Evidence generation plan
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3 Approach to evidence generation
3.1 Evidence gaps and ongoing studies
Table 1 summarises the evidence gaps and ongoing studies that might address them. Information about evidence status is derived from the external assessment group's report. More information on the studies in the table can be found in the supporting documents.
The REINFORCE trial and MASTERY cohort study are collecting data that may address many of the evidence gaps (see section 3.3).
Evidence gap | Da Vinci SP | Da Vinci X and Xi | Hugo RAS system | Senhance Surgical System | Versius |
|---|---|---|---|---|---|
Understanding the learning curve associated with RAS introduction | Limited evidence Ongoing study | No evidence Ongoing study | No evidence | Limited evidence | Limited evidence |
Resource use | Limited evidence Ongoing study | No evidence Ongoing study | Limited evidence | No evidence | Limited evidence Ongoing study |
Clinical impact of RAS | Limited evidence Ongoing study | Limited evidence Ongoing study | Limited evidence | Limited evidence | Limited evidence Ongoing study |
Surgeon opinion of RAS | Limited evidence | No evidence | No evidence | No evidence | Limited evidence |
3.2 Data sources
There are several data collections that could potentially support evidence generation. NICE's real-world evidence framework provides detailed guidance on assessing the suitability of a real-world data source to answer a specific research question.
For uses of RAS on tumours, the National Disease Registration Service (NDRS) provides the Cancer Outcomes and Services Dataset (COSD) as part of the National Cancer Registration and Analysis Service. COSD is a complied dataset that includes all patients diagnosed with or having cancer treatment in or funded by the NHS in England. It includes much of the data needed to address the evidence gaps, such as individual patient outcome data items, length of hospital stay, and the surgery type done for each surgical procedure (for example, conventional minimally invasive or open surgery).
COSD can be linked to other datasets such as NHS Digital's Hospital Episode Statistics (HES) dataset which contains details about admissions, outpatient appointments and historical A&E attendances at NHS hospitals in England. This combined dataset can be used to estimate resource use.
The quality and coverage of real-world data collections are of key importance when used in generating evidence. Active monitoring and follow up through a central coordinating point is an effective and viable approach of ensuring good-quality data with broad coverage.
3.3 Evidence collection plan
The REINFORCE and MASTERY studies may deliver comparative evidence addressing the evidence gaps for resource use, clinical impact of RAStechnologies and the learning curve associated with implementation of RAS technologies.
Technologies that are not involved in these studies may need to generate equivalent comparative evidence. Approaches to delivering this are described in the NICE's real-world evidence framework.
REINFORCE trial
The REINFORCE trial is investigating the impact of robot-assisted surgery as it is introduced to or scaled up across NHS hospitals. The primary outcome measures include outcomes at:
The trial has recruited patients across urology, colorectal, gynaecology and upper gastrointestinal soft tissue specialties from 16 NHS trusts in England and Wales. This study recruited sites that were procuring new RAS equipment and sites that were already using RAS but were considering a change in specialty or procedure using RAS methods. The study aims to recruit 2,560 participants and has an estimated completion date of December 2025.
MASTERY study
The MASTERY cohort study is using robotic systems to measure progress in surgical training and quality of surgeries. The primary outcomes measure is surgical complication rate at day 30 and other outcome measures are total operating time per surgery, time taken by surgeon to complete each surgery 'cardinal step', number of robotic-assisted surgeries carried out by surgeon before enrolment in study, number of patients with complete or incomplete surgical resection, number of patients with readmission at day 30, and number of patients with adverse events reported at day 30. The study has recruited patients who have agreed to RAS for colorectal tumours, lung tumours, gynaecological tumours, ear, nose and throat tumours, hepatobiliary tumours and prostate tumours. The study has recruited 500 participants across 15 NHS trusts in England and Scotland.
Real-world observational study
Additional evidence generation is necessary to supplement the studies and collect information that shows the evidence from them is generalisable to other implementations of the technologies.
An approach to delivering this is through a real-world observational study in which applicable outcomes are collected for patients having RAS. For example, post-surgical complications and length of stay. This data could be used to strengthen evidence for the learning curve, resource use and clinical impact evidence gaps. This study could be done both in centres that are already using RAS and those that are making new implementations.
Where RAS is being used in people with cancer, the Cancer Outcomes and Services Dataset (COSD) could be a suitable data source to support this study. Retrospective data from COSD could also be used to deliver long-term outcome information.
Qualitative survey
A qualitative survey may provide qualitative evidence on healthcare professional preferences. This study should include questions about healthcare professional experience using the technologies, their acceptability, and ideally, the ergonomic impact of these technologies to the surgeons.
Data collection should follow a predefined protocol and quality assurance processes should be put in place to ensure the integrity and consistency of data collection. See NICE's real-world evidence framework, which provides guidance on the planning, conduct and reporting of real-world cohort studies to assess comparative treatment effects.
3.4 Data to be collected
Study criteria
At recruitment, eligibility criteria for suitability of using RAS technologies and inclusion in the real-world study should be reported, along with a detailed description of the RAS technologies and the specific versions.
Patient information and outcomes
Conversion rates to open or standard minimally invasive surgery from RAS
Length of hospital stay
Clavien–Dindo score as a measure of surgical complications
Peri- and postoperative complication frequency (ideally up to 12 months)
Ideally, EQ-5D-3L or EORTC QLQ-C30 at baseline and over follow up (ideally up to 12 months)
Return to normal activities
Applicable condition-specific outcomes
Revision surgery (ideally for a minimum of 12 months follow up).
Surgeon information and outcomes
Number of RAS surgeries performed
Any prior experience of using any RAS technology before recruitment into the study.
Organisational information and outcomes
Rate of minimally invasive surgery compared with open surgery after robot-assisted surgery is introduced
Number of procedures done at the hospital
Hospital capacity and surgical waiting lists
Patient readmission rates, ideally up to 12 months
Number of RAS surgeries performed at the hospital
Information on healthcare resource use and hospitalisation costs related to RAS, including:
technology acquisition cost and how it has been paid for
technology set up costs including staff training and time costs
technology maintenance and consumable (for example, surgical blades) costs.
Safety monitoring outcomes
Any adverse events arising from using RAS technologies.
3.5 Evidence generation period
The evidence generation period should be 3 years (during which a minimum of 12 months of follow-up data will be collected). This will be enough time to implement the evidence generation study, collect the necessary information and analyse the collected data.
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