Xiaoxia Peng1, Yaqi Lv1,2, Guoshuang Feng1, Yaguang Peng1, Qiliang Li3, Wenqi Song4, Xin Ni1,5. 1. Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China. 2. Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, P.R. China. 3. Department of Clinical Laboratory Center, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China. 4. Department of Clinical Laboratory Center, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, No. 56 Nanlishi Road, Beijing, 100045, P.R. China. 5. Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck, Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, No. 56 Nanlishi Road, Beijing, 100045, P.R. China, Phone: +86-010-59617132.
Abstract
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.
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.
Entities:
Keywords:
age partitioning; clinical laboratory database; pediatric; reference intervals
Authors: Ruohua Yan; Kun Li; Yaqi Lv; Yaguang Peng; Nicholas Van Halm-Lutterodt; Wenqi Song; Xiaoxia Peng; Xin Ni Journal: BMC Med Res Methodol Date: 2022-04-10 Impact factor: 4.615