Thibaut Markarian

Can machine learning be used to quantify blood loss based on hemodynamic data? Experimental study of controlled hemorrhagic shock in anesthetized piglets

Background Hemorrhagic compensatory physiological mechanisms may initially lead to an underestimation of the severity of a hemorrhage, for which quantification of blood lost volume is difficult. The objective of our study was to assess the suitability of machine learning (ML) to...

Association between the site of emergency care and patient-centered outcomes in patients with cancer: rationale and design of the French COVER cohort

Patients living with cancer now account for a substantial and growing share of emergency department (ED) visits and carry a high burden of hospitalization and in-hospital death. Their acute presentations are frequently multifactorial - driven by disease progression, treatment tox...

Changes in LVOT velocity–time integral during whole blood donation: implications for early detection of hypovolemia in spontaneously breathing patients

Early identification of hypovolemia remains challenging in emergency settings, as vital signs may remain normal during initial blood loss. Point-of-care echocardiography allows assessment of stroke volume through left ventricular outflow tract velocity–time integral (LVOT VTI), b...