Ignite Creation Date:
2024-05-06 @ 8:06 PM
Last Modification Date:
2024-10-26 @ 3:20 PM
Study NCT ID:
NCT06256666
Status:
RECRUITING
Last Update Posted:
2024-03-26
First Post:
2024-02-03
Brief Title:
Objective Measurement of Pain in Individuals With Cognitive Deterioration Utilizing Electroencephalography
Sponsor:
Azienda USL Toscana Nord Ovest
Organization:
Azienda USL Toscana Nord Ovest
Study Overview
Official Title:
Exploring Objective Pain Assessment in Individuals With Cognitive Deterioration Electroencephalographic Markers and Machine Learning Analysis
Status:
RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This research addresses the challenge of pain assessment in individuals with cognitive deterioration CD a common aspect of aging and various neurological conditions Due to difficulties in self-reporting especially in severe cases accurate pain diagnosis and management are hindered The study explores the use of electroencephalography EEG and machine learning techniques to objectively measure pain in CD patients Utilizing a BIS device the research aims to identify EEG markers associated with pain comparing them with an objective PANAID scale The study targets patients in surgical departments providing valuable insights into enhancing pain assessment for those unable to express pain through traditional subjective scales
Detailed Description:
Cognitive deterioration CD may develop during the aging process and is a characteristic feature of various neurological and neurodegenerative diseases Individuals with CD often face significant prolonged and intricate healthcare needs frequently involving pain However effectively communicating pain characteristics becomes a challenge for individuals with CD presenting a substantial obstacle to the accurate diagnosis and treatment of pain CD affects various patient groups although current data predominantly focus on dementia patients revealing pain prevalence ranging from 40 to over 80 depending on the context
Due to its subjective nature pain assessment relies predominantly on self-reporting Individuals with CD often encounter difficulties in verbally expressing their pain due to limited intellectual and communicative abilities Even when verbal skills are present they may not guarantee valid pain reports Consequently pain assessment poses challenges for individuals with CD particularly those with severe CD elevating the risk of delayed or inaccurate pain diagnoses Self-assessments or patient-reported measures are considered the gold standard in clinical pain assessment
For individuals with compromised cognitive or linguistic abilities or when self-assessment is impractical or invalid behavioral measures can be employed These tools capture facial expressions vocalizations or body movements as indicators of pain from an external observers perspective such as nurses physicians or healthcare providers However these parameters rely entirely on others being attentive to non-verbal pain signals presenting a challenge as trained observers must reliably distinguish pain from various other facial and bodily expressions
Developing objective measures reflecting the presence of painful states appears crucial to improving pain management in various clinical situations In this regard electroencephalographic EEG activation has been described as a cortical correlate of pain processing Encouraging results have led researchers to consider increased gamma band activity as a potential indicator of pain presence applicable in clinical conditions
This study employs a commonly used BIS device in hospitals to objectively measure pain levels in subjects with cognitive deterioration Quantitative electroencephalography qEEG data will be obtained and machine learning techniques will be applied for data analysis Thirty patients experiencing cognitive decline admitted to the general surgery and orthopedics departments at Volterra Hospital for significant surgical interventions will be enrolled in the study Concurrently pain will be assessed using an objective PANAID scale and if applicable the NRS The study aims to identify electroencephalographic markers of pain through machine learning techniques and establish correlations with pain levels obtained from the use of both subjective and objective scales
Due to its subjective nature pain assessment relies predominantly on self-reporting Individuals with CD often encounter difficulties in verbally expressing their pain due to limited intellectual and communicative abilities Even when verbal skills are present they may not guarantee valid pain reports Consequently pain assessment poses challenges for individuals with CD particularly those with severe CD elevating the risk of delayed or inaccurate pain diagnoses Self-assessments or patient-reported measures are considered the gold standard in clinical pain assessment
For individuals with compromised cognitive or linguistic abilities or when self-assessment is impractical or invalid behavioral measures can be employed These tools capture facial expressions vocalizations or body movements as indicators of pain from an external observers perspective such as nurses physicians or healthcare providers However these parameters rely entirely on others being attentive to non-verbal pain signals presenting a challenge as trained observers must reliably distinguish pain from various other facial and bodily expressions
Developing objective measures reflecting the presence of painful states appears crucial to improving pain management in various clinical situations In this regard electroencephalographic EEG activation has been described as a cortical correlate of pain processing Encouraging results have led researchers to consider increased gamma band activity as a potential indicator of pain presence applicable in clinical conditions
This study employs a commonly used BIS device in hospitals to objectively measure pain levels in subjects with cognitive deterioration Quantitative electroencephalography qEEG data will be obtained and machine learning techniques will be applied for data analysis Thirty patients experiencing cognitive decline admitted to the general surgery and orthopedics departments at Volterra Hospital for significant surgical interventions will be enrolled in the study Concurrently pain will be assessed using an objective PANAID scale and if applicable the NRS The study aims to identify electroencephalographic markers of pain through machine learning techniques and establish correlations with pain levels obtained from the use of both subjective and objective scales
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None