Developing a Strategy to Identify and Treat Older Patients With Postoperative Delirium

Brooks, P.; Spillane, Jeffrey J.; Dick, Karen; Stuart‐Shor, Eileen M.

doi:10.1016/j.aorn.2013.12.009

Cited by 15 publications

(11 citation statements)

References 68 publications

(116 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patient's psychiatric history is strongly associated with misdiagnosis. There is an urgent need for education of nonpsychiatric staff [2,26]. Education should highlight pitfalls such as misinterpretation of a past psychiatric history, and clarify clinical features of hypoactive delirium and the presentation of delirium in the elderly.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm

Hercus

Hudaib

2020

BMC Health Serv Res

View full text Add to dashboard Cite

Background: Delirium is a frequent diagnosis made by Consultation-Liaison Psychiatry (CLP). Numerous studies have demonstrated misdiagnosis prior to referral to CLP. Few studies have considered the factors underlying misdiagnosis using multivariate approaches. Objectives: To determine the number of cases referred to CLP, which are misdiagnosed at time of referral, to build an accurate predictive classifier algorithm, using input variables related to delirium misdiagnosis. Method: A retrospective observational study was conducted at Alfred Hospital in Melbourne, collecting data from a record of all patients seen by CLP for a period of 5 months. Data was collected pertaining to putative factors underlying misdiagnosis. A Machine Learning-Logistic Regression classifier model was built, to classify cases of accurate delirium diagnosis vs. misdiagnosis. Results: Thirty five of 74 new cases referred were misdiagnosed. The proposed predictive algorithm achieved a mean Receiver Operating Characteristic (ROC) Area under the curve (AUC) of 79%, an average 72% classification accuracy, 77% sensitivity and 67% specificity. CONCLUSIONS: Delirium is commonly misdiagnosed in hospital settings. Our findings support the potential application of Machine Leaning-logistic predictive classifier in health care settings.

show abstract

Section: Discussionmentioning

confidence: 99%

“…There are wide implications of misdiagnosis given the demonstrated impact of delirium on length of hospital stay, cost and ultimately, patients' prognoses [2,26]. Understanding the reasons that delirium is missed is therefore crucial.…”

Section: Introductionmentioning

confidence: 99%

Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm

Hercus

Hudaib

2020

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…Delirium is overlooked by clinicians in 64-84% of patients (12). The elimination of factors that may lead to delirium and symptomatic therapy is recommended for treatment (10,11,13). Psychiatric disorders such as schizophrenia may lead to problems of drug interactions during anesthesia and surgery, confusion and treatment compliance (14).…”

Section: Discussionmentioning

confidence: 99%

Factors affecting the safety of drains and catheters in surgical patients

Yılmaz

Akıncı

Seker

et al. 2014

UCD

View full text Add to dashboard Cite

Objective: Drains and catheters are used for both prophylactic and therapeutic reasons in clinical practice. This study aimed to investigate the factors that affect safety of drains, catheters, nasogastric tube and central venous line in patients who underwent surgery.Material and Methods: Two hundred and four consecutive patients who were operated at the general surgery clinics under general anesthesia were included in the study. Factors that affect the safety of drains and catheter were followed and recorded prospectively.Results: During follow-up period, 12 (5.8%) patients have experienced problems regarding safety of drains/catheters. The mean age of patients who were followed-up in terms of security problems was 63.1 (39-86) years. Eight (66.7%) patients had been operated emergently, and four (33.3%) patients electively. Three (25%) patients had psychiatric/neurological co-morbidities and 3 (25%) patients were confused due to anesthesia/intensive care unit treatment when the drain safety was broken. Eight (66.7%) patients withdrew the drains or catheters by themselves, in 2 (16.7%) patients the drains spontaneously came out and in 2 (16.7%) patients the wrong drain was withdrawn. One patient had dementia, one patient had Alzheimer's disease and one patient was being followed-up with a diagnosis of schizophrenia. In three (25%) patients the abdominal drain, in four (33.3%) patients nasogastric tube, in one (8.3%) patient intubation tube, in one (8.3%) patient central venous catheter, and in three (25%) patients multiple drains were removed. Conclusion:The inaccurate use of drains or re-intervention for an unintentionally removed drain causes problems regarding patient safety. Close monitoring of surgical patients in terms of security, and submission of additional measures for patients with confusion and neurological/psychiatric disorders are of great importance.

show abstract

“…Delirium isan etiologically nonspecific global cerebral dysfunction, alleviated by treatment involving induction of systemic or toxic noxas (Zmeková, 2003). Brooks et al (2014) state that according to several studies, postoperative delirium results in a combination of predisposing and precipitating factors. Predisposing factors involve older age of patients, alcohol abuse, dependence on addictive substances, comorbidity and impaired cognition (present in preoperative condition), depression, unsatisfactory preoperative nutritional and functional condition, hypoxia, and abnormal levels of glycaemia.…”

Section: Introductionmentioning

confidence: 99%

Use of measurement tools for screening of postoperative delirium in nursing practice

Balková¹,

Tomagová²

2018

Cent Eur J Nurs Midw

View full text Add to dashboard Cite

Aim: The aim of the literary overview was to search quantitative research studies focusing on screening for postoperative patient delirium by nurses with the aid of measurement tools; and, afterwards, to compare the tools used with respect to their psychometric characteristics and use in clinical nursing practice. Design: Overview studyliterary review. Methods: Electronic databases were searched for relevant documents (i.e., Web of Science, SCOPUS, Science Direct, and Summon Discovery Tool), from the period 1990-2017. Key words used for databases were: postoperative delirium, screening, tool, validity, reliability, psychometric characteristic, and nursing, combined with Boolean operators. Results: A total of 253 documents were identified. Based on the chosen selection criteria, eight studies dealing with screening of postoperative delirium were included in the analysis and overview. The following measurement tools were used in the studies: the Confusion Assessment Method for Intensive Care Units-CAM-ICU, the Nursing Delirium Screening Scale-Nu-DESC, the NEECHAM Confusion Scale, the Delirium Observation Screening Scale-DOSS, and the Intensive Care Delirium Screening Checklist-ICDSC. The tool with the optimum psychometric characteristics for detection of postoperative delirium is the CAM-ICU tool. Conclusion: The results of the studies analysed indicate the importance of the implementation of a relevant tool for screening of postoperative delirium. It is essential to examine the psychometric characteristics and usability of screening tools in Slovak clinical practice.

show abstract

Developing a Strategy to Identify and Treat Older Patients With Postoperative Delirium

Cited by 15 publications

References 68 publications

Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm

Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm

Factors affecting the safety of drains and catheters in surgical patients

Use of measurement tools for screening of postoperative delirium in nursing practice

Contact Info

Product

Resources

About