On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns
In recent years, several bacterial strains have acquired significant antibiotic resistance and can, therefore, become difficult to contain. To counteract such trends, relational databases can be a powerful tool for supporting the decision-making process. The case of <i>Klebsiella pneumoniae<...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/12/4/784 |
| _version_ | 1797606677555445760 |
|---|---|
| author | Michela Gelfusa Andrea Murari Gian Marco Ludovici Cristiano Franchi Claudio Gelfusa Andrea Malizia Pasqualino Gaudio Giovanni Farinelli Giacinto Panella Carla Gargiulo Katia Casinelli |
| author_facet | Michela Gelfusa Andrea Murari Gian Marco Ludovici Cristiano Franchi Claudio Gelfusa Andrea Malizia Pasqualino Gaudio Giovanni Farinelli Giacinto Panella Carla Gargiulo Katia Casinelli |
| author_sort | Michela Gelfusa |
| collection | DOAJ |
| description | In recent years, several bacterial strains have acquired significant antibiotic resistance and can, therefore, become difficult to contain. To counteract such trends, relational databases can be a powerful tool for supporting the decision-making process. The case of <i>Klebsiella pneumoniae</i> diffusion in a central region of Italy was analyzed as a case study. A specific relational database is shown to provide very detailed and timely information about the spatial–temporal diffusion of the contagion, together with a clear assessment of the multidrug resistance of the strains. The analysis is particularized for both internal and external patients. Tools such as the one proposed can, therefore, be considered important elements in the identification of infection hotspots, a key ingredient of any strategy to reduce the diffusion of an infectious disease at the community level and in hospitals. These types of tools are also very valuable in the decision-making process related to antibiotic prescription and to the management of stockpiles. The application of this processing technology to viral diseases such as COVID-19 is under investigation. |
| first_indexed | 2024-03-11T05:18:27Z |
| format | Article |
| id | doaj.art-eaffecb715d44af4bfd1265d8440be55 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-11T05:18:27Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-eaffecb715d44af4bfd1265d8440be552023-11-17T18:04:00ZengMDPI AGAntibiotics2079-63822023-04-0112478410.3390/antibiotics12040784On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance PatternsMichela Gelfusa0Andrea Murari1Gian Marco Ludovici2Cristiano Franchi3Claudio Gelfusa4Andrea Malizia5Pasqualino Gaudio6Giovanni Farinelli7Giacinto Panella8Carla Gargiulo9Katia Casinelli10Department of Industrial Engineering, University of Rome “Tor Vergata”, 00133 Rome, ItalyConsorzio RFX (CNR, ENEA, INFN), University of Padua, 35127 Padua, ItalyDepartment of Industrial Engineering, University of Rome “Tor Vergata”, 00133 Rome, ItalyDepartment of Industrial Engineering, University of Rome “Tor Vergata”, 00133 Rome, ItalyDepartment of Industrial Engineering, University of Rome “Tor Vergata”, 00133 Rome, ItalyDepartment of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, ItalyDepartment of Industrial Engineering, University of Rome “Tor Vergata”, 00133 Rome, ItalyASL and Fabrizio Spaziani, Frosinone Hospital, 03100 Frosinone, ItalyASL and Fabrizio Spaziani, Frosinone Hospital, 03100 Frosinone, ItalyASL and Fabrizio Spaziani, Frosinone Hospital, 03100 Frosinone, ItalyASL and Fabrizio Spaziani, Frosinone Hospital, 03100 Frosinone, ItalyIn recent years, several bacterial strains have acquired significant antibiotic resistance and can, therefore, become difficult to contain. To counteract such trends, relational databases can be a powerful tool for supporting the decision-making process. The case of <i>Klebsiella pneumoniae</i> diffusion in a central region of Italy was analyzed as a case study. A specific relational database is shown to provide very detailed and timely information about the spatial–temporal diffusion of the contagion, together with a clear assessment of the multidrug resistance of the strains. The analysis is particularized for both internal and external patients. Tools such as the one proposed can, therefore, be considered important elements in the identification of infection hotspots, a key ingredient of any strategy to reduce the diffusion of an infectious disease at the community level and in hospitals. These types of tools are also very valuable in the decision-making process related to antibiotic prescription and to the management of stockpiles. The application of this processing technology to viral diseases such as COVID-19 is under investigation.https://www.mdpi.com/2079-6382/12/4/784decision support systeminfectious diseaseshotspotrelational databases<i>Klebsiella</i>nosocomial diseases |
| spellingShingle | Michela Gelfusa Andrea Murari Gian Marco Ludovici Cristiano Franchi Claudio Gelfusa Andrea Malizia Pasqualino Gaudio Giovanni Farinelli Giacinto Panella Carla Gargiulo Katia Casinelli On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns Antibiotics decision support system infectious diseases hotspot relational databases <i>Klebsiella</i> nosocomial diseases |
| title | On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns |
| title_full | On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns |
| title_fullStr | On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns |
| title_full_unstemmed | On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns |
| title_short | On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns |
| title_sort | on the potential of relational databases for the detection of clusters of infection and antibiotic resistance patterns |
| topic | decision support system infectious diseases hotspot relational databases <i>Klebsiella</i> nosocomial diseases |
| url | https://www.mdpi.com/2079-6382/12/4/784 |
| work_keys_str_mv | AT michelagelfusa onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT andreamurari onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT gianmarcoludovici onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT cristianofranchi onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT claudiogelfusa onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT andreamalizia onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT pasqualinogaudio onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT giovannifarinelli onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT giacintopanella onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT carlagargiulo onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns AT katiacasinelli onthepotentialofrelationaldatabasesforthedetectionofclustersofinfectionandantibioticresistancepatterns |