Clinical trial NCT04883879: Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward
Official title: Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: End Stage Cancer
Keywords: Wearable Device
Actigraphy Device
Survival Prediction
Deep Learning
Artificial Intelligence
Hospice Care
Palliative Care
Lead sponsor: Taipei Medical University
Sponsor class: OTHER
Collaborators: name: Ministry of Science and Technology, Taiwan
name: Taipei Medical University Hospital
name: National Yang Ming Chiao Tung University
First posted: 2021-05-12
Enrollment: 80
Sex: ALL
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
timeFrame: 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
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei City
country: Taiwan
facility: Taipei Medical University
state: TW - Taiwan
Study officials:
affiliation: Taipei Medical University
name: Shabbir Syed-Abdul, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.