Clinical Application of Overlapping Confidence Intervals for Monitoring Changes in Serial Clinical Chemistry Test Results
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Background: Interpretation of changes in serial laboratory results is necessary for both clinicians and laboratories; however, setting decision limits is not easy. Although the reference change value (RCV) has been widely used for auto-verification, it has limitations in clinical settings. We introduce the concept of overlapping confidence intervals (CIs) to determine whether the changes are statistically significant in clinical chemistry laboratory test results. Methods: In total, 1,202,096 paired results for 33 analytes routinely tested in our clinical chemistry laboratory were analyzed. The distributions of delta% absolute values and cut-off values for certain percentiles were calculated. The CIs for each analyte were set based on biological variation, and data were analyzed at various confidence levels. Additionally, we analyzed the data using RCVs and compared their clinical utility. Results: Most analytes had low indexes of individuality with large inter-individual variability. The 97.5th percentile cut-offs for each analyte were much larger than conventional RCVs. The percentages of results exceeding RCV95% and RCV99% corresponded to those with no overlap at the 83.4% and 93.2% confidence levels, respectively. Conclusions: The use of overlapping CIs in serial clinical chemistry test results can overcome the limitations of existing RCVs and replace them, especially for analytes with large intra-individual variation.
Abstract
Background: Interpretation of changes in serial laboratory results is necessary for both clinicians and laboratories; however, setting decision limits is not easy. Although the reference change value (RCV) has been widely used for auto-verification, it has limitations in clinical settings. We introduce the concept of overlapping confidence intervals (CIs) to determine whether the changes are statistically significant in clinical chemistry laboratory test results. Methods: In total, 1,202,096 paired results for 33 analytes routinely tested in our clinical chemistry laboratory were analyzed. The distributions of delta% absolute values and cut-off values for certain percentiles were calculated. The CIs for each analyte were set based on biological variation, and data were analyzed at various confidence levels. Additionally, we analyzed the data using RCVs and compared their clinical utility. Results: Most analytes had low indexes of individuality with large inter-individual variability. The 97.5th percentile cut-offs for each analyte were much larger than conventional RCVs. The percentages of results exceeding RCV95% and RCV99% corresponded to those with no overlap at the 83.4% and 93.2% confidence levels, respectively. Conclusions: The use of overlapping CIs in serial clinical chemistry test results can overcome the limitations of existing RCVs and replace them, especially for analytes with large intra-individual variation.
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