Pain ASsessment in CAncer Patients by Machine LEarning (PASCALE) (PASCALE)

Home-Based Telemedicine for Automatic Pain Assessment in Cancer Patients: Dataset Creation and Development of Machine Learning Algorithms

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.

Study Overview

• Status: Recruiting
• Conditions: Quality of Life; Oncology; Cancer Pain

Detailed Description

For the entire duration of the study, patients will remain under the care of the Early Palliative Care and Simultaneous Care Outpatient team of the Istituto Nazionale Tumori, Fondazione Pascale, at home. Pain and other symptoms will be managed according to the good clinical practice and patients will receive assistance in agreement to the routine medical care.

The following devices will be used:

1. Software
2. Instrumentation
3. Clinical Assessment Tools: European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30), Daily Pain Diary, 0-10 numeric rating scale (NRS).

The project will be divided into three main Work Packages (WPs), dedicated respectively to the creation of the IT infrastructure to support acquisitions (WP1), the patient data collection campaign (WP2), and the development of machine learning algorithms for automatic pain recognition (WP3). The application of the devices and verification of correct functioning will be carried out at the patient's home by the IT staff involved in the study.

WP1 - The system consists of three main components: the server, with the attached database, the application for mobile devices, also responsible for managing data acquisition from physiological signal acquisition devices, and the desktop application, used by the clinical staff to monitor the progress of data collection.

The mobile application will have the role of interfacing directly with the patient and acquiring biometric data from wearable devices. Specifically, the following signals will be acquired: heart rate, body temperature, non-invasive blood pressure, and galvanic skin response (GSR). The heart rate will be obtained through a wearable device (Garmin Vivosmart 4) while the body temperature, the non-invasive blood pressure, and the GSR will be acquired by an external device (a BITalino platform).To further validate the accuracy of the algorithm that will deal with pain detection, patients will also be given a QoL questionnaire (EORTC QLQ-C30).

In order to acquire the ground truth of the data, the patient will be asked to provide feedback on the level of pain, both at certain intervals of time during the day, and in case of acute pain episodes. This feedback can be based on NRS and multimedia strategies (e.g., videos). Patients will fill out a daily pain diary.

WP2 - The campaign will include a preliminary acquisition phase aimed at testing the IT infrastructure. For obtaining an adequate inter-subject and intra-subject variability, it will be necessary to enroll at least 40 patients, acquiring data for 10-14 days. Thus, the data collection campaign will be conducted for about 6 months. Each subject will use the mobile application and sensors for 2 weeks. Data will be acquired using simultaneously data collection bundles (application, sensors, and any mobile device). Upon enrolment and at the end, EORTC QLQ-C30 will be administered.

WP3 - The objective is the development of algorithms able to predict the level of pain perceived by the patient. Having a considerable amount of labelled data available, the system will learn from the examples.

• Study Type: Observational
• Enrollment (Estimated): 40

Contacts and Locations

• Study Contact: Name: Marco Cascella, MD; Phone Number: +39 0815903508; Email: m.cascella@istitutotumori.na.it
• Study Contact Backup: Name: Arturo Cuomo, MD; Phone Number: +39 0815903508; Email: a.cuomo@istitutotumori.na.it

Study Locations

• Italy
  • Campania
    • Naples, Campania, Italy, 80131
      • Recruiting
      • National Cancer Institute of Naples
      • Contact: Marco Cascella, MD; Phone Number: +39 0815903508; Email: m.cascella@istitutotumori.na.it
      • Contact: Arturo Cuomo, MD; Phone Number: +39 0815903508; Email: a.cuomo@istitutotumori.na.it
    • Napoli, Campania, Italy
      • Not yet recruiting
      • A.O.U. Federico II
      • Contact: Francesco Cutugno, PhD; Phone Number: +39 081679191; Email: francesco.cutugno@unina.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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)

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	At the beginning and at the end of the observation, up to 2 weeks

Collaborators and Investigators

• Sponsor: National Cancer Institute, Naples
• Collaborators: Federico II University
• Investigators: Principal Investigator: 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

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): June 21, 2021
• Primary Completion (Actual): February 23, 2022
• Study Completion (Estimated): June 1, 2025

Study Registration Dates

• First Submitted: December 29, 2020
• First Submitted That Met QC Criteria: January 21, 2021
• First Posted (Actual): January 27, 2021

Study Record Updates

• Last Update Posted (Actual): March 26, 2024
• Last Update Submitted That Met QC Criteria: March 25, 2024
• Last Verified: March 1, 2023

More Information

Terms related to this study

• Keywords: Telemedicine; Quality of Life; Palliative Care; Cancer pain; Machine Learning; Pain assessment; Automatic Pain Assessment
• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Cancer Pain
• Other Study ID Numbers: 41/20 oss

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Study Protocol will be shared after its publication in a peer reviewed journal.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No