Predicting Clinical Deteriorations using Wearable Sensors Peter.Charlton@kcl.ac.uk Peter H Charlton 12, T Bonnici 1, L Tarassenko 2, PJ Watkinson 3, DA Clifton 2, R Beale 1, J Alastruey 1 1 Faculty of Life Sciences and Medicine, King’s College London, UK; 2 Department of Engineering Science, University of Oxford, UK; 3 Oxford University Hospitals NHS Foundation Trust, UK Clinical Problem Aims Monitoring breathing rate unobtrusively Predicting deteriorations using wearable sensors Next Steps Hospital patients are at risk of clinical deteriorations such as heart attacks and strokes. Early signs of deteriorations can be identified from clinical measurements such as heart rate and blood pressure. These are measured by hand every 4-6 hours. It may be possible to monitor patients continuously using wearable sensors. Breathing rate is a key marker of deteriorations, but it is difficult to measure electronically. 1. To develop a technique for monitoring breathing rate unobtrusively using wearable sensors. 2. To assess whether wearable sensors can be used to reliably predict deteriorations using this technique. Wearable sensors gave advanced warning of physiological changes hours before routine practice Wearable sensors predicted deteriorations with similar accuracy to routine practice AUROCs: 0.79 0.78 AUROCs: 0.69 0.73 Many wearable sensors monitor the heart using the ECG signal, which is influenced by breathing. Errors (in breaths per minute) 0 -10 10 -5 5 By hand Intensive care monitor A novel technique was developed to estimate breathing rate from the ECG. Laboratory tests showed that it was at least as precise as existing methods. New technique cardiac arrest normal range The novel technique was able to identify elevated breathing rates (BRs) in the hours preceding a cardiac arrest, when routine practice did not. ECG (BW) Time [s] 0 2 4 6 8 10 ECG (AM) ECG Acknowledgments This work was supported by the UK EPSRC (Grant EP/H019944/1), the NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London, the NIHR Oxford Biomedical Research Centre Programme, the Oxford and King's College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grant no. WT88877/Z/09/Z and grant no. WT088641/Z/09/Z, a Royal Academy of Engineering (RAEng) Research Fellowship awarded to DAC, and an EPSRC Challenge Award to DAC. The views expressed are those of the authors and not necessarily those of the EPSRC, NHS, NIHR, Department of Health, Wellcome Trust, or RAEng. Smart watches routinely measure a signal which is influenced by both the heart and blood vessels. 0 1 2 3 4 5 6 7 8 9 10 Time [s] PPG The signal is indicative of cardiovascular health, providing opportunity to predict deteriorations in the wider population. Atrial fibrillation is an arrhythmia affecting 25% of patients after heart surgery. Here, the wearable sensor identified an increase in heart rate, indicating atrial fibrillation, prompting treatment. Here, the wearable sensor detected a drop in oxygen levels hours before routine practice, prompting earlier initiation of oxygen therapy to maintain healthy oxygen levels. A system was designed to predict deteriorations from wearable sensor data. Its performance was assessed in a clinical trial of 184 patients. Its predictions were of similar accuracy to those made in routine practice. relaxed stressed Detecting serious deteriorations Detecting minor deteriorations Source: Charlton, Peter H. (2016, July). The Processes and Benefits of Sharing Clinical Data. Zenodo. http://doi.org/10.5281/zenodo.166546 Source: Peter H Charlton. (2016). Wireless Wrist Pulse Oximeter Photo. Zenodo. http://doi.org/10.5281/zenodo.569814 Adapted from:: Charlton, P.H. et al., 2017. Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiological Measurement, 38(5), pp.669–90. DOI: 10.1088/1361-6