Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting
Vancomycin-intermediate <i>Staphylococcus aureus</i> (VISA) is a multi-drug-resistant pathogen of significant clinical concern. Various <i>S. aureus</i> strains can cause infections, from skin and soft tissue infections to life-threatening conditions such as bacteremia and pn...
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2025-01-01
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| author | Wei-Chuan Chen Jiun-Ling Wang Chi-Chuan Chang Yusen Eason Lin |
| author_facet | Wei-Chuan Chen Jiun-Ling Wang Chi-Chuan Chang Yusen Eason Lin |
| author_sort | Wei-Chuan Chen |
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| description | Vancomycin-intermediate <i>Staphylococcus aureus</i> (VISA) is a multi-drug-resistant pathogen of significant clinical concern. Various <i>S. aureus</i> strains can cause infections, from skin and soft tissue infections to life-threatening conditions such as bacteremia and pneumonia. VISA infections, particularly bacteremia, are associated with high mortality rates, with 34% of patients succumbing within 30 days. This study aimed to develop predictive models for VISA (including <i>h</i>VISA) bacteremia outcomes using data mining techniques, potentially improving patient management and therapy selection. We focused on three endpoints in patients receiving traditional vancomycin therapy: VISA persistence in bacteremia after 7 days, after 30 days, and patient mortality. Our analysis incorporated 29 risk factors associated with VISA bacteremia. The resulting models demonstrated high predictive accuracy, with 82.0–86.6% accuracy for 7-day VISA persistence in blood cultures and 53.4–69.2% accuracy for 30-day mortality. These findings suggest that data mining techniques can effectively predict VISA bacteremia outcomes in clinical settings. The predictive models developed have the potential to be applied prospectively in hospital settings, aiding in risk stratification and informing treatment decisions. Further validation through prospective studies is warranted to confirm the clinical utility of these predictive tools in managing VISA infections. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-a5c29d5df05f4de6ac089a6702acc7ba2025-01-24T13:42:39ZengMDPI AGMicroorganisms2076-26072025-01-0113110110.3390/microorganisms13010101Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care SettingWei-Chuan Chen0Jiun-Ling Wang1Chi-Chuan Chang2Yusen Eason Lin3Division of Teaching and Education, Teaching and Research Department, Kaohsiung Veterans General Hospital, Kaohsiung 813414, TaiwanDepartment of Internal Medicine, National Cheng Kung University Hospital, Tainan 701401, TaiwanDivision of Teaching and Education, Teaching and Research Department, Kaohsiung Veterans General Hospital, Kaohsiung 813414, TaiwanGraduate Institute of Human Resource and Knowledge Management, National Kaohsiung Normal University, Kaohsiung 802561, TaiwanVancomycin-intermediate <i>Staphylococcus aureus</i> (VISA) is a multi-drug-resistant pathogen of significant clinical concern. Various <i>S. aureus</i> strains can cause infections, from skin and soft tissue infections to life-threatening conditions such as bacteremia and pneumonia. VISA infections, particularly bacteremia, are associated with high mortality rates, with 34% of patients succumbing within 30 days. This study aimed to develop predictive models for VISA (including <i>h</i>VISA) bacteremia outcomes using data mining techniques, potentially improving patient management and therapy selection. We focused on three endpoints in patients receiving traditional vancomycin therapy: VISA persistence in bacteremia after 7 days, after 30 days, and patient mortality. Our analysis incorporated 29 risk factors associated with VISA bacteremia. The resulting models demonstrated high predictive accuracy, with 82.0–86.6% accuracy for 7-day VISA persistence in blood cultures and 53.4–69.2% accuracy for 30-day mortality. These findings suggest that data mining techniques can effectively predict VISA bacteremia outcomes in clinical settings. The predictive models developed have the potential to be applied prospectively in hospital settings, aiding in risk stratification and informing treatment decisions. Further validation through prospective studies is warranted to confirm the clinical utility of these predictive tools in managing VISA infections.https://www.mdpi.com/2076-2607/13/1/101Vancomycin-intermediate <i>Staphylococcus aureus</i>data mining techniquesrisk factors |
| spellingShingle | Wei-Chuan Chen Jiun-Ling Wang Chi-Chuan Chang Yusen Eason Lin Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting Microorganisms Vancomycin-intermediate <i>Staphylococcus aureus</i> data mining techniques risk factors |
| title | Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting |
| title_full | Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting |
| title_fullStr | Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting |
| title_full_unstemmed | Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting |
| title_short | Data Mining Models in Prediction of Vancomycin-Intermediate <i>Staphylococcus aureus</i> in Methicillin-Resistant <i>S. aureus</i> (MRSA) Bacteremia Patients in a Clinical Care Setting |
| title_sort | data mining models in prediction of vancomycin intermediate i staphylococcus aureus i in methicillin resistant i s aureus i mrsa bacteremia patients in a clinical care setting |
| topic | Vancomycin-intermediate <i>Staphylococcus aureus</i> data mining techniques risk factors |
| url | https://www.mdpi.com/2076-2607/13/1/101 |
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