Quantifying the association between computerised measures of attention and confusion assessment method defined delirium: a prospective study of older orthopaedic surgical patients, free of dementia

2008 ◽  
Vol 23 (12) ◽  
pp. 1253-1260 ◽  
Author(s):  
David Peter Lowery ◽  
Keith Wesnes ◽  
Nigel Brewster ◽  
Clive Ballard
Keyword(s):  
Prospective Study ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Surgical Patients ◽  
Confusion Assessment ◽  
A Prospective Study

scholarly journals Delirium in Australian Hospitals: A Prospective Study

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
C. Travers ◽  
G. J. Byrne ◽  
N. A. Pachana ◽  
K. Klein ◽  
L. Gray
Keyword(s):  
Older Patients ◽  
Prospective Observational Study ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Adverse Outcomes ◽  
Confusion Assessment ◽  
Hospital Patients ◽  
Research Nurses ◽  
High Index Of Suspicion ◽  
A Prospective Study

Objectives. Australian data regarding delirium in older hospitalized patients are limited. Hence, this study aimed to determine the prevalence and incidence of delirium among older patients admitted to Australian hospitals and assess associated outcomes.Method. A prospective observational study (n=493) of patients aged ≥70 years admitted to four Australian hospitals was undertaken. Trained research nurses completed comprehensive geriatric assessments using standardized instruments including the Confusion Assessment Method to assess for delirium. Nurses also visited the wards daily to assess for incident delirium and other adverse outcomes. Diagnoses of dementia and delirium were established through case reviews by independent physicians.Results. Overall, 9.7% of patients had delirium at admission and a further 7.6% developed delirium during the hospital stay. Dementia was the most important predictor of delirium at (OR=3.18, 95% CI: 1.65–6.14) and during the admission (OR=4.82; 95% CI: 2.19–10.62). Delirium at and during the admission predicted increased in-hospital mortality (OR=5.19, 95% CI: 1.27–21.24;OR=31.07, 95% CI: 9.30–103.78).Conclusion.These Australian data confirm that delirium is a common and serious condition among older hospital patients. Hospital clinicians should maintain a high index of suspicion for delirium in older patients.

scholarly journals Validation of the Malay 3-Minute Diagnostic Interview for Confusion Assessment Method in a surgical population

2021 ◽  
Vol 26 (4) ◽  
pp. 721-729
Author(s):  
Pui San Loh ◽  
Yi Zhe Chin ◽  
Jia Wen Lee ◽  
Angelvene Wong ◽  
Marzida Mansor ◽  
...  
Keyword(s):  
Confusion Assessment Method ◽  
Assessment Method ◽  
Diagnostic Interview ◽  
Surgical Patients ◽  
Diagnostic Study ◽  
Diagnostic And Statistical Manual ◽  
Confusion Assessment ◽  
Dsm 5 ◽  
Diagnostic Characteristics ◽  
Postoperative Setting

Background: Delirium is a common postoperative complication among elderly which can be easily missed and leads to poorer outcomes. The 3-Minute Diagnostic Assessment for Confusion Assessment Method (3D-CAM) is a short and structured tool to assess delirium by healthcare staff with minimal training. This study aimed to validate the translated Malay 3D-CAM (M3D-CAM) in postoperative surgical patients. Methods: In this prospective diagnostic study, 3D-CAM was translated into Malay and two assessors (1 and 2) independently interviewed surgical patients above 65 years old with M3D-CAM on postoperative day one. A psychiatrist diagnosed postoperative delirium according to the Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM-5) as the reference standard. The sequence of examinations was done randomly with all results blinded to each other and the diagnostic characteristics of M3D-CAM analysed with k coefficient used to evaluate reliability. Results: A total of 427 patients were screened, 111 recruited with a final 100 paired interviews completed. Their mean age was 72 (± 6) years old. Two-thirds of patients were proficient in Malay and English, therefore assessed in both 3D-CAM and M3D-CAM. Delirium was identified in 11% and 12% of patients by assessors 1 and 2 respectively while compared to DSM-5, M3D-CAM had 80% and 90% sensitivity with 96.7% and 97.7% specificity. M3D-CAM had excellent inter-rater reliability (85%), substantial parallel reliability (70%) and features 1 and 3 with substantial parallel agreement (p <0.001). Conclusion: This study demonstrated that M3D-CAM is reliable and valid for delirium assessment in the postoperative setting.

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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