End Stage CancerNCT04883879
Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward
An observational study of End Stage Cancer, run by Taipei Medical University at 1 site in Taiwan.
Trial summary
The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- End Stage Cancer
- How many people
- 80 participants
- Where
- 1 siteTaiwan
- Study dates
- 11 Dec 2019to 31 Dec 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients aged 20 years or older who were admitted to the hospice care unit at Taipei Medical University Hospital with at least one diagnosis of end-stage solid tumor diseases.
Who may be able to join
- Participants aged 20 years or older admitted to the hospice care unit at Taipei Medical University Hospital
- Participants diagnosed with at least one end-stage solid tumor diseases
- Participants consented to receive hospice care
Who cannot join
- Participants aged below 20 years of age
- Participants diagnosed with leukemia or carcinoma of unknown primary
- Participants with evident signs of approaching death upon admission
- Participants with no vital signs upon admission
- Participants who continued to receive aggressive treatment despite admission to the hospice care unit
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
Taipei Medical University
Taipei, TW - Taiwan, 110
Shabbir Syed Abdul, +886-2-66382736 ext. 1501, drshabbir@tmu.edu.tw
About this study
Official title: Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward
The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device.
Read the detailed description
This study aims to develop a deep-learning-based survival prediction model that utilizes patient movement data upon admission to predict their clinical outcomes: either death or discharge with stable condition. Objective data of the patients are recorded by a wearable device and documented as parameters of physical activity, angle, and spin. In addition to objective data, the investigators also document patients' Karnofsky Performance Status assessed subjectively by clinical doctors. Finally, the investigators aim to explore and describe the applicability, potential, and limitations of the survival prediction model based on patient movement data as a simple prognostic parameter in clinical settings.
- Conditions
- End Stage Cancer
- Keywords
- Wearable Device, Actigraphy Device, Survival Prediction, Deep Learning, Artificial Intelligence, Hospice Care, Palliative Care
What is being measured
The primary outcome is the main question the study is designed to answer.
Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
The primary outcome of the study will be to evaluate whether the analysis of the movement data captured using actigraphy device can help to predict clinical outcomes either deceased or discharged alive from hospital, with a high specificity and sensitivity, using Artificial Intelligence based prediction modelling.
Measured: From date of admission to hospice ward until the date of first documented discharge from hospital or date of death from any cause, whichever came first, assessed up to 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 May 2021
- Study start11 Dec 2019
- Primary completion31 Aug 2021 (estimated)
- Study completion31 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for End Stage CancerStudies of the same condition, open and under way.
- More trials run by Taipei Medical UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Taipei Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Science and Technology, Taiwan, Taipei Medical University Hospital, National Yang Ming Chiao Tung University
- Responsible party
- Principal investigator, Shabbir Syed Abdul, Taipei Medical University
- Investigators
- Shabbir Syed-Abdul, PhD, Principal investigator, Taipei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04883879
- Sponsor's protocol ID
- N201910041
- Status last verified
- May 2021
- First submitted
- 28 Apr 2021
- Last updated
- 12 May 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Yang TY, Kuo PY, Huang Y, Lin HW, Malwade S, Lu LS, Tsai LW, Syed-Abdul S, Sun CW, Chiou JF. Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients. Front Public Health. 2021 Dec 9;9:730150. doi: 10.3389/fpubh.2021.730150. eCollection 2021.
PubMed 34957004
Study at a glance
From the registry record, last updated by the sponsor on 12 May 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward
- ClinicalTrials.gov ID
- NCT04883879
- Acronym / sponsor ID
- N201910041
- Conditions
- End Stage Cancer
- Keywords
- Wearable Device, Actigraphy Device, Survival Prediction, Deep Learning, Artificial Intelligence, Hospice Care, Palliative Care
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Taipei Medical UniversityUniversity, hospital or other
- Collaborators
- Ministry of Science and Technology, Taiwan, Taipei Medical University Hospital, National Yang Ming Chiao Tung University
- Study start
- 11 Dec 2019 (actual)
- Primary completion
- 31 Aug 2021 (estimated)
- Study completion
- 31 Dec 2021 (estimated)
- First posted
- 12 May 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device.
Detailed description
This study aims to develop a deep-learning-based survival prediction model that utilizes patient movement data upon admission to predict their clinical outcomes: either death or discharge with stable condition. Objective data of the patients are recorded by a wearable device and documented as parameters of physical activity, angle, and spin. In addition to objective data, the investigators also document patients' Karnofsky Performance Status assessed subjectively by clinical doctors. Finally, the investigators aim to explore and describe the applicability, potential, and limitations of the survival prediction model based on patient movement data as a simple prognostic parameter in clinical settings.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged 20 years or older who were admitted to the hospice care unit at Taipei Medical University Hospital with at least one diagnosis of end-stage solid tumor diseases.
Key inclusion criteria (3 listed)
- Participants aged 20 years or older admitted to the hospice care unit at Taipei Medical University Hospital
- Participants diagnosed with at least one end-stage solid tumor diseases
- Participants consented to receive hospice care
Key exclusion criteria (5 listed)
- Participants aged below 20 years of age
- Participants diagnosed with leukemia or carcinoma of unknown primary
- Participants with evident signs of approaching death upon admission
- Participants with no vital signs upon admission
- Participants who continued to receive aggressive treatment despite admission to the hospice care unit
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
Time frame: From date of admission to hospice ward until the date of first documented discharge from hospital or date of death from any cause, whichever came first, assessed up to 1 month
The primary outcome of the study will be to evaluate whether the analysis of the movement data captured using actigraphy device can help to predict clinical outcomes either deceased or discharged alive from hospital, with a high specificity and sensitivity, using Artificial Intelligence based prediction modelling.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 May 2021.
- Derived from this study: PubMed 34957004Yang TY, Kuo PY, Huang Y, Lin HW, Malwade S, Lu LS, Tsai LW, Syed-Abdul S, Sun CW, Chiou JF. Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients. Front Public Health. 2021 Dec 9;9:730150. doi: 10.3389/fpubh.2021.730150. eCollection 2021.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Shabbir Syed-Abdul, PhD principal investigator
Taipei Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of End Stage Cancer, run by Taipei Medical University at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 May 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Shabbir Syed-Abdul, PhD (886 2-6638-2736 ext. 1514, drshabbir@tmu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data (time frame: From date of admission to hospice ward until the date of first documented discharge from hospital or date of death from any cause, whichever came first, assessed up to 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04883879, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 May 2021.