Utility of Bland-Altman Plot in the Assessment of Inter-observer Variability for Internal Quality Control in Semen Analysis: A Cross-sectional Study

Abstract

Introduction: Assessment of inter-observer variability is an essential component of quality control in semen analysis. The authors compared Bland-Altman (BA) plot, Intraclass correlation coefficient and Student’s paired t-test to determine which was the most feasible method for statistical analysis of internal quality control in their laboratory and the reason for the same. Aim: To assess inter-observer variability in sperm concentration and motility in fresh samples using Bland-Altman plot, Student’s paired t-test and Intraclass Correlation Coefficient (ICC), as a part of internal quality control. Materials and Methods: A cross-sectional observational study was conducted in the South of India, Puducherry, over a period of six months from 1st January 2020 to 30th June 2020. As a part of internal quality control two assessors independently analysed sperm concentration, progressive and non progressive motility and immotile spermson two aliquots of the samples tested by the manual method. Inter-observer variability was analysed using Bland-Altman plot, Students paired t-test and ICC. Data was analysed using Microsoft Excel® (2016), GraphPad Prism version 9, Mangold ICC calculator software and online ICC calculator at the website http://vassarstats.net/index.html Results: Nineteen men were included in the study. The ICC coefficient showed good correlation for sperm concentration, progressive motility, immotile sperms with values of 0.77, 0.84, 0.95 respectively and moderate correlation for non progressive motility with ICC=0.72. The p-value of Student’s paired t-test was above 0.05 for all parameters. There was no significant difference between the assessors for sperm concentration and motility using ICC and Student’s paired t-test, although the Student’s paired t-test does not measure agreement. BlandAltman plot showed an occasional outlier for both parameters. The authors attributed different pockets of sampling as the reason for outlier in sperm concentration. In motility testing, the two outliers were a result of delayed reporting by one of the assessors resulting in decline in the progressive motile and an increase in the non progressive motile and immotile sperms. Conclusion: In a low throughout laboratory, fresh semen is a useful sample for daily quality control. Bland-Altman plot is an easy and effective tool for monitoring internal quality control when there are two assessors, obviating the need for complex statistical tests. Adopting simple yet effective methods for internal quality control, would encourage more laboratories to come into the ambit of quality assurance.