Perioperative Smart Device Monitoring to Predict Complications

Post-Op Complication

This is a prospective, single-center, observational study designed to to quantify complications following non cardiac intermediate and high-risk surgery, and to identify digital biomarkers (collected pre, and post-operatively by a connected device) enabling early early identification of patients with post-operative complications. Patients will be invited to wear a smartdevice during the perioperative period, and will receive questionnaires about their their health status.

Worldwide, the number of surgical procedures is constantly increasing. Non cardiace intermediate and high-risk surgeries carry a high risk of complications, both because of the specific nature of the surgery and because of the various pathologies affecting the patient's functional reserves. As the population ages, it is estimated that the number of people requiring complex surgeries will continue to rise. To identify patients at high risk of developing intra- and post-operative complications, several tools have been described that are based on the physical characteristics (e.g. Revised Cardiac Risk Index, functional capacity assessment, or biological characteristics) of individuals. Once a patient is considered to be at risk, their intraoperative course should be adapted and individualised in order to reduce the rate of complications. Such preoperative management is rarely offered, either because of the resources allocated, the time available or incomplete risk stratification. In recent years, advances in the digitisation of medicine, particularly since the Covid-19 pandemic, have gradually been made available to patients. Technological advances now make it possible to collect accurate, continuous data on vital parameters, which can be analysed and exploited by the medical world, even before the patient is seen in consultation. At present, health data is collected in a standardised way preoperatively, incorporating routine examinations carried out by general practitioners or specialists in the event of specific problems known or identified at an early stage. On the other hand, the vast majority of measurements are episodic and isolated, carried out in situations that do not necessarily reflect the day-to-day lives of individuals (office-based medicine). There are now technologies that allow digital data to be collected on a daily basis, in the patient's environment (home-based medicine), and on a continuous basis over several days. The collection of digital biomarkers over a long period of time, non-invasively and remotely, enables an assessment to be made that reflects the day-to-day reality of an individual's physiology, in contrast to episodic measurements in an unfamiliar environment. With the availability of biomedical data collected on a continuous basis, combined with data based on sensors integrated into certain devices (e.g. accelerometers), relevant information on the particular lifestyle of each individual could make it possible to identify points of attention, possibly indicative of specific functional limitations. In this way, it would be possible to generate a digital clone of an individual, and to identify in greater detail the areas of reinforcement specifically required by each individual in the pre-operative phase. In addition, access to this type of data by healthcare professionals would provide an opportunity for better stratification of surgical risks and better preparation for surgery. This will make it possible to practise personalised medicine based on evidence. For high-risk surgical patients, preoperative, intraoperative and postoperative management could be optimised and personalised according to the data collected in the preoperative phase. For example, by proposing a prehabilitation programm. It would also allow better identification of the optimal surgical window. The aim of our study is to analyse the health data collected by a connected smartdevice from surgical patients in the pre-operative period, and to establish a possible link between these parameters and the occurrence of post-operative complications. We want to study the predictive potential of these variables. This connected preoperative monitoring could make it possible to identify individuals prone to complications early, non-invasively, in a personalised manner and in their usual environment. The collection of digital biomarkers specific to each patient will open the door to individualised, precision and predictive medicine, making it possible to offer a care pathway tailored to the needs of each patient prior to surgery.

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Switzerland