Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure.

Background: Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed to establish whether machine learning (ML) models could predict HFNC outcome, early in the course of treatment, with greater accuracy than currently used clinical indices.

Methods: We developed ML models trained using measurements made within the first 2 h of treatment from 184 AHRF patients (37% HFNC failures) treated at the respiratory ICU of the University Hospital of Modena between 2018 and 2023.

Statistical analysis plan for hemodynamic phenotype-based, capillary refill time-targeted resuscitation in early septic shock: the ANDROMEDA-SHOCK-2 randomized clinical trial.

Background: ANDROMEDA-SHOCK 2 is an international, multicenter, randomized controlled trial comparing hemodynamic phenotype-based, capillary refill time-targeted resuscitation in early septic shock to standard care resuscitation to test the hypothesis that the former is associated with lower morbidity and mortality in terms of hierarchal analysis of outcomes.

Objective: To report the statistical plan for the ANDROMEDA--SHOCK 2 randomized clinical trial.

Methods: We briefly describe the trial design, patients, methods of randomization, interventions, outcomes, and sample size.

The First Brazilian Cardiovascular Registry of Atrial Fibrillation: Primary Results of the RECALL Study.

Background: It is estimated that atrial fibrillation (AF) affects approximately 1.5 million people in Brazil; however, epidemiological data are limited. We sought to evaluate the characteristics, treatment patterns, and clinical outcomes in patients with AF in Brazil by creating the first nationwide prospective registry.