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refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
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DOI：10.1038/s41598-021-95301-2.png)
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Abstract
En 中文
Reference intervals are essential for the interpretation of laboratory test results in medicine. We propose a novel indirect approach to estimate reference intervals from real-world data as an alternative to direct methods, which require samples from healthy individuals. The presented refineR algorithm separates the non-pathological distribution from the pathological distribution of observed test results using an inverse approach and identifies the model that best explains the non-pathological distribution. To evaluate its performance, we simulated test results from six common laboratory analytes with a varying location and fraction of pathological test results. Estimated reference intervals were compared to the ground truth, an alternative indirect method (kosmic), and the direct method (N=120 and N=400 samples). Overall, refineR achieved the lowest mean percentage error of all methods (2.77%). Analyzing the amount of reference intervals within +/- 1 total error deviation from the ground truth, refineR (82.5%) was inferior to the direct method with N=400 samples (90.1%), but outperformed kosmic (70.8%) and the direct method with N=120 (67.4%). Additionally, reference intervals estimated from pediatric data were comparable to published direct method studies. In conclusion, the refineR algorithm enables precise estimation of reference intervals from real-world data and represents a viable complement to the direct method.
Keywords:
PEDIATRIC REFERENCE INTERVALS
IMMUNOLOGICAL REFERENCE VALUES
DETERMINING REFERENCE LIMITS
LABORATORY DATA-BASES
ROCHE
TRANSFERENCE
DERIVATION
PLASMA
ASSAYS
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