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

End Stage Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04883879
• Other study ID #: N201910041
• Status: Recruiting
• Start date: December 11, 2019
• Est. completion date: December 31, 2021

Study information

• Verified date: May 2021
• Source: Taipei Medical University
• Contact: Shabbir Syed-Abdul, PhD
• Phone: 886 2-6638-2736
• Email: drshabbir[@]tmu.edu.tw
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 80
• Est. completion date: December 31, 2021
• Est. primary completion date: August 31, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years and older

Eligibility

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

Related Conditions & MeSH terms

• End Stage Cancer

Locations

Country	Name	City	State
Taiwan	Taipei Medical University	Taipei City	TW - Taiwan

Sponsors (4)

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

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	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