Acute Pre and Post Operative Pain
Conditions
Brief summary
Managing pain, which affects 20-50% of the population, is a major issue in daily clinical practice. Evaluation of pain intensity is essential to adapt treatment but as it mainly relies on self-report, this assessment is difficult or impossible in non-communicating patients. In these cases, pain can only be evaluated by medical staff by the observation of pain-related characteristics like facial expression of pain (FEP). However, recognition of FEP is subjective, time-consuming and subject to multiple biases frequently leading to underestimation of pain and consequently under-treatment. Some of these biases could be solved by the use of facial recognition technology, allowing objective, automated and time-saving pain assessment. DEF-I aims to address technical issues and achieve the development of facial expression recognition digital tool able to evaluate severe acute pain in clinical practice, with high validity and utility by improving the quality of the images to be analyzed, by studying larger samples of patients, data and images, in order to correlate more efficiently the pain intensity felt by a patient with the expression of his face. The main objective of this study is to verify whether it is possible to quantitatively correlate the intensity of acute postoperative pain felt by a patient with his facial expression. The secondary objective is to define a reliable computer algorithm that qualitatively correlates the type of acute postoperative pain experienced by a patient with his facial expression.
Interventions
OTHERvideo taken
Patients enrolled will be operated in our institution. Facial expression will be collected before and after surgery, and pain intensity will be collected at the same time. Facial expression will be analyzed using Facial Action Coding System (FACS) .This step will seek to confirm that the subset of Action Units (AUs) defined in previous studies is correlated with the presence and intensity of an acute pain or to identify a different original subset of facial AUs better correlated to pain intensity. . The developed algorithm (or model) here should be able to correlate facial AUs to pain intensity reported by the patients on the numerical rating scale. The developed model at this stage will be then validated on an additional sample of patients.
Sponsors
Centre Hospitalier Universitaire de Nice
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Any adult patient undergoing surgery in our institution during the study duration, * able to express pain intensity on NRS
Exclusion criteria
* patient to be operated on in the area of the face or eye * Patient with altered facial morphology related to a dressing, suture, wound, trauma or oedema on the face * patient not compliant or unable to clearly express pain
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| measure of pain | t0=before surgery | numerical rating scale (NRS) is a psychometric response scale which can be used in questionnaires. It is a measurement instrument for subjective characteristics or attitudes that cannot be directly measured. When responding to a NRS item, respondents specify their level of agreement to a statement by indicating a position along a continuous line between two end-points. The range is 0=no pain to 10=acute pain |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| facial expression | t0=before surgery | facial photographs extracted from a hort 10 seconds video |
Countries
France
Outcome results
None listed