Image-based Remote Monitoring in Cardiac Surgery Patients

Overview

In this observational study, 100 patients admitted to the Cardiothoracic ward will be additionally monitored with video-cameras. The video-cameras will measure heart- and respiration rate continuously. Other features, such a cardiac arrhythmias and context analysis may be added as well. Data will be analysed retrospectively and will be compared with vital parameters measured with healthdot- and spot check measurements.

Rationale: In hospitals forty percent of unanticipated deaths occur in low-acuity departments. This alarming figure reflects the limited degree to which the cardiorespiratory status of patients is monitored in these departments, due to the obtrusiveness and expense of existing monitoring technologies, as well as the unpractically high clinical workload and costs that deployment of such technologies would entail. We have previously shown that an image-based monitoring technology reliably estimates heart rhythm and breathing rate under controlled conditions. Objective: This project explores image-based monitoring of the cardiorespiratory status of patients as an innovative unobtrusive method that could eventually aid to reduce workload for the staff and better predict (acute) deterioration or adverse events. The purpose of this study is to evaluate the feasibility, in terms of system fidelity and acceptance, of long-term image-based monitoring in a cardiothoracic ward setting. Secondary objectives are to evaluate the validity of image-based vital signs and circadian rhythms in comparison with reference devices, the discriminative ability of image-based monitoring in the prediction of clinical deterioration and effect of clinical deterioration detected with remote monitoring during hospital admission on long-term patient outcomes. Study design: Observational study Study population: 100 cardiac surgery patients Main study parameters/endpoints: Primary endpoints are (1) insight in signal loss due to artifacts and time 'out of scope' of patients, (2) storage and processing solutions to enable conversion of large amounts of image-based data into vital signs and (3) level of acceptance by healthcare staff and patients. Secondary endpoints are performance of image-based vital signs and circadian rhythms in comparison with reference devices and sensitivity and specificity for the prediction of deterioration based on the image-based data. Moreover, potential time gain and predictive value of each image-based parameter will be assessed. Another secondary endpoint is insight in the relation of occurrence of clinical deterioration detected with the image-based monitoring technology during admission and long-term patient outcomes.

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