A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia - The SABIER score
© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
BACKGROUND: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among Staphylococcus aureus bacteraemia (SAB) patients to guide clinical management. The objective of this study is to develop a novel risk score independent of subjective clinical judgment and can be used early at the time of blood culture positivity.
METHODS: We conducted a retrospective big data analysis from territory-wide electronic data and included hospitalized patients with SAB between 2009 and 2019. We applied a random forest risk scoring model to select variables from an array of parameters, according to the statistical importance of each feature in predicting SA-IE outcome. The data was divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROC) were determined.
RESULTS: We identified 15,741 SAB patients, among them 4.18% had SA-IE. The AUCROC was 0.74 (95%CI 0.70-0.76), with a negative predictive value of 0.980 (95%CI 0.977-0.983). The four most discriminatory features were age, history of infective endocarditis, valvular heart disease, and being community-onset.
CONCLUSION: We developed a novel risk score with good performance as compared to existing scores and can be used at the time of SAB and prior to subjective clinical judgment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
The Journal of infectious diseases - (2024) vom: 29. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lai, Christopher Koon-Chi [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 29.02.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1093/infdis/jiae080 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369107918 |
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| 100 | 1 | |a Lai, Christopher Koon-Chi |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia - The SABIER score |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among Staphylococcus aureus bacteraemia (SAB) patients to guide clinical management. The objective of this study is to develop a novel risk score independent of subjective clinical judgment and can be used early at the time of blood culture positivity | ||
| 520 | |a METHODS: We conducted a retrospective big data analysis from territory-wide electronic data and included hospitalized patients with SAB between 2009 and 2019. We applied a random forest risk scoring model to select variables from an array of parameters, according to the statistical importance of each feature in predicting SA-IE outcome. The data was divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROC) were determined | ||
| 520 | |a RESULTS: We identified 15,741 SAB patients, among them 4.18% had SA-IE. The AUCROC was 0.74 (95%CI 0.70-0.76), with a negative predictive value of 0.980 (95%CI 0.977-0.983). The four most discriminatory features were age, history of infective endocarditis, valvular heart disease, and being community-onset | ||
| 520 | |a CONCLUSION: We developed a novel risk score with good performance as compared to existing scores and can be used at the time of SAB and prior to subjective clinical judgment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Staphylococcus aureus | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Bloodstream infections | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Prediction Model | |
| 650 | 4 | |a Sepsis | |
| 650 | 4 | |a infective endocarditis | |
| 700 | 1 | |a Leung, Eman |e verfasserin |4 aut | |
| 700 | 1 | |a He, Yinan |e verfasserin |4 aut | |
| 700 | 1 | |a Cheung, Ching-Chun |e verfasserin |4 aut | |
| 700 | 1 | |a Oliver, Mui Oi Yat |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Qinze |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Timothy Chun-Man |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Alfred Lok-Hang |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Lui, Grace Chung-Yan |e verfasserin |4 aut | |
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