Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients

Abstract

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients. Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

Keywords: actigraphy; deep learning; long short-term memory networks; palliative care; performance status; prognostic accuracy; survival prediction; wearable technology.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Yang, Kuo, Huang, Lin, Malwade, Lu, Tsai, Syed-Abdul, Sun and Chiou.

Figures

Figure 1

The basic architecture (A) and data pre-processing and architectural flow (B) of the Long Short-Term Memory model. Symbol x and h represent the input and output values of the LSTM cell. Symbol c represents the value of the memory cell in each LSTM cell. Subscript t represents the time step.

Figure 2

The Receiver Operating Characteristic curve of Karnofsky Performance Status (blue) and Palliative Prognostic Index (green).

Figure 3

The representative activity pattern of patients with clinical outcomes of death (A) and discharged in stable condition (B). The red points on the graph indicated that the patient had taken off the device.

Figure 4

Confusion matrices of the preliminary prediction model. (A): Confusion matrix of the testing dataset, with normalization. (B): Confusion matrix of the testing dataset, without normalization.

Figure 5

Confusion matrices of the final prediction model. (A) Confusion matrix of the testing dataset, with normalization. (B) Confusion matrix of the testing dataset, without normalization.