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Artificial Intelligence to Search for Abnormalities in Ambulatory Cancer Patients

Artificial Intelligence to Search for Abnormalities in Ambulatory Cancer Patients

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05412420
Acronym
IASAAC
Enrollment
500
Registered
2022-06-09
Start date
2022-08-03
Completion date
2023-10-13
Last updated
2023-11-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Solid Tumor

Keywords

Electronic survey, Artificial intelligence, Quality of life, Patient-Reported Outcomes (PROs), Symptom management, eHealth, Machine learning

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.

Interventions

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

Sponsors

Institut de Cancérologie de Lorraine
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
PREVENTION
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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)

Design outcomes

Primary

MeasureTime frameDescription
Number of unscheduled medical consultations or re-hospitalisations3 monthsThe number of unscheduled medical consultations or re-hospitalisations will be assessed based on abnormalities identified through the patient's self-report of symptoms.

Secondary

MeasureTime frameDescription
Patient Satisfaction3 monthsPatient satisfaction will be assessed according to the Patient Assessment Chronic Illness Care Questionnaire (1= almost never : 5 = almost always)
Occurrence of toxicities3 monthsThe occurrence of toxicities will be evaluated according to the NCI-CTCAE v5.0 classification
Dose of treatments3 monthsThe total dose of treatments given will be calculated from the total dose of chemotherapy received per course and the collection of dose adjustments.
Adherence to oral treatment3 monthsAdherence to oral treatments will be assessed by the Morisky questionnaire
Anticipation of the preparation of injectable chemotherapy3 monthsAnticipation of injectable chemotherapy preparations will be evaluated based on the number of treatments ordered and actually administered, without the need to call the patient.
Predicting the occurrence of sarcopenia3 monthsThe occurrence of sarcopenia will be measured by the body mass/fat mass ratio using the CT scan performed for tumor evaluation
Handling of the digital tool3 monthsHandling of the digital tool will be assessed by the System Usability Scale ( 0 =Strongly disagree; 10=Strongly agree)

Countries

France

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026