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. 2024 Nov 18;19(11):e0310307.
doi: 10.1371/journal.pone.0310307. eCollection 2024.

Two-dimensional fetal speckle tracking; a learning curve study for offline strain analysis

Chantelle M de Vet  1   2   3 , Thomas J Nichting  1   2   3 , Annemarie F Fransen  1 , Daisy A A van der Woude  1 , Myrthe van der Ven  1   3   4 , Richard A J Post  5 , Zoé van Lier  1 , S Guid Oei  1   2   3 , Noortje H M van Oostrum  6 , Judith O E H van Laar  1   2   3
Affiliations

Two-dimensional fetal speckle tracking; a learning curve study for offline strain analysis

Chantelle M de Vet et al. PLoS One. .

Abstract

Objectives: Two-dimensional speckle tracking (2D-STE) strain analysis holds promise for assessing fetal cardiac function. Understand the learning curve before introducing 2D-STE into obstetrics is crucial. This study examined the learning curve for offline analysis of fetal left (LV) and right ventricular (RV) global longitudinal strain (GLS) using 2D-STE.

Methods: After 2D-STE training, three trainees (Maternal-Fetal Medicine fellow, OBGYN resident and medical student) analyzed 100 fetal heart clips using 2D-STE to calculate LV- and RV-GLS. Intra-class correlation coefficients (ICC) and Bland-Altman plots were compared GLS values across four sets of 25 clips for each trainee against the expert. Repeated measurements analysis compared GLS score differences between expert and trainees over time and among trainees, adjusting p-values with a Bonferroni correction.

Results: LV-GLS consistency evolved from poor-to-moderate during the first 50 measurements to moderate-to-good during the second 50 for all trainees. RV-GLS consistency evolved from poor-to-moderate during the first 75 measurements to moderate-to-good during the final 25 measurements for the fellow and resident. The student's RV-GLS consistency was poor during the first 25 measurements, moderate-to good during the second 25 measurements and again poor-to-moderate during the final 50 measurements. Repeated measurements analysis showed a significant decrease in variability of the LV- and RV-GLS score differences between the expert and trainees over time (padj<0.001), which was not significantly different between trainees. Moreover, the mean of those differences were significantly different for all trainees for LV-GLS (padj<0.001) and RV-GLS (padj = 0.029), and did significantly change over time for RV-GLS (padj<0.001) but not for LV-GLS.

Conclusions: A clear learning effect was observed by the significant decrease in variability of the difference in the score between the expert and trainees over time. The consistency of fetal GLS analysis with 2D-STE was generally found to be moderate to good after 100 measurements in trainees.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. GLS analysis with 2D-STE software.
Fig 2
Fig 2. ICC of each trainee LV-GLS value compared with the expert in four consecutive groups of 25 fetal heart clips.
Fig 3
Fig 3. Mean of the difference between the LV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips.
Fig 4
Fig 4. Standard deviation of the difference between the LV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips.
Note: with corresponding 95% confidence intervals obtained via bootstrapping.
Fig 5
Fig 5. ICC of each trainee RV-GLS value compared with the expert in four consecutive groups of 25 fetal heart clips.
Fig 6
Fig 6. Mean of the difference between the RV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips.
Fig 7
Fig 7. Standard deviation of the difference between the RV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips.
Note: with corresponding 95% confidence intervals obtained via bootstrapping.
See this image and copyright information in PMC

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