Accuracy of Cardiac Nurse Acquired and Measured Three-Dimensional Echocardiographic Left Ventricular Ejection Fraction: Comparison to Echosonographer

Ultrasound-Guided Radial Artery Puncture by Nurses in Emergency Department: A Randomized Controlled Study

INTRODUCTION

Radial artery puncture has been performed by palpation as a standard method in many emergency departments and intensive care units. Nurses play an important role in the care of patients in various settings. Ultrasonography can be performed and interpreted not only by physicians but also by nurses. This study aimed to evaluate whether emergency nurses would be more successful in radial artery puncture procedure by using ultrasonography instead of palpation.

METHODS

This single-center, prospective, randomized controlled study was conducted in the emergency department. The patients included in the study were randomized into 2 groups as ultrasonography and palpation groups. Data were recorded on the number of interventions, the duration of the procedure in seconds, total time in seconds, whether the puncture was successfully placed, whether there were complications, the types of complications (hematoma, bleeding, and infection), or whether it was necessary to switch to an alternative technique.

RESULTS

A total of 72 patients, 36 patients in the ultrasonography group and 36 patients in the palpation group, participated in the study. The success rate at the first attempt was statistically significantly higher in the ultrasonography group. Although hematoma formation among the complications occurred in the entire palpation group, it was observed in 72.2% of the ultrasonography group. Puncture time and total time were statistically significantly lower in the ultrasonography group.

DISCUSSION

Our study shows that emergency nurses can use bedside ultrasonography for radial artery puncture successfully.

Usefulness of Heart Failure Categories Based on Left Ventricular Ejection Fraction

BACKGROUND

Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high.

METHODS AND RESULTS

We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020. All values of LVEF were collected over time, and patients were followed up until death, moving out of Stockholm, or end of study. Mixed models were used to quantify within-person variance in LVEF, and multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. LVEF values followed a normal distribution. The SD of the within-person variance in LVEF over time was 7.4%. The mean time spent in any LVEF category before transition to another category was on average <1 year for heart failure with mildly reduced ejection fraction. Probabilities of transitioning between categories during the first year were substantial; patients with heart failure with mildly reduced ejection fraction had a probability of <25% of remaining in that category 1 year later.

CONCLUSIONS

LVEF follows a normal distribution and has considerable variability over time, which may impose a risk for underuse of efficient treatment. The heart failure with mildly reduced ejection fraction category is especially inconstant. Assumptions of a patient's current LVEF should take this variability and the normal distribution of LVEF into account.

Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction

BACKGROUND

Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most.

AIM

To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations.

METHODS

A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%.

RESULTS

The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348.

CONCLUSION

This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.