Original Research

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

Naseem Cassim, Ahsan Ahmad, Reubina Wadee, Jaya A. George, Deborah K. Glencross
African Journal of Laboratory Medicine | Vol 9, No 1 | a909 | DOI: https://doi.org/10.4102/ajlm.v9i1.909 | © 2020 Naseem Cassim, Ahsan Ahmad, Reubina Wadee, Jaya A. George, Deborah K. Glencross | This work is licensed under CC Attribution 4.0
Submitted: 11 September 2018 | Published: 28 September 2020

About the author(s)

Naseem Cassim, National Health Laboratory Service(NHLS), National Priority Programme, Johannesburg, South Africa; and, Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
Ahsan Ahmad, Department of Urology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
Reubina Wadee, Department of Anatomical Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
Jaya A. George, Department of Chemical Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
Deborah K. Glencross, National Health Laboratory Service(NHLS), National Priority Programme, Johannesburg, South Africa; and, Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa


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Abstract

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.


Keywords

prostate biopsy; Systemized Nomenclature of Medicine; SNOMED; morphology; topography; prostate cancer and adenocarcinoma

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Crossref Citations

1. Using text mining techniques to extract prostate cancer predictive information (Gleason score) from semi-structured narrative laboratory reports in the Gauteng province, South Africa
Naseem Cassim, Michael Mapundu, Victor Olago, Turgay Celik, Jaya Anna George, Deborah Kim Glencross
BMC Medical Informatics and Decision Making  vol: 21  issue: 1  year: 2021  
doi: 10.1186/s12911-021-01697-2