Digital biomarkers can effectively support the monitoring of chronic diseases in outpatient care. Patients with metabolic liver disease are an increasingly important target group. Here, CE IVD-marked algorithms in general practitioner and specialist care can usefully complement fibrosis checks using ultrasound or elastography as a medical device.

The number of patients with metabolic liver disease is increasing, as a result of demographic change, but also as a result of the increasing prevalence of overweight, obesity and metabolic diseases. In particular, fatty liver disease (MASLD), which is associated with metabolic dysfunction, is on the rise, with a prevalence of 20 to 30 percent of the adult population assumed for Germany [1].

Conventional hepatic risk scoring has limits

The necessary increase in diagnostics associated with the increasing number of chronic liver diseases is increasingly posing challenges for medical practices and hospitals. It is important to detect MASLD, fatty liver hepatitis (MASH) associated with metabolic dysfunction, liver fibrosis and, last but not least, hepatocellular carcinomas (HCC) at an early stage or to estimate the severity of MASLD.

Serum biomarkers can be used to estimate the severity of liver fibrosis or for early detection of HCC. However, the specificity and/or sensitivity of individual laboratory biomarkers – such as alpha-fetoprotein (AFP) in the context of HCC early detection – are often not optimal, and the recommendations in the guidelines are correspondingly inconsistent [2] [3] [4].

If there is an indication for HCC surveillance in advanced fibrosis or cirrhosis, imaging is often used at present. However, this is accompanied by logistical challenges. The results are also difficult to objectify. And there is evidence that some MASLD patients have an increased risk of HCC even with fibrosis [1].

Clinically validated digital biomarkers get more out of laboratory diagnostics

This is where so-called digital biomarkers come in, which enrich serum biomarkers or biomarkers from in vitro diagnostics (IVD) in general with additional clinical information. With the help of specially developed, clinically validated algorithms marked as a medical device CE IVD, this enables more precise risk assessments than those that would be possible with the pure laboratory value or IVD biomarker.

In addition to adequate validation and sufficiently high sensitivity and specificity, the decisive factor for such algorithms or digital biomarkers is a convenient integration into everyday clinical practice. This is where the navify® Algorithm Suite from Roche Diagnostics comes in. It is a digital platform that provides centralized access to a wide variety of CE-marked IVD algorithms from the company and selected third-party providers [5]. For optimal user convenience, the platform is connected to existing laboratory information systems and clinical information systems via standardized interfaces such as HL7 or FHIR [6].

New algorithm addresses patients with MASLD

The Roche Liver Panel, which began with the innovative early detection of HCC using the GAAD® algorithm, will be gradually expanded to include digital biomarkers/algorithms such as the ADAPT algorithm for assessing the severity of liver fibrosis in MASLD patients. It links the age of the patients and diabetes status with platelet count and the biomarker ProC3. This allows for an assessment of the severity of liver fibrosis in patients with objective findings or signs of MASLD. We are also planning

• Algorithms for the MELD scores, which provide decision support in end-stage liver disease,
• the Fib-4 algorithm for assessing liver fibrosis,
• the ALBI algorithm for evaluating liver function and survival time in HCC,
• a Child-Pugh algorithm and
• a Lille model algorithm for predicting the probability of survival in severe alcoholic hepatitis.

Digital biomarkers based on CE IVD in clinical medicine are usually provided by laboratories. In principle, digital biomarkers are not limited to laboratory diagnostics.

[1] Younossi ZM et al. Hepatology 2016; 64(5):1577-86

[2] Vogel A et al. Ann Oncol. 2018; 29 (4): 238–255.

[3] Marrero JA et al. Hepatology. 2018; 68(2): 723–750

[4] Omata M et al. Hepatol Int. 2017; 11(4): 317–370.

[5] https://www.roche.de/diagnostik/produkte-loesungen/digitale-loesungen/navify-algorithm-suite; last accessed Jul 9, 2025

[6] https://www.roche.de/diagnostik/produkte-loesungen/digitale-loesungen/navify-algorithm-suite/leberpanel-gaad; last accessed Jul 9, 2025

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