View in EDS

Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: A retrospective cohort study.

Saved in:
Bibliographic Details
Title: Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: A retrospective cohort study.
Authors: Tandan, Meera1 (AUTHOR) m.tandan1@nuigalway.ie, Timilsina, Mohan2 (AUTHOR), Cormican, Martin3,4 (AUTHOR), Vellinga, Akke1,3 (AUTHOR)
Source: International Journal of Medical Informatics. Jul2019, Vol. 127, p127-133. 7p.
Subjects: Antibiotics, Comparative studies, Decision trees, Drug resistance in microorganisms, Escherichia coli, Escherichia coli diseases, Medical cooperation, Research, Urinary tract infections, Retrospective studies
Abstract: Background: In general practice, many infections are treated empirically prior to or without microbiological confirmation. Prediction of antimicrobial susceptibility could optimise prescribing thus improving patient outcomes. Decision tree models are a novel idea to predict AMR at the time of clinical presentation. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of pathogens causing urinary tract infections (UTI) for patients over 65 years based on pre-existing routine laboratory data.Methods: Data were extracted from the database of the microbiological laboratory of the University Hospitals Galway (UHG). All urine results from patients over 65 years, their microbiological analysis and susceptibility (AST) results from January 2011 to December 2015 were included. The primary endpoint was culture result and resistance to antimicrobials (nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav, and amoxicillin) commonly used to treat UTI. A non-parametric regression tree analysis i.e. a decision tree model was generated with the 75% of the dataset (training set) and validated with the remaining 25% (test set). The model performance was evaluated measuring Area Under the Curve Receiver Operating Characteristic (AUC_ROC) curve.Results: A total of 99,101 urine samples of patients over 65 years were submitted for culture over the five years and 27% had significant bacteriuria (≥104 cfu/ml) and AST. The most common identified causative organisms were E.coli, Klebsiella spp. and Proteus spp. E.coli was more often resistant to amoxicillin (66%) followed by Proteus spp. (41%). Klebsiella spp. and Proteus spp. were more often resistant to trimethoprim (78% and 54% respectively). E. coli resistance to nitrofurantoin is low (<10%). The decision tree model showed an AUC-ROC score of 0.68 for culture and in between 0.60 to 0.97 for antimicrobial resistance of the pathogens, with the inclusion of patient's descriptors only. Including the uropathogen in the model did not change model performance.Conclusions: The decision tree models using patient descriptors available at the time of presentation showed fair to excellent performance in predicting culture and antimicrobial resistance. The presented models provide an alternative approach to decision making on antimicrobial prescribing for UTIs. Increasing more predictors in the model could improve the model performance. Prospective data collection, validation and feasibility testing of the model including data from other laboratories will progress the practical implementation of similar models. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Medical Informatics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 136583016
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: A retrospective cohort study.
– Name: Author
  Label: Authors
  Group: Au
  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Tandan%2C+Meera%22&quot;&gt;Tandan, Meera&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; m.tandan1@nuigalway.ie&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Timilsina%2C+Mohan%22&quot;&gt;Timilsina, Mohan&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Cormican%2C+Martin%22&quot;&gt;Cormican, Martin&lt;/searchLink&gt;&lt;relatesTo&gt;3,4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Vellinga%2C+Akke%22&quot;&gt;Vellinga, Akke&lt;/searchLink&gt;&lt;relatesTo&gt;1,3&lt;/relatesTo&gt; (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22International+Journal+of+Medical+Informatics%22&quot;&gt;International Journal of Medical Informatics&lt;/searchLink&gt;. Jul2019, Vol. 127, p127-133. 7p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Antibiotics%22&quot;&gt;Antibiotics&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Comparative+studies%22&quot;&gt;Comparative studies&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Decision+trees%22&quot;&gt;Decision trees&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Drug+resistance+in+microorganisms%22&quot;&gt;Drug resistance in microorganisms&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Escherichia+coli%22&quot;&gt;Escherichia coli&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Escherichia+coli+diseases%22&quot;&gt;Escherichia coli diseases&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Medical+cooperation%22&quot;&gt;Medical cooperation&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Research%22&quot;&gt;Research&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Urinary+tract+infections%22&quot;&gt;Urinary tract infections&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Retrospective+studies%22&quot;&gt;Retrospective studies&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: &lt;bold&gt;Background: &lt;/bold&gt;In general practice, many infections are treated empirically prior to or without microbiological confirmation. Prediction of antimicrobial susceptibility could optimise prescribing thus improving patient outcomes. Decision tree models are a novel idea to predict AMR at the time of clinical presentation. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of pathogens causing urinary tract infections (UTI) for patients over 65 years based on pre-existing routine laboratory data.&lt;bold&gt;Methods: &lt;/bold&gt;Data were extracted from the database of the microbiological laboratory of the University Hospitals Galway (UHG). All urine results from patients over 65 years, their microbiological analysis and susceptibility (AST) results from January 2011 to December 2015 were included. The primary endpoint was culture result and resistance to antimicrobials (nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav, and amoxicillin) commonly used to treat UTI. A non-parametric regression tree analysis i.e. a decision tree model was generated with the 75% of the dataset (training set) and validated with the remaining 25% (test set). The model performance was evaluated measuring Area Under the Curve Receiver Operating Characteristic (AUC_ROC) curve.&lt;bold&gt;Results: &lt;/bold&gt;A total of 99,101 urine samples of patients over 65 years were submitted for culture over the five years and 27% had significant bacteriuria (≥104 cfu/ml) and AST. The most common identified causative organisms were E.coli, Klebsiella spp. and Proteus spp. E.coli was more often resistant to amoxicillin (66%) followed by Proteus spp. (41%). Klebsiella spp. and Proteus spp. were more often resistant to trimethoprim (78% and 54% respectively). E. coli resistance to nitrofurantoin is low (&lt;10%). The decision tree model showed an AUC-ROC score of 0.68 for culture and in between 0.60 to 0.97 for antimicrobial resistance of the pathogens, with the inclusion of patient&#39;s descriptors only. Including the uropathogen in the model did not change model performance.&lt;bold&gt;Conclusions: &lt;/bold&gt;The decision tree models using patient descriptors available at the time of presentation showed fair to excellent performance in predicting culture and antimicrobial resistance. The presented models provide an alternative approach to decision making on antimicrobial prescribing for UTIs. Increasing more predictors in the model could improve the model performance. Prospective data collection, validation and feasibility testing of the model including data from other laboratories will progress the practical implementation of similar models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;Copyright of International Journal of Medical Informatics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=136583016
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ijmedinf.2019.04.020
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
        StartPage: 127
    Subjects:
      – SubjectFull: Antibiotics
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Decision trees
        Type: general
      – SubjectFull: Drug resistance in microorganisms
        Type: general
      – SubjectFull: Escherichia coli
        Type: general
      – SubjectFull: Escherichia coli diseases
        Type: general
      – SubjectFull: Medical cooperation
        Type: general
      – SubjectFull: Research
        Type: general
      – SubjectFull: Urinary tract infections
        Type: general
      – SubjectFull: Retrospective studies
        Type: general
    Titles:
      – TitleFull: Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: A retrospective cohort study.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tandan, Meera
      – PersonEntity:
          Name:
            NameFull: Timilsina, Mohan
      – PersonEntity:
          Name:
            NameFull: Cormican, Martin
      – PersonEntity:
          Name:
            NameFull: Vellinga, Akke
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2019
              Type: published
              Y: 2019
          Identifiers:
            – Type: issn-print
              Value: 13865056
          Numbering:
            – Type: volume
              Value: 127
          Titles:
            – TitleFull: International Journal of Medical Informatics
              Type: main
ResultId 1