“What’s in a Name?” Delirium by Any Other Name Would Be as Deadly. A Review of the Nature of Delirium Consultations

Joshi, Aditya; Krishnamurthy, Venkatesh; Purichia, Heather; Hollar-Wilt, Laura; Bixler, Edward O.; Rapp, Mark

doi:10.1097/01.pra.0000422739.49377.17

Cited by 13 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The individual is, therefore, likely to have reduced orientation to their environment, and at times to oneself. This symptom has sometimes been referred to as 'reduced level of conscious ness' or confusional state, 11 although disturbance in awareness is a more accurate description. The disturb ance of awareness tends to develop over hours to days, and typically fluctuates in the course of the day, often worsening in the evening.…”

Section: Deliriummentioning

confidence: 99%

Classifying neurocognitive disorders: the DSM-5 approach

et al. 2014

View full text Add to dashboard Cite

Neurocognitive disorders--including delirium, mild cognitive impairment and dementia--are characterized by decline from a previously attained level of cognitive functioning. These disorders have diverse clinical characteristics and aetiologies, with Alzheimer disease, cerebrovascular disease, Lewy body disease, frontotemporal degeneration, traumatic brain injury, infections, and alcohol abuse representing common causes. This diversity is reflected by the variety of approaches to classifying these disorders, with separate groups determining criteria for each disorder on the basis of aetiology. As a result, there is now an array of terms to describe cognitive syndromes, various definitions for the same syndrome, and often multiple criteria to determine a specific aetiology. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides a common framework for the diagnosis of neurocognitive disorders, first by describing the main cognitive syndromes, and then defining criteria to delineate specific aetiological subtypes of mild and major neurocognitive disorders. The DSM-5 approach builds on the expectation that clinicians and research groups will welcome a common language to deal with the neurocognitive disorders. As the use of these criteria becomes more widespread, a common international classification for these disorders could emerge for the first time, thus promoting efficient communication among clinicians and researchers.

show abstract

Section: Deliriummentioning

confidence: 99%

Classifying neurocognitive disorders: the DSM-5 approach

et al. 2014

View full text Add to dashboard Cite

show abstract

“…37 Factors that have been directly linked to misdiagnosis include atypical presentation, inexperience of the diagnosing clinician and a documented past psychiatric diagnosis. 35,38 In addition, womenwith misdiagnosed delirium are more commonly labelled asdepression, while men are more likely to receive an unspecified diagnosis. 35 …”

Section: Assessment In Clinical Practicementioning

confidence: 99%

Delirium and depression: inter-relationship and clinical overlap in elderly people

O’Sullivan

Inouye

Meagher

2014

The Lancet Psychiatry

View full text Add to dashboard Cite

Delirium and depression are complex neuropsychiatric syndromes that are common in the elderly and associated with a variety of poor healthcare outcomes. Accurate detection is key to providing optimal care for these conditions but is complicated by their considerable clinical overlap. This includes shared symptom profiles as well as comorbidity. Careful assessment of symptom character as well as the context and course of disturbances can allow for more accurate diagnosis. Prior depressive illness is a common finding in patients with delirium, while depressive illness is a recognised sequel of delirium. Evidence points to similar pathophysiological mechanisms involving disturbances in stress and inflammatory responses, monoaminergic and melatonergic functions, that in turn point to avenues for therapeutic intervention. Development of better tools for systematic assessment for delirium and depression in populations at high risk by virtue of age, diminished cognitive reserve and frailty is a key target to achieve improved healthcare outcomes.

show abstract

“…A past psychiatric history has been shown in two studies to make delirium misdiagnosis more likely [10,15], though not in a palliative care setting [13]. In another study, Swigart et al did not find that an overall psychiatric diagnosis made misdiagnosis more likely, but specific diagnosesbipolar disorder and psychosisconferred nine times greater odds of misdiagnosis [3].…”

Section: Introductionmentioning

confidence: 95%

“…Otani et al found a low misdiagnosis rate of 8.1%, and related this to a relatively better understanding of psychiatric diagnosis by physicians in their hospital [14]. In contrast, Joshi et al found a much higher misdiagnosis rate of 73%, relating to their stringent classification as inaccurate in all cases where delirium was not mentioned specifically in the referral, even if delirium diagnosis was documented in the patient file [15]. In non-CLP settings, similar rates have been observed, with 61% of delirium misdiagnosed by the primary team prior to referral to a palliative care inpatient service [13].…”

Section: Introductionmentioning

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

“What’s in a Name?” Delirium by Any Other Name Would Be as Deadly. A Review of the Nature of Delirium Consultations

Abstract: This study highlights the need for educational programs and easy to implement screening tools to ensure delirium is not overlooked.

Cited by 13 publications

References 18 publications

Classifying neurocognitive disorders: the DSM-5 approach

Classifying neurocognitive disorders: the DSM-5 approach

Delirium and depression: inter-relationship and clinical overlap in elderly people

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

Contact Info

Product

Resources

About