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Identification of Pediatric Patients With Celiac Disease Based on Serology and a Classification and Regression Tree Analysis.

ABSTRACT: BACKGROUND & AIMS:Celiac disease is detected using serology and endoscopy analyses. We used multiple statistical analyses of a geographically isolated population in the United States to determine whether a single serum screening can identify individuals with celiac disease. METHODS:We performed a retrospective study of 3555 pediatric patients (18 years old or younger) in the intermountain West region of the United States from January 1, 2008, through September 30, 2013. All patients had undergone serologic analyses for celiac disease, including measurement of antibodies to tissue transglutaminase (TTG) and/or deamidated gliadin peptide (DGP), and had duodenal biopsies collected within the following year. Modified Marsh criteria were used to identify patients with celiac disease. We developed models to identify patients with celiac disease using logistic regression and classification and regression tree (CART) analysis. RESULTS:Single use of a test for serum level of IgA against TTG identified patients with celiac disease with 90% sensitivity, 90% specificity, a 61% positive predictive value (PPV), a 90% negative predictive value, and an area under the receiver operating characteristic curve value of 0.91; these values were higher than those obtained from assays for IgA against DGP or IgG against TTG plus DGP. Not including the test for DGP antibody caused only 0.18% of celiac disease cases to be missed. Level of TTG IgA 7-fold the upper limit of normal (ULN) identified patients with celiac disease with a 96% PPV and 100% specificity. Using CART analysis, we found a level of TTG IgA 3.2-fold the ULN and higher to most accurately identify patients with celiac disease (PPV, 89%). Multivariable CART analysis showed that a level of TTG IgA 2.5-fold the ULN and higher was sufficient to identify celiac disease in patients with type 1 diabetes (PPV, 88%). Serum level of IgA against TTG in patients with versus those without trisomy 21 did not affect diagnosis predictability in CART analysis. CONCLUSIONS:In a population-based study, we found that serum level of IgA against TTG can identify patients with celiac disease with PPVs of about 90%. Predictive values increase greatly when levels are markedly above the ULN or when the assay is used in combination with other variables. Measurement of IgG against TTG or DGP does not increase the accuracy of detection of celiac disease based against TTG IgA levels. There is a low risk of false-positive results from serologic analysis in patients with type I diabetes or persistent increases in antibody against TTG on repeat testing.

SUBMITTER: Ermarth A

PROVIDER: S-EPMC5316297 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Identification of Pediatric Patients With Celiac Disease Based on Serology and a Classification and Regression Tree Analysis.

Ermarth Anna A Bryce Matthew M Woodward Stephanie S Stoddard Gregory G Book Linda L Jensen M Kyle MK

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 20161112 3

<h4>Background & aims</h4>Celiac disease is detected using serology and endoscopy analyses. We used multiple statistical analyses of a geographically isolated population in the United States to determine whether a single serum screening can identify individuals with celiac disease.<h4>Methods</h4>We performed a retrospective study of 3555 pediatric patients (18 years old or younger) in the intermountain West region of the United States from January 1, 2008, through September 30, 2013. All patien ...[more]

PMID: 27847281

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Project description:BackgroundIn the typical setting of gene-selection problems from high-dimensional data, e.g., gene expression data from microarray or next-generation sequencing-based technologies, an enormous volume of high-throughput data is generated, and there is often a need for a simple, computationally-inexpensive, non-parametric screening procedure than can quickly and accurately find a low-dimensional variable subset that preserves biological information from the original very high-dimensional data (dimension p > 40,000). This is in contrast to the very sophisticated variable selection methods that are computationally expensive, need pre-processing routines, and often require calibration of priors.ResultsWe present a tree-based sequential CART (S-CART) approach to variable selection in the binary classification setting and compare it against the more sophisticated procedures using simulated and real biological data. In simulated data, we analyze S-CART performance versus (i) a random forest (RF), (ii) a fully-parametric Bayesian stochastic search variable selection (SSVS), and (iii) the moderated t-test statistic from the LIMMA package in R. The simulation study is based on a hierarchical Bayesian model, where dataset dimensionality, percentage of significant variables, and substructure via dependency vary. Selection efficacy is measured through false-discovery and missed-discovery rates. In all scenarios, the S-CART method is seen to consistently outperform SSVS and RF in both speed and detection accuracy. We demonstrate the utility of the S-CART technique both on simulated data and in a control-treatment mouse study. We show that the network analysis based on the S-CART-selected gene subset in essence recapitulates the biological findings of the study using only a fraction of the original set of genes considered in the study's analysis.ConclusionsThe relatively simple-minded gene selection algorithms like S-CART may often in practical circumstances be preferred over much more sophisticated ones. The advantage of the "greedy" selection methods utilized by S-CART and the likes is that they scale well with the problem size and require virtually no tuning or training while remaining efficient in extracting the relevant information from microarray-like datasets containing large number of redundant or irrelevant variables.AvailabilityThe MATLAB 7.4b code for the S-CART implementation is available for download from https://neyman.mcg.edu/posts/scart.zip.

Dataset Information

Identification of Pediatric Patients With Celiac Disease Based on Serology and a Classification and Regression Tree Analysis.

Publications

Identification of Pediatric Patients With Celiac Disease Based on Serology and a Classification and Regression Tree Analysis.

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