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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2020

Open Access 01-12-2020 | Research article

Comparison of four algorithms on establishing continuous reference intervals for pediatric analytes with age-dependent trend

Authors: Kun Li, Lixin Hu, Yaguang Peng, Ruohua Yan, Qiliang Li, Xiaoxia Peng, Wenqi Song, Xin Ni

Published in: BMC Medical Research Methodology | Issue 1/2020

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Abstract

Background

Continuous reference intervals (RIs) allow for more precise consideration of the dynamic changes of physiological development, which can provide new strategies for the presentation of laboratory test results. Our study aimed to establish continuous RIs using four different simulation methods so that the applicability of different methods could be further understood.

Methods

The data of alkaline phosphatase (ALP) and serum creatinine (Cr) were obtained from the Pediatric Reference Interval in China study (PRINCE), in which healthy children aged 0–19 years were recruited. The improved non-parametric method, the radial smoothing method, the General Additive Model for Location Scale and Shape (GAMLSS), and Lambda-Median-Sigma (LMS) were used to develop continuous RIs. The accuracy and goodness of fit of the continuous RIs were evaluated based on the out of range (OOR) and Akaike Information Criterion (AIC) results.

Results

Samples from 11,517 and 11,544 participants were used to estimate the continuous RIs of ALP and Cr, respectively. Time frames were partitioned to fulfill the following two criteria: sample size = 120 in each subgroup and mean difference = 2 between adjacent time frames. Cubic spline or penalized spline was used for curve smoothing. The RIs estimated by the four methods approximately overlapped. However, more obvious edge effects were shown in the curves fit by the non-parametric methods than the semi-parametric method, which may be attributed to insufficient sample size. The OOR values of all four methods were smaller than 10%.

Conclusions

All four methods could be used to establish continuous RIs. GAMLSS and LMS are more reliable than the other two methods for dealing with edge effects.
Appendix
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Literature
1.
Daly CH, Higgins V, Adeli K, Grey VL, Hamid JS. Reference interval estimation: methodological comparison using extensive simulations and empirical data. Clin Biochem. 2017;50(18):1145–58.PubMed
2.
Peng X, Lv Y, Feng G, Peng Y, Li Q, Song W, Ni X. Algorithm on age partitioning for estimation of reference intervals using clinical laboratory database exemplified with plasma creatinine. Clin Chem Lab Med. 2018;56(9):1514–23.PubMed
3.
Zierk J, Arzideh F, Haeckel R, Rascher W, Rauh M, Metzler M. Indirect determination of pediatric blood count reference intervals. Clin Chem Lab Med. 2013;51(4):863–72.PubMed
4.
CLSI. Establishing, and verifying reference intervals in the clinical laboratory; approved guideline— third edition. CLSI Document EP28-A3c 2008.
5.
Zierk J, Arzideh F, Haeckel R, Cario H, Fruhwald MC, Gross HJ, Gscheidmeier T, Hoffmann R, Krebs A, Lichtinghagen R, et al. Pediatric reference intervals for alkaline phosphatase. Clin Chem Lab Med. 2017;55(1):102–10.PubMed
6.
Zierk J, Arzideh F, Rechenauer T, Haeckel R, Rascher W, Metzler M, Rauh M. Age- and sex-specific dynamics in 22 hematologic and biochemical analytes from birth to adolescence. Clin Chem. 2015;61(7):964–73.PubMed
7.
Borghi E, de Onis M, Garza C, Van den Broeck J, Frongillo EA, Grummer-Strawn L, Van Buuren S, Pan H, Molinari L, Martorell R, et al. Construction of the World Health Organization child growth standards: selection of methods for attained growth curves. Stat Med. 2006;25(2):247–65.PubMed
8.
Wan X, Qu Y, Huang Y, Zhang X, Song H, Jiang H. Nonparametric estimation of age-specific reference percentile curves with radial smoothing. Contemp Clin Trials. 2012;33(1):13–22.PubMed
9.
De Henauw S, Michels N, Vyncke K, Hebestreit A, Russo P, Intemann T, Peplies J, Fraterman A, Eiben G, de Lorgeril M, et al. Blood lipids among young children in Europe: results from the European IDEFICS study. Int J Obes. 2014;38(Suppl 2):S67–75.
10.
Ni X, Song W, Peng X, Shen Y, Peng Y, Li Q, Wang Y, Hu L, Cai Y, Shang H, et al. Pediatric reference intervals in China (PRINCE): design and rationale for a large, multicenter collaborative cross-sectional study. Sci Bull. 2018;63(24):1626–34.
11.
Stasinopoulos DM, Rigby RA. Generalized additive models for location scale and shape (GAMLSS) in R. J R Stat Soc. 2005;54(3):507–54.
12.
Cole TJ. Using the LMS method to measure skewness in the NCHS and Dutch National height standards. Ann Hum Biol. 1989;16(5):407–19.PubMed
13.
Cole TJ, Green PJ. Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med. 1992;11(10):1305–19.PubMed
14.
Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modeling. J R Stat Soc. 1994;43(3):429–67.
15.
Arzideh F, Wosniok W, Haeckel R. Indirect reference intervals of plasma and serum thyrotropin (TSH) concentrations from intra-laboratory data bases from several German and Italian medical centres. Clin Chem Lab Med. 2011;49(4):659–64.PubMed
16.
Pavlov IY, Wilson AR, Delgado JC. Reference interval computation: which method (not) to choose? Clin Chim Acta. 2012;413(13–14):1107–14.
17.
Akaike H. A new look at the statistical model identification. IEEE Trans Automatic Control. 1974;19(6):716–23.
18.
Hastie T, Tibshirani R. Generalized additive models. Stat Sci. 1986;1(3):297–310.
19.
Lane PW, Wood S, Jones MC, Nelder JA, Lee YJ, Borja MC, Longford NT, Bowman A, Cole TJ. Generalized additive models for location, scale and shape - discussion. Appl Stat. 2005;54:544–54.
20.
Rigby RA, Stasinopoulos DM. Using the box-cox t distribution in GAMLSS to model skewness and kurtosis. Stat Model. 2006;6(6):209–29.
21.
Rigby RA, Stasinopoulos DM. Smooth centile curves for skew and kurtotic data modelled using the box-cox power exponential distribution. Stat Med. 2004;23(19):3053–76.PubMed
22.
van Buuren S, Fredriks M. Worm plot: a simple diagnostic device for modelling growth reference curves. Stat Med. 2001;20(8):1259–77.PubMed
23.
Jaschke SR: The cornish-fisher-expansion in the context of delta - gamma - normal approximations. Sfb Discussion Papers 2001.
24.
Rigby RA, Stasinopoulos DM. Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat Methods Med Res. 2014;23(4):318–32.PubMed
25.
Asgari S, Higgins V, McCudden C, Adeli K. Continuous reference intervals for 38 biochemical markers in healthy children and adolescents: comparisons to traditionally partitioned reference intervals. Clin Biochem. 2019;73:82–9.PubMed
26.
Schnabl K, Chan MK, Gong Y, Adeli K. Closing the gaps in paediatric reference intervals: the CALIPER initiative. Clin Biochem Rev. 2008;29(3):89–96.PubMedPubMedCentral
27.
Lv Y, Feng G, Ni X, Song W, Peng X. The critical gap for pediatric reference intervals of complete blood count in China. Clin Chim Acta. 2017;469:22–5.PubMed
28.
Horn PS, Pesce AJ, Copeland BE. A robust approach to reference interval estimation and evaluation. Clin Chem. 1998;44(3):622–31.PubMed
29.
Morkrid L, Rowe AD, Elgstoen KB, Olesen JH, Ruijter G, Hall PL, Tortorelli S, Schulze A, Kyriakopoulou L, Wamelink MM, et al. Continuous age- and sex-adjusted reference intervals of urinary markers for cerebral creatine deficiency syndromes: a novel approach to the definition of reference intervals. Clin Chem. 2015;61(5):760–8.
Metadata
Title
Comparison of four algorithms on establishing continuous reference intervals for pediatric analytes with age-dependent trend
Authors
Kun Li
Lixin Hu
Yaguang Peng
Ruohua Yan
Qiliang Li
Xiaoxia Peng
Wenqi Song
Xin Ni
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2020
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-020-01021-y

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