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Screening for ketosis using multiple logistic regression based on milk yield and composition
Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Japanese Society of Veterinary Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667666/ https://www.ncbi.nlm.nih.gov/pubmed/26074408 http://dx.doi.org/10.1292/jvms.14-0691 |
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author | KAYANO, Mitsunori KATAOKA, Tomoko |
author_facet | KAYANO, Mitsunori KATAOKA, Tomoko |
author_sort | KAYANO, Mitsunori |
collection | PubMed |
description | Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively. |
format | Online Article Text |
id | pubmed-4667666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | The Japanese Society of Veterinary Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46676662015-12-03 Screening for ketosis using multiple logistic regression based on milk yield and composition KAYANO, Mitsunori KATAOKA, Tomoko J Vet Med Sci Internal Medicine Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively. The Japanese Society of Veterinary Science 2015-06-14 2015-11 /pmc/articles/PMC4667666/ /pubmed/26074408 http://dx.doi.org/10.1292/jvms.14-0691 Text en ©2015 The Japanese Society of Veterinary Science http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License. |
spellingShingle | Internal Medicine KAYANO, Mitsunori KATAOKA, Tomoko Screening for ketosis using multiple logistic regression based on milk yield and composition |
title | Screening for ketosis using multiple logistic regression based on milk yield
and composition |
title_full | Screening for ketosis using multiple logistic regression based on milk yield
and composition |
title_fullStr | Screening for ketosis using multiple logistic regression based on milk yield
and composition |
title_full_unstemmed | Screening for ketosis using multiple logistic regression based on milk yield
and composition |
title_short | Screening for ketosis using multiple logistic regression based on milk yield
and composition |
title_sort | screening for ketosis using multiple logistic regression based on milk yield
and composition |
topic | Internal Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667666/ https://www.ncbi.nlm.nih.gov/pubmed/26074408 http://dx.doi.org/10.1292/jvms.14-0691 |
work_keys_str_mv | AT kayanomitsunori screeningforketosisusingmultiplelogisticregressionbasedonmilkyieldandcomposition AT kataokatomoko screeningforketosisusingmultiplelogisticregressionbasedonmilkyieldandcomposition |