Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression

Objectives: To determine the trend of bacterial resistance of Escherichia coli to Imipenem (IPM) and Meropenem (MEM), by means of a linear regression model, taking the information collected in the bulletins of bacterial resistance generated by the GREBO group of Bogotá between 2010 and 2014. Methods...

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Autores:
Viloria Silva, Amelec Jesus
Campo Urbina, Myrna
Gómez Rodríguez, Lucila
Parody Muñoz, Alexander
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1317
Acceso en línea:
https://hdl.handle.net/11323/1317
https://repositorio.cuc.edu.co/
Palabra clave:
Bacterial drug resistance
Escherichia coli
Imipenem and Meropenem
Linear regression model
Rights
openAccess
License
Atribución – No comercial – Compartir igual
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dc.title.eng.fl_str_mv Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
title Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
spellingShingle Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
Bacterial drug resistance
Escherichia coli
Imipenem and Meropenem
Linear regression model
title_short Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
title_full Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
title_fullStr Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
title_full_unstemmed Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
title_sort Predicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regression
dc.creator.fl_str_mv Viloria Silva, Amelec Jesus
Campo Urbina, Myrna
Gómez Rodríguez, Lucila
Parody Muñoz, Alexander
dc.contributor.author.spa.fl_str_mv Viloria Silva, Amelec Jesus
Campo Urbina, Myrna
Gómez Rodríguez, Lucila
Parody Muñoz, Alexander
dc.subject.eng.fl_str_mv Bacterial drug resistance
Escherichia coli
Imipenem and Meropenem
Linear regression model
topic Bacterial drug resistance
Escherichia coli
Imipenem and Meropenem
Linear regression model
description Objectives: To determine the trend of bacterial resistance of Escherichia coli to Imipenem (IPM) and Meropenem (MEM), by means of a linear regression model, taking the information collected in the bulletins of bacterial resistance generated by the GREBO group of Bogotá between 2010 and 2014. Methods/Statistical Analysis: From the information published in newsletters GREBO group between 2010 and 2014, the behavior of E. coli bacterial resistance to antibiotics was analyzed. From this information simple linear regression models using the statistical software Statgraphics XVI were generated. Findings: The generated mathematical models to predict the evolution of antibiotic resistance as a function of time and that were significant are: Resistance IPM * Year = 0.00000208772 (p value 0.0020; adjusted R2 = 92.86%); Resistance MEM = 0.00000149115 * Year (p value 0.0026; adjusted R2 = 91.84%). Application/Improvements: There is a relationship between the values of resistance and over the years, with variable time sufficient to explain the behavior of the resistance of E. coli variable. In 2015 IPM resistance is estimated that this in 0.42% (CI 0.02% - 0.8%) and MEM 0.3% (CI 0.17% - 0.42%).
publishDate 2016
dc.date.issued.none.fl_str_mv 2016
dc.date.accessioned.none.fl_str_mv 2018-11-19T19:17:59Z
dc.date.available.none.fl_str_mv 2018-11-19T19:17:59Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 09746846
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/1317
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 09746846
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/1317
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv Atribución – No comercial – Compartir igual
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
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rights_invalid_str_mv Atribución – No comercial – Compartir igual
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Indian Journal of Science and Technology
institution Corporación Universidad de la Costa
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spelling Viloria Silva, Amelec JesusCampo Urbina, MyrnaGómez Rodríguez, LucilaParody Muñoz, Alexander2018-11-19T19:17:59Z2018-11-19T19:17:59Z201609746846https://hdl.handle.net/11323/1317Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Objectives: To determine the trend of bacterial resistance of Escherichia coli to Imipenem (IPM) and Meropenem (MEM), by means of a linear regression model, taking the information collected in the bulletins of bacterial resistance generated by the GREBO group of Bogotá between 2010 and 2014. Methods/Statistical Analysis: From the information published in newsletters GREBO group between 2010 and 2014, the behavior of E. coli bacterial resistance to antibiotics was analyzed. From this information simple linear regression models using the statistical software Statgraphics XVI were generated. Findings: The generated mathematical models to predict the evolution of antibiotic resistance as a function of time and that were significant are: Resistance IPM * Year = 0.00000208772 (p value 0.0020; adjusted R2 = 92.86%); Resistance MEM = 0.00000149115 * Year (p value 0.0026; adjusted R2 = 91.84%). Application/Improvements: There is a relationship between the values of resistance and over the years, with variable time sufficient to explain the behavior of the resistance of E. coli variable. In 2015 IPM resistance is estimated that this in 0.42% (CI 0.02% - 0.8%) and MEM 0.3% (CI 0.17% - 0.42%).Viloria Silva, Amelec Jesus-1be008bb-6eeb-4db4-bc0b-68bb6cb663e2-600Campo Urbina, Myrna-54f5cedd-7a38-4d60-a7df-0fe0780b7841-600Gómez Rodríguez, Lucila-96f477ee-c3db-4c48-82f9-5f94b19cda8e-600Parody Muñoz, Alexander-729969a6-104c-4359-90e4-915cdc430463-600engIndian Journal of Science and TechnologyAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bacterial drug resistanceEscherichia coliImipenem and MeropenemLinear regression modelPredicting of behavior of escherichia coli resistance to Imipenem and Meropenem, using a simple mathematical model regressionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALPredicting of Behavior of Escherichia Coli Resistance.pdfPredicting of Behavior of Escherichia Coli Resistance.pdfapplication/pdf236907https://repositorio.cuc.edu.co/bitstreams/cab25b9a-d92c-4f37-83c8-4e7492a7c22c/download694c1f593d99236afb50a6925e9ad640MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/93601d1c-d72f-411d-857d-610173ae4c45/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILPredicting of Behavior of Escherichia Coli Resistance.pdf.jpgPredicting of Behavior of Escherichia Coli Resistance.pdf.jpgimage/jpeg68793https://repositorio.cuc.edu.co/bitstreams/d929f567-73c7-4b92-8e72-e12460b13cc7/download5aeb9e26dd3a4ce28ae2d7691dc18587MD54TEXTPredicting of Behavior of Escherichia Coli Resistance.pdf.txtPredicting of Behavior of Escherichia Coli Resistance.pdf.txttext/plain16190https://repositorio.cuc.edu.co/bitstreams/4982bf9b-3a3e-43e4-8bbd-706dde496e96/download5cf05f260f104c7aee902999ee54c073MD5511323/1317oai:repositorio.cuc.edu.co:11323/13172024-09-17 14:09:36.129open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=