Volume 23 Issue 9
Sep.  2025
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GONG Yiwen, LIU Ying, XU Ye. Construction and validation of a logistic regression model for predicting pulmonary infection risk in stroke patients undergoing left-right contralateral C7 nerve transfer surgery[J]. Chinese Journal of General Practice, 2025, 23(9): 1539-1543. doi: 10.16766/j.cnki.issn.1674-4152.004173
Citation: GONG Yiwen, LIU Ying, XU Ye. Construction and validation of a logistic regression model for predicting pulmonary infection risk in stroke patients undergoing left-right contralateral C7 nerve transfer surgery[J]. Chinese Journal of General Practice, 2025, 23(9): 1539-1543. doi: 10.16766/j.cnki.issn.1674-4152.004173

Construction and validation of a logistic regression model for predicting pulmonary infection risk in stroke patients undergoing left-right contralateral C7 nerve transfer surgery

doi: 10.16766/j.cnki.issn.1674-4152.004173
Funds:

 10000015Z155080000004

 2024HL02

  • Received Date: 2024-11-15
    Available Online: 2025-11-17
  •   Objective   To identify sensitive indicators for assessing pulmonary infection risk in stroke patients following left-right contralateral C7 nerve transfer surgery, and to develop and validate a risk-prediction nomogram to support clinical condition assessment and clinical decision-making.   Methods   A retrospective analysis was conducted on 294 stroke patients who underwent left-right contralateral C7 nerve transfer surgery at Jing' an District Central Hospital between March 2021 and December 2023. Patients were stratified into an infection group (n=29) and a non-infection group (n=265) based on standardized diagnostic criteria for pulmonary infection. Demographic statistical indicators, laboratory indicators and other data of the two groups were collected. LASSO and logistic regression were used to analyze the risk factors of pulmonary infection after the left-right contralateral C7 nerve transfer surgery of stroke patients. The postoperative pulmonary infection risk prediction model was established, and the ROC curve was evaluated to analyze the efficiency and verify the stability of the model.   Results   There were significant differences in age, diabetes mellitus, peak expiratory flow (PEF), FEV1/FVC, VCmax, operation time, Beck score, American Society of Anesthesiologists (ASA) grading, and preoperative EMG abnormalities between the two groups (P < 0.05). Multifactor LASSO regression analysis showed that when λ. 1se=0.040 7, 7 predictors finally fit the characteristics. Multivariate analysis identified that age >60 years old and high Beck score as independent risk factors, while PEF≥320 L/min and FEV1/FVC≥92% were protective factors (P < 0.05). The model achieved an AUC of 0.983 (95% CI: 0.968-0.998), with sensitivity and specificity of 0.931 and 0.936, respectively. Internal validation Bootstrap (B=1 000) confirmed stable predictive performance, and decision curve analysis showed clinical utility.   Conclusion   Age>60 years old and elevated Beck score are independent risk factors, whereas PEF≥320 L/min and FEV1/FVC≥92% are protective factors. The nomogram model showed excellent predictive effect on pulmonary infection, and can provide data support for clinical evaluation and decision-making.

     

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