Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients

Gonzalez, Filipe; Varudo, Rita; Leote, João; Martins, Cristina; Bacariza, Jacobo; Fernandes, Antero; Michard, Frédèric

doi:10.1016/j.bja.2022.07.037

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…In a recent study, 60 in order to identify phenotypes with comparable behavior for forecasting unfavorable outcomes, the authors looked at unsupervised clustering to categorize directly recorded LV diastolic function measures in patients with heart failure, including PALS, only slightly agreeing with the classification determined by the 2016 guidelines. 18 This analysis yielded three separate groups that outperformed guideline-based categorization for forecasting unfavorable occurrences during follow-up (66) learning-based methodologies have adequately demonstrated that a cluster-based classification works better than a consensus-based classification. [61][62][63] At the same time, the proposed algorithm 60 draws attention to the possible value of PALS in DD grading.…”

Section: Future Diagnostic Approaches To Lvdd and La Pressure In Icumentioning

confidence: 97%

“…64 Much literature already exists regarding systolic function and fluid status assessment using machine learning algorithms applying automatic measurements of LVEF and left ventricular outflow tract velocity time integral. [65][66][67] Considering all the pitfalls described for the assessment of DD in critically ill patients, we consider a more holistic approach to the dias-tolic function in the ICU more valuable. Experienced clinicians should integrate the hemodynamic information (based not only on echocardiographic variables) with other data from like lung ultrasound (LUS), the venous excess ultrasound (VExUS), and the ventriculo-arterial coupling, 68,69 to better stratify critically ill patients according to their pathophysiologic profile and prognostic impact.…”

Section: Future Diagnostic Approaches To Lvdd and La Pressure In Icumentioning

confidence: 99%

“…Hence, we think that it is likely that the current cardiology guidelines will be at some point revised with the chance to introduce PALS or other strain‐based parameters describing LA function, and possibly with a contribution from artificial intelligence 64 . Much literature already exists regarding systolic function and fluid status assessment using machine learning algorithms applying automatic measurements of LVEF and left ventricular outflow tract velocity time integral 65–67 . Considering all the pitfalls described for the assessment of DD in critically ill patients, we consider a more holistic approach to the diastolic function in the ICU more valuable.…”

Section: Future Diagnostic Approaches To Lvdd and La Pressure In Icumentioning

confidence: 99%

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Diastology in the intensive care unit: Challenges for the assessment and future directions

Gonzalez,

Santonocito,

Maybauer

et al. 2024

Echocardiography

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Myocardial dysfunction is common in patients admitted to the intensive care unit (ICU). Septic disease frequently results in cardiac dysfunction, and sepsis represents the most common cause of admission and death in the ICU. The association between left ventricular (LV) systolic dysfunction and mortality is not clear for critically ill patients. Conversely, LV diastolic dysfunction (DD) seems increasingly recognized as a factor associated with poor outcomes, not only in sepsis but also more generally in critically ill patients. Despite recent attempts to simplify the diagnosis and grading of DD, this remains relatively complex, with the need to use several echocardiographic parameters. Furthermore, the current guidelines have several intrinsic limitations when applied to the ICU setting. In this manuscript, we discuss the challenges in DD classification when applied to critically ill patients, the importance of left atrial pressure estimates for the management of patients in ICU, and whether the study of cardiac dysfunction spectrum during critical illness may benefit from the integration of left ventricular and left atrial strain data to improve diagnostic accuracy and implications for the treatment and prognosis.

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Section: Future Diagnostic Approaches To Lvdd and La Pressure In Icumentioning

confidence: 97%

Section: Future Diagnostic Approaches To Lvdd and La Pressure In Icumentioning

confidence: 99%

Section: Future Diagnostic Approaches To Lvdd and La Pressure In Icumentioning

confidence: 99%

See 1 more Smart Citation

Diastology in the intensive care unit: Challenges for the assessment and future directions

Gonzalez,

Santonocito,

Maybauer

et al. 2024

Echocardiography

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“…VTI aids in assessing changes in stroke volume and is useful for assessing changes in cardiac output. The clinical use of the VTI component is well-known and widely studied in adult cardiology [7][8][9]. In contrast to the utility of VTI assessment in various postnatal clinical conditions, it is utilized much less often in fetuses.…”

Section: Introductionmentioning

confidence: 99%

Velocity Time Integral: A Novel Method for Assessing Fetal Anemia

et al. 2023

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The velocity time integral (VTI) is a clinical Doppler ultrasound measurement of blood flow, measured by the area under the wave curve and equivalent to the distance traveled by the blood. This retrospective study assessed the middle cerebral artery (MCA) VTI of fetuses in pregnancies complicated by maternal alloimmunization. Doppler indices of the MCA were retrieved from electronic medical records. Systolic deceleration-diastolic time, systolic acceleration time, VTI, and peak systolic velocity (PSV) were measured at 16–40 weeks gestation. Cases with PSV indicating fetal anemia (cutoff 1.5 MoM) and normal PSV were compared. The study included 255 Doppler ultrasound examinations. Of these, 41 were at 16–24 weeks (group A), 100 were at 25–32 weeks (group B), and 114 were at 33–40 weeks (group C). VTI increased throughout gestation (5.5 cm, 8.6 cm, and 12.1 cm in groups A, B, and C, respectively, p = 0.003). VTI was higher in waveforms calculated to have MCA-PSV ≥ 1.5 MoM compared to those with MCA-PSV < 1.5 MoM (9.1 cm vs. 14.1 cm, respectively, p < 0.001), as was VTI/s (22.04 cm/s vs. 33.75 cm/s, respectively; p < 0.001). The results indicate that the MCA VTI increases significantly among fetuses with suspected anemia, indicating higher perfusion of hemodiluted blood to the brain. This feasible measurement might provide a novel additional marker for the development of fetal anemia.

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“…It has the *Correspondence: frederic.michard@bluewin.ch disadvantage of being time-consuming and operator dependent. Machine learning algorithms have recently been developed to facilitate, automate, and decrease the variability of echocardiographic measurements [4][5][6][7]. Several algorithms have been designed specifically for the real-time assessment of LVEF [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography

et al. 2022

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Background Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. Methods Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEFNov) and by experts (LVEFExp) were compared with LVEF reference measurements (LVEFRef) taken manually by echo experts. Results LVEFRef ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEFRef < 50% (left ventricular systolic dysfunction). Real-time LVEFExp and LVEFNov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEFExp (5 ± 4%) and for LVEFNov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEFRef and both real-time LVEFExp (r = 0.86, p < 0.001) and LVEFNov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEFExp and 0 ± 7% for LVEFNov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEFExp and 73% for LVEFNov. The specificity to detect systolic dysfunction was 98% both for LVEFExp and LVEFNov. Conclusion Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved. Trial registration: NCT05336448. Retrospectively registered on April 19, 2022.

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Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients

Cited by 7 publications

References 10 publications

Diastology in the intensive care unit: Challenges for the assessment and future directions

Diastology in the intensive care unit: Challenges for the assessment and future directions

Velocity Time Integral: A Novel Method for Assessing Fetal Anemia

Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography

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