Volume 21 Issue 9
Sep.  2023
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Article Navigation >  Chinese Journal of General Practice  > 2023  >  21(9): 1503-1507
YANG Chenjie, CHEN Hui, GUO Chunfeng, ZHOU Chenhui. Construction and validation of a predictive model for the risk of intracranial infection in patients after decompressive craniectomy[J]. Chinese Journal of General Practice, 2023, 21(9): 1503-1507. doi: 10.16766/j.cnki.issn.1674-4152.003156
Citation: YANG Chenjie, CHEN Hui, GUO Chunfeng, ZHOU Chenhui. Construction and validation of a predictive model for the risk of intracranial infection in patients after decompressive craniectomy[J]. Chinese Journal of General Practice, 2023, 21(9): 1503-1507. doi: 10.16766/j.cnki.issn.1674-4152.003156
shu

Construction and validation of a predictive model for the risk of intracranial infection in patients after decompressive craniectomy

doi: 10.16766/j.cnki.issn.1674-4152.003156
  • 1.

    Operating Room, the First Hospital of Ningbo, Ningbo, Zhejiang 315000, China

Funds:

 82101354

  • Received Date: 2022-12-25
    Abstract
  •   Objective  To analyze the risk factors for intracranial infection in patients after decompressive craniectomy (DC), to construct a risk prediction model for intracranial infection in DC patients, and to conduct internal and external validation of the model.  Methods  The 61 DC patients who had intracranial infections in the neurosurgery department of a tertiary care hospital from January 2020 to September 2021 were retrospectively analyzed and included in the infection group, and 122 DC patients of the same department and similar age who did not have intracranial infections were selected according to the ratio of 1:2 to be included in the non-infection group for a case-control study, and the column line graph model was established and internally validated by applying R software using univariate analysis and multi-factor logistic regression analysis. Sixty-one clinical data of patients receiving DC from January to December 2019 were selected for external validation of the model.  Results  The incidence of intracranial infection in the modeled group of patients was 33.3% (61/183). Logistic regression analysis showed that surgery time (OR=1.006, P=0.039), hematoma size (OR=1.037, P=0.004), ICU stay (OR=1.092, P=0.004), cerebral hemorrhage (OR=16.128, P=0.019), traumatic brain injury (OR=18.100, P=0.028), and brain tumor (OR=44.286, P=0.003) were independent risk factors for intracranial infection in DC patients. The column line graph model was developed accordingly, and the model AUC was 0.863 (95% CI: 0.760-0.967), and the external validation results showed an AUC of 0.933 (95% CI: 0.862-1.000) for the model.  Conclusion  The longer the surgery time, the larger the hematoma, the longer the ICU stay, the history of cerebral hemorrhage, the history of traumatic brain injury, and the history of brain tumor, the higher the risk of intracranial infection in DC patients. The prediction model of intracranial infection in DC patients constructed in this study has good accuracy and has some clinical application value.

     

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