One Million Cancer Treatment Months

Study Overview
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Brief Summary
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The OMCAT Register aims to provide learning databases in cancer comprising both PRO data using PRO-React and ground truth (outcome data verified by the physician during patient examinations). Intelligent learning and knowledge engineering procedures will utilize this PRO data to provide high-quality event prediction algorithms. The ground-truth data enables so-called supervised learning techniques of artificial intelligence, because predicted events can be verified with a high level of certainty from ground-truth data.

Detailed Description
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The next generation of PRO-React by CANKADO is designed to predict impending incident threats at an earlier stage than previously feasible and -- by more timely intervention -- help physicians to eliminate or mitigate the severity of an unfavourable event, reduce the required intensity of countermeasures, or otherwise reduce patient risks. A highly reliable identification of situations classified as low-risk by CANKADO could also enable a more focused utilization of resources as well as enhanced patient comfort and decreased stress, e.g., due to less frequent monitoring visits or reduced need for invasive diagnostics. The OMCAT Register aims to provide learning databases in cancer comprising both PRO data using PRO-React and ground truth (outcome data verified by the physician during patient examinations). Intelligent learning and knowledge engineering procedures will utilize this PRO data to provide high-quality event prediction algorithms. The ground-truth data enables so-called supervised learning techniques of artificial intelligence, because predicted events can be verified with a high level of certainty from ground-truth data. The PRO data of a patient provide what is known in engineering, physics, and statistics as time series of observations. The unique feature of PRO time series for applications in cancer is the very high sampling frequency (e.g., daily or better) compared to examinations, which generally occur at fixed, and much less frequent intervals. Prediction algorithms based on PRO data would thus be ideally suited to reduce the delay in detecting events, for example, by triggering physician appointments or indicating the need for more intensive medical diagnostics.

Official Title
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Development of an Artificial Intelligence-based Incident Prediction Algorithm to Improve Cancer Patient Care and Patient Safety

Conditions
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Cancer

Intervention / Treatment
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* Device: CANKADO PRO-React Onco


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Signed informed consent Age ≥ 18 years Diagnosed with cancer Prescribed CANKADO PRO-React Onco Exclusion Criteria: Lack of consent to study participation or lack of patient's ability to consent Enrolled in this trial within a further treatment

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Device: CANKADO PRO-React Onco|CANKADO PRO-React Onco is approved as class I medical device within the European Union (registration number DE /CA59 /371/2020-R/Hd) and is compliant with the FDA classification for Mobile Medical Devices (2015) Appendix B. The purpose of CANKADO PRO-React Onco is to be an automated digital support for patients to help them decide how urgent it is to contact the attending physician based on the symptoms they independently record in the system. It supports patients with cancer under systemic, anti-tumor or anti-hormonal therapy in adjuvant, neoadjuvant, post-neoadjuvant or palliative situations. It is unsuitable for patients undergoing radiotherapy, cell and gene therapy, surgical procedures or alternative healing methods.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Health Status | Using the EuroQol-visual analogue scale, abbreviated as EQ-VAS Scale, containing values between 100 (best imaginable health) and 0 (worst imaginable health), (answered by patients) | 6 months | 
| Complaints/Symptoms | Assessed using a question set aligned with the PRO-CTCAE and CTCAE (answered by patients) | 6 months | 
| Presence or Absence of SAEs | yes/no (answered by physician) | 6 months | 
| Presence or Absence of dosis reductions | yes/no (answered by physician) | 6 months | 
| Presence or Absence of treatment interruptions | yes/no (answered by physician) | 6 months | 
| Presence or Absence of disease progression | yes/no (answered by physician) | 6 months | 
| Presence or Absence of disease regression | yes/no (answered by physician) | 6 months | 
| Presence or Absence of death | yes/no (answered by physician) | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cancer type | according to ICD classification | 6 months | 
| Patient Typology | According to Bloem et al (PMID: 32771005) | 6 months | 
| Timepoints of patient documentation | The timepoints at which a patient uses the CANKADO System to document patient-reported outcomes are retrieved from the system including date and time | 6 months | 
| Frequency of patient documentation | The frequency at which a patient uses the CANKADO System to document patient-reported outcomes are calculated using the timepoints of patient documentation | 6 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence