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A step forward in the diagnosis of urinary tract infections: from machine learning to clinical practice.
Flores, Emilio; Martínez-Racaj, Laura; Blasco, Álvaro; Diaz, Elena; Esteban, Patricia; López-Garrigós, Maite; Salinas, María.
Afiliação
  • Flores E; Department of Laboratory, Hospital Universitario San Juan de Alicante, Carretera de Valencia, 03550 San Juan de Alicante, Alicante, Spain.
  • Martínez-Racaj L; Department of Clinical Medicine, Universidad Miguel Hernandez, Elche, Spain.
  • Blasco Á; Department of Laboratory, Hospital Universitario San Juan de Alicante, Carretera de Valencia, 03550 San Juan de Alicante, Alicante, Spain.
  • Diaz E; Department of Laboratory, Hospital Universitario San Juan de Alicante, Carretera de Valencia, 03550 San Juan de Alicante, Alicante, Spain.
  • Esteban P; Department of Emergency, Hospital Universitario San Juan de Alicante, Carretera de Valencia, 03550 San Juan de Alicante, Alicante, Spain.
  • López-Garrigós M; Department of Laboratory, Hospital Universitario San Juan de Alicante, Carretera de Valencia, 03550 San Juan de Alicante, Alicante, Spain.
  • Salinas M; Department of Laboratory, Hospital Universitario San Juan de Alicante, Carretera de Valencia, 03550 San Juan de Alicante, Alicante, Spain.
Comput Struct Biotechnol J ; 24: 533-541, 2024 Dec.
Article em En | MEDLINE | ID: mdl-39220685
ABSTRACT

Objectives:

Urinary tract infections (UTIs) are common infections within the Emergency Department (ED), causing increased laboratory workloads and unnecessary antibiotics prescriptions. The aim of this study was to improve UTI diagnostics in clinical practice by application of machine learning (ML) models for real-time UTI prediction.

Methods:

In a retrospective study, patient information and outcomes from Emergency Department patients, with positive and negative culture results, were used to design models - 'Random Forest' and 'Neural Network' - for the prediction of UTIs. The performance of these predictive models was validated in a cross-sectional study. In a quasi-experimental study, the impact of UTI risk assessment was investigated by evaluating changes in the behaviour of clinicians, measuring changes in antibiotic prescriptions and urine culture requests.

Results:

First, we trained and tested two different predictive models with 8692 cases. Second, we investigated the performance of the predictive models in clinical practice with 962 cases (Area under the curve was between 0.81 to 0.88). The best performance was the combination of both models. Finally, the assessment of the risk for UTIs was implemented into clinical practice and allowed for the reduction of unnecessary urine cultures and antibiotic prescriptions for patients with a low risk of UTI, as well as targeted diagnostics and treatment for patients with a high risk of UTI.

Conclusion:

The combination of modern urinalysis diagnostic technologies with digital health solutions can help to further improve UTI diagnostics with positive impact on laboratory workloads and antimicrobial stewardship.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Espanha País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Espanha País de publicação: Holanda