Algorithm on age partitioning for estimation of reference intervals using clinical laboratory database exemplified with plasma creatinine

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Background:We describe an algorithm to determine age-partitioned reference intervals (RIs) exemplified for creatinine using data collection from the clinical laboratory database.Methods:The data were acquired from the test results of creatinine of 164,710 outpatients aged <18 years in Beijing Children's Hospital laboratories’ databases between January 2016 and December 2016. The tendency of serum creatinine with age was examined visually using box plot by gender first. The age subgroup was divided automatically by the decision tree method. Subsequently, the statistical tests of the difference between subgroups were performed by Harris-Boyd and Lahti methods.Results:A total of 136,546 samples after data cleaning were analyzed to explore the partition of age group for serum creatinine from birth to 17 years old. The suggested age partitioning of RIs for creatinine by the decision tree method were for eight subgroups. The difference between age subgroups was demonstrated to be statistically significant by Harris-Boyd and Lahti methods. In addition, the results of age partitioning for RIs estimation were similar to the suggested age partitioning by the Canadian Laboratory Initiative in Pediatric Reference Intervals study. Lastly, a suggested algorithm was developed to provide potential methodological considerations on age partitioning for RIs estimation.Conclusions:Appropriate age partitioning is very important for establishing more accurate RIs. The procedure to explore the age partitioning using clinical laboratory data was developed and evaluated in this study, and will provide more opinions for designing research on establishment of RIs.

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