Nomogram and randomized survival forest model for predicting sepsis risk in patients with cerebral infarction in the intensive care unit

Background: To construct a nomogram and a Randomized Survival Forest (RSF) model for predicting the occurrence of sepsis in patients with cerebral infarction in intensive care units (ICUs). Methods: A total of 1,963 patients were included from the Medical Information Mart for Intensive Care IV database version 2.0 (MIMIC-IV v2.0). Screening features based on Cox regression and Lasso regression for nomogram and RSF modeling. Results: Patients were randomly split into a training set (1,374 cases) and a validation set (589 cases) at a ratio of 7:3. Risk factors in the nomogram model included atenolol, bicarbonate, calcium, clopidogrel, dipyridamole, heart failure, lymphocyte percent, midazolam, propofol, rhabdomyolysis, vancomycin, white blood cells, and antibiotics. In the training and validation sets, the nomogram predicted sepsis on the 3rd day of admission with an AUC of 0.798 and 0.765 and predicted sepsis on the 7th day with an AUC of 0.808 and 0.736, respectively. In the training and validation sets, the RSF model predicted sepsis on the 3rd day of admission with an AUC of 0.899 and 0.775 and predicted sepsis on the 7th day with an AUC of 0.913 and 0.768, respectively Conclusions: The two models can reliably predict the probability of sepsis in patients with cerebral infarction in the intensive care unit, which can help clinicians to assess the condition and provide timely medical interventions for patients. The RSF model has better performance.