Research

In 2023, Ottens et al. A panel of 37 internationally recognized delirium experts designed a set of three delirium treatment algorithms. For ward patients, patients after cardiac surgery and for patients in the intensive care unit. The set of algorithms support healthcare providers in the management of underlying EEG changes that are seen in acute encephalopathies underlying delirium.

In 2023, Ditzel et al. Published the results of a prospective cross-sectional study in 494 patients. The diagnostic accuracy of DeltaScan was investigated. This large and robust study proved that DeltaScan can accurately diagnose acute encephalopathy manifesting as delirium within a few minutes at the patient’s bedside.

This study explores barriers to delirium screening and management in hospitalized patients from a nursing perspective.

In 2009, Van Eijk, Slooter et al. published an article about the under-diagnosis of delirium in general and the hypo-active form of delirium in particular.

In 2011, Van Eijck, Slooter et al. examined how delirium screening instruments performed in daily clinical practice. The results demonstrated a low sensitivity of 47%. In the case of hypo-active delirium – the most prevalent form – the sensitivity was only 31%. This formed the motivation to develop an objective screening instrument. 

In 2015, Van der Kooi, Slooter et al. published an article describing the technological basic principles of screening for delirium using EEG. 

In 2017, Numan et al. published a study demonstrating how difficult it is to recognise delirium. The gold standard for the diagnosis of delirium is an evaluation by a delirium expert. In 21% of the cases these experts differed in their opinions. Only 32% of the actual cases of delirium were recorded in the nursing files, which points to under-detection of delirium in clinical practice. There is a clear need for a user-friendly, reliable method to recognise delirium in clinical practice. EEG could potentially serve as an objective screening method for delirium. 

In 2019, Numan et al. published results which demonstrate that it is possible to diagnose delirium in older post-operative patients using an automatically analysed 1-channel EEG. This method could facilitate objective screening of delirium, in which the result is not dichotomised but instead provides a continuous scale.

In 2019, a study by Fleischmann et al. demonstrated that a combination of several EEG electrodes can be used for accurate identification and diagnosis of patients with delirium in a mixed cohort of several thousand patients. The diagnostic performance of qualitative EEG exceeded that of classical clinical delirium tests, despite the absence of a priori information about the condition of the patient. 

In 2019, Kimchi et al. demonstrated that EEG retardation detected in clinical practice was correlated to the severity of delirium and was a predictor of unfavourable clinical outcomes, such as prolonged hospitalisation, poorer functional outcomes and increased mortality.

In 2020, Tanabe et al. published research stating that delirium is related to retarded frontal and occipital EEG activity. 

In 2021, Slooter et al. published a position paper that urges to end segregation between acute encephalopathy and delirium under the title “Updated nomenclature of delirium and acute encephalopathy: statement of ten Societies“. 

In 2021 Hut et al. published a study that demonstrates that acute encephalopathy in single channel EEG largely overlaps with a clinical diagnosis of delirium. 

In 2022 Ditzel et al. published that automated polymorphic delta activity detection in 1-channel EEG [ed.: DeltaScan algorithm] had high sensitivity and specificity for acute encephalopathy and delirium. 

In 2010, Heymann (Germany) demonstrated that treating delirium too late results in worse outcomes for the patient

In 2011, Leslie and Inouye (USA) determined that the healthcare costs attributable to delirium are so high that they are ranked between the costs for diabetes and cardiovascular diseases. 

In 2013, Pandharipande, Ely et al. (USA) demonstrated that a longer duration of delirium is associated with more significant long-term cognitive deterioration

Ely and later Pisani demonstrated that each day that delirium persists is independently associated with an increase in the mortality risk by 10%.

Ely and later Pisani demonstrated that each day that delirium persists is independently associated with an increase in the mortality risk by 10%.

Slooter (NL) has demonstrated that delirium results in worse outcomes in many cases and that this deterioration is independent of the severity of the underlying disease.

As early as 2001, Marcantonio (USA) and others had determined that intensive screening by geriatric specialists can limit the burden of delirium

A large meta-analysis by Marcantonio (USA) in 2017 concluded that up to 75% of the patients had a hypo-active or silent delirium and that only 12% to 35% of delirium patients are diagnosed. The patients that do receive a diagnosis are mainly those with hyperactive delirium 

Inouye et al. (USA) described in 2014 that suffering from delirium not only contributes to a higher risk of mortality, but also to an increased risk of admission to a nursing home due to loss of ADL functionalities.

In 2010, Martin et al. (USA) studied the relationship between delirium and (post-operative) sepsis, in which they stated that delirium is an independent predictor of sepsis.

Van den Boogaard et al. (NL) demonstrated that the cognitive problems following hospitalisation were significantly higher in patients who had suffered delirium, compared to patients without delirium.

Mulkey et al. (USA) recently described the use of EEG in the detection of delirium, stating that EEG – with use of the latest techniques – allows for earlier and improved detection of delirium

Numan published her thesis “EEG in delirium” in 2017. Numan, T. (2017) EEG in delirium (Thesis, University of Utrecht).