Septic cardiomyopathy is an important contributor to organ dysfunction in sepsis. Guided treatment of septic cardiomyopathy may affect patients' prognosis, especially when their cardiac index is substantially decreased. The implication of septic cardiomyopathy for both short- and long-term outcomes is an important area for future investigation.
Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians’ interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.
Purpose of review Severe sepsis with septic shock is the most common cause of death among critically ill patients. Mortality has decreased substantially over the last decade but recent data has shown that opportunities remain for the improvement of early and targeted therapy. This review discusses published data regarding the role of focused ultrasonography in septic shock resuscitation. Recent findings Early categorization of the cardiovascular phenotypes with echocardiography can be crucial for timely diagnosis and targeted therapy of patients with septic shock. In the last few years, markers of volume status and volume responsiveness have been investigated, serving as valuable tools for targeting volume therapy in the care of both spontaneously breathing and mechanically ventilated patients. In tandem, investigators have highlighted findings of extravascular volume with ultrasonographic evaluation to compliment de-escalation of resuscitation efforts when appropriate. Furthermore, special attention has been given to resuscitation efforts of patients in septic shock with right ventricular failure. Summary Severe sepsis with septic shock is an insidious disease process that continues to take lives. In more recent years, data have emerged suggesting the utility of bedside ultrasonography for early cardiovascular categorization, goal directed resuscitation, and appropriate cardiovascular support based on its changing phenotypes.
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