ICU DeliriumNCT06187792
Constructing a Model of Pupillary Parameters in Predicting Delirium Among Critically Ill Patients in the Intensive Unit
An observational study of ICU Delirium, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
Delirium is commonly observed in critically ill patients in intensive care units (ICUs), imposing significant burdens on both patients and the healthcare system. Existing assessment tools have certain limitations. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- ICU Delirium
- How many people
- 200 participants
- Where
- 1 siteTaiwan
- Study dates
- 4 Oct 2023to Sep 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients admitted to the intensive care unit from internal and surgical departments.
Who may be able to join
- Patients admitted to the intensive care unit from internal and surgical departments.
- Ages 18 and above.
Who cannot join
- Acute brain injury (hemorrhagic, ischemic stroke).
- Other brain-related diseases (brain tumor, brain infection, oculomotor nerve paralysis, etc.).
- Ophthalmic diseases that prevent monitoring of pupil measurements.
- Patients with pre-hospital cardiac arrest or in-hospital cardiac arrest.
- Estimated stay in the intensive care unit not exceeding 72 hours.
- Refusal to participate in this study.
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
Ming-Chen Chiang
Taipei, 220
Ming-Chen Chiang, (886)02-23123456 ext. 262653, jane.c0203@gmail.com
About this study
Delirium is commonly observed in critically ill patients in intensive care units (ICUs), imposing significant burdens on both patients and the healthcare system. Existing assessment tools have certain limitations. Studies have indicated a correlation between pupil parameters and neurological disorders including delirium. Automated Infrared Pupillometry, widely used in neurological disorders, is employed in this study to assess its accuracy and predictive power in evaluating delirium among critically ill patients. The aim is to investigate the accuracy and predictive capability of these parameters in assessing delirium, while identifying the optimal cut-off points. The research findings will contribute to enhancing early detection and prevention of delirium in ICU settings.
Read the detailed description
Delirium is an acute impairment of attention and cognitive function commonly observed in critically ill patients in intensive care units (ICUs). It leads to long-term cognitive impairment and increased risk of mortality for patients, while also causing distress for healthcare providers and family members, imposing substantial burdens on patients, families, and healthcare systems. Although there are assessment tools and predictive models available for detecting delirium, they have certain limitations.
Recent studies have indicated an association between delirium and the neurotransmitter acetylcholine (ACh). Acetylcholine not only regulates consciousness and cognitive wakefulness but also modulates pupil constriction and light reflex. In clinical practice, the Automated Infrared Pupillometry (AIP) has emerged as a robust tool for assessing acetylcholine, aiding in early delirium detection. However, more research is needed to clearly establish their relationship. This study aims to investigate the accuracy and predictive power of Automated Infrared Pupillometry in assessing delirium among critically ill patients. It involves collecting pupil parameters from critically ill patients and examining the correlation between delirium and pupil parameters using the Intensive Care Delirium Screening Checklist (ICDSC). The goal is to explore the accuracy and predictive capability of these parameters in evaluating delirium, identifying optimal cut-off points. The findings will contribute to enhancing early detection and prevention of delirium in intensive care settings.
- Conditions
- ICU Delirium
- Keywords
- Delirium, Predicting delirium, Automated Infrared Pupillometry (AIP), Pupil Parameters, Intensive Care Delirium Screening Checklist (ICDSC)
- Medical subject headings
- Delirium
What is being measured
The primary outcome is the main question the study is designed to answer.
Constructing a model based on pupillary parameters and delirium:
By analyzing the relationship between pupillary parameters and delirium, identifying the optimal cut-off point, and constructing a formula.
Measured: 2023/09/1-2025/08/30
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Jan 2024
- Study start4 Oct 2023
- Primary completionSep 2025 (estimated)
- Study completionSep 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (41)Papers the sponsor has linked to this study.
- Other trials for ICU DeliriumStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06187792
- Sponsor's protocol ID
- 202306099DINA
- Status last verified
- Oct 2023
- First submitted
- 17 Dec 2023
- Last updated
- 3 Jan 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Almeida IC, Soares M, Bozza FA, Shinotsuka CR, Bujokas R, Souza-Dantas VC, Ely EW, Salluh JI. The impact of acute brain dysfunction in the outcomes of mechanically ventilated cancer patients. PLoS One. 2014 Jan 22;9(1):e85332. doi: 10.1371/journal.pone.0085332. eCollection 2014.
PubMed 24465538Bergeron N, Dubois MJ, Dumont M, Dial S, Skrobik Y. Intensive Care Delirium Screening Checklist: evaluation of a new screening tool. Intensive Care Med. 2001 May;27(5):859-64. doi: 10.1007/s001340100909.
PubMed 11430542Bujang MA, Adnan TH. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. J Clin Diagn Res. 2016 Oct;10(10):YE01-YE06. doi: 10.7860/JCDR/2016/18129.8744. Epub 2016 Oct 1.
PubMed 27891446Carnahan RM, Lund BC, Perry PJ, Pollock BG, Culp KR. The Anticholinergic Drug Scale as a measure of drug-related anticholinergic burden: associations with serum anticholinergic activity. J Clin Pharmacol. 2006 Dec;46(12):1481-6. doi: 10.1177/0091270006292126.
PubMed 17101747Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychother Psychosom. 2022;91(1):8-35. doi: 10.1159/000521288. Epub 2022 Jan 6.
PubMed 34991091Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8.
PubMed 3558716Chougule PS, Najjar RP, Finkelstein MT, Kandiah N, Milea D. Light-Induced Pupillary Responses in Alzheimer's Disease. Front Neurol. 2019 Apr 12;10:360. doi: 10.3389/fneur.2019.00360. eCollection 2019.
PubMed 31031692de Vries L, Fouquaet I, Boets B, Naulaers G, Steyaert J. Autism spectrum disorder and pupillometry: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2021 Jan;120:479-508. doi: 10.1016/j.neubiorev.2020.09.032. Epub 2020 Oct 22.
PubMed 33172600Detroyer E, Timmermans A, Segers D, Meyfroidt G, Dubois J, Van Assche A, Joosten E, Milisen K. Psychometric properties of the intensive care delirium screening checklist when used by bedside nurses in clinical practice: a prospective descriptive study. BMC Nurs. 2020 Apr 10;19:21. doi: 10.1186/s12912-020-00415-z. eCollection 2020.
PubMed 32308558Devlin JW, Skrobik Y, Gelinas C, Needham DM, Slooter AJC, Pandharipande PP, Watson PL, Weinhouse GL, Nunnally ME, Rochwerg B, Balas MC, van den Boogaard M, Bosma KJ, Brummel NE, Chanques G, Denehy L, Drouot X, Fraser GL, Harris JE, Joffe AM, Kho ME, Kress JP, Lanphere JA, McKinley S, Neufeld KJ, Pisani MA, Payen JF, Pun BT, Puntillo KA, Riker RR, Robinson BRH, Shehabi Y, Szumita PM, Winkelman C, Centofanti JE, Price C, Nikayin S, Misak CJ, Flood PD, Kiedrowski K, Alhazzani W. Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. Crit Care Med. 2018 Sep;46(9):e825-e873. doi: 10.1097/CCM.0000000000003299.
PubMed 30113379Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992 Jun;45(6):613-9. doi: 10.1016/0895-4356(92)90133-8.
PubMed 1607900Ely EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, Truman B, Speroff T, Gautam S, Margolin R, Hart RP, Dittus R. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001 Dec 5;286(21):2703-10. doi: 10.1001/jama.286.21.2703.
PubMed 11730446Favre E, Bernini A, Morelli P, Pasquier J, Miroz JP, Abed-Maillard S, Ben-Hamouda N, Oddo M. Neuromonitoring of delirium with quantitative pupillometry in sedated mechanically ventilated critically ill patients. Crit Care. 2020 Feb 24;24(1):66. doi: 10.1186/s13054-020-2796-8.
PubMed 32093710Fiest KM, Soo A, Hee Lee C, Niven DJ, Ely EW, Doig CJ, Stelfox HT. Long-Term Outcomes in ICU Patients with Delirium: A Population-based Cohort Study. Am J Respir Crit Care Med. 2021 Aug 15;204(4):412-420. doi: 10.1164/rccm.202002-0320OC.
PubMed 33823122Gelinas C, Berube M, Chevrier A, Pun BT, Ely EW, Skrobik Y, Barr J. Delirium Assessment Tools for Use in Critically Ill Adults: A Psychometric Analysis and Systematic Review. Crit Care Nurse. 2018 Feb;38(1):38-49. doi: 10.4037/ccn2018633.
PubMed 29437077
Study at a glance
From the registry record, last updated by the sponsor on 3 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Constructing a Model of Pupillary Parameters in Predicting Delirium Among Critically Ill Patients in the Intensive Unit
- ClinicalTrials.gov ID
- NCT06187792
- Acronym / sponsor ID
- 202306099DINA
- Conditions
- ICU Delirium
- Keywords
- Delirium, Predicting delirium, Automated Infrared Pupillometry (AIP), Pupil Parameters, Intensive Care Delirium Screening Checklist (ICDSC)
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 4 Oct 2023 (actual)
- Primary completion
- Sep 2025 (estimated)
- Study completion
- Sep 2025 (estimated)
- First posted
- 3 Jan 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Delirium is commonly observed in critically ill patients in intensive care units (ICUs), imposing significant burdens on both patients and the healthcare system. Existing assessment tools have certain limitations. Studies have indicated a correlation between pupil parameters and neurological disorders including delirium. Automated Infrared Pupillometry, widely used in neurological disorders, is employed in this study to assess its accuracy and predictive power in evaluating delirium among critically ill patients. The aim is to investigate the accuracy and predictive capability of these parameters in assessing delirium, while identifying the optimal cut-off points. The research findings will contribute to enhancing early detection and prevention of delirium in ICU settings.
Detailed description
Delirium is an acute impairment of attention and cognitive function commonly observed in critically ill patients in intensive care units (ICUs). It leads to long-term cognitive impairment and increased risk of mortality for patients, while also causing distress for healthcare providers and family members, imposing substantial burdens on patients, families, and healthcare systems. Although there are assessment tools and predictive models available for detecting delirium, they have certain limitations.
Recent studies have indicated an association between delirium and the neurotransmitter acetylcholine (ACh). Acetylcholine not only regulates consciousness and cognitive wakefulness but also modulates pupil constriction and light reflex. In clinical practice, the Automated Infrared Pupillometry (AIP) has emerged as a robust tool for assessing acetylcholine, aiding in early delirium detection. However, more research is needed to clearly establish their relationship. This study aims to investigate the accuracy and predictive power of Automated Infrared Pupillometry in assessing delirium among critically ill patients. It involves collecting pupil parameters from critically ill patients and examining the correlation between delirium and pupil parameters using the Intensive Care Delirium Screening Checklist (ICDSC). The goal is to explore the accuracy and predictive capability of these parameters in evaluating delirium, identifying optimal cut-off points. The findings will contribute to enhancing early detection and prevention of delirium in intensive care settings.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients admitted to the intensive care unit from internal and surgical departments.
Key inclusion criteria (2 listed)
- Patients admitted to the intensive care unit from internal and surgical departments.
- Ages 18 and above.
Key exclusion criteria (6 listed)
- Acute brain injury (hemorrhagic, ischemic stroke).
- Other brain-related diseases (brain tumor, brain infection, oculomotor nerve paralysis, etc.).
- Ophthalmic diseases that prevent monitoring of pupil measurements.
- Patients with pre-hospital cardiac arrest or in-hospital cardiac arrest.
- Estimated stay in the intensive care unit not exceeding 72 hours.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Constructing a model based on pupillary parameters and delirium:
Time frame: 2023/09/1-2025/08/30
By analyzing the relationship between pupillary parameters and delirium, identifying the optimal cut-off point, and constructing a formula.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jan 2024.
- Results publication: PubMed 24465538Almeida IC, Soares M, Bozza FA, Shinotsuka CR, Bujokas R, Souza-Dantas VC, Ely EW, Salluh JI. The impact of acute brain dysfunction in the outcomes of mechanically ventilated cancer patients. PLoS One. 2014 Jan 22;9(1):e85332. doi: 10.1371/journal.pone.0085332. eCollection 2014.
- Results publication: PubMed 11430542Bergeron N, Dubois MJ, Dumont M, Dial S, Skrobik Y. Intensive Care Delirium Screening Checklist: evaluation of a new screening tool. Intensive Care Med. 2001 May;27(5):859-64. doi: 10.1007/s001340100909.
- Results publication: PubMed 27891446Bujang MA, Adnan TH. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. J Clin Diagn Res. 2016 Oct;10(10):YE01-YE06. doi: 10.7860/JCDR/2016/18129.8744. Epub 2016 Oct 1.
- Results publication: PubMed 17101747Carnahan RM, Lund BC, Perry PJ, Pollock BG, Culp KR. The Anticholinergic Drug Scale as a measure of drug-related anticholinergic burden: associations with serum anticholinergic activity. J Clin Pharmacol. 2006 Dec;46(12):1481-6. doi: 10.1177/0091270006292126.
- Results publication: PubMed 34991091Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychother Psychosom. 2022;91(1):8-35. doi: 10.1159/000521288. Epub 2022 Jan 6.
- Results publication: PubMed 3558716Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8.
- Results publication: PubMed 31031692Chougule PS, Najjar RP, Finkelstein MT, Kandiah N, Milea D. Light-Induced Pupillary Responses in Alzheimer's Disease. Front Neurol. 2019 Apr 12;10:360. doi: 10.3389/fneur.2019.00360. eCollection 2019.
- Results publication: PubMed 33172600de Vries L, Fouquaet I, Boets B, Naulaers G, Steyaert J. Autism spectrum disorder and pupillometry: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2021 Jan;120:479-508. doi: 10.1016/j.neubiorev.2020.09.032. Epub 2020 Oct 22.
- Results publication: PubMed 32308558Detroyer E, Timmermans A, Segers D, Meyfroidt G, Dubois J, Van Assche A, Joosten E, Milisen K. Psychometric properties of the intensive care delirium screening checklist when used by bedside nurses in clinical practice: a prospective descriptive study. BMC Nurs. 2020 Apr 10;19:21. doi: 10.1186/s12912-020-00415-z. eCollection 2020.
- Results publication: PubMed 30113379Devlin JW, Skrobik Y, Gelinas C, Needham DM, Slooter AJC, Pandharipande PP, Watson PL, Weinhouse GL, Nunnally ME, Rochwerg B, Balas MC, van den Boogaard M, Bosma KJ, Brummel NE, Chanques G, Denehy L, Drouot X, Fraser GL, Harris JE, Joffe AM, Kho ME, Kress JP, Lanphere JA, McKinley S, Neufeld KJ, Pisani MA, Payen JF, Pun BT, Puntillo KA, Riker RR, Robinson BRH, Shehabi Y, Szumita PM, Winkelman C, Centofanti JE, Price C, Nikayin S, Misak CJ, Flood PD, Kiedrowski K, Alhazzani W. Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. Crit Care Med. 2018 Sep;46(9):e825-e873. doi: 10.1097/CCM.0000000000003299.
- Results publication: PubMed 1607900Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992 Jun;45(6):613-9. doi: 10.1016/0895-4356(92)90133-8.
- Results publication: PubMed 11730446Ely EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, Truman B, Speroff T, Gautam S, Margolin R, Hart RP, Dittus R. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001 Dec 5;286(21):2703-10. doi: 10.1001/jama.286.21.2703.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of ICU Delirium, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 3 Jan 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Ming-Chen Chiang ((886) 02-23123456 ext. 262653, jane.c0203@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Constructing a model based on pupillary parameters and delirium: (time frame: 2023/09/1-2025/08/30). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06187792, which also lists 41 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Jan 2024.