Pediatric Fibrosis Score–Continuous (pFIB-c)

Compute pFIB-c: logistic probability of advanced liver fibrosis (F3–F4) in pediatric MASLD from AST, ALT, platelets (×10⁹/L), and BMI z-score. Educational tool.

Pediatric Fibrosis Score–Continuous (pFIB-c)

Enter AST, ALT, platelet count, and BMI z-score for a child or adolescent with MASLD. Output is a modeled probability of advanced fibrosis (F3–F4). Not a substitute for clinical judgment or hepatology referral pathways.

AST (U/L)

ALT (U/L)

Platelet count (×10⁹/L)

Use the same numeric platelet value as on a CBC (not ×10³/µL unless you convert).

BMI z-score (SDS)

Standard deviation score for BMI for age and sex (CDC, WHO, or local growth charts).

Important: External validation is evolving for pediatric MASLD fibrosis tools. Use this probability alongside elastography, biopsy indications, and specialist pathways. Performance differs between community obesity cohorts and tertiary referral populations.

How pFIB-c is calculated

The Pediatric Fibrosis Score–Continuous (pFIB-c) is a logistic regression probability for advanced liver fibrosis (stage F3–F4) in pediatric metabolic dysfunction–associated steatotic liver disease (MASLD). This implementation uses the continuous model with four routinely available variables, as documented in the Hepatology derivation and in the clinical indices literature (see references below).

Logit = −6.892 + 0.023×AST + 0.028×ALT − 0.010×Platelets + 1.126×BMI z-score

pFIB-c = 1 / (1 + e^−Logit)

AST, ALT: U/L (same units as standard chemistry panels).
Platelets: ×10⁹/L (enter the same number as on a typical CBC report, for example 250 for 250 × 10⁹/L).
BMI z-score: age- and sex-specific z-score from CDC, WHO, or your institutional growth tool (not BMI kg/m²).

Interpretation bands

A common three-band summary treats probability below 0.10 as lower modeled risk, 0.10 up to (but not including) 0.50 as intermediate, and 0.50 or higher as higher modeled risk. These thresholds are aids only; management should follow society guidance and local pathways.

About other calculator versions

Some online tools collect different clinical fields (for example sex, hypertension, ethnicity, fasting glucose and insulin for HOMA-IR, weight z-score, and ALT). See for example MDCalc's pFIB-c interface. Those layouts may correspond to an alternate documented model from the same research program. This page uses the four-variable logistic equation above because its coefficients are explicitly published in indexed references and clinical software libraries.

References

Alkhouri N, et al. Development and validation of the Pediatric Fibrosis Score (pFIB) and the Pediatric Fibrosis Score Continuous (pFIB-c) for the prediction of liver fibrosis in children with non-alcoholic fatty liver disease. Hepatology. 2024.
pfib_c_score() in the cliot R package (CRAN), mirroring the logistic coefficients shown above.

What pFIB-c estimates

The Pediatric Fibrosis Score–Continuous (pFIB-c) returns a modeled probability between 0 and 1 for advanced liver fibrosis, meaning histologic stages F3 through F4, in children with metabolic dysfunction–associated steatotic liver disease (MASLD). It was developed for non-invasive risk stratification using routine blood tests and growth data instead of immediate biopsy.

The score does not diagnose steatosis by itself, grade inflammation, or replace elastography, imaging, specialist examination, or tissue diagnosis when the clinical picture calls for those steps. It summarizes how several commonly discordant signals align with patterns seen in the derivation cohort when advanced fibrosis was the outcome of interest.

Clinical background

Pediatric fatty liver disease often presents silently while fibrosis can accumulate over years. Advanced fibrosis identifies patients who merit closer longitudinal monitoring, intensified lifestyle and metabolic treatment, and sometimes pharmacologic trials or transplant pathway discussions according to age, comorbidities, and local hepatology networks. Because biopsy is invasive and subject to sampling variability, multivariable blood-based models are attractive for triage, particularly when repeated testing can track response to intervention.

MASLD replaces older nomenclature such as pediatric NAFLD in many settings; this calculator assumes the same underlying clinical problem domain as the published pediatric fibrosis modeling work: steatotic liver disease with metabolic risk features in children and adolescents.

Model structure

pFIB-c uses a standard logistic regression. A linear combination of predictors forms the logit (log-odds). The probability is the logistic transform of that logit, which constrains output to the open interval between zero and one.

Logit = −6.892 + (0.023 × AST) + (0.028 × ALT) − (0.010 × Platelets) + (1.126 × BMI z-score)

pFIB-c probability = 1 ÷ (1 + e^−Logit)

The negative coefficient on platelets reflects biology familiar from adult fibrosis scores: portal hypertension and altered hepatic architecture associate with thrombocytopenia, so lower platelet counts push risk upward when other variables are held constant. Higher AST and ALT generally push risk upward in this parameterization, reflecting hepatocellular stress and necro-inflammatory activity as captured in the training data. Higher BMI z-score pushes risk upward, encoding heavier adiposity-related metabolic load on the liver in children.

Required inputs and units

Aspartate aminotransferase (AST): U/L as reported on routine chemistry.
Alanine aminotransferase (ALT): U/L on the same scale.
Platelets: enter as ×10⁹/L using the same numeric value printed on the CBC (for example enter 220 when the report reads 220 × 10⁹/L).
BMI z-score: the age- and sex-specific standard deviation score from your growth reference (CDC, WHO, or institutional curves). Do not substitute raw BMI in kg/m²; the model expects the z-score.

Negative z-scores are permissible mathematically if they reflect your growth tool output; interpret such cases in context because extreme anthropometric outliers may be poorly represented in published validation subsets.

Translating probability to common risk bands

Many implementations summarize the continuous probability with three bands:

Low modeled risk: probability below 0.10 (under 10%).
Intermediate (indeterminate) risk: probability from 0.10 up to but not including 0.50.
Higher modeled risk: probability 0.50 or greater (50% or more).

These thresholds are operational conveniences, not universal treatment rules. An intermediate band explicitly signals uncertainty where clinicians often seek concordant fibrosis assessment (for example vibration-controlled transient elastography), interval labs, or referral discussion rather than a single binary action.

How to read the number at the bedside

Think of the output as a modeled probability under the assumptions of the derivation study, not the patient’s literal chance in isolation from all other data. Concordance between non-invasive tests strengthens confidence; conflict between tests should trigger reassessment of sampling issues (hemolysis, acute illness), medications, alternate diagnoses (Wilson disease, autoimmune hepatitis, biliary disease), or the need for advanced imaging and specialty evaluation.

Acute illness, hemolysis, or marrow disorders can distort AST, ALT, or platelets independent of chronic fibrosis stage. Medications, vigorous exercise, and intercurrent viral illness also perturb transaminases. Platelet counts may be elevated for reactive reasons as well as depressed for portal hypertension-related reasons, so platelet trends and peripheral smear context matter.

Other calculator versions and scope drift

Public tools sometimes collect additional fields (sex, blood pressure categories, glycemic indices, insulin resistance surrogates, alternate anthropometric z-scores). Those forms may implement an expanded specification from the same research program rather than the four-variable logistic equation shown here. If your institution standardizes a particular field set, align documentation and quality review with that variant rather than mixing outputs across incompatible implementations.

This build follows the four-variable logistic coefficients used in the codebase implementation (intercept −6.892; AST, ALT, platelets, BMI z-score only).

Limitations and responsible use

External validity varies by age band, ethnicity, obesity prevalence, and referral intensity. The model can underperform when training populations differ materially from your patient. It should not be used as the sole criterion for enrollment in drug trials, transplant listing, or medicolegal determinations.

Biopsy remains the traditional fibrosis reference standard but carries risk and sampling error; elastography also has technical failure modes and interpretation nuances in pediatric body habitus. pFIB-c is best viewed as one layer in a composite strategy: trend over time, corroborate with physics-based fibrosis estimates when available, and personalize decisions using family engagement and coordinated metabolic care.