Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece
Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/9/2/50 |
| _version_ | 1818265519650766848 |
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| author | Georgios Feretzakis Evangelos Loupelis Aikaterini Sakagianni Dimitris Kalles Maria Martsoukou Malvina Lada Nikoletta Skarmoutsou Constantinos Christopoulos Konstantinos Valakis Aikaterini Velentza Stavroula Petropoulou Sophia Michelidou Konstantinos Alexiou |
| author_facet | Georgios Feretzakis Evangelos Loupelis Aikaterini Sakagianni Dimitris Kalles Maria Martsoukou Malvina Lada Nikoletta Skarmoutsou Constantinos Christopoulos Konstantinos Valakis Aikaterini Velentza Stavroula Petropoulou Sophia Michelidou Konstantinos Alexiou |
| author_sort | Georgios Feretzakis |
| collection | DOAJ |
| description | Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample’s Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient’s clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden. |
| first_indexed | 2024-12-12T19:52:06Z |
| format | Article |
| id | doaj.art-198d399c94bd47cc8e0da0510960dafa |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-12-12T19:52:06Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-198d399c94bd47cc8e0da0510960dafa2022-12-22T00:13:57ZengMDPI AGAntibiotics2079-63822020-01-01925010.3390/antibiotics9020050antibiotics9020050Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in GreeceGeorgios Feretzakis0Evangelos Loupelis1Aikaterini Sakagianni2Dimitris Kalles3Maria Martsoukou4Malvina Lada5Nikoletta Skarmoutsou6Constantinos Christopoulos7Konstantinos Valakis8Aikaterini Velentza9Stavroula Petropoulou10Sophia Michelidou11Konstantinos Alexiou12School of Science and Technology, Hellenic Open University, 26335 Patras, GreeceIT Department, Sismanogleio General Hospital, 15126 Marousi, GreeceIntensive Care Unit, Sismanogleio General Hospital, 15126 Marousi, GreeceSchool of Science and Technology, Hellenic Open University, 26335 Patras, GreeceMicrobiology Laboratory, Sismanogleio General Hospital, 15126 Marousi, Greece2nd Internal Medicine Department, Sismanogleio General Hospital, 15126 Marousi, GreeceMicrobiology Laboratory, Sismanogleio General Hospital, 15126 Marousi, Greece1st Internal Medicine Department, Sismanogleio General Hospital, 15126 Marousi, GreeceIntensive Care Unit, Sismanogleio General Hospital, 15126 Marousi, GreeceMicrobiology Laboratory, Sismanogleio General Hospital, 15126 Marousi, GreeceIT Department, Sismanogleio General Hospital, 15126 Marousi, GreeceIntensive Care Unit, Sismanogleio General Hospital, 15126 Marousi, Greece1st Surgery Department, Sismanogleio General Hospital, 15126 Marousi, GreeceHospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample’s Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient’s clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.https://www.mdpi.com/2079-6382/9/2/50antibiotic resistanceantimicrobial resistanceintensive care uniticumachine learningpredictionartificial intelligenceml techniques |
| spellingShingle | Georgios Feretzakis Evangelos Loupelis Aikaterini Sakagianni Dimitris Kalles Maria Martsoukou Malvina Lada Nikoletta Skarmoutsou Constantinos Christopoulos Konstantinos Valakis Aikaterini Velentza Stavroula Petropoulou Sophia Michelidou Konstantinos Alexiou Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece Antibiotics antibiotic resistance antimicrobial resistance intensive care unit icu machine learning prediction artificial intelligence ml techniques |
| title | Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece |
| title_full | Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece |
| title_fullStr | Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece |
| title_full_unstemmed | Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece |
| title_short | Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece |
| title_sort | using machine learning techniques to aid empirical antibiotic therapy decisions in the intensive care unit of a general hospital in greece |
| topic | antibiotic resistance antimicrobial resistance intensive care unit icu machine learning prediction artificial intelligence ml techniques |
| url | https://www.mdpi.com/2079-6382/9/2/50 |
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