Prediction of vancomycin dose on high-dimensional data using machine learning techniques.
Expert Rev Clin Pharmacol
; 14(6): 761-771, 2021 Jun.
Article
de En
| MEDLINE
| ID: mdl-33835879
ABSTRACT
OBJECTIVES:
Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods.METHODS:
This study proposed a variable engineering process that automatically generates second-order variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables.RESULTS:
Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups.CONCLUSION:
The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Vancomycine
/
Apprentissage machine
/
Antibactériens
/
Modèles biologiques
Type d'étude:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limites:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Langue:
En
Journal:
Expert Rev Clin Pharmacol
Année:
2021
Type de document:
Article
Pays d'affiliation:
Chine