Periodic Reporting for period 1 - IPSIBiM (Improved Patient Safety through Intensive Biosignal Monitoring)
Reporting period: 2015-09-01 to 2017-08-31
The other main objective of the project included multidisciplinary scientific training in vital sign monitoring, machine learning algorithms and clinical decision support to enable Dr. Vania Almeida to expand her expertise in the area of sensors, electronics and data acquisition to other relevant areas of Biomedical Engineering. In addition, the project included training in project management, financial, planning experience, communication skills and technical writing. The Fellow also collaborated actively during a clinical secondment with The Royal Orthopaedic Hospital, which allowed her to gain experience in communication and effective collaboration with users outside the academic domain.
The successful implementation of this project helped Dr. Vania Almeida to secure a position as Lecturer in Electronics and Medical Instrumentation at Middlesex University.
The Fellow collaborated with The Royal Orthopaedic Hospital during a clinical secondment that provided the essential clinical training, namely training in the assessment, measurement and monitoring of vital signs and mandatory training sessions (including infection control, information governance and data protection, dignified treatment of patients, health and safety).
Dr Almeida also participated in dissemination activities through Aston’s Marie Curie Fellowship blog (http://www.mariecurie.astonblogs.co.uk/category/vania-almeida/) where she wrote posts about her own experience as Marie Curie Fellow, outreach activities and its impact on general public, the importance of a well-balanced training program to achieve a position of professional maturity, and how to prepare a career development program.
The Fellow’s training needs were addressed through a personal Career Development Plan aimed at diversifying and complementing her research skills and knowledge, providing her with a range of special technical training in the area of machine learning techniques and in the assessment, measurement and monitoring of vital signs in medical environment.
A wireless system was implemented in a clinical ward at The Royal Orthopaedics Hospital NHS Trust in collaboration with Sensium Healthcare Ltd. In the first year, all the clinical approvals were obtained, from the NHS Research Ethics Committee (REC)/ Health Research Authority (HRA), and NIHR Clinical Research Network. A data management plan was also designed to recruit two groups of subjects: knee or hip replacement patients (revision patients) and clinical staff on the wards that care for adult patients who undergone joint or spinal surgery. The recruitment ended on 30 September and 202 participants were recruited.
The data collected in this study included vital signs, information on the patients’ care, wellbeing and pain levels, and the experience of patients with wireless vital sign monitoring. A second study, a hospital-wide survey was carried out with patients and staff from joint and spinal surgery wards. These tasks were added to the original plan aiming to improve the clinical impact of this study, contributing to a better understanding of the patients’ needs, and to provide additional information about patient feedback to the clinical and industrial partners.
Dr. Vania Almeida studied potential indicators of physiological deterioration by the analysis of vital-sign tipping points. The first results were achieved using a dataset comprising heart rate (HR) time series. Different indicators were considered: 1) generic early warning indicators used in ecosystems analysis (autocorrelation at-1-lag (ACF1), standard deviation (SD), skewness, kurtosis and heteroskedasticity) and 2) entropy analysis (kernel entropy and multi scale entropy). The results were published in the IEEE EMBC 2016 in Orlando, Florida, USA.
In another study the objective was to quantify the potential bias of switching models in the presence of non-stationarities, when the inputs are spectral, symbolic and entropy indices. To distinguish stationary from non-stationary periods, a test was used to verify the stability of the mean and variance over short periods. The results were published in the IEEE EMBC 2017 in Jeju, South Korea.
Dr. Almeida continues to collaborate with Aston University as a Visiting Lecturer, and she is currently preparing further papers to be submitted over the next year focusing on the data collected during the project.
This study recruited 202 participants, more than five times the estimated number in the submitted proposal (35 subjects). These numbers demonstrate the clinical interest of this project. The study also collected data that we believe could be used to improve the clinical impact of the study, contributing to a better understanding of the patients’ needs, as well as to provide additional information about patient feedback for the clinical and industrial partners, which was not initially considered in the project proposal.
From the clinical point of view, the technology and results can provide pilot data for a large randomised controlled trial to investigate the effectiveness and cost effectiveness of the new technology in orthopaedic patients after major surgery. We continue to work on the data analysis, and hope be able to demonstrate health benefits to patients, including a reduction in serious patient safety incidents.