Artificial Intelligence to Search for Abnormalities in Ambulatory Cancer Patients (IASAAC)

During treatment, cancer patients may experience side effects related to their disease but also to the different treatments they receive.

Currently, adverse effects and toxicities are well codified in the oncology community, notably via the NCI CTCAE criteria.

Unlike objective data such as a blood sample or a CTscan, a major bias in patient assessment is the subjective assessment of the physician or its team at a given time, which may not reflect the overall situation (for better or worse). Several studies had already highlighted the discrepancies between medical and patient data collection.

Self-assessment of symptoms is one way to overcome this bias. Moreover, there are now a large number of solutions that allow to perform these self-assessments at home.

Thanks to these tools, there are now two situations, the scheduled evaluation (before a chemotherapy treatment, or after a surgical procedure for instance) and the unscheduled situations, where it is the patient himself who can trigger an evaluation form.

These new evaluation methods also allow to take a quality of life approach. Patient-reported outcomes (PROs) is now a valid evidence-based assay to detect patient's symptoms and therefore provide helpful clinical information to healthcare providers.

The goal of this study is to go one step further than the previous PROs studies and evaluate the ability to train a machine learning algorithm to detect at-risk situations and lay the foundation for a viable solution for future prospective and randomized trials.

Study Overview

• Status: Completed
• Conditions: Solid Tumor
• Intervention / Treatment: Other: Patient Self-Reporting of Symptoms

• Study Type: Interventional
• Enrollment (Actual): 500
• Phase: Not Applicable

Contacts and Locations

Study Locations

• France
  • Vandœuvre-lès-Nancy, France, 54500
    • Institut de Cancerologie de Lorraine

Participation Criteria

Eligibility Criteria

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

Description

Inclusion Criteria:

• Follow-up for a solid tumor
• Chemotherapy treatment (oral and/or injectable) scheduled or in progress
• Life expectancy > 3 months
• Performance Status (PS) < 3
• Have an internet connection or assistance to answer questions throughout the study (nurse, family members, etc.)
• Patient having understood, signed and dated the consent form
• Patient affiliated to the social security system

Exclusion Criteria:

• Lack of means to answer the online questionnaires
• Patient in another therapeutic trial with an experimental molecule
• Patients and their families who cannot read or speak French
• Persons deprived of liberty or under guardianship (including curatorship)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Patient Self-Reporting of Symptoms

Other: Patient Self-Reporting of Symptoms; At baseline, clinical research staff will: verify the possibility of an internet connection at the patient's home; help the patient to fill in the 1st questionnaire (baseline questionnaire - frailty) Every two weeks for 3 months: patients complete questionnaires via app (toxicity; quality of life, medication adherence); responses are verified by clinical research staff; In case of severe symptoms, the clinician contacts the patient and arranges for management. At the end of the study : - patients answer a satisfaction questionnaire

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of unscheduled medical consultations or re-hospitalisations	The number of unscheduled medical consultations or re-hospitalisations will be assessed based on abnormalities identified through the patient's self-report of symptoms.	3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient Satisfaction	Patient satisfaction will be assessed according to the Patient Assessment Chronic Illness Care Questionnaire (1= almost never : 5 = almost always)	3 months
Occurrence of toxicities	The occurrence of toxicities will be evaluated according to the NCI-CTCAE v5.0 classification	3 months
Dose of treatments	The total dose of treatments given will be calculated from the total dose of chemotherapy received per course and the collection of dose adjustments.	3 months
Adherence to oral treatment	Adherence to oral treatments will be assessed by the Morisky questionnaire	3 months
Handling of the digital tool	Handling of the digital tool will be assessed by the System Usability Scale ( 0 =Strongly disagree; 10=Strongly agree)	3 months
Anticipation of the preparation of injectable chemotherapy	Anticipation of injectable chemotherapy preparations will be evaluated based on the number of treatments ordered and actually administered, without the need to call the patient.	3 months
Predicting the occurrence of sarcopenia	The occurrence of sarcopenia will be measured by the body mass/fat mass ratio using the CT scan performed for tumor evaluation	3 months

Collaborators and Investigators

• Sponsor: Institut de Cancérologie de Lorraine
• Investigators: Principal Investigator: LAMBERT AURELIEN, MD, Institut de Cancerologie de Lorraine

Study record dates

Study Major Dates

• Study Start (Actual): August 3, 2022
• Primary Completion (Actual): October 13, 2023
• Study Completion (Actual): October 13, 2023

Study Registration Dates

• First Submitted: May 30, 2022
• First Submitted That Met QC Criteria: June 7, 2022
• First Posted (Actual): June 9, 2022

Study Record Updates

• Last Update Posted (Actual): November 30, 2023
• Last Update Submitted That Met QC Criteria: November 29, 2023
• Last Verified: November 1, 2023

More Information

Terms related to this study

• Keywords: Quality of life; eHealth; Artificial intelligence; Symptom management; Machine learning; Electronic survey; Patient-Reported Outcomes (PROs)
• Additional Relevant MeSH Terms: Congenital Abnormalities
• Other Study ID Numbers: 2022-A01034-39

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

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