Pain ASsessment in CAncer Patients by Machine LEarning (PASCALE)
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorNational Cancer Institute, Naples
About this trial
In cancer patients, the integration between anticancer therapies and palliative care is of fundamental importance. In this context, telemedicine can improve the quality of life (QoL) of chronic patients through self-management and remote monitoring solutions. This approach can favor the effectiveness of the treatment and therapeutic adherence. Of note, telemedicine can also be applied to the management of cancer pain. In the advanced stages of cancer disease, pain is one of the most obvious and most disabling symptoms. Consequently, proper pain management has a significant impact on the QoL, the ability to withstand treatment, and the recovery of patients. On the other hand, given the complexity of cancer pain, the main obstacle to its proper management is the lack of adequate measurement methods. Although in recent years a great deal of effort has been made in the direction of automatic pain assessment, both concerning the creation of datasets and the development of classification algorithms, the literature is lacking regarding the automatic measurement of pain in the setting of cancer patients. Observation by experienced clinical staff and self-assessment by patients could be useful for obtaining the ground truth and, in turn, for training automatic pain recognition systems.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients aged > 18 years
Home care patients diagnosed with advanced cancer disease and life expectancy ≤ 1 year
Patients receiving treatment for cancer pain
Patients who have given their consent
Disqualifiers
Patients aged < 18 years
Willingness to sign the informed consent form (unable to read or write)
Cognitive deficit (e.g. Alzheimer disease or senile dementia)
Trial population
Home care patients aged \> 18 years, diagnosed with advanced oncological disease (life expectancy ≤ 1 year), suffering from cancer pain.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
No trial groups listed
Trial outcomes
Primary outcomes
1
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Heart rate (beats per minute, bpm)
Time frame
Up to 2 weeks2
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
Time frame
Whenever the patient has a worsening of his/her pain, up to 2 weeks3
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Non-invasive Blood Pressure (mmHg). The patient will use the device provided (BITalino).
Time frame
Whenever the patient has a worsening of his/her pain, up to 2 weeks4
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: The Galvanic Skin Response (GSR) refers to changes in sweat gland activity that are reflective of the intensity of the emotional state. The patient will use the device provided (BITalino).
Time frame
Whenever the patient has a worsening of his/her pain, up to 2 weeksSecondary outcomes
1
Patients' quality of life assessed by the EORTC QLQ-C30 questionnaire.
Quality of life (QoL) of patients according to European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. It is scored on a metric from 0 to 100. Higher scores mean better outcome.
Time frame
At the beginning and at the end of the observation, up to 2 weeksSponsors and contacts
Click on the lead sponsor to view all of their trials.
National Cancer Institute, Naples
Lead sponsor
Federico II University
Collaborator
Source ClinicalTrials.gov