Clinical trial NCT04531995: One Million Cancer Treatment Months
Official title: Development of an Artificial Intelligence-based Incident Prediction Algorithm to Improve Cancer Patient Care and Patient Safety
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Cancer
Keywords: Artificial Intelligence
Lead sponsor: Cankado GmbH
Sponsor class: INDUSTRY
First posted: 2020-08-31
Enrollment: 166000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Cancer Patients under systemic, anti-tumor or anti-hormonal therapy in adjuvant, neoadjuvant, post-neoadjuvant or palliative situations with prescribed CANKADO PRO-React Onco will be enrolled.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Health Status
timeFrame: 6 months
measure: Complaints/Symptoms
timeFrame: 6 months
measure: Presence or Absence of SAEs
timeFrame: 6 months
measure: Presence or Absence of dosis reductions
timeFrame: 6 months
measure: Presence or Absence of treatment interruptions
timeFrame: 6 months
measure: Presence or Absence of disease progression
timeFrame: 6 months
measure: Presence or Absence of disease regression
timeFrame: 6 months
measure: Presence or Absence of death
timeFrame: 6 months
Secondary outcomes:
measure: Cancer type
timeFrame: 6 months
measure: Patient Typology
timeFrame: 6 months
measure: Timepoints of patient documentation
timeFrame: 6 months
measure: Frequency of patient documentation
timeFrame: 6 months
Other outcomes:
Locations:
city: Moers
country: Germany
facility: Onkologische Praxis Moers
city: Mönchengladbach
country: Germany
facility: Ev. Krankenhaus Bethesda Praxis für gynäkologische Onkologie
city: Soest
country: Germany
facility: Schwerpunktpraxis für Hämatologie und Onkologie
city: Würzburg
country: Germany
facility: Hämatologisch-Onkologische Schwerpunktpraxis - Novum medicum
Study officials:
affiliation: Cankado GmbH
name: Timo Schinköthe, PhD
role: STUDY_DIRECTOR
References: