New Score Could Predict Mucus Plugs in Asthma

A simplified, non-invasive scoring system could help clinicians identify people with asthma who are likely to have airway mucus plugs without immediately relying on high-resolution computed tomography (HRCT), according to research.

Airway mucus plugs are an important pathological feature of asthma, particularly severe disease, and are associated with airflow obstruction and poorer outcomes. However, identifying them generally requires imaging, creating a need for practical screening approaches that can indicate which patients are most likely to benefit from further investigation.

Researchers have now combined evidence from a systematic review and meta-analysis with clinical validation to develop scores capable of estimating both the presence and severity of mucus plugging.

Researchers Identify Key Predictors of Mucus Plugs

The investigators searched six major databases for studies examining factors associated with airway mucus plugs in asthma.

Ten studies involving 837 patients were included in the subsequent meta-analysis. Several characteristics emerged as important factors associated with mucus plugging, including age, measures of pulmonary function and biomarkers of type 2 inflammation.

Relevant lung function measures included forced expiratory volume in one second (FEV1%), forced vital capacity (FVC%), and the FEV1/FVC ratio. Fractional exhaled nitric oxide (FeNO), blood eosinophil count, and immunoglobulin E (IgE) were among the inflammatory markers associated with mucus plugs.

Researchers used standardised effect sizes from the meta-analysis to construct both a mathematical equation-based model and a simplified clinical scoring system.

Simplified Score Shows Strong Predictive Performance

The scoring approach was internally validated using a separate cohort of 105 adults with asthma treated at Tongji Hospital between 2024 and 2026.

For estimating whether mucus plugs were present, the simplified scoring system achieved an area under the receiver operating characteristic curve (AUC) of 0.864. Specificity reached 94.1%, while sensitivity was 71.8%.

The model also demonstrated promising performance for identifying patients with a higher mucus plug burden. In this analysis, the AUC was 0.859, with sensitivity of 91.7% and specificity of 68.4%.

These results suggest the score may be useful for identifying both patients likely to have mucus plugs and those potentially experiencing more extensive mucus plugging.

Non-Invasive Screening Could Guide Imaging Decisions

The scoring system could offer clinicians a relatively simple method for estimating mucus plug risk using patient characteristics, pulmonary function measurements, and routinely assessed inflammatory biomarkers.

Rather than replacing HRCT, the researchers propose that the tool could help determine which patients should be prioritised for imaging. This could potentially make screening more efficient while identifying patients whose airway disease warrants closer investigation.

The approach may be particularly relevant given the relationship between mucus plugging, airflow limitation, and severe asthma.

External Validation Remains Essential

Despite the promising performance, the authors emphasise that the findings remain preliminary. The score was internally validated in only 105 patients from a single hospital, meaning its performance across different populations and clinical settings is not yet established.

Larger external validation studies will therefore be required before the scoring system can be incorporated into routine asthma management.

If validated, the non-invasive approach could provide clinicians with a practical tool for recognising patients at increased risk of airway mucus plugging and targeting HRCT assessment more effectively.

Reference

Wang Z et al. A non-invasive simplified scoring system to predict airway mucus plugs in asthma. BMC Pulm Med. 2026;DOI: 10.1186/s12890-026-04560-0.