The application of sigma metrics in the laboratory to assess quality control processes in South Africa

Objective: This study aimed to determine the sigma metrics of analytes when using different total allowable error guidelines.Methods: A retrospective analysis was performed on 19 general chemistry analytes at Charlotte Maxeke Johannesburg Academic Hospital in South Africa between January 2017 and December 2017. Sigma metrics were calculated on two identical analysers, using internal quality control data and total allowable error guidelines from the Ricos biological variation database and three alternative sources (the Royal College of Pathologists of Australasia, the Clinical Laboratory Improvements Amendment, and the European Federation of Clinical Chemistry and Laboratory Medicine). Results: The sigma performance was similar on both analysers but varied based on the guideline used, with the Clinical Laboratory Improvements Amendment guidelines resulting in the best sigma metrics (53% of analytes on one analyser and 46% on the other had acceptable sigma metrics) and the Royal College of Pathologists of Australia guidelines being the most stringent (21% and 23%). Sodium and chloride performed poorly across all guidelines (sigma < 3). There were also month-to-month variations that may result in acceptable sigma despite poor performance during certain months.Conclusion: The sigma varies greatly depending on the total allowable error, but could be a valuable tool to save time and decrease costs in high-volume laboratories. Sigma metrics calculations need to be standardised

Using Systematized Nomenclature of Medicine clinical term codes to assign histological findings for prostate biopsies in the Gauteng province, South Africa: Lessons learnt

Background: Prostate cancer (PCa) is a leading male neoplasm in South Africa.Objective: The aim of our study was to describe PCa using Systemized Nomenclature of Medicine (SNOMED) clinical terms codes, which have the potential to generate more timely data.Methods: The retrospective study design was used to analyse prostate biopsy data from our laboratories using SNOMED morphology (M) and topography (T) codes where the term 'prostate' was captured in the narrative report. Using M code descriptions, the diagnosis, sub-diagnosis, sub-result and International Classification of Diseases for Oncology (ICD-O-3) codes were assigned using a lookup table. Topography code descriptions identified biopsies of prostatic origin. Lookup tables were prepared using Microsoft Excel and combined with the data extracts using Access. Contingency tables reported M and T codes, diagnosis and sub-diagnosis frequencies.Results: An M and T code was reported for 88% (n = 22 009) of biopsies. Of these, 20 551 (93.37%) were of prostatic origin. A benign diagnosis (ICD-O-3:8000/0) was reported for 10 441 biopsies (50.81%) and 45.26% had a malignant diagnosis (n = 9302). An adenocarcinoma (8140/3) sub-diagnosis was reported for 88.16% of malignant biopsies (n = 8201). An atypia diagnosis was reported for 760 biopsies (3.7%). Inflammation (39.03%) and hyperplasia (20.82%) were the predominant benign sub-diagnoses.Conclusion: Our study demonstrated the feasibility of generating PCa data using SNOMED codes from national laboratory data. This highlights the need for extending the results of our study to a national level to deliver timeous monitoring of PCa trends