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Development and validation of artificial intelligence models to predict urinary tract infections and secondary bloodstream infections in adult patients

Authors
 Min Hyuk Choi  ;  Dokyun Kim  ;  Yongjung Park  ;  Seok Hoon Jeong 
Citation
 JOURNAL OF INFECTION AND PUBLIC HEALTH, Vol.17(1) : 10-17, 2024-01 
Journal Title
JOURNAL OF INFECTION AND PUBLIC HEALTH
ISSN
 1876-0341 
Issue Date
2024-01
MeSH
Adult ; Algorithms ; Artificial Intelligence* ; Humans ; ROC Curve ; Urinalysis / methods ; Urinary Tract Infections* / diagnosis ; Urinary Tract Infections* / urine
Keywords
Artificial intelligence ; Machine learning ; Secondary bloodstream infection ; Urinalysis ; Urinary tract infection
Abstract
Background

Traditional culture methods are time-consuming, making it difficult to utilize the results in the early stage of urinary tract infection (UTI) management, and automated urinalyses alone show insufficient performance for diagnosing UTIs. Several models have been proposed to predict urine culture positivity based on urinalysis. However, most of them have not been externally validated or consisted solely of urinalysis data obtained using one specific commercial analyzer.



Methods

A total of 259,187 patients were enrolled to develop artificial intelligence (AI) models. AI models were developed and validated for the diagnosis of UTI and urinary tract related-bloodstream infection (UT-BSI). The predictive performance of conventional urinalysis and AI algorithms were assessed by the areas under the receiver operating characteristic curve (AUROC). We also visualized feature importance rankings as Shapley additive explanation bar plots.



Results

In the two cohorts, the positive rates of urine culture tests were 25.2% and 30.4%, and the proportions of cases classified as UT-BSI were 1.8% and 1.6%. As a result of predicting UTI from the automated urinalysis, the AUROC were 0.745 (0.743–0.746) and 0.740 (0.737–0.743), and most AI algorithms presented excellent discriminant performance (AUROC > 0.9). In the external validation dataset, the XGBoost model achieved the best values in predicting both UTI (AUROC 0.967 [0.966–0.968]) and UT-BSI (AUROC 0.955 [0.951–0.959]). A reduced model using ten parameters was also derived.



Conclusions

We found that AI models can improve the early prediction of urine culture positivity and UT-BSI by combining automated urinalysis with other clinical information. Clinical utilization of the model can reduce the risk of delayed antimicrobial therapy in patients with nonspecific symptoms of UTI and classify patients with UT-BSI who require further treatment and close monitoring.
Files in This Item:
T202306613.pdf Download
DOI
10.1016/j.jiph.2023.10.021
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Laboratory Medicine (진단검사의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Dokyun(김도균) ORCID logo https://orcid.org/0000-0002-0348-5440
Park, Yong Jung(박용정) ORCID logo https://orcid.org/0000-0001-5668-4120
Jeong, Seok Hoon(정석훈) ORCID logo https://orcid.org/0000-0001-9290-897X
Choi, Min Hyuk(최민혁) ORCID logo https://orcid.org/0000-0001-9801-9874
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/198130
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