LiverPRO: a new screening test for fibrosis in steatotic liver disease

Background

Following the new nomenclature by the American Association for the Study of Liver Diseases (AASLD), patients with fatty liver disease now fall under the umbrella term of steatotic liver disease (SLD) (1). Patients with SLD are further classified into metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related liver disease (MetALD), and alcohol-associated liver disease (ALD). SLD affects 25–40% of adults worldwide. Among this large population of patients, less than 10% of patients develop liver-related complications (2); thus, identifying patients at risk is crucial step to direct resources to patients in need. Since hepatic fibrosis is a key predictor of liver-related outcomes in SLD, fibrosis assessment is the initial step in stratifying patient risk and guiding tailored care.

According to current guidelines, individuals with risk factors for SLD, such as type 2 diabetes, obesity, hyperlipidemia, or excessive alcohol use, should undergo screening for hepatic fibrosis (3). While liver biopsy is the gold standard for evaluating fibrosis, several non-invasive tests have emerged to reduce the risk and cost of fibrosis assessment. Imaging-based modalities such as transient elastography, magnetic resonance (MR) elastography, and shear wave ultrasound have demonstrated reliable performance to stage liver fibrosis. However, their use is constrained by limited access and higher cost in the primary care setting (4). Accordingly, AASLD recommends serum-based tests as the first modality of hepatic fibrosis assessment.

Many serum-based tests have been designed to assess hepatic fibrosis. These include, but are not limited to, Fibrosis-4 (FIB-4), NAFLD Fibrosis Score (NFS), and Enhanced Liver Fibrosis (ELF) test. While scoring systems such as FIB-4 and NFS use routine laboratory data that are commonly available, ELF is based on specific biomarkers not available in standard laboratory centers (5) (see Table 1). Patients who are at intermediate or high risk for hepatic fibrosis based on FIB-4 or NFS are recommended to undergo transient elastography. If, after elastography, the risk remains intermediate or high, liver biopsy is recommended (12,13).

The currently available serum-based tests demonstrate only modest performance, leading to missed cases and unnecessary work up in low-risk individuals. The number of patients with an intermediate to high-risk FIB-4 requiring transient elastography is even more pronounced in patients with type 2 diabetes and those greater than 70 years old (14). Given the overwhelming burden of SLD, there remains a need for more sensitive, cost-effective, non-invasive, and accessible tools that can be implemented at the primary care level.

Summary of findings

In the January 2025 issue of Lancet Gastroenterology and Hepatology, Lindvig et al. proposed a new scoring system, LiverPRO, to detect clinically significant liver fibrosis, with a particular focus on implementation in the primary care setting (6). They used 6 independent prospective cohorts in three countries to develop and validate the score. In both development and validation cohorts, a wide spectrum of patients with SLD, including MetALD and ALD, were present. This score was tested in both low-prevalence fibrosis cohorts [DECIDE, Scarred Liver Project (SLP), and Inter99] as well as high-prevalence fibrosis cohorts (development cohort and German SLD).

The LiverPRO scoring system was created based on available, routine data including age, aspartate aminotransferase (AST), alkaline phosphatase (AP), gamma-glutamyl transferase (GGT), international normalized ratio (INR), albumin, sodium, bilirubin, platelets, and cholesterol. LiverPRO employs a multivariable logistic regression that incorporates age along with 3 to 9 additional laboratory predictors, based on what is available in the electronic health record. The key advantage of LiverPRO is its flexibility to generate prediction models even when some variables are unavailable, with the overall platform comprised of 466 unique sub-models. In the DECIDE cohort, all patients had nine input variables available, which enabled the software to assess the accuracy of each sub-model using nine-variable calculations. Although detailed area under the curve (AUC) and NPV values for individual sub-models were not reported, the majority demonstrated an AUC >75% compared with models using the full set of variables.

In a cohort with high prevalence of histologically confirmed fibrosis, LiverPRO has good diagnostic performance to identify patients with >F2 and >F3 fibrosis with AUC 0.86 and 0.89, respectively. LiverPRO’s accuracy is modest in cohorts with low prevalence of fibrosis with AUC ranging between 0.7 to 0.86. When comparing the diagnostic performance of different scoring systems, LiverPRO had a higher AUC compared to FIB-4 and NFS in both low- and high-prevalence cohorts, whereas its accuracy is comparable to LiverRisk and ELF. The rule-out performance of LiverPRO is high with negative predictive value (NPV) ranging from 88–98%, confirming that it is a reliable screening test.

When comparing LiverPRO to FIB-4, NFS, LiverRisk, and ELF, the rule-out NPV is superior in high-prevalence cohorts; however, it is not consistently higher in low prevalence cohorts.

Critical commentary

LiverPRO is designed to assist primary care providers in screening patients at risk for liver fibrosis. The score demonstrates reliable performance in identifying patients with advanced fibrosis and ruling out individuals at low risk for hepatic fibrosis. It is a sophisticated, well-constructed scoring system based on readily available objective parameters. A key advantage of LiverPRO is its flexibility, allowing for fibrosis assessment even when some input variables are missing. These characteristics contribute to its low cost and broad applicability in primary care settings. As a result, LiverPRO offers advantages over scoring systems that require specialized testing, such as ELF.

While LiverPRO demonstrates reliable performance, its superiority over existing scoring tests is not consistent, particularly with respect to diagnostic features relevant for screening. When compared to FIB-4, NFS, LiverRisk, and ELF, LiverPRO showed overall better diagnostic performance with higher AUC. As LiverPRO is intended as an initial screening test, the rule-out sensitivity and rule-out NPV are key characteristics to accurately categorize patients as low risk. LiverPRO did not consistently outperform FIB-4 and NFS in ruling out patients across different cohorts. Given that both FIB-4 and NFS rely on routine laboratory tests, it remains uncertain whether LiverPRO offers sufficient incremental benefit to justify a change in clinical practice among primary care providers.

The methodology of this study is robust with a development cohort with histologic staging and multiple validation cohorts of different prevalence. Another strength of this study is the inclusion of patients across the full spectrum of SLD. By including individuals with moderate to heavy alcohol use, the study highlights LiverPRO’s performance in patients with both alcohol-related risk factors and metabolic dysfunction. Despite the study’s rigorous methodology, a key limitation lies in the lack of diversity within the study population—99% of the development cohort and 57–76% of the validation cohorts were Caucasian. To enhance generalizability, future validation of the scoring system should include more racially and ethnically diverse populations, particularly those from African, Asian, and Latin American regions.

Summary

The advent of complex models with flexible variable inputs can optimize fibrosis screening in patients with risk factors for SLD. LiverPRO is a flexible, low-cost, and accessible screening tool designed to identify patients with hepatic fibrosis in the primary care setting. Its performance in ruling out individuals at low risk of fibrosis is acceptable, with consistent results observed across both low- and high-prevalence cohorts. This study demonstrates that LiverPRO performs adequately in patients across the spectrum of SLD. Although it offers modest advantages over existing tests such as FIB-4, the incremental improvement in diagnostic accuracy may not be sufficient to warrant a change in clinical practice yet.

Future studies should focus on enhancing the accuracy of LiverPRO by incorporating variables that reflect the unique risk factors and clinical characteristics of individual communities. Given its adaptable framework, LiverPRO offers the potential for modification to align with population-specific needs. For example, while a high body mass index (BMI) is strongly associated with fibrosis progression in North America, this association appears weaker in many Asian populations (15,16). In contrast, in regions such as sub-Saharan Africa where hepatitis C remains prevalent, viral hepatitis plays a more significant role in fibrosis progression than metabolic risk factors (17). Like many other prognostic tools, LiverPRO does not include quantity of alcohol use into the algorithm. While capturing alcohol consumption objectively remains challenging, its inclusion may improve predictive accuracy in patients with SLD. Further validation in geographically and demographically diverse populations is essential. Lastly, studies should assess the practical integration of LiverPRO into electronic health records, with an emphasis on ensuring usability for patients and providers.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the Editorial Office, Translational Gastroenterology and Hepatology. The article has undergone external peer review.

Peer Review File: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-25-102/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tgh.amegroups.com/article/view/10.21037/tgh-25-102/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Kanwal F, Neuschwander-Tetri BA, Loomba R, et al. Metabolic dysfunction-associated steatotic liver disease: Update and impact of new nomenclature on the American Association for the Study of Liver Diseases practice guidance on nonalcoholic fatty liver disease. Hepatology 2024;79:1212-9. [Crossref] [PubMed]
2. Kalligeros M, Vassilopoulos A, Vassilopoulos S, et al. Prevalence of Steatotic Liver Disease (MASLD, MetALD, and ALD) in the United States: NHANES 2017-2020. Clin Gastroenterol Hepatol 2024;22:1330-1332.e4. [Crossref] [PubMed]
3. Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology 2023;77:1797-835. [Crossref] [PubMed]
4. Sterling RK, Duarte-Rojo A, Patel K, et al. AASLD Practice Guideline on imaging-based noninvasive liver disease assessment of hepatic fibrosis and steatosis. Hepatology 2025;81:672-724. [Crossref] [PubMed]
5. Tamaki N, Takaura K, Higuchi M, et al. Enhanced Liver Fibrosis Score for Diagnosing Liver Fibrosis in Chronic Hepatitis. Diagnostics (Basel) 2024;14:1317. [Crossref] [PubMed]
6. Lindvig KP, Thorhauge KH, Hansen JK, et al. Development, validation, and prognostic evaluation of LiverPRO for the prediction of significant liver fibrosis in primary care: a prospective cohort study. Lancet Gastroenterol Hepatol 2025;10:55-67. [Crossref] [PubMed]
7. Nsumbu JB, Makulo JR, Mutombo Tshiswaka T, et al. Performance of APRI and FIB-4 Scores Compared to FibroScan: A Cross-Sectional Study in a Black Sub-Saharan African Population. Hepat Med 2025;17:27-37. [Crossref] [PubMed]
8. Mathew JF, Panackel C, Jacob M, et al. A Validation Study of Non-invasive Scoring Systems for Assessing Severity of Hepatic Fibrosis in a Cohort of South Indian Patients With Non-alcoholic Fatty Liver Disease. J Clin Exp Hepatol 2024;14:101407. [Crossref] [PubMed]
9. Serra-Burriel M, Juanola A, Serra-Burriel F, et al. Development, validation, and prognostic evaluation of a risk score for long-term liver-related outcomes in the general population: a multicohort study. Lancet 2023;402:988-96. Erratum in: Lancet 2023;402:964. [Crossref] [PubMed]
10. Sripongpun P, Kim WR, Mannalithara A, et al. The steatosis-associated fibrosis estimator (SAFE) score: A tool to detect low-risk NAFLD in primary care. Hepatology 2023;77:256-67. [Crossref] [PubMed]
11. Hsu C, Caussy C, Imajo K, et al. Magnetic Resonance vs Transient Elastography Analysis of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Pooled Analysis of Individual Participants. Clin Gastroenterol Hepatol 2019;17:630-637.e8. [Crossref] [PubMed]
12. Castera L, Friedrich-Rust M, Loomba R. Noninvasive Assessment of Liver Disease in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology 2019;156:1264-1281.e4. [Crossref] [PubMed]
13. Wattacheril JJ, Abdelmalek MF, Lim JK, et al. AGA Clinical Practice Update on the Role of Noninvasive Biomarkers in the Evaluation and Management of Nonalcoholic Fatty Liver Disease: Expert Review. Gastroenterology 2023;165:1080-8. [Crossref] [PubMed]
14. Silverii GA, Profili F, Francesconi P, et al. Fibrosis-4 score for screening of metabolic dysfunction-associated steatotic liver disease (MASLD): Data from a population-based sample in Tuscany. Nutr Metab Cardiovasc Dis 2024;34:2405-8. [Crossref] [PubMed]
15. Fan JG, Kim SU, Wong VW. New trends on obesity and NAFLD in Asia. J Hepatol 2017;67:862-73. [Crossref] [PubMed]
16. Wong RJ, Ahmed A. Obesity and non-alcoholic fatty liver disease: Disparate associations among Asian populations. World J Hepatol 2014;6:263-73. [Crossref] [PubMed]
17. Hasanoglu I, Rivero-Juárez A, Özkaya Şahin G, et al. When Metabolic Dysfunction-Associated Steatotic Liver Disease Meets Viral Hepatitis. J Clin Med 2025;14:3422. [Crossref] [PubMed]