Utilizing augmented artificial intelligence for aminoacidopathies using collaborative laboratory integrated reporting- A cross-sectional study. (October 2022)

• Record Type: Journal Article
• Authors: Khan, Zaib Un Nisa; Jafri, Lena; Hall, Patricia L.; Schultz, Matthew J.; Ahmed, Sibtain; Khan, Aysha Habib; Majid, Hafsa
• Is Part Of: Annals of medicine and surgery. Volume 82(2022)
• Publication Date: 2022-10
• Subjects: Amino acids -- inherited Metabolic disorders -- Maple syrup urine disease -- Phenylketonuria -- Artificial intelligence; Surgery -- Periodicals; Medicine -- Periodicals; General Surgery -- Periodicals; Education, Medical -- Periodicals; Periodicals; 617
• DOI: 10.1016/j.amsu.2022.104651
• Languages: English
• ISSNs: 2049-0801

Abstract

Abstract: Introduction: Plasma amino acids profiling can aid in the screening and diagnosis of aminoacidopathies. The goal of the current study was to analyze and report the metabolic profiles of plasma amino acid (PAA) and additionally to compare PAA-reference intervals (RI) from Pakistan with more countries utilizing Clinical Laboratory Integrated Reports (CLIR). Methods: This was a cross sectional prospective single center study. Twenty-two amino acids were analyzed in each sample received for one year at the clinical laboratory. Data was divided into reference and case data files after interpretation by a team of pathologists and technologists. All PAA samples were analyzed using ion-exchange high-performance chromatography. The CLIR application of Amino Acid in Plasma (AAQP) was used for statistical analysis for both data sets and post-analytical interpretive tools using a single condition tool was applied. Result: The majority of 92% (n = 1913) of PAA profiles out of the total 2081 tests run were non-diagnostic; the PAA values were within the age-specific RI. The PAA median was in close comparison close to the 50th percentile of reference data available in CLIR software. Out of the total 2081 tests run, one hundred and sixty-eight had abnormal PAA levels; 27.38% were labeled as non-fasting samples, and the main aminoacidopathies identified were Phenylketonuria and Maple Syrup Urine Disorder. Conclusion: An agreement of >95% was observed between the reporting done by the pathologists and technologists' team and then after the application of CLIR. Augmented artificial intelligence using CLIR can improve the accuracy of reporting rare aminoacidopathies in a developing country like ours. Highlights: Plasma amino acids helps in diagnosing and monitoring of various aminoacidopathies. Few aminoacidopathies present with a grossly abnormal investigation profile, with few diseases having subtle deviations. Their is 98% concordance of diagnosis concordance of diagnosis of aminoacidopathies between our lab and CLIR. CLIR tools can be utilized in a newborn screening program for screening and diagnosis in future.