Categories: Healthcare / Medical Research

CORE Model: Simple Blood Tests Predict Liver Disease Risk

CORE Model: Simple Blood Tests Predict Liver Disease Risk

What is the CORE model?

A new tool, the CORE model, is being developed to help primary care teams assess the risk of serious liver diseases. The aim is to identify patients who are at higher risk of conditions such as cirrhosis, liver cancer, or the potential need for a liver transplant within the next decade. The model relies on three simple blood tests that are commonly collected during routine visits, making it feasible to implement in everyday primary care from the start.

According to an affiliated researcher, the method is designed to be practical for general practitioners who are often the first point of contact for patients. The CORE model provides a ten-year risk estimate, allowing clinicians to tailor monitoring and intervention strategies before illness progresses unchecked.

“This is about diseases that are becoming more common and have poor prognosis if discovered late. Our method can predict the risk of illness within ten years and builds on three routine blood tests,” explains Rickard Strandberg, an attached researcher at the Department of Medicine in Huddinge, Karolinska Institutet, in a press release.

How three blood tests power the prediction

The core idea behind CORE is to convert three standard blood test results into a risk score for future liver disease. While the exact markers are described as routine and commonly measured during regular health checks, the overarching principle is that a compact data set can reveal meaningful differences in a patient’s risk profile. The model does not require advanced imaging or costly tests, which means it could be applied widely in primary care settings to flag individuals who deserve closer follow-up.

By focusing on a ten-year horizon, the CORE model supports proactive management rather than reactive treatment. Patients flagged as higher risk can be offered targeted lifestyle advice, vaccination where relevant, closer laboratory surveillance, and timely referrals to hepatology or transplant evaluation if needed.

Why this matters for primary care

Many liver diseases progress insidiously, and late detection is associated with poorer outcomes. In primary care, where most patients first seek help, a practical risk tool can help clinicians prioritize care and allocate resources more efficiently. Early lifestyle interventions—such as weight management, reducing alcohol intake, and maintaining metabolic health—can alter the disease trajectory for at-risk individuals.

Potential benefits for patients and clinicians

  • Early identification of patients at elevated risk, enabling timely intervention.
  • Personalized follow-up plans based on individual risk, potentially reducing unnecessary testing for low-risk patients.
  • Improved patient engagement through clear, data-backed risk communication.
  • Better use of specialist referrals by prioritizing those at higher risk.

Implementation considerations and limitations

While promising, the CORE model will need broad external validation across diverse populations to ensure reliability beyond the initial study group. Clinicians should consider potential biases and ensure that risk estimates are integrated with clinical context, including measurements of alcohol use, metabolic health, viral hepatitis status, and other liver-related factors. Privacy and data security are essential when integrating risk tools into electronic health records. Training will be important so that primary care teams can explain risk scores to patients and discuss appropriate next steps.

What happens next?

Researchers envision a phased rollout with rigorous validation, followed by pilot programs in primary care networks. If successful, the CORE model could become a standard part of routine health checks, guiding surveillance strategies and helping to catch liver disease earlier when interventions are most effective. As with any predictive tool, ongoing monitoring and updates will be essential as more data become available and the patient population evolves.