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Evaluation of Seismocardiography(SCG) for Assessing Fitness and Predicting Outcomes in Oesophageal Cancer Surgery
Sponsor: Guy's and St Thomas' NHS Foundation Trust
Summary
Oesophageal cancer is a common cause of cancer death worldwide. Curative treatment involves chemotherapy and surgery but has significant risks. Therefore, patient selection and improving physical fitness to withstand such major treatment is important to reduce the risk of complications. Physical fitness is traditionally measured by a specialised exercise test called Cardiopulmonary exercise testing (CPET), which can take up to one hour and requires specialised staff and expensive equipment such as a graded exercise bike or treadmill. Seismofit is a small device (smaller than a smartphone) used to estimate fitness in patients in under three minutes while lying down at rest. It measures the vibrations generated by the heart and, together with patient height, weight, age, and gender, accurately estimates fitness using an algorithm developed in healthy patients. The device has never been tested in a large group of oesophageal cancer patients to see if it can be used to predict complications in patients undergoing cancer treatment. In this study, patients undergoing Oesophageal cancer treatment with chemotherapy or chemoradiotherapy and surgery will have Seismofit measurements at various points during their treatment to see if we can predict complications and hospital stay. Secondly, this study will also evaluate the accuracy of Seismofit compared to the gold standard CPET results in cancer patients.
Official title: Evaluation of Seismocardiography (SCG) as a Tool for Assessing Fitness and Predicting Outcomes in Oesophageal Cancer Surgery
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
164
Start Date
2025-09-15
Completion Date
2027-06
Last Updated
2025-09-08
Healthy Volunteers
No
Conditions
Interventions
Seismofit
Seismofit® is a 3 x 5 x 1.5 cm Class I medical device manufactured by Ventriject, which utilizes SCG principles and machine learning to estimate an individual's fitness. It is afixed to a patient's sternum with an adhesive patch. It measures the amplitude and timing of vibrations on the chest using accelerometers. The device then averages the data collected over 45 seconds to create the SCG. From the SCG, several maxima and minima (fiducial points) are identified, which correlate to the opening and closing of the mitral and aortic valves. Features of the SCG, including the timing, frequency, amplitude, and variability of these points, are then incorporated into an algorithm that also considers patient height, weight, age, and sex to calculate the VO2 peak. Data is transmitted via Bluetooth to an allocated smartphone app.