Literature DB >> 21524525

Latent class evaluation of a milk test, a urine test, and the fat-to-protein percentage ratio in milk to diagnose ketosis in dairy cows.

M A Krogh1, N Toft, C Enevoldsen.   

Abstract

In this study, 3 commonly used tests to diagnose ketosis were evaluated with a latent class model to avoid the assumption of an available perfect test. The 3 tests were the KetoLac BHB (Sanwa Kagaku Kenkyusho Co. Ltd., Nagoya, Japan) test strip that tests milk for β-hydroxybutyrate, the KetoStix (Bayer Diagnostics Europe Ltd., Dublin, Ireland) test strip that tests urine for acetoacetate, and the fat-to-protein percentage ratio (FPR) in milk. A total of 8,902 cows were included in the analysis. The cows were considered to be a random sample from the population of Danish dairy cattle under intensive management, thus representing a natural spectrum of ketosis as a disease. All cows had a recorded FPR between 7 and 21 d postpartum. The KetoLac BHB recordings were available from 2,257 cows and 6,645 cows had a KetoStix recording. The recordings were analyzed with a modified Hui-Walter model, in a Bayesian framework. The specificity of the KetoLac BHB test and the KetoStix test were both high [0.99 (0.97-0.99)], whereas the specificity of FPR was somewhat lower [0.79 (0.77-0.81)]. The best sensitivity was for the KetoStix test [0.78 (0.55-0.98)], followed by the FPR [0.63 (0.58-0.71)] and KetoLac BHB test [0.58 (0.35-0.93)].
Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21524525     DOI: 10.3168/jds.2010-3816

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  5 in total

Review 1.  Estimation of diagnostic test accuracy without full verification: a review of latent class methods.

Authors:  John Collins; Minh Huynh
Journal:  Stat Med       Date:  2014-06-09       Impact factor: 2.373

2.  A quantitative screening method to detect rater-introduced bias in clinical ratings.

Authors:  Mogens A Krogh; Carsten Enevoldsen
Journal:  Acta Vet Scand       Date:  2012-09-21       Impact factor: 1.695

3.  Screening for ketosis using multiple logistic regression based on milk yield and composition.

Authors:  Mitsunori Kayano; Tomoko Kataoka
Journal:  J Vet Med Sci       Date:  2015-06-14       Impact factor: 1.267

4.  Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example.

Authors:  Haifang Ni; Irene Klugkist; Saskia van der Drift; Ruurd Jorritsma; Gerrit Hooijer; Mirjam Nielen
Journal:  PLoS One       Date:  2021-01-14       Impact factor: 3.240

5.  Diagnostic accuracy of a bovine specific electronic beta-hydroxybutyrate handheld meter in fresh blood and stored serum samples.

Authors:  Z Rodriguez; L S Caixeta; G Cramer
Journal:  Vet Anim Sci       Date:  2020-12-14
  5 in total

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