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Influencing factors and construction of a nomogram prediction model for secondary fungal infection in pneumoconiosis patients with bacterial pneumonia
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Yi-fei WANG, Yun-di JIANG, Wen DU, Li-jun PENG, Yu-qin YAO, Qin ZHANG, Ding-zi ZHOU
Modern Preventive Medicine | 2026, 53(6) : 1009 - 1016
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Modern Preventive Medicine | 2026, 53(6): 1009-1016
• Environmental and Occupational Health •
Influencing factors and construction of a nomogram prediction model for secondary fungal infection in pneumoconiosis patients with bacterial pneumonia
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Yi-fei WANG, Yun-di JIANG, Wen DU, Li-jun PENG, Yu-qin YAO, Qin ZHANG, Ding-zi ZHOU
Affiliations
  • West China School of Public Health (West China Fourth Hospital), Sichuan University, Chengdu, Sichuan 610041, China
Published: 2026-03-25 doi: 10.20043/j.cnki.MPM.202510041
Outline
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Objective

To identify risk factors for secondary fungal infection in patients with pneumoconiosis complicated by bacterial pneumonia and to develop a nomogram prediction model.

Methods

Clinical data of patients admitted to a tertiary occupational disease hospital between December 2021 and July 2024 were retrospectively analyzed and randomly divided into training and testing sets (7:3). Variables were selected using LASSO and multivariable logistic regression to construct a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

Results

Among 641 patients, 213 developed secondary pulmonary fungal infection. Multivariable analysis showed that invasive procedures (OR=3.440, 95% CI:2.111-5.699), elevated immature granulocyte percentage (OR=2.294, 95% CI:1.254-4.402), systemic glucocorticoid use (OR=2.196, 95% CI:1.114-4.327), repeated hospitalization (OR=2.094, 95% CI:1.280-3.464), and increasing age (OR=1.059, 95% CI:1.034-1.085) were associated with increased risk, whereas higher large platelet count was associated with reduced risk (OR=0.980, 95% CI:0.968-0.992). The nomogram achieved AUCs of 0.818 (95% CI:0.777-0.858) in the training set and 0.797 (95% CI:0.688-0.906) in the testing set. Calibration and DCA demonstrated good agreement and clinical net benefit.

Conclusion

The nomogram accurately predicts secondary fungal infection risk in this population, demonstrating favorable performance and potential for early risk stratification and targeted intervention.

Pneumoconiosis  /  Bacterial pneumonia  /  Lung diseases, Fungal  /  Nomograms  /  Predictive model
王艺霏, 江云笛, 杜文, 彭莉君, 姚于勤, 张勤, 周丁子. 尘肺合并细菌性肺炎患者继发真菌感染的影响因素分析及列线图预测模型构建. 现代预防医学, 2026 , 53 (6) : 1009 -1016 . DOI: 10.20043/j.cnki.MPM.202510041
Yi-fei WANG, Yun-di JIANG, Wen DU, Li-jun PENG, Yu-qin YAO, Qin ZHANG, Ding-zi ZHOU. Influencing factors and construction of a nomogram prediction model for secondary fungal infection in pneumoconiosis patients with bacterial pneumonia[J]. Modern Preventive Medicine, 2026 , 53 (6) : 1009 -1016 . DOI: 10.20043/j.cnki.MPM.202510041
Year 2026 volume 53 Issue 6
27
13
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doi: 10.20043/j.cnki.MPM.202510041
  • Receive Date:2025-10-09
  • Online Date:2026-11-02
  • Published:2026-03-25
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  • Received:2025-10-09
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    West China School of Public Health (West China Fourth Hospital), Sichuan University, Chengdu, Sichuan 610041, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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