Descriptive analysis of the unwarranted continuation of antipsychotics for the management of ICU delirium during transitions of care: A multicenter evaluation across New Jersey

2021 ◽  
Author(s):  
Deepali Dixit ◽  
Liza Barbarello Andrews ◽  
Sara Radparvar ◽  
Christopher Adams ◽  
Samir T Kumar ◽  
...  
Keyword(s):  
Risk Factors ◽  
New Jersey ◽  
Descriptive Analysis ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Transitions Of Care ◽  
Adverse Event Profile ◽  
Icu Patients ◽  
Number Of Patients ◽  
Icu Delirium

Abstract Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose Nearly half of intensive care unit (ICU) patients will develop delirium. Antipsychotics are used routinely for the management of ICU delirium despite limited reliable data supporting this approach. The unwarranted continuation of antipsychotics initiated for ICU delirium is an emerging transitions of care concern, especially considering the adverse event profile of these agents. We sought to evaluate the magnitude of this issue across 6 centers in New Jersey and describe risk factors for continuation. Methods This multicenter, retrospective study examined adult ICU patients who developed ICU delirium from June 2016 to June 2018. Patients were included in the study if they received at least 3 doses of antipsychotics while in the ICU with presence of either a clinical diagnosis of delirium or a positive Confusion Assessment Method score. Patients were excluded if they were on an antipsychotic before ICU admission. Results Of the 300 patients included and initiated on antipsychotics for ICU delirium, 157 (52.3%) were continued on therapy upon transfer from the ICU to another level of inpatient care. The number of patients continued on newly initiated antipsychotics further increased to 183 (61%) upon discharge from the hospital. Conclusion The continuation of antipsychotics for the management of delirium during transitions of care was a common practice across ICUs in New Jersey. Several risk factors for continuation of antipsychotics were identified. Efforts to reduce unnecessary continuation of antipsychotics at transitions of care are warranted.

scholarly journals A Systematic Review and Pooled Prevalence of Paediatric Delirium in Critically Ill Children

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i31-i32
Author(s):  
D Semple ◽  
M M Howlett ◽  
J D Strawbridge ◽  
C V Breatnach ◽  
J C Hayden
Keyword(s):  
Risk Factors ◽  
Critical Care ◽  
Critically Ill ◽  
Observational Studies ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Assessment Tools ◽  
Critically Ill Children ◽  
Confusion Assessment ◽  
Pooled Prevalence

Abstract Introduction Paediatric Delirium (PD) is a neuropsychiatric complication that occurs during the management of children in the critical care environment (Paediatric Intensive Care (PICU) and Neonatal Intensive Care (NICU). Delirium can be classified as hypoactive (decreased responsiveness and withdrawal), hyperactive (agitation and restlessness), and mixed (combined) (1). PD can be assessed using a number of assessment tools. PD has been historically underdiagnosed or misdiagnosed, having many overlapping symptoms with other syndrome such as pain and iatrogenic withdrawal syndrome (2). An appreciation of the extent of PD would help clinicians and policy makers drive interventions to improve recognition, prevention and management of PD in clinical practice. Aim To estimate the pooled prevalence of PD using validated assessment tools, and to identify risk factors including patient-related, critical-care related and pharmacological factors. Methods A systematic search of PubMed, EMBASE and CINAHL databases was undertaken. Eligible articles included observational studies or trials that estimated a prevalence of PD in a NICU/PICU population using a validated PD assessment tool. Validated tools are the paediatric Confusion Assessment Method-ICU (pCAM-ICU), the Cornell Assessment of Pediatric Delirium (CAPD), the PreSchool Confusion Assessment Method for the ICU (psCAM-ICU), pCAM-ICU severity scale (sspCAM-ICU), and the Sophia Observation Withdrawal Symptoms scale Paediatric Delirium scale (SOS-PD) (1). Only full text studies were included. No language restrictions were applied. Two reviewers independently screened records. Data was extracted using a pre-piloted form and independently verified by another reviewer. Quality was assessed using tools from the National Institutes of Health. A pooled prevalence was calculated from the studies that estimated PD prevalence using the most commonly applied tool, the CAPD (1). Results Data from 23 observational studies describing prevalence and risk factors for PD in critically ill children were included (Figure 1). Variability in study design and outcome reporting was found. Study quality was generally good. Using the validated tools prevalence ranged from 10–66% of patients. Hypoactive delirium was the most prevalent sub-class identified. Using the 13 studies that used the CAPD tool, a pooled prevalence of 35% (27%-43% 95%CI) was calculated. Younger ages, particularly less than two years old, sicker patients, particularly those undergoing mechanical and respiratory ventilatory support were more at risk for PD. Restraints, the number of sedative medications, including the cumulative use of benzodiazepines and opioids were identified as risk factors for the development of PD. PD was associated with longer durations of mechanical ventilation, longer stays and increased costs. Data on association with increased mortality risk is limited and conflicting. Conclusion PD affects one third of critical care admissions and is resource intense. Routine assessment in clinical practice may facilitate earlier detection and management strategies. Modifiable risk factors such as the class and number of sedative and analgesic medications used may contribute to the development of PD. Early mobility and lessening use of these medications present strategies to prevent PD occurrence. Longitudinal prospective multi-institutional studies to further investigate the presentations of the different delirium subtypes and modifiable risk factors that potentially contribute to the development of PD, are required. References 1. Semple D (2020) A systematic review and pooled prevalence of PD, including identification of the risk factors for the development of delirium in critically ill children. doi: 10.17605/OSF.IO/5KFZ8 2. Ista E, te Beest H, van Rosmalen J, de Hoog M, Tibboel D, van Beusekom B, et al. Sophia Observation withdrawal Symptoms-Paediatric Delirium scale: A tool for early screening of delirium in the PICU. Australian Critical Care. 2018;31(5):266–73

scholarly journals Do we know if our Patients are Delirious?

2008 ◽  
Vol 9 (3) ◽  
pp. 269-269
Author(s):  
Callum Kaye
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Critically Ill Patient ◽  
Confusion Assessment ◽  
Surgical Icu ◽  
Symptoms And Signs

Delirium in the intensive care unit (ICU) setting is a significant cause of morbidity, mortality and increases ICU, as well as hospital length of stay1,2. Furthermore, with so many of the risk factors being present in the critically ill patient in the ICU environment, it's not surprising that other studies have found that up to 80% of patients will be delirious at some point during admission3,4. We performed a small study in a Toronto Medical-Surgical ICU using the Confusion Assessment Method for the ICU (CAM-ICU)5 to determine the prevalence of delirium in this unit. We concurrently reviewed medical and nursing notes to identify documentation of symptoms and signs that could indicate possible delirium during routine clinical assessment of the patient.

scholarly journals Postoperative Delirium after Urological Surgery: A Literature Review

2019 ◽  
Vol 13 (3) ◽  
pp. 133-140 ◽  
Author(s):  
Ioannis Leotsakos ◽  
Ioannis Katafigiotis ◽  
Ofer N. Gofrit ◽  
Mordechai Duvdevani ◽  
Dionysios Mitropoulos
Keyword(s):  
Risk Factors ◽  
Literature Search ◽  
Postoperative Delirium ◽  
Mini Mental State Examination ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Factors Associated ◽  
Confusion Assessment ◽  
Urological Surgery ◽  
State Examination

Purpose: We aimed to thoroughly search and identify studies referring to risk factors associated with postoperative delirium (POD) in patients undergoing open as well as en-doscopic urological surgery. Methods: The review after a systematic literature search included 5 studies. Results: The incidence of POD was reported to be between 7.8 and 30% depending on the type of the urologic surgery, while in the majority of the studies the onset happened on the first postoperative day and the symptoms lasted 3 ± 0.8 days. Seventeen different risk factors for POD were identified and presented in detail. Conclusion: The Mini-Mental State Examination score and older age were significantly associated with the development of POD. However, the Confusion Assessment Method is very well validated against the diagnosis of delirium from the specialists.

scholarly journals Risk Factors of Delirium in Sequential Sedation Patients in Intensive Care Units

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Yang ◽  
Yongfang Zhou ◽  
Yan Kang ◽  
Binbin Xu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Sofa Score ◽  
Maintenance Dose ◽  
Proportional Hazards ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Cox Proportional Hazards ◽  
Older Age ◽  
Independent Risk Factors

Background. Delirium is a primary adverse event in ventilated patients who receive long-term monosedative treatment. Sequential sedation may reduce these adverse effects. This study evaluated risk factors for delirium in sequential sedation patients. Methods. A total of 141 patients who underwent sequential sedation were enrolled. Delirium was diagnosed using Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) scale. Univariate and multivariate Cox proportional hazards regressions were used to predict risk factors. Results. Older age (≥51) (RR = 2.432, 95% CL 1.316–4.494, p=0.005), higher SOFA score (≥14) (RR = 2.022, 95% CL 1.076–3.798, p=0.029), regular smoking (RR = 2.366, 95% CL 1.277–4.382, p=0.006), and higher maintenance dose of midazolam (RR = 1.052, 95% CL 1.000–1.107, p=0.049) and fentanyl (RR = 1.045, 95% CL 1.019–1.072, p=0.001) when patients met sequential criteria, were independent risk factors of delirium. Sequential sedation with dexmedetomidine (RR = 0.448, 95% CL 0.209–0.963, p=0.040) was associated with a lower risk of delirium. Conclusions. Older age, higher SOFA score, regular smoking, and higher maintenance dose of midazolam and fentanyl when patients met sequential criteria were independent risk factors of delirium in sequential sedation patients. Sequential sedation with dexmedetomidine reduced risk of delirium.

Prevalence and Characteristics of Pre-Operative Delirium in Hip Fracture Patients

Gerontology ◽  
2015 ◽  
Vol 62 (4) ◽  
pp. 396-400 ◽  
Author(s):  
Susan Freter ◽  
Michael Dunbar ◽  
Katalin Koller ◽  
Chris MacKnight ◽  
Kenneth Rockwood
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Hip Fracture ◽  
Wait Time ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Negative Outcomes ◽  
Time To Surgery ◽  
Hip Fracture Surgery ◽  
Post Surgery

Background: Delirium is a common complication of hip fracture and is associated with negative outcomes. Previous studies document risk factors for post-operative delirium but have frequently excluded patients with pre-operative delirium. Objective: This study endeavours to document prevalence and risk factors for pre-operative delirium in hip fracture patients and compares risk factor profiles and outcomes between pre- and post-operative delirium. Methods: 283 hip fracture patients were assessed pre-operatively with the Delirium Elderly At Risk (DEAR) instrument, Mini-Mental State Examination (MMSE), and Confusion Assessment Method (CAM). They were followed on post-operative days 1, 3, and 5 for the presence of delirium. Doses of opioids were recorded. Wait time to surgery, length of stay, and discharge site were noted. Results: Delirium was present in 57.6% patients pre-operatively and 41.7% post-surgery. Not all patients (62%) with pre-operative delirium also had post-operative delirium. There was a considerable overlap in risk factors, with some differences. Wait time to surgery, number of comorbidities, and total pre-operative opioid and lorazepam doses were associated with pre- but not post-operative delirium. Negative outcomes were more closely associated with post-operative delirium. Conclusion: Delirium is common in pre-hip fracture surgery patients, and not all patients with pre-operative delirium go on to have post-operative delirium. Risk factor profiles are not identical, raising the possibility of identifying and intervening in patients at high risk of delirium pre-operatively.

Delirium in Hospitalized Older Patients: Recognition and Risk Factors

1998 ◽  
Vol 11 (3) ◽  
pp. 118-125 ◽  
Author(s):  
Sharon K. Inouye
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Older Patients ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Precipitating Factors ◽  
Acute Confusional State ◽  
Confusional State ◽  
Confusion Assessment ◽  
Simplified Algorithm

Delirium, or acute confusional state, represents a common, serious, potentially preventable and increasing problem for older hospitalized patients. This study is intended to improve overall understanding of the problem of delirium and thus to lessen its adverse impact on the older population. The specific aims of this study are (1) to examine the epidemiology of delirium in older patients; (2) to evaluate barriers to recognition; (3) to present the Confusion Assessment Method (CAM) simplified algorithm to improve recognition; (4) to elucidate predisposing and precipitating factors for delirium; and (5) to propose preventive strategies. Delirium occurs in 10-60% of the older hospitalized population and is unrecognized in 32-66% of cases. The CAM algorithm provides a sensitive (94-100%), specific (90-95%), reliable, and easy to use means for identification of delirium. Four predisposing and five precipitating factors were identified and validated to identify patients at high risk for development of delirium. Primary prevention of delirium should address important delirium risk factors and target patients at intermediate to high risk for delirium at admission.

scholarly journals Incidence and Risk Factors of Delirium in the Intensive Care Unit: A Prospective Cohort

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Farshid Rahimi-Bashar ◽  
Ghazal Abolhasani ◽  
Nahid Manouchehrian ◽  
Nasrin Jiryaee ◽  
Amir Vahedian-Azimi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Prospective Cohort ◽  
Cox Regression ◽  
Cox Model ◽  
Scoring Systems ◽  
Confusion Assessment Method ◽  
Assessment Method

Purpose. The purpose of this study was to determine the incidence, risk factors, and impact of delirium on outcomes in ICU patients. In addition, the scoring systems were measured consecutively to characterize how these scores changed with time in patients with and without delirium. Material and Methods. A prospective cohort study enrolling 400 consecutive patients admitted to the ICU between 2018 and 2019 due to trauma or surgery. Patients were followed up for the development of delirium over ICU days using the Confusion Assessment Method (CAM) for the ICU and Intensive Care Delirium Screening Checklist (ICDSC). Cox model logistic regression analysis was used to explore delirium risk factors. Results. Delirium occurred in 108 (27%) patients during their ICU stay, and the median onset of delirium was 4 (IQR 3–4) days after admission. According to multivariate cox regression, the expected hazard for delirium was 1.523 times higher in patients who used mechanical ventilator as compared to those who did not (HR: 1.523, 95% CI: 1.197-2.388, P < 0.001 ). Conclusion. Our findings suggest that an important opportunity for improving the care of critically ill patients may be the determination of modifiable risk factors for delirium in the ICU. In addition, the scoring systems (APACHE IV, SOFA, and RASS) are useful for the prediction of delirium in critically ill patients.

scholarly journals E.04 Coma and delirium are associated with low levels of brain tissue oxygen in critically ill patients

2016 ◽  
Vol 43 (S2) ◽  
pp. S17-S17
Author(s):  
MD Wood ◽  
D Maslove ◽  
J Muscedere ◽  
JG Boyd
Keyword(s):  
Brain Tissue ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Near Infrared ◽  
Neurological Status ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Brain Tissue Oxygenation ◽  
Low Levels ◽  
Icu Delirium

Background: The cause of ICU delirium is unknown. We used near infrared spectroscopy (NIRS) to measure brain tissue oxygenation (BtO2) in critically ill patients, to test the hypothesis that poor cerebral oxygen delivery contributes to ICU delirium. Methods: Adult patients were enrolled if they required mechanical ventilation for >24 hours, and/or vasoactive agents. Patients were excluded if they had previous cognitive dysfunction, brain injury on admission, or a life expectancy <24 hours. BtO2 was measured for the first 24 hours of ICU admission. The confusion assessment method-ICU (CAM-ICU) was used to screen for delirium. Participants were designated to one of three groups on the basis of their predominant neurological status (comatose, delirious, or intact). Results: To date, 47 patients have been recruited. Both delirious and comatose patients’ had significantly lower BtO2 levels compared to intact patients (P<0.001). There was a significant correlation between hemoglobin and BtO2 (R2=0.347, P<0.01). However, when correlation analysis was conducted separately amongst the three groups, the delirious patients (R2=0.485, P<0.05) were the strongest contributors to this positive correlation. Conclusions: Delirious patients exhibited the lowest BtO2 recordings and demonstrated a significant association between Hb and BtO2. This study offers potential insight into the pathophysiology of ICU delirium.

Agitation

2018 ◽  
pp. 180-183
Author(s):  
Megan Rashid
Keyword(s):  
Correct Diagnosis ◽  
Altered Mental Status ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Early Mobility ◽  
Confusion Assessment ◽  
Life Threatening ◽  
High Risk Populations ◽  
Icu Delirium ◽  
Abcdef Bundle

The case illustrates a classic example of intensive care unit (ICU) delirium, which often goes unrecognized but can adversely affect both morbidity and mortality. The Confusion Assessment Method for the ICU (CAM-ICU) is a validated tool for diagnosing delirium, but it remains a diagnosis of exclusion, and it is important to rule out potentially life-threatening medical causes of altered mental status. Treatment is difficult even with the correct diagnosis, and prevention is key. The ABCDEF bundle (assessing and managing pain, both SAT and SBT, choice of analgesia/sedation, delirium, early mobility, and family engagement) is a tool that identifies high-risk populations, and can help mitigate the prevalence of ICU delirium.

scholarly journals Cerebrospinal Fluid Apolipoprotein E Levels in Delirium

2017 ◽  
Vol 7 (2) ◽  
pp. 240-248 ◽  
Author(s):  
Gideon A. Caplan ◽  
JIan Tai ◽  
Fazrul Mohd Hanizan ◽  
Catherine L. McVeigh ◽  
Mark A. Hill ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cerebrospinal Fluid ◽  
Apolipoprotein E ◽  
Late Onset ◽  
The Elderly ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Apolipoprotein A1 ◽  
Ε4 Allele ◽  
State Examination

Background/Aims: Delirium and the apolipoprotein E ε4 allele are risk factors for late-onset Alzheimer disease (LOAD), but the connection is unclear. We looked for an association. Methods: Inpatients with delirium (n = 18) were compared with LOAD outpatients (n = 19), assaying blood and cerebrospinal fluid (CSF) using multiplex ELISA. Results: The patients with delirium had a higher Confusion Assessment Method (CAM) score (5.6 ± 1.2 vs. 0.0 ± 0.0; p < 0.001) and Delirium Index (13.1 ± 4.0 vs. 2.9 ± 1.2; p = 0.001) but a lower Mini-Mental State Examination (MMSE) score (14.3 ± 6.8 vs. 20.8 ± 4.6; p = 0.003). There was a reduction in absolute CSF apolipoprotein E level during delirium (median [interquartile range]: 9.55 μg/mL [5.65–15.05] vs. 16.86 μg/mL [14.82–20.88]; p = 0.016) but no differences in apolipoprotein A1, B, C3, H, and J. There were no differences in blood apolipoprotein levels, and no correlations between blood and CSF apolipoprotein levels. CSF apolipoprotein E correlated negatively with the CAM score (r = –0.354; p = 0.034) and Delirium Index (r = –0.341; p = 0.042) but not with the Acute Physiology and Chronic Health Evaluation (APACHE) index, or the MMSE or Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Conclusion: Reduced CSF apolipoprotein E levels during delirium may be a mechanistic link between two important risk factors for LOAD.

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