Development and Validation of a Prognostic Nomogram for Cerebral Infarction Patients in Intensive Care Units: a Retrospective Cohort Study

Abstract

OBJECTIVES: Our study aimed to establish a utility risk prediction model for the prognosis of patients with cerebral infarction.BACKGROUND: Despite large number of studies focus on the prognosis risk factors of patients with cerebral infarction, there were still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with cerebral infarction.METHODS: The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of cerebral infarction patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. RESULTS: Overall, 1,564 patients (1315 survivals and 249 deaths) with cerebral infarction included in our research from MIMIC-IV database. The incident of in-hospital mortality is 15.9%. Lasso regression model verified that age, white blood cell count, anion gap (AG), SOFA score were significantly correlated with hospital mortality. The risk prediction model demonstrated a good discrimination with an AUC of ROC 0.789 (95% CI 0.752–0.826) in training set and 0.829 (95% CI 0.791–0.867) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that this model has good clinical benefits.Conclusion: We developed a nomogram that predicts hospital mortality in patients with cerebral infarction according to the real world’s data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.