NCT07159711

Evaluation of Seismocardiography(SCG) for Assessing Fitness and Predicting Outcomes in Oesophageal Cancer Surgery

Evaluation of Seismocardiography (SCG) as a Tool for Assessing Fitness and Predicting Outcomes in Oesophageal Cancer Surgery

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 164 (estimated)
• Sponsor: Guy's and St Thomas' NHS Foundation Trust · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed description

Oesophageal cancer is the 8th most common cause of cancer and the 6th leading cause of cancer-related deaths worldwide(West et al., 2016; Liu et al., 2023). Surgery with curative intent has been associated with significant morbidity and mortality despite recent advancements in anaesthesia and surgical techniques(Wu et al., 2014). Good patient selection with optimisation of cardiorespiratory fitness (CRF) is essential to improve surgical outcomes(West et al., 2014; Ozova et al., 2022). This is particularly important given that most oesophageal cancer patients undergo neo-adjuvant treatment (e.g. chemotherapy), which is known to have a significant adverse effect on patient fitness before surgery (mean reduction of 12.1% in VO2Peak) (Jack et al., 2014). A lower baseline level of fitness has also been shown to be associated with morbidity and mortality in patients undergoing treatment with curative intent (Hennis et al., 2011; West et al., 2024). Prehabilitation is now considered the standard of care in oesophageal cancer surgery, with societal guidance recently recommending a baseline fitness assessment to tailor the intervention as well as a means of monitoring response (Walker et al., 2024). Cardiopulmonary Exercise Testing (CPET) is considered the gold standard in measuring the CRF of an individual by obtaining the maximal volume of oxygen consumed (VO2Peak) in samples of expired gas during a graded effort cycle ergometer test (Krogh et al., 2020; Levett et al., 2018a). However, CPET is a resource-intensive, costly, time-consuming test (requiring up to 60 minutes) and creates a significant time burden on patients with already busy treatment schedules. In a UK survey of prehabilitation practice, 50% of Upper Gastrointestinal (UGI) units used CPET at baseline, with only 80% of these (40% total) using CPET for response assessment (Barman et al. 2024 in revision for publication Annals RCS Eng). Seismofit® is a device that accurately estimates the VO2Peak using machine learning algorithms incorporating seismocardiography (SCG) and patient parameters such as height, weight, age, and gender. SCG is a process by which vibrations generated by the heart during its regular physiological cycles are characterised and mapped for changes in morphology, frequency, intensity and character(Sørensen et al., 2018). Key advantages of Seismofit® include its inter-test reliability, rapid availability of results (i.e., estimation of fitness in under 3 minutes in an outpatient setting), and accurate estimation of fitness in a resting patient (Hansen et al., 2023a). The accuracy of Seismofit® in comparison to CPET-measured CRF has previously been demonstrated with a Mean Absolute Percentage Error (MAPE) of 12.3% in healthy adult populations(Hansen et al., 2023b). However, there is minimal data on cancer patients, and no studies have correlated VO2Peak generated by Seismofit® to clinical outcomes. Given the potential benefits of Seismofit® over CPET, this study will investigate its validity in a clinical setting across four major Oesophageal cancer resection centres (Guy's and St Thomas NHS Foundation Trust (GSTT), University Hospitals Southampton (UHS), Royal Marsden NHS Foundation Trust (RM) and Royal Surrey NHS Foundation Trust (RS). This will be the first study to evaluate the use of Seismofit® in a large volume of oesophageal cancer patients. With correlation to clinical outcomes, this study will serve as the basis for future research into the use of Seismofit® in a clinical setting.

Conditions

• Oesophageal Cancer
• Oesophageal Adenocarcinoma
• Prehabilitation
• Oesophagectomy
• Neoadjuvant Chemotherapy

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	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.

Timeline

• Start date: 2025-09-15
• Primary completion: 2027-06-01
• Completion: 2027-06-01
• First posted: 2025-09-08
• Last updated: 2025-09-08