What question did this study set out to answer?

The aim is to explore the connection between blood inflammatory markers and short-term prognosis in pneumoconiosis patients, developing a predictive model.

February 27, 2026Open Access

Blood Marker-Based Machine Learning Model for Survival Prediction in Patients with Pneumoconiosis: Construction and External Validation

Key Points

The aim is to explore the connection between blood inflammatory markers and short-term prognosis in pneumoconiosis patients, developing a predictive model.
Retrospective collection of clinical data from 813 pneumoconiosis patients from two hospitals.
Data divided into training (568) and validation sets (245).
Variables analyzed using LASSO regression and Cox regression.
Model performance evaluated with ROC curve, C index, and decision curve analysis.
Mean ages were 62.1 years in training and 63.3 years in validation sets.
Mortality rates were 11.1% (training) and 11.9% (validation).
CRP and IL-6 identified as independent risk factors for poor prognosis (both P<0.001).
Model demonstrated excellent discriminatory power with AUCs of 0.905 and 0.920.
High CRP/IL-6 levels correlated with significantly reduced survival (log-rank P<0.001).

Abstract

Purpose: This study aimed to investigate the association between blood inflammatory markers, including C-reactive protein (CRP) and interleukin-6 (IL-6), and the short-term prognosis of pneumoconiosis, and to develop a multifactorial prediction model. Patients and Methods: Clinical data of 813 pneumoconiosis patients admitted to two regional tertiary hospitals from October 2016 to August 2023 were retrospectively collected and randomly divided into a training set (n=568) and an external validation set (n=245). Variables were screened by least absolute shrinkage and selection operator (LASSO) regression, a multifactorial logistic regression column-line graph model was constructed, and the relationship between risk factors and prognosis was analyzed by multifactorial Cox regression and Kaplan-Meier survival curves. The model performance was verified by consistency index (C index), receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC). Results: The mean age was 62.1 years in the training set and 63.3 years in the validation set, with mortality rates of 11.1% and 11.9%, respectively. LASSO regression identified age, dust exposure duration, dyspnea, blood oxygen saturation (SpO 2 ), neutrophil count, CRP, and IL-6 as predictors. CRP (OR=1.028, 95% CI: 1.016– 1.040) and IL-6 (OR=1.020, 95% CI: 1.010– 1.030) were independent risk factors for poor short-term prognosis (both P 0.05). DCA and CIC indicated high clinical value. High CRP/IL-6 levels were associated with significantly reduced survival (log-rank P< 0.001). Conclusion: The prediction model based on CRP, IL-6, and clinical characteristics effectively identifies pneumoconiosis patients at high risk of short-term poor prognosis, providing a reliable basis for early intervention due to its high discriminatory power and clinical applicability. Keywords: pneumoconiosis, C-reactive protein, interleukin-6, prediction model, inflammation

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Cite This Study

Huang et al. (Sun,) studied this question.

synapsesocial.com/papers/69a134dded1d949a99abe4e9 https://doi.org/https://doi.org/10.2147/jir.s555051

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