Journal of Shandong University (Health Sciences) ›› 2026, Vol. 64 ›› Issue (3): 108-115.doi: 10.6040/j.issn.1671-7554.0.2024.0940

• Clinical Medicine • Previous Articles    

Construction and validation of a risk prediction model for pulmonary encephalopathy associated with acute exacerbation of chronic obstructive pulmonary disease based on dynamic nomogram and machine learning

LU Chenlin, XU Lu, YANG Junfa, PAN Huaqin, NI Qingtao   

  1. Department of Respiratory and Critical Care Medicine, Taizhou Peoples Hospital, Taizhou 225300, Jiangsu, China
  • Published:2026-03-19

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Abstract: Objective To construct a model for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease(AECOPD)associated with pulmonary encephalopathy based on dynamic nomogram and machine learning, and to verify the predictive efficacy of the model. Methods A total of 272 patients with AECOPD admitted to Taizhou Peoples Hospital from January 2022 to June 2024 were selected as research objects. According to whether they were accompanied by pulmonary encephalopathy, the patients were divided into pulmonary encephalopathy group(n=54)and non-pulmonary encephalopathy group(n=218). Univariate and multivariate logistic regression were used to analyze the risk factors of AECOPD associated with pulmonary encephalopathy, and the related prediction models were established. Results Univariate analysis showed that there was no significant difference in gender, age, body mass index(BMI), smoking history, hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease(COPD)course, acute exacerbations, heart rate and serum sodium between the two groups(P>0.05). There were significant differences in partial pressure of oxygen(PaO2), partial pressure of carbon dioxide(PaCO2), blood pH, serum potassium, serum albumin and C-reactive protein(CRP)between the two groups(P<0.05). The results of multifactor analysis showed that lower PaO2, higher PaCO2, lower blood pH, lower serum potassium and higher CRP levels were independent risk factors for pulmonary encephalopathy in AECOPD patients(P<0.05). Hosmer-Lemeshow fit test showed the following results: for dynamic nomogram, χ2=2.912, P=0.940; for random forest, χ2=12.628, P=0.125; for decision tree models, χ2=9.232, P=0.323. The AUCs were 0.874(95%CI: 0.822-0.925), 0.802(95%CI: 0.727-0.877)and 0.847(95%CI: 0.788-0.905), respectively. Conclusion Dynamic nomogram, random forest and decision tree models based on risk factors can effectively predict the risk of pulmonary encephalopathy in patients with AECOPD.

Key words: Acute exacerbation of chronic obstructive pulmonary disease, Pulmonary encephalopathy, Dynamic nomogram, Random forest, Decision tree

CLC Number: 

  • R563
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