Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward

May 6, 2021 updated by: Shabbir Syed Abdul, Taipei Medical University

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.

Study Overview

Status

Recruiting

Conditions

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 Type

Observational

Enrollment (Anticipated)

80

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Taiwan
    • TW - Taiwan
      • Taipei City, TW - Taiwan, Taiwan, 110
        • Recruiting
        • Taipei Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Taipei Medical University

Collaborators

Taipei Medical University Hospital
National Yang Ming University
Ministry of Science and Technology, Taiwan

Investigators

  • Principal Investigator: Shabbir Syed-Abdul, PhD, Taipei Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 11, 2019

Primary Completion (Anticipated)

August 31, 2021

Study Completion (Anticipated)

December 31, 2021

Study Registration Dates

First Submitted

April 28, 2021

First Submitted That Met QC Criteria

May 6, 2021

First Posted (Actual)

May 12, 2021

Study Record Updates

Last Update Posted (Actual)

May 12, 2021

Last Update Submitted That Met QC Criteria

May 6, 2021

Last Verified

May 1, 2021

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • N201910041

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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