refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data

Abstract 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 refin...

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Autores principales:	Tatjana Ammer, André Schützenmeister, Hans-Ulrich Prokosch, Manfred Rauh, Christopher M. Rank, Jakob Zierk
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e221b54fbd294da994f4057aa0be46c4
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spelling	oai:doaj.org-article:e221b54fbd294da994f4057aa0be46c42021-12-02T16:35:46ZrefineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data10.1038/s41598-021-95301-22045-2322https://doaj.org/article/e221b54fbd294da994f4057aa0be46c42021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95301-2https://doaj.org/toc/2045-2322Abstract 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.Tatjana AmmerAndré SchützenmeisterHans-Ulrich ProkoschManfred RauhChristopher M. RankJakob ZierkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tatjana Ammer André Schützenmeister Hans-Ulrich Prokosch Manfred Rauh Christopher M. Rank Jakob Zierk refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
description	Abstract 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.
format	article
author	Tatjana Ammer André Schützenmeister Hans-Ulrich Prokosch Manfred Rauh Christopher M. Rank Jakob Zierk
author_facet	Tatjana Ammer André Schützenmeister Hans-Ulrich Prokosch Manfred Rauh Christopher M. Rank Jakob Zierk
author_sort	Tatjana Ammer
title	refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
title_short	refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
title_full	refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
title_fullStr	refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
title_full_unstemmed	refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data
title_sort	refiner: a novel algorithm for reference interval estimation from real-world data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e221b54fbd294da994f4057aa0be46c4
work_keys_str_mv	AT tatjanaammer refineranovelalgorithmforreferenceintervalestimationfromrealworlddata AT andreschutzenmeister refineranovelalgorithmforreferenceintervalestimationfromrealworlddata AT hansulrichprokosch refineranovelalgorithmforreferenceintervalestimationfromrealworlddata AT manfredrauh refineranovelalgorithmforreferenceintervalestimationfromrealworlddata AT christophermrank refineranovelalgorithmforreferenceintervalestimationfromrealworlddata AT jakobzierk refineranovelalgorithmforreferenceintervalestimationfromrealworlddata
_version_	1718383723915771904

refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data

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