The ICU-Recover Box

The ICU-Recover Box: Using Smart Technology For Monitoring Health Status After ICU Admission

Schouten, Janno (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Biomechanical Engineering)

Reinders, M.J.T. (mentor)
Arbous, M.S. (mentor)
Rintjap, J.D. (graduation committee)
Albayrak, A. (graduation committee)

Delft University of Technology
Leiden University Medical Center
Erasmus MC

Technical Medicine | Sensing and Stimulation

2023-03-27

Patients who are admitted to the Intensive Care Unit (ICU) are extremely ill and at high risk of organ failure and death. Being admitted to the ICU is known to cause long lasting physical, cognitive, and physiological symptoms, which is called Post-Intensive Care syndrome (PICS). To provide better management of PICS, early recognition and intervention are required. Two LUMC intensivists are exploring methods to increase ICU involvement in follow-up care for ICU patients, and thereby improve outcome related to PICS. Home monitoring may provide a method feasible for the ICU. To better assess feasibility, a pilot study, called "The ICU-Recover Box", was conducted. Additionally, since patients admitted to the ICU vary widely, it is essential to determine which patients are most likely to benefit from increased ICU involvement in follow-up care. An attempt was made to determine whether measurements recorded during ICU admission contain valuable information to predict ICU mortality. When this data contains valuable information, it may also be used to predict which patients should receive the ICU-Recover Box.

Fifteen patients who were admitted to the ICU received a weight scale, blood pressure monitor and smartwatch at hospital discharge. During the twelve week follow-up period, these patients would monitor their health status, which could be monitored remotely. Based on the preliminary results of six patients, a list of recommendations for large-scale clinical research and eventual implementation of the ICU-Recover Box into standard ICU practices, was formulated. The most important recommendations were to reduce the dependency on third parties and to substitute the home monitoring devices for devices that are medically CE-marked and have better ergonomics.

One recommendation was to assess which patients are most likely to benefit from home monitoring before implementing the ICU-Recover Box into standard ICU procedures. An attempt was made to predict ICU outcome using a machine learning approach. Data of 1364 patients, admitted to the LUMC ICU in 2017 and 2018, was collected and labeled "Survivor" or "ICU-Death", based on the ICU mortality. Features consisted of patient characteristics, hemodynamic status, and medication administrations. An Extreme Gradient Boosting (XGB) classifier was selected and optimized. Training and testing of this classifier was iterated 100 times. Mean AUC over all iterations was 0.809 with a standard deviation of 0.036. Mean sensitivity was 0.298 and mean specificity was 0.964, with standard deviations of 0.067 and 0.011 respectively. The mean corresponding F1 score was 0.851 with a standard deviation of 0.020. These results indicated that data recorded on the ICU seems to contain valuable information to predict ICU mortality. To identify whether this data could be used to determine which patients should receive the ICU-Recover Box, further research is required.

Intensive Care
Home Monitoring
Prediction Model
Machine Learning

http://resolver.tudelft.nl/uuid:42a32303-a2e6-47df-858f-78e6a57c162a

https://git.lumc.nl/jsschouten/icu-recover-box.git Link to used python scripts

Student theses

master thesis

© 2023 Janno Schouten