54 No One Told Me I Couldn’t Drive with Delirium

2021 ◽  
Vol 50 (Supplement_1) ◽  
pp. i12-i42
Author(s):  
H Watson ◽  
L Ralston
Keyword(s):  
Screening Tool ◽  
Hospital Admissions ◽  
District General Hospital ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Lead Author ◽  
Common Cause ◽  
Confusion Assessment ◽  
Number Of Patients ◽  
Patients At Risk

Abstract Introduction Delirium is a common cause and complication of hospital admissions. DVLA1, and Consensus guidelines2 exist for driving with dementia or mild cognitive impairment, but there are no specific guidelines pertaining to delirium. This audit set out to find the prevalence of delirium in a district general hospital prior to implementation of a standard screening tool. It was noteworthy that a significant number of patients with delirium were drivers. Methods The notes of 114 patients under the care of nine specialties, both medical and surgical were prospectively reviewed. Of those with risk factors for delirium, data was collected on the number of patients who had a diagnosis of delirium made during their admission. For patients at risk with no documented screening already completed a Confusion Assessment Method (CAM) screening test was performed by the lead author. In patients identified with delirium it was also established if they were current drivers via clerking documentation or by discussion with the patient/family. Drivers with delirium were highlighted in the medical notes and where possible discussions were had with the patient and their families regarding driving advice until the delirium had resolved. Results The prevalence of delirium in this group was 23% (n = 26/114). 20 patients had documented evidence of delirium and a further 6 patients were diagnosed as a result of this project. 15.4% (n = 4/26) of patients with delirium were current drivers. For this group there was no documented evidence that driving advice had been given to the patient or family. Conclusion This baseline audit has identified that delirium is not consistently screened for and identified. In patients with delirium, driving history is not being sought and consequently the opportunity for driving advice is being missed. Clear guidance from the DVLA on driving for patients with a resolving delirium is needed. References 1. DVLA, 2018. 2. RCPsych, 2019.

Evaluation of the confusion assessment method (CAM) as a screening tool for delirium in the emergency room

2001 ◽  
Vol 23 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Johanne Monette ◽  
Guillaume Galbaud du Fort ◽  
Shek H. Fung ◽  
Fadi Massoud ◽  
Yola Moride ◽  
...  
Keyword(s):  
Emergency Room ◽  
Screening Tool ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Confusion Assessment

scholarly journals Performance of the French version of the 4AT for screening the elderly for delirium in the emergency department

CJEM ◽  
2018 ◽  
Vol 20 (6) ◽  
pp. 903-910 ◽  
Author(s):  
Anne-Julie Gagné ◽  
Philippe Voyer ◽  
Valérie Boucher ◽  
Alexandra Nadeau ◽  
Pierre-Hugues Carmichael ◽  
...  
Keyword(s):  
Emergency Department ◽  
Screening Tool ◽  
The Elderly ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Systematic Screening ◽  
French Version ◽  
Administration Time ◽  
Older Inpatients ◽  
Patients At Risk

CLINICIAN’S CAPSULEWhat is known about the topic?Delirium is frequent in older inpatients but often goes undetected. A short tool, the 4 A’s Test (4AT), was created and validated for the detection of delirium.What did this study ask?This study compared the performance of the French version of the 4AT (4AT-F) with the Confusion Assessment Method (CAM) for the screening of delirium.What did this study find?The 4AT-F was a fast and reliable screening tool for delirium in the emergency department (ED).Why does this study matter to clinicians?Because of its quick administration time, it allows for systematic screening of patients at risk of delirium and cognitive impairment.

Abstract 1122‐000208: Imaging Characteristics for Post Stroke Delirium

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Mohammad Jamil Ahmad ◽  
Sahar Anjum ◽  
Aditya Kumar ◽  
Jacob Sebaugh ◽  
Michele Joseph ◽  
...  
Keyword(s):  
Infarct Size ◽  
Hospital Stay ◽  
Hospital Admissions ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Post Stroke ◽  
Imaging Characteristics ◽  
Patients At Risk ◽  
A Prospective Study ◽  
Expected Mortality

Introduction : Delirium after acute ischemic stroke (AIS) is a common clinical occurrence, present in 13–48% of patients. Post‐stroke delirium is associated with longer hospital admissions, worse functional outcomes, and increased mortality in the short term and has been associated with worse long‐term outcomes. Prior studies have shown right‐sided strokes are more associated with delirium, but very few other imaging characteristics of post‐stroke delirium have been described. We conducted a prospective study evaluating imaging characteristics for patients with delirium. Methods : Between Sept 2019 and June 2021, patients diagnosed with AIS within 48 hrs of stroke onset were prospectively evaluated for delirium using the Confusion Assessment Method (CAM)‐ICU daily for the first eight days of their hospital stay. Patients with severe stroke and expected mortality within the first month at the time of admission or with severe aphasia unable to follow commands were excluded. Data regarding demographics, comorbidities, hospital stay, stroke metrics, lab work and medications were analyzed. Imaging characteristics were adjudicated by authors based on either the patient’s first MRI or the 24 hr CT after admission. Infarct size measured based on ABC/2 formula based on diffusion‐weighted imaging on MRI or stroke appearance on CT. Results : Over the course of 12 non‐consecutive months, we evaluated 213 patients, of which 177 could be assessed with the CAM‐ICU. Delirium was present in 88 (49.7%). There were no statistically significant differences in age, gender, race, co‐morbidities, or TOAST etiology among patients with and without delirium (Table 1). Patients with delirium had higher NIHSS and were more likely to receive tPA. Patients with delirium were more likely to have MCA territory strokes, strokes involving the insula, and to have infarct sizes ≥10 cc. On multivariate modeling, NIHSS (OR 1.07; 95% CI 1.01, 1.13), MCA territory stroke (OR 2.62; 95% CI 1.09, 6.30), and infarct size ≥10 cc (OR 3.23; 95% CI 1.46, 6.90) were associated with delirium. Conclusions : In a cohort of AIS patients without significant expected mortality on admission, the incidence of delirium is high. On evaluation, infarct size ≥10 cc and in the MCA territory were more associated with delirium than NIHSS. These imaging characteristics should be considered in any future predictive models for identifying patients at risk for delirium.

Delir-Diagnostik

2007 ◽  
Vol 20 (2-3) ◽  
pp. 135-139
Author(s):  
B. Dittrich ◽  
G. Gatterer ◽  
T. Frühwald ◽  
U. Sommeregger
Keyword(s):  
Confusion Assessment Method ◽  
Assessment Method ◽  
Psychische Störung ◽  
Confusion Assessment

Zusammenfassung: Das Delir (“akuter Verwirrtheitszustand”) bezeichnet eine psychische Störung, die plötzlich auftritt, durch eine rasche Fluktuation von Bewusstseinslage und Aufmerksamkeitsleistung gekennzeichnet ist und eine organische Ursache hat. Dieses Störungsbild nimmt bei Patienten im höheren Lebensalter deutlich an Häufigkeit zu und verursacht durch verlängerte Krankenhausaufenthalte und ungünstige Krankheitsverläufe erhebliche Kosten im Gesundheitssystem. Daher erscheint eine möglichst frühe Erkennung deliranter Zustandsbilder gerade im Rahmen der Geriatrie von großer Bedeutung. Zu diesem Zweck wurde eine deutsche Version der international weit verbreiteten Confusion Assessment Method entwickelt, die für die Bedürfnisse einer Abteilung für Akutgeriatrie modifiziert wurde. Dargestellt werden die Entwicklung und erste Erfahrungen mit diesem Instrument.

3-Minute Diagnostic Interview for Confusion Assessment Method--Chinese Version

2020 ◽  
Author(s):  
Dong-Liang Mu ◽  
Pan-Pan Ding ◽  
Shu-Zhe Zhou ◽  
Mei-Jing Liu ◽  
Xin-Yu Sun ◽  
...  
Keyword(s):  
Confusion Assessment Method ◽  
Assessment Method ◽  
Diagnostic Interview ◽  
Chinese Version ◽  
Confusion Assessment

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 Postoperative delirium after cardiac surgery of elderly patients as an independent risk factor for prolonged length of stay in intensive care unit and in hospital

2021 ◽  
Author(s):  
Andrea Kirfel ◽  
Jan Menzenbach ◽  
Vera Guttenthaler ◽  
Johanna Feggeler ◽  
Andreas Mayr ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Length Of Stay ◽  
Elderly Patients ◽  
Postoperative Delirium ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Hospital Length Of Stay ◽  
Confusion Assessment

Abstract Background Postoperative delirium (POD) is a relevant and underdiagnosed complication after cardiac surgery that is associated with increased intensive care unit (ICU) and hospital length of stay (LOS). The aim of this subgroup study was to compare the frequency of tested POD versus the coded International Statistical Classification of Diseases and Related Health Problems (ICD) diagnosis of POD and to evaluate the influence of POD on LOS in ICU and hospital. Methods 254 elective cardiac surgery patients (mean age, 70.5 ± 6.4 years) at the University Hospital Bonn between September 2018 and October 2019 were evaluated. The endpoint tested POD was considered positive, if one of the tests Confusion Assessment Method for ICU (CAM-ICU) or Confusion Assessment Method (CAM), 4 'A's Test (4AT) or Delirium Observation Scale (DOS) was positive on one day. Results POD occurred in 127 patients (50.0%). LOS in ICU and hospital were significantly different based on presence (ICU 165.0 ± 362.7 h; Hospital 26.5 ± 26.1 days) or absence (ICU 64.5 ± 79.4 h; Hospital 14.6 ± 6.7 days) of POD (p < 0.001). The multiple linear regression showed POD as an independent predictor for a prolonged LOS in ICU (48%; 95%CI 31–67%) and in hospital (64%; 95%CI 27–110%) (p < 0.001). The frequency of POD in the study participants that was coded with the ICD F05.0 and F05.8 by hospital staff was considerably lower than tests revealed by the study personnel. Conclusion Approximately 50% of elderly patients who underwent cardiac surgery developed POD, which is associated with an increased ICU and hospital LOS. Furthermore, POD is highly underdiagnosed in clinical routine.

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