This study aimed to investigate the association between systemic immune-inflammation (SII) and the risk of in-hospital death for patients with intracerebral hemorrhage (ICH) in the intensive care units (ICU) and to further develop a prediction model related to SII in predicting the risk of in-hospital death for patients with ICH.
In this retrospective cohort study, we included 1,176 patients with ICH from the Medical Information Mart for Intensive Care III (MIMIC-III) database. All patients were randomly assigned to the training group for the construction of the nomogram and the testing group for the validation of the nomogram based on a ratio of 8:2. Predictors were screened by the least absolute shrinkage and selection operator (LASSO) regression analysis. A multivariate Cox regression analysis was used to investigate the association between SII and in-hospital death for patients with ICH in the ICU and develop a model for predicting the in-hospital death risk for ICU patients with ICH. The receiver operator characteristic curve was used to assess the predicting performance of the constructed nomogram.
In the training group, 232 patients with ICH died while 708 survived. LASSO regression showed some predictors, including white blood cell count, glucose, blood urea nitrogen, SII, the Glasgow Coma Scale, age, heart rate, mean artery pressure, red blood cell, bicarbonate, red blood cell distribution width, liver cirrhosis, respiratory failure, renal failure, malignant cancer, vasopressor, and mechanical ventilation. A prediction model integrating these predictors was established. The area under the curve (AUC) of the nomogram was 0.810 in the training group and 0.822 in the testing group, indicating that this nomogram might have a good performance.
Systemic immune-inflammation was associated with an increased in-hospital death risk for patients with ICH in the ICU. A nomogram for in-hospital death risk for patients with ICH in the ICU was developed and validated.