Optimal Prone Position Duration in Patients With ARDS Due to COVID-19: The Omelette Pilot Trial.

BACKGROUND: Prone position (PP) has been widely used in the COVID-19 pandemic for ARDS management. However, the optimal length of a PP session is still controversial. This study aimed to evaluate the effects of prolonged versus standard PP duration in subjects with ARDS due to COVID-19. METHODS: This was a single-center, randomized controlled, parallel, and open pilot trial including adult subjects diagnosed with severe ARDS due to COVID-19 receiving invasive mechanical ventilation that met criteria for PP between March-September 2021. Subjects were randomized to the intervention group of prolonged PP (48 h) versus the standard of care PP (∼16 h). The primary outcome variable for the trial was ventilator-free days (VFDs) to day 28. RESULTS: We enrolled 60 subjects. VFDs were not significantly different in the standard PP group (18 [interquartile range [IQR] 0-23] VFDs vs 7.5 [IQR 0-19.0] VFDs; difference, -10.5 (95% CI -3.5 to 19.0, P = .08). Prolonged PP was associated with longer time to successful extubation in survivors (13.00 [IQR 8.75-26.00] d vs 8.00 [IQR 5.00-10.25] d; difference, 5 [95% CI 0-15], P = .001). Prolonged PP was also significantly associated with longer ICU stay (18.5 [IQR 11.8-25.3] d vs 11.50 [IQR 7.75-25.00] d, P = .050) and extended administration of neuromuscular blockers (12.50 [IQR 5.75-20.00] d vs 5.0 [IQR 2.0-14.5] d, P = .005). Prolonged PP was associated with significant muscular impairment according to lower Medical Research Council values (59.6 [IQR 59.1-60.0] vs 56.5 [IQR 54.1-58.9], P = .02). CONCLUSIONS: Among subjects with severe ARDS due to COVID-19, there was no difference in 28-d VFDs between prolonged and standard PP strategy. However, prolonged PP was associated with a longer ICU stay, increased use of neuromuscular blockers, and greater muscular impairment. This suggests that prolonged PP is not superior to the current recommended standard of care.

¿Podrían ayudarnos los algoritmos de machine learning en la predicción de hemorragia masiva a nivel prehospitalario? / Could machine learning algorithms help us in predicting massive hemorrhage at the prehospital level?

Objetivo: Comparación de la capacidad predictiva de diferentes algoritmos de machine learning (AML) respecto a escalas tradicionales de predicción de hemorragia masiva en pacientes con enfermedad traumática grave (ETG). Diseño: Sobre una base de datos de una cohorte retrospectiva con variables clínicas prehospitalarias y de resultado de hemorragia masiva se realizó un tratamiento de la base de datos para poder aplicar los AML, obteniéndose un conjunto total de 473 pacientes (80% entrenamiento, 20% validación). Para la modelización se realizó imputación proporcional y validación cruzada. El poder predictivo se evaluó con la métrica ROC y la importancia de las variables mediante los valores Shapley. Ámbito: Atención extrahospitalaria del paciente con ETG. Pacientes: Pacientes con ETG atendidos en el medio extrahospitalario por un servicio médico extrahospitalario desde enero de 2010 hasta diciembre de 2015 y trasladados a un centro de trauma en Madrid. Intervenciones: Ninguna. Variables de interés principales: Obtención y comparación de la métrica ROC de 4 AML: random forest, support vector machine, gradient boosting machine y neural network con los resultados obtenidos con escalas tradicionales de predicción. Resultados: Los diferentes AML alcanzaron valores ROC superiores al 0,85, teniendo medianas cercanas a 0,98. No encontramos diferencias significativas entre los AML. Cada AML ofrece un conjunto de variables diferentes, pero con predominancia de las variables hemodinámicas, de reanimación y de deterioro neurológico. Conclusiones: Los AML podrían superar a las escalas tradicionales de predicción en la predicción de hemorragia masiva. (AU)

Objective: Comparison of the predictive ability of various machine learning algorithms (MLA) versus traditional prediction scales for massive hemorrhage in patients with severe traumatic injury (ETG). Design: On a database of a retrospective cohort with prehospital clinical variables and massive hemorrhage outcome, a treatment of the database was performed to be able to apply the different MLA, obtaining a total set of 473 patients (80% training and 20% validation). For modeling, proportional imputation and cross validation were performed. The predictive power was evaluated with the ROC metric and the importance of the variables using the Shapley values. Setting: Out-of-hospital care of patients with ETG. Participants: Patients with ETG treated out-of-hospital by a prehospital medical service from January 2010 to December 2015 and transferred to a trauma center in Madrid. Interventions: None. Main variables of interest: Obtaining and comparing the ROC curve metric of 4 MLAs: random forest, support vector machine, gradient boosting machine and neural network with the results obtained with traditional prediction scales. Results: The different MLA reached ROC values higher than 0.85, having medians close to 0.98. We found no significant differences between MLAs. Each MLA offers a different set of more important variables with a predominance of hemodynamic, resuscitation variables and neurological impairment. Conclusions: MLA may be helpful in patients with massive hemorrhage by outperforming traditional prediction scales. (AU)

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. The predictive power was evaluated with the ROC metric and the importance of the variables using the Shapley values. SETTING: Out-of-hospital care of patients with STI. PARTICIPANTS: Patients with STI treated out-of-hospital by a out-of-hospital medical service from January 2010 to December 2015 and transferred to a trauma center in Madrid. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Obtaining and comparing the "Receiver Operating Characteristic curve" (ROC curve) metric of four MLAs: "random forest" (RF), "vector support machine" (SVM), "gradient boosting machine" (GBM) and "neural network" (NN) with the results obtained with TPS. RESULTS: The different AML reached ROC values higher than 0.85, having medians close to 0.98. We found no significant differences between AMLs. Each AML offers a different set of more important variables with a predominance of hemodynamic, resuscitation variables and neurological impairment. CONCLUSIONS: MLA may be helpful in patients with HM by outperforming TPS.

Plasmapheresis in the Treatment of Refractory Myoclonic Status. A Case Report.

A case of myoclonic status treated with plasmapheresis in a patient of 63 years of age who was admitted to a Spanish intensive care unit is reported. The patient showed clinical and radiological evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection; molecular tests did not verify this.