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

NCT04883879 | Unknown

• 1 other identifier: N201910041
• Study Type: observational
• Target: 80
• Locations: 1 country
• Sites: 1

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.

Conditions

End Stage Cancer

Keywords

Wearable Device; Actigraphy Device; Survival Prediction; Deep Learning; Artificial Intelligence; Hospice Care; Palliative Care

Outcome Measures

Primary Outcomes (1)

• 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

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Taipei Medical University: lead
• Ministry of Science and Technology, Taiwan: collaborator
• Taipei Medical University Hospital: collaborator
• National Yang Ming Chiao Tung University: collaborator

Study Sites (1)

Taipei Medical University

Taipei, TW - Taiwan, 110, Taiwan

RECRUITING

Related Publications (1)

• 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.

  PMID: 34957004 DERIVED

Study Officials

• Shabbir Syed-Abdul, PhD

  Taipei Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Shabbir Syed-Abdul, PhD

CONTACT

886 2-6638-2736; drshabbir@tmu.edu.tw

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: April 28, 2021
• First Posted: May 12, 2021
• Study Start: December 11, 2019
• Primary Completion: August 31, 2021
• Study Completion: December 31, 2021
• Last Updated: May 12, 2021

Record last verified: 2021-05

Locations

TW (1)