| Title |
Comparison of left ventricular manual versus automated derived longitudinal strain: implications for clinical practice and research
|
|---|---|
| Published in |
The International Journal of Cardiovascular Imaging, November 2015
|
| DOI | 10.1007/s10554-015-0804-x |
| Pubmed ID | |
| Authors |
Yukari Kobayashi, Miyuki Ariyama, Yuhei Kobayashi, Genevieve Giraldeau, Dominik Fleischman, Mirta Kozelj, Bojan Vrtovec, Euan Ashley, Tatiana Kuznetsova, Ingela Schnittger, David Liang, Francois Haddad |
| Abstract |
Systolic global longitudinal strain (GLS) is emerging as a useful metric of ventricular function in heart failure and usually assessed using post-processing software. The purpose of this study was to investigate whether longitudinal strain (LS) derived using manual-tracings of ventricular lengths (manual-LS) can be reliable and time efficient when compared to LS obtained by post-processing software (software-LS). Apical 4-chamber view images were retrospectively examined in 50 healthy controls, 100 patients with dilated cardiomyopathy (DCM), and 100 with hypertrophic cardiomyopathy (HCM). We measured endocardial and mid-wall manual-LS and software-LS, using peak of average regional curve [software-LS(a)] and global ventricular lengths [software-LS(l)] according to definition of Lagragian strain. We compared manual-LS and software-LS by using Bland-Altman plot and coefficient of variation (COV). In addition, test-retest was also performed for further assessment of variability in measurements. While manual-LS was obtained in all subjects, software-LS could be obtained in 238 subjects (95 %). The time spent for obtaining manual-LS was significantly shorter than for the software-LS (94 ± 39 s vs. 141 ± 79 s, P < 0.001). Overall, manual-LS had an excellent correlation with both software-LS (a) (R(2) = 0.93, P < 0.001) and software-LS(l) (R(2) = 0.84, P < 0.001). The bias (95 %CI) between endocardial manual-LS and software-LS(a) was 0.4 % [-2.8, 3.6 %] in absolute and 3.5 % [-17.0, 24.0 %] in relative difference while it was 0.4 % [-2.5, 3.3 %] and 3.4 % [-16.2, 23.1 %], respectively with software-LS(l). Mid-wall manual-LS and mid-wall software-LS(a) also had good agreement [a bias (95 % CI) for absolute value of 0.1 % [-2.1, 2.5 %] in HCM, and 0.2 % [-2.2, 2.6 %] in controls]. The COV for manual and software derived LS were below 6 %. Test-retest showed good variability for both methods (COVs were 5.8 and 4.7 for endocardial and mid-wall manual-LS, and 4.6 and 4.9 for endocardial and mid-wall software-LS(a), respectively. Manual-LS appears to be as reproducible as software-LS; this may be of value especially when global strain is the metric of interest. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Saudi Arabia | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 19% |
| Student > Postgraduate | 5 | 19% |
| Other | 4 | 15% |
| Professor > Associate Professor | 3 | 11% |
| Researcher | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 5 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 44% |
| Computer Science | 2 | 7% |
| Agricultural and Biological Sciences | 1 | 4% |
| Psychology | 1 | 4% |
| Engineering | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 10 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 November 2015.
All research outputs
#19,942,887
of 25,371,288 outputs
Outputs from The International Journal of Cardiovascular Imaging
#1,116
of 2,012 outputs
Outputs of similar age
#272,565
of 392,657 outputs
Outputs of similar age from The International Journal of Cardiovascular Imaging
#13
of 39 outputs
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