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

Sponsor

Taipei Medical University (Other)

Overall Status

Recruiting

CT.gov ID

NCT04883879

Collaborator

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

Enrollment

Location

24.7

Anticipated Duration (Months)

3.2

Patients Per Site Per Month

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
End Stage Cancer

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

Study Type:

Observational

Anticipated Enrollment :

80 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward

Actual Study Start Date :

Dec 11, 2019

Anticipated Primary Completion Date :

Aug 31, 2021

Anticipated Study Completion Date :

Dec 31, 2021

Outcome Measures

Primary Outcome Measures

Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data [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.

Eligibility Criteria

Criteria

Ages Eligible for Study:

20 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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