Brief Summary

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.

Conditions

• Oncology
• Cancer Pain
• Quality of Life

Primary Outcome Measures

• 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. (Up to 2 weeks)
  Clinical data: Heart rate (beats per minute, bpm)
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Clinical data: Non-invasive Blood Pressure (mmHg). The patient will use the device provided (BITalino).
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  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).
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Pain features: A daily Pain Diary will be used. Type: how pain is felt (e.g., sharp, ache, shooting, tingling).
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Pain features: A daily Pain Diary will be used. Degree: 0-10 numeric rating scale (NRS) where 0 is no pain and 10 is the worst pain imaginable.
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Pain features: A daily Pain Diary will be used. Duration (minutes, hours, days).
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Pain features: A daily Pain Diary will be used. Precipitating factors.
• 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. (Whenever the patient has a worsening of his/her pain, up to 2 weeks)
  Pain features: A daily Pain Diary will be used. Name and amount of drug used and time it was taken.

Countries

Italy

Locations and Investigators

• National Cancer Institute of Naples, Naples, Italy (Recruiting)
  • Marco Cascella, MD
  • Arturo Cuomo, MD
• A.O.U. Federico II, Napoli, Italy (Not yet recruiting)
  • Francesco Cutugno, PhD

Study Officials

• Marco Cascella, MD (Principal Investigator) — Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy
• Arturo Cuomo, MD (Principal Investigator) — Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy