Résumé
Background: Standardization of the semen analysis may improve reproducibility. We assessed variability between laboratories in semen analyses and evaluated whether a transformation using Z scores and regression statistics was able to reduce this variability
Materials and Methods: We performed a retrospective cohort study. We calculated between-laboratory coefficients of variation [CVB] for sperm concentration and for morphology. Subsequently, we standardized the semen analysis results by calculating laboratory specific Z scores, and by using regression. We used analysis of variance for four semen parameters to assess systematic differences between laboratories before and after the transformations, both in the circulation samples and in the samples obtained in the prospective cohort study in the Netherlands between January 2002 and February 2004
Results: The mean CVB was 7% for sperm concentration [range 3 to 13%] and 32% for sperm morphology [range 18 to 51%]. The differences between the laboratories were statistically significant for all semen parameters [all P<0.001]. Standardization using Z scores did not reduce the differences in semen analysis results between the laboratories [all P<0.001]
Conclusion: There exists large between-laboratory variability for sperm morphology and small, but statistically significant, between-laboratory variation for sperm concentration. Standardization using Z scores does not eliminate between-laboratory variability
Résumé
Like any other medical technology or intervention, diagnostic tests should be thoroughly evaluated before their introduction into daily practice. Increasingly, decision makers, physicians, and other users of diagnostic tests request more than simple measures of a test's analytical or technical performance and diagnostic accuracy; they would also like to see testing lead to health benefits. In this last article of our series, we introduce the notion of clinical utility, which expresses-preferably in a quantitative form-to what extent diagnostic testing improves health outcomes relative to the current best alternative, which could be some other form of testing or no testing at all. In most cases, diagnostic tests improve patient outcomes by providing information that can be used to identify patients who will benefit from helpful downstream management actions, such as effective treatment in individuals with positive test results and no treatment for those with negative results. We describe how comparative randomized clinical trials can be used to estimate clinical utility. We contrast the definition of clinical utility with that of the personal utility of tests and markers. We show how diagnostic accuracy can be linked to clinical utility through an appropriate definition of the target condition in diagnostic-accuracy studies.