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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patient Self-Reporting of Symptoms | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Patient Self-Reporting of Symptoms | Other | At baseline, clinical research staff will:
Every two weeks for 3 months:
At the end of the study : - patients answer a satisfaction questionnaire |
| 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 |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| LAMBERT AURELIEN, MD | Institut de Cancérologie de Lorraine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Institut de Cancerologie de Lorraine | Vandœuvre-lès-Nancy | 54500 | France |
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|
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 |