A Machine Learning-Based Risk Score for Prediction of Infective Endocarditis Among Patients With Staphylococcus aureus Bacteremia-The SABIER Score.
J Infect Dis
; 230(3): 606-613, 2024 Sep 23.
Article
in En
| MEDLINE
| ID: mdl-38420871
ABSTRACT
BACKGROUND:
Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among patients with S. aureus bacteremia (SAB) to guide clinical management. The objective of the current study was to develop a novel risk score that is 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 in predicting SA-IE outcome. The data were divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROCs) were determined.RESULTS:
We identified 15 741 SAB patients, among them 658 (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 community onset.CONCLUSIONS:
We developed a novel risk score with performance comparable with existing scores, which can be used at the time of SAB and prior to subjective clinical judgment.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Staphylococcal Infections
/
Staphylococcus aureus
/
Bacteremia
/
Endocarditis, Bacterial
/
Machine Learning
Limits:
Adult
/
Aged
/
Female
/
Humans
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Male
/
Middle aged
Language:
En
Journal:
J Infect Dis
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Estados Unidos