Extension of an ICU-based noninvasive model to predict latent shock in the emergency department: an exploratory study

Artificial intelligence (AI) has been widely adopted for the prediction of latent shock occurrence in critically ill patients in intensive care units (ICUs). However, the usefulness of an ICU-based model to predict latent shock risk in an emergency department (ED) setting remains unclear. This study aimed to develop an AI model to predict latent shock risk in patients admitted to EDs.

Multiple regression analysis was used to compare the difference between Medical Information Mart for Intensive Care (MIMIC)-IV-ICU and MIMIC-IV-ED datasets. An adult noninvasive model was constructed based on the MIMIC-IV-ICU v3.0 database and was externally validated in populations admitted to an ED. Its efficiency was compared with efficiency of testing with noninvasive systolic blood pressure (nSBP) and shock index.

A total of 50,636 patients from the MIMIC-IV-ICU database was used to develop the model, and a total of 2,142 patients from the Philips IntelliSpace Critical Care and Anesthesia (ICCA)-ED and 425,087 patients from the MIMIC-IV-ED were used for external validation. The modeling and validation data revealed similar non-invasive feature distributions. Multiple regression analysis of the MIMIC-IV-ICU and MIMIC-IV-ED datasets showed mostly similar characteristics. The area under the receiver operating characteristic curve (AUROC) of the noninvasive model 10 min before the intervention was 0.90 (95% CI: 0.84-0.96), and the diagnosis accordance rate (DAR) was above 80%. More than 80% of latent shock patients were identified more than 70 min earlier using the noninvasive model; thus, it performed better than evaluating shock index and nSBP.

The adult noninvasive model can effectively predict latent shock occurrence in EDs, which is better than using shock index and nSBP.