# A novel inflammation-nutrition risk score (INRS) and its related nomogram model to predict radiological bronchiectasis in patients after tuberculosis infection in Wuhan, China

**Authors:** Qi Yu, Jisong Yan, Jianping Song, Fan Yu, Nanchuan Jiang, Yaya Zhou, Xinliang He, Fengyun Gong, Xiaorong Wang

PMC · DOI: 10.1080/07853890.2026.2625545 · Annals of Medicine · 2026-02-17

## TL;DR

This study introduces a new score and model to predict bronchiectasis in tuberculosis survivors, helping identify high-risk patients for better follow-up care.

## Contribution

The study introduces a novel Inflammation-Nutrition Risk Score (INRS) and a nomogram model for predicting radiological bronchiectasis after tuberculosis infection.

## Key findings

- The INRS and nomogram model showed strong predictive power for radiological bronchiectasis in post-TBI patients.
- INRS ≥1.86 was a significant independent risk factor for RBE with an odds ratio of 5.04.
- The model demonstrated good performance across development, internal validation, and external validation cohorts.

## Abstract

Tuberculosis infection (TBI) is a significant cause of bronchiectasis (BE). Identifying risk factors for radiological BE (RBE) could enhance the early detection of high-risk individuals following TB infection. This study aimed to develop and validate a novel Inflammation-Nutrition Risk Score (INRS) and a corresponding nomogram model to predict the risk of RBE after TBI.

We enrolled 2,210 post-TBI patients from two medical centres. Data from 1,825 patients at Wuhan Jinyintan Hospital were used to develop the INRS and the RBE nomogram. An independent cohort of 385 patients from Wuhan Union Hospital served as an external validation set.

The INRS was derived from four parameters: PNI, HALP score, Lg(SII) and CAR. Multivariate analysis identified the following independent risk factors for RBE: age ≥60 years (OR = 1.19, p = 0.030), current smoking (OR = 1.71, p = 0.009), COPD (OR = 3.13, p < 0.001), RDW-CV ≥12.8% (OR = 1.09, p = 0.005), ALB <35.5 g/L (OR = 1.04, p = 0.003) and INRS ≥1.86 (OR = 5.04, p < 0.001). The RBE nomogram model demonstrated strong discriminatory power, accuracy and clinical utility across the development, internal validation and external validation cohorts.

In post-TBI patients, the INRS represents a novel predictive biomarker for RBE. The INRS-based nomogram is a clinically applicable and efficient tool for risk stratification and guiding follow-up management to prevent RBE progression.

## Linked entities

- **Diseases:** tuberculosis (MONDO:0018076), bronchiectasis (MONDO:0004822), COPD (MONDO:0005002)

## Full-text entities

- **Genes:** ALB (albumin) [NCBI Gene 213] {aka FDAHT, HSA, PRO0883, PRO0903, PRO1341}, TNF (tumor necrosis factor) [NCBI Gene 7124] {aka DIF, IMD127, TNF-alpha, TNFA, TNFSF2, TNLG1F}, FGB (fibrinogen beta chain) [NCBI Gene 2244] {aka HEL-S-78p}, CRP (C-reactive protein) [NCBI Gene 1401] {aka PTX1}, CP (ceruloplasmin) [NCBI Gene 1356] {aka AB073614, CP-2}
- **Diseases:** chronic cough (MESH:D003371), bronchi (MESH:D055091), cystic fibrosis (MESH:D003550), DM (MESH:D009223), hepatitis B virus (MESH:D006509), pulmonary tuberculosis (MESH:D014397), lung cancer (MESH:D008175), dilation (MESH:D002311), post (MESH:D000094025), lung disease (MESH:D008171), Diabetes Melitus (MESH:D003920), Hypertension (MESH:D006973), Nutrition (MESH:D044342), immunodeficiency (MESH:D007153), respiratory diseases (MESH:D012140), Bronchiectasis (MESH:D001987), Inflammation (MESH:D007249), Infectious Disease (MESH:D003141), nutritional deficit (MESH:D009748), TB infection (MESH:D014390), Chronic pulmonary heart disease (MESH:D011660), COPD (MESH:D029424), Chronic hepatitis B (MESH:D019694), interstitial lung disease (MESH:D017563), HALP (OMIM:194470), TB (MESH:D014376), respiratory disorders (MESH:D012131), smoking (MESH:D015208)
- **Chemicals:** nicotine (MESH:D009538), rifampicin (MESH:D012293), DCA (-), alcohol (MESH:D000438)
- **Species:** Homo sapiens (human, species) [taxon 9606], Mus musculus (house mouse, species) [taxon 10090]

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## References

41 references — full list in the complete paper: https://tomesphere.com/paper/PMC12918364/full.md

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Source: https://tomesphere.com/paper/PMC12918364