reliability of minimally trained operator's velocity-time integral measurement guided by artificial intelligence vti

Conditions: Velocity-time Integral Measurement; Critically Ill Patients; Tissue Hypoperfusion

Summary: stroke volume is a major determinant of tissue perfusion and therefore a key parameter to monitor in patients with hemodynamic instability and hypoperfusion. left ventricular outflow tract (lvot) velocity-time integral (vti) measured using pulsed wave doppler is widely used as an estimation of stroke volume and should be a competence required for every intensive care unit (icu) physician. recently, research in artificial intelligence (ai) applied to medical imaging constituted a breakthrough in the acquisition of images. the goal of the present study is to characterize and quantify the reliability and reproducibility of lvot vti measurements by comparing the measures obtained by minimally-trained operators and expert physicians, guided by ultrasight ai software.

the main goal of intensive care unit (icu) physicians is to ensure cellular oxygenation by maintaining adequate organ perfusion in their patients. stroke volume is a major determinant of tissue perfusion and therefore a key parameter to monitor in patients with hemodynamic instability. left ventricular outflow tract (lvot) velocity-time integral (vti) measured using pulsed wave doppler is widely used as an estimation of stroke volume to assess hemodynamic modifications. this value reflects the stroke distance, which varies proportionately to stroke volume in case of hemodynamic variations resulting from therapeutic interventions (fluid administration, vasoactive drugs...) or disease processes. an increase in stroke volume (or lvot vti) is expected in response to fluid administration and attests for its efficacy. a lack of increase indicates that the cardiovascular system is no longer fluid-responsive, and that fluid administration is not improving tissue perfusion and creates congestion. therefore, measuring aortic vti should be a competence required for every icu physician. however, international icu guidelines on echocardiography do not consider lvot vti measurement as a basic skill but rather as a competence of advanced operators. more recently, the european society of intensive care medicine published expert recommendations on echocardiography, setting the evaluation of lvot vti as basic skill but with a weak recommendation, lacking published evidence to support this statement. the main difficulty in measuring lvot vti is obtaining an adequate apical 5-chamber view. recently, research in artificial intelligence (ai) applied to medical imaging constituted a breakthrough in the acquisition of images. ultrasight is a company specialized in ai applied to echocardiography. their software is based on neural network using machine learning to analyse extremely precisely the image obtained by an operator. the software indicates to the operator in real time on-screen how to optimize the image by mobilizing the probe until the desired view is correctly obtained, with the best quality. the main objective of the present study is to characterize and to quantify the reliability and reproducibility of lvot vti measurements by comparing the measures obtained by minimally trained operators and experts, using an ultrasound platform equipped with real-time ai-based guidance (ultrasight). if interchangeability of minimally trained operators and expert measurements can be demonstrated, this will constitute a strong basis to upgrade the measurement of lvot vti as a basic competence in critical care ultrasound. the secondary objectives are to assess the concordance of therapeutic decisions made by the icu clinician in charge of the patient (i.e.: continue or interrupt fluid administration) based on the vti variation obtained by the minimally-trained operator, and that based on the vti variation obtained by the expert, the agreement of the absolute value of the measure of lvot vti obtained by the minimally trained operators and the experts, the correlation between the measures of the vti variation (% change following a fluid challenge of 250 ml or a passive leg-raising test) between the minimally-trained operators and those obtained by experts.

Eligibility criteria:
inclusion criteria: all patients aged 18 and more hospitalized in icu, in whom fluid administration is considered necessary by the clinician in charge, based on the presence of hypoperfusion criterion: * \>10% decrease in mean arterial pressure with respect to baseline value * skin mottling, oliguria (\<0,5 ml/kg/h) * change in the level of consciousness * hyperlactatemia * decrease in central venous oxygen saturation affiliation to a french social security system (beneficiary or legal) participant's or next of kin non-opposition or emergency procedure exclusion criteria: patients with atrial fibrillation, due to the higher variability in lvot vti; patient on emergency medical assistance; patient under guardianship, curatorship, deprived of liberty.

Keywords: velocity-time integral echocardiography artificial intelligence minimally-trained operator reproducibility fluid challenge fluid responsiveness