Abstract: Objective: To explore the status quo and influencing factors of unplanned ICU readmission in patients undergoing intracranial tumor surgery so as to establish a prediction model and validate it, in order to provide a basis for clinical intervention. Methods: A total of 2383 patients undergoing intracranial tumor surgery admitted to the neurosurgery ICU of a tertiary grade A hospital in Beijing from December 2019 to September 2022 were enrolled by convenience sampling method. Propensity Score (PSM) was used to match 420 cases at 1:4, with the first 300 cases as the training set and the last 120 cases as the validation set. Univariate and multivariate logistic regression models were used to evaluate the independent influencing factors of unplanned readmission to ICU in patients undergoing intracranial tumor surgery, and the prediction model was constructed and validated. Results: The incidence of unplanned readmission to ICU in patients with intracranial tumor surgery was 3.52%(84/2383). A total of 420 cases were included in the analysis after PSM, of which 84 cases were included in the ICU readmission group, and 336 cases were included in the non-readmission group. Regression analysis showed that tracheal intubation, duration of mechanical ventilation, and the GCS score before ICU discharge were the influencing factors (P<0.05). The Area Under the ROC Curve (AUC) of the model established by the training set was 0.834 with the sensitivity being 81.2%, the specificity being 77.0%, and the best critical value being 0.206. The AUC value of the model established in the external validation set was 0.854 with a sensitivity of 81.4% and a specificity of 82.6%. Conclusion: The risk prediction model established in this study for patients with craniocerebral tumor who are not scheduled to return to ICU after surgery is scientific and reasonable, with good prediction effect, which is suitable for clinical screening, and can provide reference for medical staff to identify high-risk groups early to take targeted intervention measures.

Key words: adult; intracranial tumor; unplanned ICU readmission; risk factors; risk prediction model; nursing