The Journal of Practical Medicine ›› 2026, Vol. 42 ›› Issue (18): 3399-3407.doi: 10.3969/j.issn.1006-5725.2026.18.019

• Treatise：Mechanism and Practice • Previous Articles    

Development and validation of a nomogram for predicting postoperative pulmonary complications in patients with spontaneous intracerebral hemorrhage

Meijuan Liu1, Yong Zhang2, Yanna Si1, Jun Lu1, Fangzhou Lu1, Juan Liu1()   

  1. 1. Department of Anesthesiology，Brain Hospital Affiliated to Nanjing Medical University，Nanjing 210029，Jiangsu，China
    2. Department of Anesthesiology，Nanjing First Hospital，Nanjing Medical University，Nanjing 210006，Jiangsu，China
  • Received:2026-06-23 Revised:2026-08-08 Accepted:2026-08-18 Online:2026-09-25 Published:2026-10-19
  • Contact: Juan Liu
  • About author:

    Liu Meijuan performed the experiments and wrote the article. Zhang Yong， Lu Jun and Lu Fangzhou performed the experiments. Si Yanna revised the article. Liu Juan designed the study and reviewed the article. All authors read and approved the final manuscript as submitted.

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Abstract:

Objective To construct and validate a predictive model for postoperative pulmonary complications （PPCs） following craniotomy in patients with spontaneous intracerebral hemorrhage （sICH）. Methods A total of 308 sICH patients who underwent emergency craniotomy for hematoma evacuation under general anesthesia at Brain Hospital Affiliated to Nanjing Medical University from June 2023 to June 2025 were retrospectively enrolled as the training cohort. Additionally， 132 sICH patients who received the same surgical procedure at Nanjing First Hospital during the same period were included as the validation cohort. Patients were divided into PPCs and non-PPCs groups based on the occurrence of PPCs within 7 postoperative days. LASSO regression and multivariate Logistic regression were performed on the training cohort to screen risk factors and develop a nomogram prediction model. Model performance and clinical applicability were evaluated using receiver operating characteristic （ROC） curve and area under the curve （AUC）， calibration curve， and decision curve analysis （DCA）. Results This study enrolled 440 patients， with a PPCs incidence of 60.0% （264/440）. Multivariate Logistic regression analysis identified age ≥ 65 years， preoperative hypoxemia， dysphagia， low Glasgow Coma Scale （GCS） score， and elevated systemic immune-inflammation index （SII） as independent risk factors for PPCs in sICH patients （all P < 0.05）. The developed nomogram demonstrated an AUC of 0.876 （95%CI： 0.793 ⁃ 0.958） in the training cohort， with sensitivity of 91.3% and specificity of 66.7%； and an AUC of 0.817 （95%CI： 0.749 - 0.885） in the validation cohort， with sensitivity of 90.2% and specificity of 72.0%. Calibration curve and Hosmer-Lemeshow test （training cohort： χ² = 2.621， P = 0.955； validation cohort： χ² = 2.212， P = 0.973） indicated good model fit. DCA revealed positive net benefit within threshold probabilities of 17% ⁃ 91% in the training cohort and 16% ⁃ 99% in the validation cohort， suggesting favorable clinical utility. Conclusions Age ≥ 65 years， preoperative hypoxemia， dysphagia， low GCS score， and elevated SII are independent risk factors for PPCs in sICH patients. The nomogram model constructed based on these factors demonstrates good predictive performance and clinical application value.

Key words: intracerebral hemorrhage, nomogram, predictive model, postoperative pulmonary complications, risk factors

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