Using Wearable Device to Improve Quality of Palliative Care

NCT05054907 | Unknown

• 1 other identifier: 202105097RIND
• Study Type: observational
• Target: 75
• Locations: 1 country
• Sites: 2

Brief Summary

This study is going to use wearable devices and smartphones to collect physical data from terminal patients and build a survival predicting model for terminal patients with machine learning. Investigators hypothesize that continuous physical data monitoring could offer a hint to better predictability in end-of-life care.

Conditions

Terminal Cancer; End Stage Cancer

Keywords

palliative; cancer; survival prediction; artificial intelligence; wearable devices

Outcome Measures

Primary Outcomes (2)

• Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment

  Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patients' death or survival within specific time range. The primary outcome is to evaluate the Area Under the Receiver Operating Characteristic curve (AUC - ROC) of the machine-learning model in predicting patients' survival.

  From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Other clinical assessments are performed every week. Death or survival is recorded at the time the case closed.

• Area Under the Receiver Operating Characteristic curve (AUC-ROC) of machine-learning model to predict unexpected medical needs using wearable device parameters and clinical assessment

  Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patient's unexpected medical needs (which is defined as emergency department visit or unplanned admission to hospital). The primary outcome is to evaluate Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model in predicting unexpected medical needs.

  From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Other clinical assessments are performed every week. Events are recorded upon happening or afterwards.

Secondary Outcomes (3)

• Correlation between symptoms and wearable device parameters

  From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Symptoms assessed every week.

• Correlation between Australia-modified Karnofsky Performance Status (AKPS) and wearable device parameters

  From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Functional status assessed every week.

• Correlation between palliative care phase and wearable device parameters

  From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Palliative care phase assessed every week.

Other Outcomes (10)

• Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Performance Scale (PPS)

  From date of enrollment until the date of death, or assessed up to 26 weeks. PPS are assessed every week. Death or survival is recorded at the time the case closed.

• Comparison of AUC-ROC in survival prediction between machine learning model and Glasgow Prognostic Score (GPS)

  GPS assessed retrospectively if data available. Death or survival is recorded at the time the case closed.

• Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Index (PPI)

  From date of enrollment until the date of death, or assessed up to 26 weeks. PPI are assessed every week. Death or survival is recorded at the time the case closed.

Study Arms (1)

Wearable devices + Smartphone

The only arm in the study.

Eligibility Criteria

• Age: 20 Years - 105 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Terminal cancer patients are receiving palliative care in outpatient clinic, home care or ward admission and will receive regular follow-up in the future.

You may qualify if:

• Age: 20 years old or older
• Clinical diagnosis: cancer in terminal stage.

You may not qualify if:

• \- Cannot cooperate with use of wearable devices or smartphones.

Sponsors & Collaborators

• National Taiwan University Hospital: lead
• National Taiwan University: collaborator

Study Sites (2)

National Taiwan University Hospital

Taipei, 100, Taiwan

COMPLETED

National Taiwan University, Cancer Center

Taipei, 106, Taiwan

RECRUITING

Related Publications (2)

• Pavic M, Klaas V, Theile G, Kraft J, Troster G, Blum D, Guckenberger M. Mobile Health Technologies for Continuous Monitoring of Cancer Patients in Palliative Care Aiming to Predict Health Status Deterioration: A Feasibility Study. J Palliat Med. 2020 May;23(5):678-685. doi: 10.1089/jpm.2019.0342. Epub 2019 Dec 23.

  PMID: 31873052 BACKGROUND

• Liu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res. 2023 Aug 18;25:e47366. doi: 10.2196/47366.

  PMID: 37594793 DERIVED

MeSH Terms

Conditions

Neoplasms

Study Officials

• Jaw-Shiun Tsai, MDPHD

  National Taiwan University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jen-Hsuan Liu, MD

CONTACT

+886922068868; b98401001@ntu.edu.tw

Jaw-Shiun Tsai, MDPHD

CONTACT

jawshiun@ntu.edu.tw

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 30, 2021
• First Posted: September 23, 2021
• Study Start: September 23, 2021
• Primary Completion: December 31, 2022
• Study Completion: April 30, 2023
• Last Updated: November 9, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

TW (2)