Volv, supported by Sanofi, and working with Optimum Patient Care, and collaborating with a specialist Consultant Clinician, is performing research to build algorithms in the UK which are aimed at finding ways to better identify people living with Fabry or Pompe disease.

This novel and innovative methodology, inTrigue, is highlighting ways in which we can be much more precise in detecting people living with either disease much earlier.

Are you a Fabry or Pompe specialist in the UK and want to know more, or collaborate? Please contact us.

 

 

inTrigue: helping people living with disease get better outcomes

In the sections below you will find an overview of how we create models to help predict which people might be at risk of disease, some of the current performance metrics, and also some background information on both Fabry and Pompe disease.

By using the inTrigue methodology in collaboration with Optimum Patient Care (OPC) in the UK and the OPC Research Database and supported by Sanofi, we are learning novel patterns of disease, we do this because using published medical criteria does not help find the patients that remain undiagnosed and in fact highlights many more patients that do not in fact have disease (false positives).

The inTrigue approach looks for people that cannot be found using those methods. inTrigue is designed to help clinicians detect the people who are living with a rare or difficult-to-diagnose disease and help uncover those people who are therefore otherwise unlikely to get a diagnosis.

Importantly, this is a research project that

• focusses on a limited population at first
• works with a population of clinicians that have signed up for the OPC quality improvement (QI) programme to improve the quality of care for patients in general practice
• aims to use the feedback from clinicians to improve the approach

 

This is a completely different level of performance that promises to reduce the time to a diagnosis, and also importantly, uncover the undiagnosed patients.

OPC quality improvement (QI) programme:
(https://www.primescholars.com/articles/strategies-that-promote-sustainability-in-quality-improvement-activities-for-chronic-disease-management-in-healthcare-se-100520.html)

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.

 

Introduction

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 Patient 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 inTrigue methodology 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

Model performance: Fabry disease in UK

Task

Use model learned via Algorithm SLSL to find undiagnosed FD patients in OPCRD EHR database GP-EHR-DB-UK (18M patients).

 

Evaluation procedure

Request that FD specialist practicing in UK review EHRs of top 50 candidate patients (candidates have predicted probabilities exceeding FD threshold FD).

 

Evaluation outcome

Results are very promising showing that out of 50 patients the top 25 have a precision of 88%, and when the total 50 patients are considered the precision remains high at 76% using the precision@k metric.

 

Model performance: Pompe disease in UK

Task

Use model learned via Algorithm SLSL to find undiagnosed PD patients in OPCRD EHR database GP-EHR-DB-UK (18M patients).

 

Evaluation procedure

Request that PD specialist practicing in UK review EHRs of top 30 candidate patients (candidates have predicted probabilities exceeding PD threshold xPD).

 

Evaluation outcome

The results for Pompe are also very promising showing that out of 30 patients the top 20 have a precision of 80%, and when the total 30 patients are considered the precision remains high at 73% using the precision@k metric.