EMUs: Enhanced Monitoring Using Sensors After Surgery

Patients can become critically unwell following surgical operations. Delay in recognition of this deterioration can result in patient harm and even death. Wearable wireless sensors that record patients vital signs such as heart rate could help improve recognition of patient deterioration. The goal of this observational study: Enhanced Monitoring Using Sensors After Surgery (EMUs) is to determine if data from wearable physiological monitors can be used for the early detection of postoperative deterioration, while being acceptable to patients and healthcare staff. The study participants and surgical inpatients undergoing open surgery. There are 3 objectives which each represent a stage of the study: 1. To perform usability testing of device with clinicians, nurses, and healthcare workers in non-clinical environment. 2. To determine baseline postoperative monitoring practice across our network and perform device usability testing in clinical environment. 3. To perform a shadow-mode cohort study with collection of time-stamped sensor clinical event data to determine relationships between physiological waveforms and patient deterioration. This registration focuses on the shadow-mode cohort study. Participants will wear wireless sensors on their chest and fingers, pre-, intra-, and post-operatively for up to 10 days. The sensors will record their vital signs such as heart rate, and oxygen levels. This will then be analysed, and used to aid the design of early detection algorithms that may be able to predict clinical illness or complications in this patient group. This is an observational study gathering real time data only. No changes in patient care will result, and in Stages 2 and 3 no sensor data will be available to clinical teams. This study will be performed in departments of general surgery in Benin, Ghana, Guatemala, India, Mexico, Nigeria, Rwanda, and the United Kingdom.

Patients who die after surgery frequently have treatable complications which are not identified in a timely manner. This is due to a failure to "recognize", "relay" or "react" to the deterioration of a patient in the postoperative period. This study aims to determine whether data from wearable physiological monitors can be used for the early detection of patient deterioration, while being acceptable to patients and healthcare staff. If found useful, future studies would be conducted to determine the performance and safety of such a device. This study has three objectives which will be addressed in three stages. STAGE 1. Usability testing of device with clinicians, nurses, and healthcare workers in non-clinical environment. STAGE 2. Baseline postoperative monitoring practice assessment and device usability testing in clinical environment. STAGE 3. Shadow-mode cohort study with collection of time-stamped sensor and clinical event data to determine relationships between physiological waveforms and patient deterioration. This registration focuses on the shadow-mode cohort study. This study will be performed in departments of general surgery in Benin, Ghana, Guatemala, India, Mexico, Nigeria, Rwanda, and the UK. In Stages 2 and 3, patients will have undergone major surgery and will be recovering in postoperative wards. This study can be performed using any suitable wearable device (it is device agnostic), as it seeks to gather generalisable information. In the first instance, the Sibel ANNE® One device will be used. ANNE® One is a wireless ICU-grade dual sensor system that provides real-time physiological monitoring. The system features two skin-mounted, bio-integrated sensors that provide continuous storage of vital sign measurements and physiological waveforms. This is an observational study gathering real time data only. No changes in patient care will result, and in Stages 2 and 3 no sensor data will be available to clinical teams. True equipoise exists: it is not clear whether these data are useful or how they should be used. Patients will be managed with standard care throughout. Wearable sensors have potential application in improving postoperative monitoring and consequently, the reduction of avoidable morbidity and mortality. Sensor data may be used to generate prediction algorithms providing a continuous readout of individual patient risk. Such algorithms could enhance healthcare workers' capacity to identify and intervene upon patients with early complications. However, few high-quality studies have yet been performed in this area. This study has approvals form the following ethical review boards: Edinburgh Medical School Research Ethics Committee West of Scotland Research Ethics Service (on behalf of NHS Health Research Authority) Health and Care Research Wales (on behalf of NHS Health Research Authority) Ghana Health Service Ethics Review Committee Comite de Investigacion, Hospital General San Juan de Dios, Guatemala, Guatemala Christian Medical College and Hospital, Institutional Ethics Committee, Ludhiana, India El Comite de Etica en Investigacion del Hospital General de Boca del Rio, Veracruz, Mexico Lagos University Teaching Hospital Health Research Ethics Committee, Lagos Nigeria Lagos State University Teaching Hospital Health Research Ethics Committee, Ikeja, Nigeria Obafemi Awolowo University Hospitals Teaching Complex, Ethics and Research Committee, Ife, Nigeria Ethics Committee of University Teaching Hospital of Kigali, Kigali, Rwanda