Clinical trial NCT05412420: Artificial Intelligence to Search for Abnormalities in Ambulatory Cancer Patients
Official title: Artificial Intelligence to Search for Abnormalities in Ambulatory Cancer Patients
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Solid Tumor
Keywords: Electronic survey
Artificial intelligence
Quality of life
Patient-Reported Outcomes (PROs)
Symptom management
eHealth
Machine learning
Lead sponsor: Institut de Cancérologie de Lorraine
Sponsor class: OTHER
First posted: 2022-06-09
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: PREVENTION

Brief summary: 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.


Eligibility criteria: 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)

Primary outcomes:
measure: Number of unscheduled medical consultations or re-hospitalisations
timeFrame: 3 months
Secondary outcomes:
measure: Patient Satisfaction
timeFrame: 3 months
measure: Occurrence of toxicities
timeFrame: 3 months
measure: Dose of treatments
timeFrame: 3 months
measure: Adherence to oral treatment
timeFrame: 3 months
measure: Handling of the digital tool
timeFrame: 3 months
measure: Anticipation of the preparation of injectable chemotherapy
timeFrame: 3 months
measure: Predicting the occurrence of sarcopenia
timeFrame: 3 months
Other outcomes:
Locations:
city: Vandœuvre-lès-Nancy
country: France
facility: Institut de Cancerologie de Lorraine
Study officials:
affiliation: Institut de Cancérologie de Lorraine
name: LAMBERT AURELIEN, MD
role: PRINCIPAL_INVESTIGATOR
References: