number of predictors to prevent overfitting of models.

Delirium in Older Adults: What a Surgeon Needs to Know

Delirium remains a challenging clinical problem in hospitalized older adults, especially for postoperative patients. This complication, with a high risk of postoperative mortality and an increased length of stay, frequently occurs in older adult patients. This brief narrative paper aims to review the recent literature regarding delirium and its most recent update. We also offer physicians a brief and essential clinical practice guide to managing this acute and common disease.

Multicomponent nursing program to prevent delirium in critically ill patients: a randomized clinical trial

OBJECTIVES

To determine the efficacy of a multicomponent nursing program to prevent delirium in critically ill patients.

METHODS

Parallel controlled randomized clinical trial to prevent delirium in 81 critically ill patients: 41 in the control group and 40 in the intervention group (intervention: spatial and temporal guidance, visual stimulus, auditive stimulus, and family support). Participants were recruited from September 2017 to March 2018 in the university hospital Los Comuneros, Bucaramanga, Colombia. Clinical Trials record NCT03215745.

RESULTS

The incidence of delirium was 5% in the intervention group and 24% in the control group. The relative risk was 0.20 (95% CI 0.05 to 0.88). The absolute risk reduction was 19.39% (95% CI 4.61 to 34.17) and the number needed to treat was 5 (95 CI % 3 to 26%).

CONCLUSION

The multicomponent nursing program is efficient to prevent delirium in critically ill patients.

Risk factors associated with the development of delirium in general ICU patients. A prospective observational study

Objective

We aimed to analyze risk factors related to the development of delirium, aiming for early intervention in patients with greater risk.

Material and methods

Observational study, including prospectively collected patients treated in a single general ICU. These were classified into two groups, according to whether they developed delirium or not (screening performed using CAM-ICU tool). Demographics and clinical data were analyzed. Multivariate logistic regression analyses were performed to quantify existing associations.

Results

1462 patients were included. 93 developed delirium (incidence: 6.3%). These were older, scored higher on the Clinical Frailty Scale, on the risk scores on admission (SAPS-3 and SOFA), and had a greater number of organ failures (OF). We observed more incidence of delirium in patients who (a) presented more than two OF (20.4%; OR 4.9; CI95%: 2.9–8.2), and (b) were more than 74 years old albeit having <2 OF (8.6%; OR 2.1; CI95%: 1.3–3.5). Patients who developed delirium had longer ICU and hospital length-of-stays and a higher rate of readmission.

Conclusions

The highest risk observed for developing delirium clustered in patients who presented more than 2 OF and patients over 74 years old. The detection of patients at high risk for developing delirium could imply a change in management and improved quality of care.

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.

The Nexus Between Sleep Disturbance and Delirium Among Intensive Care Patients

Sleep in intensive care is hampered due to many factors; the clinical environment itself exacerbates sleep disturbance. Research suggests that interventions aimed at improving sleep quality have produced positive effects in reducing incidences and duration of delirium. Sleep disturbance is well documented among intensive care patients; however, its prognostic impact is not fully understood. Delirium, disproportionally prevalent among intensive care patients, has significant prognostic factors related to patient outcomes, in which sleep disturbance often is present. The relationship between sleep disturbance and delirium is complex, sharing commonalities in relation to neurobiological and neurohormonal alterations, which may contribute to a bidirectional relationship.

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.

Cognitive and psychosocial outcomes of mechanically ventilated intensive care patients with and without delirium

Objective

Delirium is common in intensive care patients and is associated with short- and long-term adverse outcomes. We investigated the long-term risk of cognitive impairment and post-traumatic stress disorder (PTSD) in intensive care patients with and without delirium.

Methods

This is a prospective cohort study in ICUs in two Australian university-affiliated hospitals. Patients were eligible if they were older than 18 years, mechanically ventilated for more than 24 h and did not meet exclusion criteria. Delirium was assessed using the Confusion Assessment Method for Intensive Care Unit. Variables assessing cognitive function and PTSD symptoms were collected at ICU discharge, after 6 and 12 months: Mini-Mental State Examination, Telephone Interview for Cognitive Status, Impact of Events Scale-Revised and Informant Questionnaire for Cognitive Decline (caregiver).

Results

103 participants were included of which 36% developed delirium in ICU. Patients with delirium were sicker and had longer duration of mechanical ventilation and ICU length of stay. After 12 months, 41/60 (68.3%) evaluable patients were cognitively impaired, with 11.6% representing the presence of symptoms consistent with dementia. When evaluated by the patient’s caregiver, the patient’s cognitive function was found to be severely impaired in a larger proportion of patients (14/60, 23.3%). Delirium was associated with worse cognitive function at ICU discharge, but not with long-term cognitive function. IES-R scores, measuring PTSD symptoms, were significantly higher in patients who had delirium compared to patients without delirium. In regression analysis, delirium was independently associated with cognitive function at ICU discharge and PTSD symptoms at 12 months.

Conclusions

Intensive care survivors have significant rates of long-term cognitive decline and PTSD symptoms. Delirium in ICU was independently associated with short-term but not long-term cognitive function, and with long-term PTSD symptoms.

Trial registration Australian New Zealand Clinical Trials Registry, ACTRN12616001116415, 15/8/2016 retrospectively registered, https://www.anzctr.org.au

Association between delirium prediction scores and days spent with delirium

PURPOSE

To determine the correlation and discriminative value of the E-PRE-DELIRIC and PRE-DELIRIC scores with delirium exposure to evaluate the prognostic value of both models.

METHODS

A secondary analysis of a randomized clinical trial enrolling 1506 delirium-free, critically ill adults with an anticipated ICU stay of ≥2 days. Days spent with delirium (≥1 positive CAM-ICU) or coma (≥1 RASS ≤-4) in the 28-days after ICU admission were calculated. Patients were categorized into four groups: no delirium, short-exposure (1 delirium day), moderate-exposure (2-5 delirium days), and long- exposure (≥6 delirium days) to determine the correlation and discriminative value of the E-PRE-DELIRIC and the PRE-DELIRIC with days spent with delirium.

RESULTS

The correlation between the overall E-PRE-DELIRIC and PRE-DELIRIC scores and days spent with delirium were: R = 0.08 (P = .005) and R = 0.26 (P < .001), respectively. The correlation between both prediction scores and days spent with coma or delirium were R = 0.21 (P < .0001) and R = 0.46 (P < .0001), respectively. The highest Area Under the Receiver Operating Characteristic for both E-PRE-DELIRIC [0.57 (95% CI:0.51-0.62)] and PRE-DELIRIC [0.58 (95% CI:0.53-0.62)] was found in the long delirium exposure group.

CONCLUSION

The E-PRE-DELIRIC and PRE-DELIRIC model each poorly correlate and discriminate with days spent with delirium in the 28 days after ICU admission.