RESUMEN
OBJECTIVE: The aim of this study was to describe the clinical and procedural risk factors associated with the unplanned neurosurgical intensive care unit (NICU) readmission of patients after elective supratentorial brain tumor resection and serves as an exploratory analysis toward the development of a risk stratification tool that may be prospectively applied to this patient population. METHODS: This was a retrospective observational cohort study. The electronic medical records of patients admitted to an institutional NICU between September 2018 and November 2021 after elective supratentorial brain tumor resection were reviewed. Demographic and perioperative clinical factors were recorded. A prognostic model was derived from the data of 4892 patients recruited between September 2018 and May 2021 (development cohort). A nomogram was created to display these predictor variables and their corresponding points and risks of readmission. External validation was evaluated using a series of 1118 patients recruited between June 2021 and November 2021 (validation cohort). Finally, a decision curve analysis was performed to determine the clinical usefulness of the prognostic model. RESULTS: Of the 4892 patients in the development cohort, 220 (4.5%) had an unplanned NICU readmission. Older age, lesion type, Karnofsky Performance Status (KPS) < 70 at admission, longer duration of surgery, retention of endotracheal intubation on NICU entry, and longer NICU length of stay (LOS) after surgery were independently associated with an unplanned NICU readmission. A total of 1118 patients recruited between June 2021 and November 2021 were included for external validation, and the model's discrimination remained acceptable (C-statistic = 0.744, 95% CI 0.675-0.814). The decision curve analysis for the prognostic model in the development and validation cohorts showed that at a threshold probability between 0.05 and 0.8, the prognostic model showed a positive net benefit. CONCLUSIONS: A predictive model that included age, lesion type, KPS < 70 at admission, duration of surgery, retention of endotracheal intubation on NICU entry, and NICU LOS after surgery had an acceptable ability to identify elective supratentorial brain tumor resection patients at high risk for an unplanned NICU readmission. These risk factors and this prediction model may facilitate better resource allocation in the NICU and improve patient outcomes.
RESUMEN
BACKGROUND: Our aim of this study was to identify risk factors and develop a prediction model for unplanned neurological intensive care unit (NICU) events after elective infratentorial brain tumor resection in order to propose an individualized admission to the NICU tailored to patient needs. METHODS: Patients admitted to our NICU between September 2018 and May 2021 after elective infratentorial brain tumor resection were reviewed. Prolonged NICU stays and unplanned NICU admissions were defined as unplanned NICU events. The prognostic model of unplanned NICU events was developed using a forward stepwise logistic regression analysis, and external validation was evaluated. The C-statistic was used to assess discrimination, and a smooth, nonparametric calibration line was used to assess calibration graphically in the model. RESULTS: Of the 1,710 patients in the development cohort, unplanned NICU events occurred in 162 (9.5%). Based on the lesion type, a Karnofsky Performance Status score <70 at admission, longer duration of surgery, bleeding in the operative area evident on postoperative computed tomography, higher fibrinogen and blood glucose levels at admission, and more intraoperative blood loss were independently associated with unplanned NICU events. The external validation test showed good discrimination (C-statistic = 0.811) and calibration (Hosmer-Lemeshow P = 0.141) for unplanned NICU events. CONCLUSIONS: Several patient and operative characteristics are associated with a greater likelihood of the occurrence of unplanned NICU events. In the future, we may be able to provide better help for the resource allocation of NICUs according to these risk factors and prediction models.