Shock Index and Physiological Stress Index for reestratifying patients with intermediate-high risk pulmonary embolism.

Objective: Study and Evaluation of Two Scores: Shock Index (SI) and Physiological Stress Index (PSI) as discriminators for proactive treatment (reperfusion before decompensated shock) in a population of intermediate-high risk pulmonary embolism (PE).

Design: Using a database from a retrospective cohort with clinical variables and the outcome variable of "proactive treatment", a comparison of the populations was conducted. Optimal cut-off for "proactive treatment" points were obtained according to the SI and PSI.

Could machine learning algorithms help us predict massive bleeding at prehospital level?

Objective: Comparison of the predictive ability of various machine learning algorithms (MLA) versus traditional prediction scales (TPS) for massive hemorrhage (MH) in patients with severe traumatic injury (STI).

Design: On a database of a retrospective cohort with prehospital clinical variables and MH outcome, a treatment of the database was performed to be able to apply the different AML, obtaining a total set of 473 patients (80% training, 20% validation). For modeling, proportional imputation and cross validation were performed.