Volv OPC sanofi

Volv, Sanofi and OPC: collaborating for people living with disease

Volv, supported by Sanofi, and leveraging the data from OPC in the UK, is creating a unique collaboration that does not stop here.


The first phase of this project was to collaborate to build new types of models for two rare diseases: Fabry and Pompe. To do this, we focussed on primary health care records, i.e. the records that general practitioners use.

Both diseases are difficult to diagnose for primary care clinicians, and as a result, remain underdiagnosed. For Pompe disease in the UK, it is estimated that 50% of people with the disease are not being diagnosed, leading to a longer delay until they eventually do get diagnosed. This data is managed by Optimum Patientt Care, which provides de-identified data, of around 8.5 million patient records, for research purposes. Data security and protection are paramount. This means that the data remains anonymous and secure during the disease model development process.

The data complies with:

  • GDPR/ DPA 2018 compliant
  • Secured EHR data extraction
  • Data is de-identified (no PID)
  • Data is pseudonymised SHA256
  • Secure data encryption AES256
  • Secure data transfer via HSCN
  • NHS DSP Toolkit (ref: 8HR5)
  • Non-identifiable data is contributed to OPCCRD for ethically approved research
  • NHS IHRA REC (ref: 20/EM/0148)

Phase 1: Learn an algorithm/model for the diseases and validate with expert clinicians

The first phase of the inTriguemethodology involved an iterative process of finding a way to determine what makes patients with Fabry and Pompe disease stand out from all other patients. We used a combination of data science (or AI) approaches to get to a list of patients that plausibly have a disease.

Within this phase, crucially and differentiatingly, we also needed to validate whether the approach has worked by checking the inTrigue results with an expert clinician. We did this with a consultant in a specialist Fabry and Pompe department in a UK teaching hospital. The results of this evaluation can be seen in the results section.

Once the clinician's validation was complete, we then take those inputs and optimise the algorithm, which will again boost the performance. Once this is done, we are ready to move to Phase 2.

Phase 2: Clinical follow-up on plausible patients, more accurately and earlier

In this second phase, the algorithm is applied to the data, and clinicians are asked if they want to participate in the model deployment programme. The clinicians need to give their consent to be part of this quality improvement programme. Several QI programmes are already in place and if they agree, they can then check to see if any of the patients in their practice are at risk of these diseases. This is done through the remote installation of reports in the GP system. We can then monitor to see if there is an improvement in terms of quality of clinical care.

More results on this aspect of the deployment of the models will be published at a later stage, but the optimisation steps post clinician validation shows significant improvement on these results presented here.

Later phases

After this programme, consideration is being given to deploying the models more widely by embedding them into GP systems nationwide.

Initial metrics on model performance

Fabry Pre clinical results UK
Pompe Pre clinical results UK

Refinement of models post clinical validation step