Observational study in healthy volunteers to define interobserver reliability of ultrasound haemodynamic monitoring techniques performed by trainee doctors

Bussmann, Benjamin Mothibe; Sharma, Shivom; McGregor, David; Hulme, William; Harris, Tim

doi:10.1097/mej.0000000000000533

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…This training programme ensured that operators' experience on all methods was identical and achievable by junior residents and ED nurses. Interobserver reliability for LVOT VTI amongst operators was satisfactory and reported in a separate study [43].…”

Section: Operator Trainingmentioning

confidence: 73%

Emergency department non-invasive cardiac output study (EDNICO): an accuracy study

McGregor

Sharma

Gupta

et al. 2020

Scand J Trauma Resusc Emerg Med

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Background: There is little published data investigating non-invasive cardiac output monitoring in the emergency department (ED). We assess here the accuracy of five non-invasive methods in detecting fluid responsiveness in the ED: (1) common carotid artery blood flow, (2) suprasternal aortic Doppler, (3) bioreactance, (4) plethysmography with digital vascular unloading method, and (5) inferior vena cava collapsibility index. Left ventricular outflow tract echocardiography derived velocity time integral is the reference standard. This follows an assessment of feasibility and repeatability of these methods in the same cohort of ED patients. Methods: This is a prospective observational study of non-invasive methods for assessing fluid responsiveness in the ED. Participants were non-ventilated ED adult patients requiring intravenous fluid resuscitation. Sensitivity and specificity of each method in determining the fluid responsiveness status of participants is determined in comparison to the reference standard. Results: Thirty-three patient data sets were included for analysis. The specificity and sensitivity to detect fluid responders was 46.2 and 45% for common carotid artery blood flow (CCABF), 61.5 and 63.2% for suprasternal artery Doppler (SSAD), 46.2 and 50% for bioreactance, 50 and 41.2% for plethysmography vascular unloading technique (PVUT), and 63.6 and 47.4% for inferior vena cava collapsibility index (IVCCI), respectively. Analysis of agreement with Cohen's Kappa − 0.08 for CCABF, 0.24 for SSAD, − 0.04 for bioreactance, − 0.08 for PVUT, and 0.1 for IVCCI. Conclusion: In this study, non-invasive methods were not found to reliably identify fluid responders. Non-invasive methods of identifying fluid responders are likely to play a key role in improving patient outcome in the ED in fluid depleted states such as sepsis. These results have implications for future studies assessing the accuracy of such methods.

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Section: Operator Trainingmentioning

confidence: 73%

Emergency department non-invasive cardiac output study (EDNICO): an accuracy study

McGregor

Sharma

Gupta

et al. 2020

Scand J Trauma Resusc Emerg Med

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“…However, work by our group showed that the test characteristics were not as favorable when POCUS examinations were performed by novice sonologists (AUROC, 0.69) 15 . In addition, poor‐to‐moderate interrater agreement in cIVC measurement among nonexpert sonologists has been reported by other authors 16–18 . Training clinicians to a level at which they obtain the requisite skills necessary to accurately use POCUS‐measured cIVC to guide IV fluid resuscitation might be the most substantial hurdle this approach faces before it can be widely adopted.…”

mentioning

confidence: 75%

“…15 In addition, poor-to-moderate interrater agreement in cIVC measurement among nonexpert sonologists has been reported by other authors. [16][17][18] Training clinicians to a level at which they obtain the requisite skills necessary to accurately use POCUS-measured cIVC to guide IV fluid resuscitation might be the most substantial hurdle this approach faces before it can be widely adopted. Artificial intelligence using deep learning (DL) algorithms has been proposed as a solution to overcome skill gaps to make the widespread use of POCUS-directed IV fluid resuscitation more feasible, reliable, and useful.…”

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confidence: 99%

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness

Blaivas

Philips

et al. 2020

J of Ultrasound Medicine

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Objectives-To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients.Methods-We used a data set of IVC ultrasound (US) videos to train the LSTM network. The data set was created from IVC US videos of spontaneously breathing critically ill patients undergoing intravenous fluid resuscitation as part of 2 prior prospective studies. We randomly selected 90% of the IVC videos to train the LSTM network and 10% of the videos to test the LSTM network's ability to predict fluid responsiveness. Fluid responsiveness was defined as a greater than 10% increase in the cardiac index after a 500-mL fluid bolus, as measured by bioreactance.Results-We analyzed 211 videos from 175 critically ill patients: 191 to train the LSTM network and 20 to test it. Using standard data augmentation techniques, we increased our sample size from 191 to 3820 videos. Of the 175 patients, 91 (52%) were fluid responders. The LSTM network was able to predict fluid responsiveness moderately well, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval [CI], 0.43-1.00), a positive likelihood ratio of infinity, and a negative likelihood ratio of 0.3 (95% CI, 0.12-0.77). In comparison, point-of-care US experts using video review offline and manual diameter measurement via software caliper tools achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.83-0.99).Conclusions-We demonstrated that an LSTM network can be trained by using videos of IVC US to classify IVC collapse to predict fluid responsiveness. Our LSTM network performed moderately well given the small training cohort but worse than point-of-care US experts. Further training and testing of the LSTM network with a larger data sets is warranted.

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“…However, non‐invasive devices have emerged as reliable alternatives. One such device is the Ultrasound Cardiac Output Monitor 1A (USCOM‐1A) (USCOM Ltd, Sydney, NSW, Australia), which employs continuous‐wave Doppler ultrasound and requires much less training and specialised knowledge than gold‐standard techniques 12–14 . Assessments are painless and take a skilled operator approximately 10 min to perform.…”

Section: Introductionmentioning

confidence: 99%

Reference ranges for third‐trimester maternal cardiovascular function parameters measured in normotensive pregnant women using a non‐invasive cardiac output monitor: A study based on data from the prospective PEACH cohort study

Lihme,

Basit,

Persson

et al. 2023

BJOG

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ObjectiveWe defined reference ranges for maternal cardiac output, systemic vascular resistance, and stroke volume measured in the third trimester of pregnancy using the Ultrasound Cardiac Output Monitor 1A.DesignBased on data from the prospective PEACH (PreEclampsia, Angiogenesis, Cardiac dysfunction and Hypertension) cohort study.SettingRigshospitalet and Hvidovre Hospital, Denmark.SampleNormotensive pregnant women aged 18–45 years with singleton pregnancies, enrolled in the PEACH study in 2016–2018.MethodsWe modelled cardiac output, systemic vascular resistance and stroke volume as a function of gestational age using multilevel linear models with fractional polynomials.Main Outcome MeasuresUnconditional and conditional reference ranges for cardiovascular parameters measured in gestational weeks 28–40.ResultsOur study cohort included 405 healthy pregnant women who contributed 1210 cardiovascular function measurements for analysis. Maximum cardiac output and stroke volume values were measured in gestational weeks 30–32 and decreased over the third trimester, whereas systemic vascular resistance increased during the same period. We created reference ranges for eight combinations of maternal height, age and parity. We also created a simple calculator to allow for implementation of the reference ranges in clinical practice.ConclusionsOur reference ranges allow the use of a bedside ultrasound device to non‐invasively assess cardiac function in pregnancy and identify women at risk of complications. The unconditional ranges allow clinicians to evaluate isolated measurements and identify women needing follow‐up. The conditional ranges incorporate information from previous measurements and improve monitoring over time.

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Observational study in healthy volunteers to define interobserver reliability of ultrasound haemodynamic monitoring techniques performed by trainee doctors

Cited by 11 publications

References 20 publications

Emergency department non-invasive cardiac output study (EDNICO): an accuracy study

Emergency department non-invasive cardiac output study (EDNICO): an accuracy study

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness

Reference ranges for third‐trimester maternal cardiovascular function parameters measured in normotensive pregnant women using a non‐invasive cardiac output monitor: A study based on data from the prospective PEACH cohort study

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