Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach

Aims Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of applied microbiology Ročník 128; číslo 3; s. 688 - 696
Hlavní autori: Chowdhury, A.S., Lofgren, E.T., Moehring, R.W., Broschat, S.L.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Oxford University Press 01.03.2020
Predmet:
Algorithms
Anti-Infective Agents - therapeutic use
antibiotic resistance
Antibiotics
Antimicrobial agents
Antimicrobial resistance
Antimicrobial Stewardship
antimicrobial utilization
artificial intelligence
Benchmarks
cubist regression
data imputation
Drug resistance
Female
Hospitalization
hospitals
Humans
Learning algorithms
Machine Learning
Male
patient features
Patients
Regression analysis
Risk
risk adjustment
Southeastern United States
standardized antimicrobial administration ratio
Statistical analysis
Support vector machines
support vector regression
therapeutics
Southeastern United States
support vector regression
data imputation
standardized antimicrobial administration ratio
antimicrobial stewardship
antimicrobial resistance
patient features
antimicrobial utilization
machine learning
cubist regression
ISSN:1364-5072, 1365-2672, 1365-2672
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Aims Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate AU and for fair inter‐facility comparison. In this study we identify patient‐ and facility‐level predictors of antimicrobial usage in hospitalized patients using a machine learning approach, which can be used to inform a risk adjustment model to facilitate assessment of AU. To our knowledge, this is the first time machine learning has been applied for this purpose. Methods and Results Patient admission records were retrieved from the Duke Antimicrobial Stewardship Outreach Network which include clinical data for 27 community hospitals in the southeastern United States. Candidate features (predictors) were then generated from these records. The number of features was reduced using a statistical approach, and missing values of the reduced feature set were imputed using bootstrapping and expectation‐maximization algorithm. Finally, support vector regression (SVR) and cubist regression (CB) models were applied to find root‐mean‐square error values which were used to evaluate the selected feature set. The performance of the SVR and CB models was found to be better than that of linear null and negative binomial null models, thereby demonstrating the effectiveness of our selected features. Conclusions Relevant patient‐ and facility‐level predictors of antimicrobial usage in days of therapy were obtained and evaluated. The potential predictor set can be used in risk adjustment strategies for benchmarking antimicrobial use. Significance and Impact of the Study One reason for the rapid emergence of antimicrobial resistance is inappropriate use of antibiotics in hospitalized patients. Identifying predictors of antimicrobial exposure using a machine learning technique can improve the use of AU, enhance patient health outcomes, and reduce the infection spread caused by antimicrobial‐resistant organisms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1364-5072
1365-2672
1365-2672
DOI:10.1111/jam.14499