Pain ASsessment in CAncer Patients by Machine LEarning (PASCALE)

NCT04726228 | Recruiting

• 1 other identifier: 41/20 oss
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 2

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

Keywords

Pain assessment; Machine Learning; Automatic Pain Assessment; Palliative Care; Telemedicine; Cancer pain; Quality of Life

Outcome Measures

Primary Outcomes (9)

• 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)

  Up to 2 weeks

• 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).

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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).

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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).

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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.

  Pain features: A daily Pain Diary will be used. Type: how pain is felt (e.g., sharp, ache, shooting, tingling).

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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.

  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.

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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.

  Pain features: A daily Pain Diary will be used. Duration (minutes, hours, days).

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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.

  Pain features: A daily Pain Diary will be used. Precipitating factors.

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

• 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.

  Pain features: A daily Pain Diary will be used. Name and amount of drug used and time it was taken.

  Whenever the patient has a worsening of his/her pain, up to 2 weeks

Secondary Outcomes (1)

• Patients' quality of life assessed by the EORTC QLQ-C30 questionnaire.

  At the beginning and at the end of the observation, up to 2 weeks

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Home care patients aged \> 18 years, diagnosed with advanced oncological disease (life expectancy ≤ 1 year), suffering from cancer pain.

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• National Cancer Institute, Naples: lead
• Federico II University: collaborator

Study Sites (2)

National Cancer Institute of Naples

Naples, Campania, 80131, Italy

RECRUITING

A.O.U. Federico II

Napoli, Campania, Italy

NOT YET RECRUITING

Related Publications (10)

• Reis-Pina P, Lawlor PG, Barbosa A. Adequacy of cancer-related pain management and predictors of undertreatment at referral to a pain clinic. J Pain Res. 2017 Aug 31;10:2097-2107. doi: 10.2147/JPR.S139715. eCollection 2017.

  PMID: 28919809 BACKGROUND

• Adamse C, Dekker-Van Weering MG, van Etten-Jamaludin FS, Stuiver MM. The effectiveness of exercise-based telemedicine on pain, physical activity and quality of life in the treatment of chronic pain: A systematic review. J Telemed Telecare. 2018 Sep;24(8):511-526. doi: 10.1177/1357633X17716576. Epub 2017 Jul 11.

  PMID: 28696152 BACKGROUND

• Sirintrapun SJ, Lopez AM. Telemedicine in Cancer Care. Am Soc Clin Oncol Educ Book. 2018 May 23;38:540-545. doi: 10.1200/EDBK_200141.

  PMID: 30231354 BACKGROUND

• Aung MSH, Kaltwang S, Romera-Paredes B, Martinez B, Singh A, Cella M, Valstar M, Meng H, Kemp A, Shafizadeh M, Elkins AC, Kanakam N, de Rothschild A, Tyler N, Watson PJ, de C Williams AC, Pantic M, Bianchi-Berthouze N. The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset. IEEE Trans Affect Comput. 2016 Oct-Dec;7(4):435-451. doi: 10.1109/TAFFC.2015.2462830. Epub 2015 Jul 30.

  PMID: 30906508 BACKGROUND

• Gruss S, Geiger M, Werner P, Wilhelm O, Traue HC, Al-Hamadi A, Walter S. Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli. J Vis Exp. 2019 Apr 5;(146). doi: 10.3791/59057.

  PMID: 31009005 BACKGROUND

• Pfeifer AC, Uddin R, Schroder-Pfeifer P, Holl F, Swoboda W, Schiltenwolf M. Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J Clin Med. 2020 Nov 5;9(11):3557. doi: 10.3390/jcm9113557.

  PMID: 33167300 BACKGROUND

• Cuomo A, Bimonte S, Forte CA, Botti G, Cascella M. Multimodal approaches and tailored therapies for pain management: the trolley analgesic model. J Pain Res. 2019 Feb 19;12:711-714. doi: 10.2147/JPR.S178910. eCollection 2019.

  PMID: 30863143 BACKGROUND

• Rashidi P, Edwards DA, Tighe PJ. Primer on machine learning: utilization of large data set analyses to individualize pain management. Curr Opin Anaesthesiol. 2019 Oct;32(5):653-660. doi: 10.1097/ACO.0000000000000779.

  PMID: 31408024 BACKGROUND

• Dawes TR, Eden-Green B, Rosten C, Giles J, Governo R, Marcelline F, Nduka C. Objectively measuring pain using facial expression: is the technology finally ready? Pain Manag. 2018 Mar;8(2):105-113. doi: 10.2217/pmt-2017-0049. Epub 2018 Feb 22.

  PMID: 29468939 BACKGROUND

• Cascella M, Di Gennaro P, Crispo A, Vittori A, Petrucci E, Sciorio F, Marinangeli F, Ponsiglione AM, Romano M, Ovetta C, Ottaiano A, Sabbatino F, Perri F, Piazza O, Coluccia S. Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain. BMC Palliat Care. 2024 Aug 3;23(1):198. doi: 10.1186/s12904-024-01526-z.

  PMID: 39097739 DERIVED

MeSH Terms

Conditions

Neoplasms; Cancer Pain

Condition Hierarchy (Ancestors)

Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Marco Cascella, MD

  Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy

  PRINCIPAL INVESTIGATOR

• Arturo Cuomo, MD

  Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Marco Cascella, MD

CONTACT

+39 0815903508; m.cascella@istitutotumori.na.it

Arturo Cuomo, MD

CONTACT

+39 0815903508; a.cuomo@istitutotumori.na.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 29, 2020
• First Posted: January 27, 2021
• Study Start: June 21, 2021
• Primary Completion: February 23, 2022
• Study Completion: October 1, 2025
• Last Updated: July 29, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

IT (2)