CARDIOVASCULAR JOURNAL OF AFRICA • Volume 35, No 1, January – April 2024 AFRICA 45 a frequency of 1.7 MHz and receives a frequency of 3.4 MHz. All data were analysed offline after being transferred to an Xcelera workstation (Philips). The RALS analysis was performed separately offline using Philips Q lab 11.0 speckletracking software. Two-dimensional echocardiography was used for measurements of RA dimensions. They were performed in agreement with ASE chamber quantification guidelines of 2015 and the 2010 ASE guidelines on right heart assessment. The RA parameters (area and volume) were measured at the end of systole on an apical four-chamber view modified to optimise the RA visualisation. The RAV was measured using the single-plane method of discs by tracing an outline of the RA blood–tissue interface, ensuring that the RA appendage, superior vena cava and inferior vena cava were excluded. The tricuspid tenting area was also excluded (Fig. 1).12,14,15 The RALS was measured by an experienced cardiologist and clinical technologist using STE. For STE using 2D grey-scale echocardiography, apical four-chamber views were captured during breath holding at the end of the expiratory phase for a few seconds, and with an electrocardiogram recording attached. A suitable image of myocardial tissue was obtained completely separated from surrounding structures. Three successive cardiac cycles were recorded and averaged. The frame rate was set between 60 and 80 frames per second. Analysis of specklebased strain was done using Philips Q lab 11.0 speckle-tracking software. In four-chamber RA focused views, the endocardial surface of the RA was traced manually by a three-point-and-click approach. The system automatically generates an epicardial surface tracing. The region of interest (ROI) was therefore created, composed of seven segments. The ROI was manually adjusted as needed to allow for enough speckle tracking. The software generates the longitudinal curves for each segment with its mean value (Fig. 2).20-22 The number of healthy controls who were enrolled in the parent study was 100, after excluding 23 patients who did not meet the study inclusion criteria. Using the power command in Stata, we conducted a one-sample correlation test to estimate the minimum sample required to detect a correlation of at least r = 0.3 between RALS and age. The minimum sample size required was 100. All participants were subdivided into four age groups (group 1: 18−29 years, group 2: 30−39 years, group 3: 40−49 years and group 4: ≥ 50 years). This study is a secondary data analysis, and we utilised data from an existing database. Only variables in our data collection sheet were used in this study. Statistical analysis Continuous variables are described using the mean and standard deviation, or median and interquartile range when variables were not normally distributed. The independent t-test was used to compare means of continuous variables by gender while the Mann−Whitney test was used for a comparison of median values by gender. One-way analysis of variance (ANOVA) was used to compare means of normally distributed continuous variables by age group or body mass index (BMI) category, while the Kruskal−Wallis test was used for comparison of medians when variables were not normally distributed. Pearson’s correlation coefficients determined the association between age and RA parameters using a statistical significance threshold of 0.05. All statistical analyses were conducted in Stata version 15. Univariate and multivariable linear regression was used to explore the association between RALS and independent Fig. 1. Apical four-chamber view in a normal participant, showing measurement of RA volume using the discs method. Fig. 2. STE showing decreased peak RA longitudinal strain of 25.5% in an older subject (A), compared to a younger subject of 46.5% (B). A B
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