Delirium detection in Intensive Care patients Willemijn van der Kooi Department of Intensive Care Medicine University Medical Center Utrecht, The Netherlands

Disclosures Orion Pharma: contributed to printing costs of my thesis NPK design: contributed to printing costs of my thesis Prevalentie delirium is 50 tot 80 % voor IC patiënten, afhankelijk of je onderscheid maakt tussen beademd en niet beademd. Voor gemengde populatie zo rond de 50 %, voor alleen mechanisch geventileerde patiënten gata het richting de 80 % As we all know, delirium is a common syndrome in the Icu with reported prevalences of over 70 %. Delirium is associated with impaired long term outcome. Although very sensitive screening tools for delirium exist in a research setting. Daily delirium screening in clinical practice shows dissapointing sensitivities. One large multi center trial, showed a sensitivity of only 49 %

Introduction Delirium prevalence: 50%-80% for ICU patients 10-15% for cardiac surgery patients ICU delirium is associated with: Long term cognitive impairment Increased hospital and ICU length of stay Increased mortality * Actor Prevalentie delirium is 50 tot 80 % voor IC patiënten, afhankelijk of je onderscheid maakt tussen beademd en niet beademd. Voor gemengde populatie zo rond de 50 %, voor alleen mechanisch geventileerde patiënten gata het richting de 80 % As we all know, delirium is a common syndrome in the Icu with reported prevalences of over 70 %. Delirium is associated with impaired long term outcome. Although very sensitive screening tools for delirium exist in a research setting. Daily delirium screening in clinical practice shows dissapointing sensitivities. One large multi center trial, showed a sensitivity of only 49 %

Introduction Delirium often (71%) missed by ICU physicians1 questionnaires developed for screening Daily practice Sensitivity of questionnaire with best performance (Cam-ICU): 47% in ICU patients2 28% in post-operative patients3 Cognitive screening may not fit well in the culture of the ICU Therefore we may need a new approach for delirium detetecion. We would like to focus this new approach on delirium detetcion using physiological alterations, for example the already known alterations in the EEG during delirium, but maybe also temperature. 1 Van Eijk et al. Crit Care Med 2009;37:1881-5 2 Van Eijk et al. Am J Respir Crit Care Med 2011;184:340-4 3 Neufeld et al. Br J Anaesth 2013;111:612-8

Introduction New approach: delirium detection using physiological alterations Ultimate goal: 2 sensors coupled to a monitor Monitor shows on a scale the chance of having delirium

Content Three physiological parameters studied: Temperature variability Eye movements Brain activity (EEG) Future perspective Delirium may be a manbifestation of an encephalopathy. For ther syndromes sharing symtpoms with delirium as ….. It has bene described that these suffer from disturbed temperature regulation

Temperature variability during delirium in ICU patients Van der kooi et al. PLoS One. 2013; 8:e78923

Introduction Delirium: manifestation of encephalopathy In delirium tremens, Wernicke encephalopathy and schizophrenia: temperature regulation is disturbed Does delirium affect thermoregulation? Delirium may be a manbifestation of an encephalopathy. For ther syndromes sharing symtpoms with delirium as ….. It has bene described that these suffer from disturbed temperature regulation

Aim of the study To investigate whether: ICU delirium is related to absolute body temperature ICU delirium is related to temperature variability

Methods Subjects from 3 previous delirium studies Daily delirium assessments by research- nurse/physician Temperature: measured per minute 24/7

Methods Inclusion: Patients with delirious + non-delirious days during ICU admission of >24 hrs Exclusion criteria: Disturbed body temperature regulation (treatment/diagnoses) Neurological/neurosurgical disease Days with sepsis, coma or death were excluded from analysis *All patients received paracetamol 1000 mg 4 times daily All adult patients with at least one delirious and one delirious-free episode during an ICU admission of at least 24 hours were included, except for the following five exclusion criteria: (1) disturbed regulation of body temperature: renal replacement therapy, extra corporal membrane oxygenation, therapeutic hypothermia or admission because of an intoxication; (2) no temperature data in the medical record; (3) persistent delirium or comatose state during the whole ICU admission, which makes comparison of delirium- with non-delirium days impossible; (4) admission because of a neurological- or neurosurgical disease, as it may be difficult to diagnose delirium in these patients; or (5) impossibility to be tested with the CAM-ICU, for example because of an inability to understand Dutch or English. A comatose state was defined as a Glasgow Coma Score lower than 9 or a Richmond Agitation and Sedation Scale (RASS) score lower than minus 3. Furthermore , we excluded patients with sepsis throughout their whole ICU admission as well as days with sepsis in other patients. Sepsis was defined as two or more systemic inflammatory response syndrome criteria together with a suspected or proven infection described in the medical record. All  included patients were treated with paracetamol 1000 mg 4 times daily, both on days with delirium, as on days without.

Methods Coma No Delirium Delirium Temperature data was measured every  minute in the inguinal crease or rectum with a temperature probe  comment 2 and 15. For artifact detection, temperature measurements below 35 degrees were excluded, together with data from the preceding and following 20 minutes, in order to overcome decreases in temperature due to a removed thermometer. Coma No Delirium Delirium

Methods Linear Mixed models: Univariable (unadjusted) Multivariable (adjusted for confounders RASS and SOFA) Outcome: body temperature [°C] temperature variability (absolute second derivative) [°C/min2] Linear mixed models were used to account for clustering of multiple, daily measurement averages per patient. Rass for activity level/agitataion/sedation and sofa for severity of disease. Patients with and without delirium were compared for differences in temperature variability and absolute body temperature, and additionally adjusted for level of activity (mean RASS) and disease severity (maximal SOFA score). Linear mixed models were used to account for clustering of multiple, daily measurement averages per patient. Delirium scores, as well as possible confounders, mean RASS and maximal SOFA scores, were included as fixed effects. All models included a random intercept. Random slopes for the fixed effects were included when the Akaike Information Criterion [23] of that particular model was five points lower than the AIC of the same model with only a random intercept.

Results Patients with and without delirium were compared for differences in temperature variability and absolute body temperature, and additionally adjusted for level of activity (mean RASS) and disease severity (maximal SOFA score). Linear mixed models were used to account for clustering of multiple, daily measurement averages per patient. Delirium scores, as well as possible confounders, mean RASS and maximal SOFA scores, were included as fixed effects. All models included a random intercept. Random slopes for the fixed effects were included when the Akaike Information Criterion of that particular model was five points lower than the AIC of the same model witThe used covariance type for models with only a random intercept was ‘identity’; in all other cases it was ‘unstructured’. Statistical analyses were performed with Statistical Package for the Social Sciences (IBM SPSS Statistics, version 20, Armonk, New York, U.S.A.). A two-tailed p-value less than 0.05 was considered to be statistically significant. h only a random intercept.

Results Patient characteristics Age: mean (SD) 68 (14) Gender: number of males (%) 15 (63%) Admission type: number (%)   -internal medicine 3 (12%) -surgery 12 (50%) -cardiothoracic surgery 9 (38%) Delirium type: number (%) -Hypoactive 6 (25%) -Hyperactive 0 (0%) -Mixed type 18 (75%) Number of analyzed days: median (IQR) -Delirium 2.0 (1.0) -Non-delirium 1.0 (1.8) Nine patients were female. The mean age was 68 years old (SD 14) and mean Acute Physiology and Chronic Health Evaluation IV score was 52 (SD 21). Median length of ICU stay in these patients was 5 days (IQR 3.3 to 9.8). The median number of delirium days in the study population was 2 (IQR 1.0 to 2.0) and the median number of non-delirium days 1 (IQR 1.0 to 2.8).

Results Body Temperature: Model Variable Effect estimate 95% Confidence interval p-value Unadjusted   Delirium [yes] -0.03 -0.17; 0.10 0.61 Adjusted 0.63 Rass 0.01 -0.09; 0.10 0.90 Sofa 0.001 -0.04; 0.04 0.95 Overall, the median (interquartile range) of the number of samples per measurement day was  755 (506-1027). In Figure 2, the determination of temperature variability is explained for one patient. The differences per patient for temperature variability are shown in Figure 3. Of the 24 patients, 21 patients (88%) showed increased temperature variability during delirium when compared to non-delirium. The mean temperature variability on delirium days was 0.021 (SD 0.008) and non-delirium days 0.015 (SD 0.010). The best unadjusted and adjusted linear mixed models for temperature variability included only a random intercept and no random slopes. Both the unadjusted and adjusted linear mixed models showed that temperature variability is increased during delirium (βunadjusted=0.005, 95% CI=0.003 to 0.008, p<0.001 and βadjusted=0.005, 95% CI= 0.002 to 0.008, p<0.001). The mean absolute body temperature on delirium days was 36.9 ºC (SD=0.50) and on non-delirium days 36.9 ºC (SD=0.58). Of the 24 patients, 13 patients (54%) showed decreased temperature during delirium when compared to non-delirium. The best unadjusted and adjusted linear mixed models for absolute body temperature also included only a random intercept and no random variables. Both the unadjusted and adjusted linear mixed models showed that delirium is not associated with absolute body temperature (βunadjusted=-0.03, 95% CI=-0.17 to 0.10, p=0.61 and βadjusted=-0.03, 95% CI=-0.17 to 0.10, p=0.63).

Results Temperature Variability: Model Variable Effect estimate 95% Confidence interval p-value Unadjusted   Delirium [yes] 0.005 0.003; 0.008 <0.001 Adjusted 0.002; 0.008 Rass -0.001 -0.003; 0.001 0.20 Sofa -0.0001 -0.001; 0.001 0.71 Overall, the median (interquartile range) of the number of samples per measurement day was  755 (506-1027). In Figure 2, the determination of temperature variability is explained for one patient. The differences per patient for temperature variability are shown in Figure 3. Of the 24 patients, 21 patients (88%) showed increased temperature variability during delirium when compared to non-delirium. The mean temperature variability on delirium days was 0.021 (SD 0.008) and non-delirium days 0.015 (SD 0.010). The best unadjusted and adjusted linear mixed models for temperature variability included only a random intercept and no random slopes. Both the unadjusted and adjusted linear mixed models showed that temperature variability is increased during delirium (βunadjusted=0.005, 95% CI=0.003 to 0.008, p<0.001 and βadjusted=0.005, 95% CI= 0.002 to 0.008, p<0.001). The mean absolute body temperature on delirium days was 36.9 ºC (SD=0.50) and on non-delirium days 36.9 ºC (SD=0.58). Of the 24 patients, 13 patients (54%) showed decreased temperature during delirium when compared to non-delirium. The best unadjusted and adjusted linear mixed models for absolute body temperature also included only a random intercept and no random variables. Both the unadjusted and adjusted linear mixed models showed that delirium is not associated with absolute body temperature (βunadjusted=-0.03, 95% CI=-0.17 to 0.10, p=0.61 and βadjusted=-0.03, 95% CI=-0.17 to 0.10, p=0.63).

Discussion Strengths: Limitations Delirium diagnoses prospectively Within subjects comparisons Easy method temperature variability Limitations Possible effect of medication Natural circadian rhythm bias Sedatie en analgesie kan lichaamstemperatuur verlagen. Voor mate van sedatie gecorrigeerd met de rass Natuurlijk variatie van lichaamstemperatuur niet meegenomen, maar is 1000 keer kleiner dan de variatie in temperatuur die hier gemeten is

Discussion Temperature variability: increased during delirium in ICU patients encephalopathy that underlies delirium Future studies: Monitoring temperature variability in total ICU population Combine with EEG for objective tool to detect delirium

Delirium detection based on monitoring of blinks and eye movements Van der kooi et al. Am J Geriatr Psychiatry. 2014

Introduction Delirium associated with change in motor level activity Actigraphy not practical Eye movements less affected by muscle weakness, restraints, pain

Goal Determine whether eye blinks and eye movements differ in patients with delirium compared to patients without delirium.

Methods Population: post-cardiac surgery patients Reference: psychiatrist, geriatrist, neurologist using DSM 4 criteria Als eerste stap, alleen gekeken naar de 2 extreme situaties van delirant en niet delirant !!!!

Methods Standard 21 electrode EEG recording (30 minutes) with periods of eyes open and closed First artifact free minute selected with eyes closed and open Exclusie: schade aan brein door, dementie, schedeletsel, epilepsie etc.

Methods: Eye movements Eye movements compared between delirium and non-delirium Number (per min) and duration (sec) of: Blinks Vertical eye movements Horizontal eye movements

Results: study population Delirious patients (n=28) Non-delirious patients (n=28) p-value Age, mean (SD) 76 (5.6) 74 (8.6) 0.16 Gender: male, n (%) 16 (57%) 1 Apache IV score, median (IQR) 58 (45-65) 43 (35-51) <0.01 Charlson comorbidity index, median (IQR) 2 (1-3) 1 (0-1) 0.02 Haloperidol use past 24 hours n (%) 17 (61%) 2 (7%) Postsurgical day of EEG, median (IQR) 3 (2-5) 3 (2-4) 0.78 Haloperidol in 2 controle patiënten niet uitleggen, is verwarrend

Results: eye movements Eyes Open Variable Delirium Median (IQR) Non-delirium p-value Number of Vertical eye movements (min-1) 1 (0-13) 15 (2-54) 0.01 Number of Blinks (min-1) 12 (5-18) 18 (8-25) 0.02 Duration of Blinks (s) 0.50 (0.36-0.96) 0.34 (0.23-0.53) <0.01 Er was geen verschil in deze parameters tussen delirante patiënten met en delirante patiënten zonder haloperidol

Results: eye movements Eyes Closed Variable Delirium Median (IQR) Non-delirium p-value Duration of Horizontal eye movements (s) 0.41 (0.15-0.75) 0.08 (0.06-0.22) <0.01 Er was geen verschil in deze parameters tussen delirante patiënten met en delirante patiënten zonder haloperidol

Results: Eye movements haloperidol Eyes Variable Delirium with haloperidol Median (IQR) Delirium without haloperidol p-value Open  Number of vertical eye movements 2 (0-17) 0 (0-17) 0.69 Number of blinks   12 (4-19) 12 (6-17) 0.87  Open Duration of blinks (s) 0.49 (0.39-1.01) 0.52 (0.34-0.93) 0.81  Closed Duration of horizontal of eye movements (s) 0.59 (0.23-1.40) 0.27 (0.13-0.69) 0.19 EEG chracteristics|: relative power of delta theta alpha etc.

Conclusion Especially blinks are affected in delirious patients Strengths: non-invasive Only 1 minute of data necessary Limitations: 22 electrodes needed for eye movement measurement, except for blinks Difference in Apache and Charlson Comorbidity score Future studies: Detection of eye movements in general population of ICU patients Determining whether eye movements can detect delirium at early stage 22 electrodes needed for eye movement measurement, except for blinks (hier mara 2 voor nodig) en deze wijken ook het meest af Difference in Apache and Charlson Comorbidity score (risk factors delirium)

Delirium detection using EEG: what and how to measure? Van der kooi et al. Chest. 2014

Introduction Delirium characterized by EEG abnormalities EEG not practical Without Delirium With Delirium Therefore we may need a new approach for delirium detetecion. We would like to focus this new approach on delirium detetcion using physiological alterations, for example the already known alterations in the EEG during delirium, but maybe also eye movements

Goal Determine the electrode derivation and EEG characteristic that have the best capability of discriminating delirium from non-delirium

Methods Standard 21 electrode EEG recording (30 minutes) with periods of eyes open and closed First artifact free minute selected with eyes closed Exclusie: schade aan brein door, dementie, schedeletsel, epilepsie etc.

Methods: EEG Eyes closed= 210 different derivations Patient selection same as Eye movement study

Methods: EEG For every derivation 6 parameters: 1 θ 4-8 Hz α 8-13 Hz Relative delta power (0.5-4 Hz), Relative theta power (4-8 Hz),Relative alpha power (8-13 Hz), Relative beta power (13-20 Hz), Peak frequency, Slow-fast ratio Ruwe EEG δ 0-4 Hz θ 4-8 Hz α 8-13 Hz β 13-20 Hz Patient selection same as Eye movement study 1van der Kooi, et al. J Neuropsychiatry Clin Neurosci 2012; 24: 472-477.

Methods: EEG 210 derivations x 6 parameters = 1260 combinations All 1260 combinations Compared between delirium and non-delirium (Mann-whitney U) P-values ranked smallest p-value is optimal combination (Bonferoni correction ) Patient selection same as Eye movement study 1van der Kooi, et al. J Neuropsychiatry Clin Neurosci 2012; 24: 472-477.

Results: EEG Eyes closed Rank p-value* Deriviation Parameter 1 1.8e-12 F8-Pz Relative δ 2 3.7e-12 F8-P3 3 1.1e-11 F8-O2 4 1.5e-11 Fp2-O1 5 1.7e-11 F8-F4 6 2.2e-11 F8-O1 7 2.4e-11 F8-Cz 8 F8-C3 9 2.9e-11 Fp2-Pz 10 3.0e-11 Cz-O1 EEG with eyes closed showed larger differences than eeg with eyes open. For eyes closed the 10 most optimal combinations (smallesty p-value) are shown. Met alleen 2*28 patienten zagen we een verschil aantonen met een p-waarde van 2*10-12. Dit was met een frontaal-parietale afleiding in combinatie met het meten van relatieve delta. Verder waren ook buur electrodes van deze afleiding erg goed in het onderscheid maken tussen delirium en niet delirium zoals Fp2-Pz en F8-P3 en allemala in combinatie met het meten van de parameter relatieve delta. Zoals u kunt zien in de grafiek, was er bijna een 100 % verschil tussen de twee groepen. Er was geen verschil in deze beste parameter tussen delirante patiënten met en delirante patiënten zonder haloperidol. *p< 4.0*10-5 is significant

Results: EEG Most optimal electrode locations, based on first 4 rankings. Bepaalde electrodes op voorhoofd en achterhoofd zijn vooral erg goed in onderscheid te maken. Deze liggen dicht bij elkaar in de buurt wat, erg praktisch is voor ene toekomstige toepasisng, warabij je niet wilt dat je op de millimeter nauwkeurig moet plakken om een goed resultata te krijgen.

Conclusion EEG easily detects delirium from non-delirium using 2 electrodes in frontal-parietal derivation and relative delta power Strengths: new approach, non-invasive, only 2 electrodes and 1 minute data necessary Future studies: Validation study in unselected population of postoperative- and critically ill patients Determine whether it recognizes delirium at an early stage Een validatie studie is nodig om de sensitiviteit en specificiteit in een ongeselecteerde populatie postoperatieve en IC patiënten te bepalen

Future Directions

Overall Conclusion EEG most promising method for delirium detection. Project started: Development of delirium monitor EEG chracteristics|: relative power of delta theta alpha etc.

Product development Product and algorithm EEG chracteristics|: relative power of delta theta alpha etc.

Validation study Goal: To determine sensitivity, specificity and predictive values of the delirium monitor when compared to reference standard (specialized geriatric nurse) in elderly postoperative patients (n=154). POS: Mmse, verhoogd risico op delirium (VMS criteria), kwetsbaarheid ouderen (ISAR-HP)->arts/vpk geriatrie Inclusion: major surgery, age >70 years old, preoperative increased vulnerability for delirium Exclusion: no communication possible, neurological surgery, patient in isolation, patient already participated

Usability study Practical? Easy to Use? Opinion of nurses of different medical departments Een validatie studie is nodig om de sensitiviteit en specificiteit in een ongeselecteerde populatie postoperatieve en IC patiënten te bepalen

Extra slides EEG chracteristics|: relative power of delta theta alpha etc.

Results: EEG eyes open Ogen Open Rang p-waarde* Afleiding Parameter 1 P7-P4 Relative alpha 2 4.2e-07 P3-P4 3 1.6e-06 P7-O1 Relative delta 4 3.2e-06 5 3.5e-06 Slow Fast ratio 6 4.0e-06 P4-O1 7 6.1e-06 P7-P8 8 7.9e-06 9 9.4e-06 P3-P8 10 1.1e-05 P7-O2 Exclusie: schade aan brein door, dementie, schedeletsel, epilepsie etc. *p< 5.6*10-4 is significant Delirium met/zonder haloperidol geen verschil (p=0.37)

Results: Eye movements eyes open Variable Delirium Median (IQR) Non-delirium p-value AUC (95% CI)   Open Number of eye movements Horizontal 6 (0-51) n=23 26 (0-55) n=28 0.54 0.55 (0.39-0.71) Vertical 1 (0-13) 15 (2-54) 0.01 0.70 (0.55-0.85) Blinks 12 (5-18) 18 (8-25) n=27 0.02 0.65 (0.50-0.80) Duration of eye movements (s) 0.24 (0.10-0.56) n=14 0.14 (0.04-0.27) n=17 0.14 0.66 (0.47-0.85) 0.14 (0.06-0.49) n=10 0.07 (0.04-0.60) n=18 0.46 0.59 (0.37-0.81) 0.50 (0.36-0.96) n=20 0.34 (0.23-0.53) <0.01 0.74 (0.59-0.88) EEG chracteristics|: relative power of delta theta alpha etc.

Results: Eye movements eyes closed Variable Delirium Median (IQR) Non-delirium p-value AUC (95% CI)   Closed Number of eye movements Horizontal 0 (0-42) n=27 0 (0-51) 0.37 0.57 (0.41-0.72) Vertical 5 (0-47) 10 (0-52) 0.40 0.56 (0.41-0.72) Duration of eye movements (s) 0.41 (0.15-0.75) n=12 0.08 (0.06-0.22) n=13 <0.01 0.81 (0.64-0.99) 0.15 (0.07-0.29) n=15 0.07 (0.03-0.27) n=17 0.19 0.64 (0.44-0.84) EEG chracteristics|: relative power of delta theta alpha etc.

Results: Eye movements haloperidol Eyes Variable Delirium with haloperidol Median (IQR) Delirium without haloperidol p-value Number of eye movements   Open  Vertical  2 (0-17) n=14 0 (0-17) n=9 0.69 Blinks 12 (4-19) 12 (6-17) 0.87 Duration of eye movements (s)  Open 0.49 (0.39-1.01) 0.52 (0.34-0.93) 0.81  Closed Horizontal 0.59 (0.23-1.40) n=6 0.27 (0.13-0.69) 0.19 EEG chracteristics|: relative power of delta theta alpha etc.

Stap1 Van onderzoek naar klinische prakti Ontwikkeling van delirium monitor Product Algoritme Validatie studie Gebruiksvriendelijkheids- studie

Validatie studie Doel: Het bepalen van de sensitiviteit, specificiteit en voorspellende waarden van de delirium monitor in vergelijking met de referentie standaard in oudere postoperatieve patiënten (n=154). Elderly (>70 years) Patienten worden preoperatief gescreend door de geriatrie op kwetsbaarheid (ISAR-HP) en verhoogd risico op delirium. Als patienten positief scoren op één van die twee, worden ze geincludeerd. Het is nu nog een monocenterstudie, waarbij we zijn gestart in Utrecht. Berlijn en Nijmegen gaan sowieso meedoen, Zwolle is nog niet helemaal duidelijk.

Validatie studie Inclusie: ≥ 70 jaar Opname voor grote operatie (min. 2 opname dagen ZH na operatie) Preoperatieve verhoogde kwetsbaarheid en/of verhoogd risico op delirium Exclusie: Geen communicatie mogelijk Neurologische chirurgische ingreep Eerdere deelname studie Patient in isolatie vanwege resistente bacterie Elderly (>70 years) Major surgery Patienten worden preoperatief gescreend door de geriatrie op kwetsbaarheid (ISAR-HP in het UMCU) en verhoogd risico op delirium. Als patiënten positief scoren op één van die twee, worden ze geïncludeerd.

Validatie studie - Studie verloop Operatie T0 T1 T2 T3 = Delirium monitor = Referentie standaard = POS Geriatrische screening

Validatie studie Delirium monitor 4 elektrodes 5 minuten EEG meting OD Relatieve δ power Referentie standaard onderzoeker/vpk DRS-R-98 Ernst van delirium VAS (0-10) Kans dat patiënt delirant is Classificatie Deliriant/Mogelijk delirant/Niet delirant (Op basis van DSM-V criteria) Zoals we het nu hebben, is de referentie standaard een geriatrisch verpleegkundig specialist met veel ervaring met delirium We gaan het aanpassen naar de ‘echte’ gouden standaard. De onderzoeker/verpleegkundige neemt de DRS-R98 en VAS af en dit wordt gefilmd. Achteraf gaat de geriater/psychiater/neuroloog dit beoordelen en die geven de uiteindelijke classificatie.

Validatie studie - Analyses 1e artefact vrije minute  relatieve δ power ROC curve relatieve δ power vs. classificatie van referentie standaard

Validatie studie - Secundaire doelen 1) Schaal voor ernst van delirium (relatieve δ vs. DRS-R-98) 2) Vroegtijdig herkennen van delirium? 1 2 4 5 6 7 8 9 10 3 Operatie T0 T1 T2 T3 = Delirium monitor = Referentie standaard = Geriatrische screening

Stap2 Van onderzoek naar klinische praktijk Gebruiksvriendelijkheidsonderzoek Handig product? Ervaring verpleegkundige

Stap3 Van onderzoek naar klinische praktijk Delirium monitor bredere doelgroep Dementie Neurotrauma IC: Effect sedatie op EEG

Samenvatting EEG in delirium studie = het idee Relatieve δ power Frontaal- Pariëtaal 2) Ontwikkeling prototype 3) Validatiestudie 4) Gebruiksvriendelijkheidsstudie 5) Hoe krijgen we het naar de IC

Delirium monitor project UMCU - IC Arjen Slooter Willemijn van der Kooi Tianne Numan Annemieke Hoekman Pontes Medical Rutger van Merkerk NPK design Tessa Souhoka Marlies van Dullemen Jos Oberdorf Medische Techniek Leonard van Schelven Rene van de Vosse Bert Westra Maurice Konings Geriatrie Marielle Emmelot-Vonk Jolanda Peijster- de Waal Marcel Weterman KNF Geert-Jan Huiskamp Frans Leijten