End Stage Cancer
Conditions
Keywords
Wearable Device, Actigraphy Device, Survival Prediction, Deep Learning, Artificial Intelligence, Hospice Care, Palliative Care
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.
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.
Interventions
None listed
Sponsors
Ministry of Science and Technology, Taiwan
Taipei Medical University Hospital
National Yang Ming Chiao Tung University
Taipei Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
Taiwan
Contacts
Primary ContactShabbir Syed-Abdul, PhD
Outcome results
None listed