Metabolic liver disorder
Identifying hidden patients with a rare genetic metabolic liver-based disorder through dialysis landscape analysis
Client
A global biopharmaceutical company specialising in therapies for a rare inherited metabolic disorder originating in the liver and ultimately affecting kidney function.
The objective
Estimating the potential number and geographic distribution of patients aged 18-40 across Germany who may be affected by this rare inherited metabolic disorder, and determine how these patients are distributed across individual dialysis centres.
The client requested a particular focus on dialysis centres connected to the largest dialysis networks in Germany, while also analysing independent centres. The project also aimed to compile key information on centre characteristics, available treatments, and contact persons, while supporting the integration of these insights into the organisation’s existing CRM system.
The challenge
- Hidden patients; When the disease progresses to kidney failure, patients may enter dialysis programmes where they remain undiagnosed and are only treated for the consequences of the disease. In other cases, the underlying diagnosis has been established but is not clearly documented as the cause of kidney failure.
- Underlying diagnosis not captured in coding; Because the underlying diagnosis is often not documented, patients appear in healthcare data under broader diagnostic or procedural categories, making it challenging to identify them through diagnosis-based coding systems. Identifying these patients therefore requires analysing treatment situations and care pathways rather than relying solely on diagnostic codes.
- Care concentrated in specialised centres; Many patients are managed in a limited number of centres. Local hospitals or dialysis centres often do not diagnose the disease but rather treat symptoms. This makes understanding referral networks crucial for the identification of where patients might be diagnosed.
GDS’s Approach
To overcome the limitations of traditional healthcare coding data, GDS developed a structured analytical framework combining demographic analysis, healthcare infrastructure mapping, and detailed centre-level analysis.
- The dialysis population in Germany was analysed to estimate the proportion of patients aged 18–40, and combined with municipality-level population data to identify geographic concentrations of this age group across the country.
- Dialysis centres across Germany were mapped, including centres within the largest dialysis networks as well as independent centres, using a combination of client-provided data and additional research.
- Using postal code proximity, catchment areas around dialysis centres were modelled to allocate estimated patient numbers to the most likely treatment locations.
- These areas were then combined with population distribution data to identify centres with a higher potential concentration of relevant patients. Centre staff language skills were also considered, as the disease has a higher prevalence in some populations, including individuals of Arabic origin.
- Referral pathways between dialysis centres and university hospitals were analysed to identify where patients are most likely to be referred for diagnosis and specialist evaluation, enabling the identification of key centres and clinicians involved in these pathways.