Intensive Care Med Exp
January 2025
Crit Care Sci
September 2024
Purpose: Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.
View Article and Find Full Text PDFObjective: To determine the prevalence of sonographic vasospasm and delayed ischemic deficit in patients with aneurysmal subarachnoid hemorrhage, to evaluate the correlation between different tomographic scales and these complications, and to study prognostic factors in this group of patients.
Methods: This was a prospective study of patients admitted to the intensive care unit with a diagnosis of aneurysmal subarachnoid hemorrhage. The prevalence of sonographic vasospasm and radiological delayed cerebral ischemia was analyzed, as was the correlation between different tomographic scales and these complications.