B-125 Artificial Intelligence Application to Improve the Algorithm for Autoverification of Results of Biochemical Markers that Assess Thyroid Function

Abstract

Abstract Background The results verification process is the most important control step of the post-analytical phase. It can be done in two ways, manually by a professional, or using autoverification systems. Appropriate results are verified and submitted to the Laboratory Information System (LIS). Corrective actions are initiated for discordant results prior to their submission to the LIS. Autoverification is a process where the laboratory tests results are published without any professional’s intervention. It is recommended that each laboratory creates the necessary cut-off values for autoverification, according to its population and its biochemical-medical criteria. In our laboratory, an autoverification algorithm is applied to evaluate thyroid function, which involves the measurement of 2 parameters: Thyrotropin (TSH) and Free Thyroxine (FT4). These parameters were chosen because they are the most used for characterizing thyroid function. The autoverification algorithm was defined for euthyroid, hypothyroid and hyperthyroid patients. Our professionals proposed a “safe range” for both parameters and we relate those parameters results for autoverification. We study the usefulness of artificial intelligence models by clustering non-supervised machine learning algorithms, to find behavioral patterns of not autoverified sample results, to support and even broaden the “safe ranges” established by our professionals. The goal was to improve the current autoverification algorithm by using artificial intelligence for thyroid function test results and to increase the autoverification percentage. Methods The autoverification process was carried out at TURNER Laboratories, Rosario, Argentina. The autoverification algorithm was designed according to the Laboratory and Clinical Standards Institute Guide AUTO10-A. The Atellica Data Manager (ADM) middleware was used to provide the connection between the Atellica Solution Immunoassay & Clinical Chemistry Analyzers and the LIS. ADM receives the results from the analyzers and processes them in consideration of the pre-analytical (age, sex, etc.), analytical (QC, flags, etc.) and post-analytical (Delta Check, etc.) conditions. It then applies the autoverification algorithm to send the results to the LIS. In the first semester of 2022, we used the autoverification algorithm with the existing “safe ranges” in 18 626 samples of outpatients between the ages of 16 and 60. On the other hand, we applied different Clustering algorithms to the samples that were not autoverified, to find behavior patterns. The ones we used are: KMeans, Fuzzy C-Means, DBSCAN, Agglomerative Clustering, Dendrogram and Affinity Propagation. We chose KMeans with k = 4. Our professionals thoroughly analyzed each of the clusters and defined “new safe ranges” for autoverification. In the second semester of 2022, 18 947 samples were analyzed and the autoverification algorithm was applied with the “safe ranges” and the “new safe ranges”. Results In the first semester of 2022, by applying the existing “safe ranges” we obtained an autoverification of 19.0%. We then simulated an autoverification with “safe ranges” and “new safe ranges” and obtained an autoverification of 71.5%. In the second semester, we applied autoverification with “safe ranges” and “new safe ranges” obtaining an autoverification of 72.7%. Conclusion The application of machine learning algorithms has significantly improved the level of autoverification of biochemical markers that evaluate thyroid function.