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Hospital-acquired infections surveillance: The machine-learning algorithm mirrors National Healthcare Safety Network definitions.
Lukasewicz Ferreira, Stephani Amanda; Franco Meneses, Arateus Crysham; Vaz, Tiago Andres; da Fontoura Carvalho, Otavio Luiz; Hubner Dalmora, Camila; Pressotto Vanni, Daiane; Ribeiro Berti, Isabele; Pires Dos Santos, Rodrigo.
Afiliação
  • Lukasewicz Ferreira SA; Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
  • Franco Meneses AC; Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
  • Vaz TA; Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
  • da Fontoura Carvalho OL; Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
  • Hubner Dalmora C; Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
  • Pressotto Vanni D; Tacchini Hospital, Bento Gonçalves, Rio Grande do Sul, Brazil.
  • Ribeiro Berti I; Tacchini Hospital, Bento Gonçalves, Rio Grande do Sul, Brazil.
  • Pires Dos Santos R; Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
Infect Control Hosp Epidemiol ; 45(5): 604-608, 2024 May.
Article em En | MEDLINE | ID: mdl-38204340
ABSTRACT

BACKGROUND:

Surveillance of hospital-acquired infections (HAIs) is the foundation of infection control. Machine learning (ML) has been demonstrated to be a valuable tool for HAI surveillance. We compared manual surveillance with a supervised, semiautomated, ML method, and we explored the types of infection and features of importance depicted by the model.

METHODS:

From July 2021 to December 2021, a semiautomated surveillance method based on the ML random forest algorithm, was implemented in a Brazilian hospital. Inpatient records were independently manually searched by the local team, and a panel of independent experts reviewed the ML semiautomated results for confirmation of HAI.

RESULTS:

Among 6,296 patients, manual surveillance classified 183 HAI cases (2.9%), and a semiautomated method found 299 HAI cases (4.7%). The semiautomated method added 77 respiratory infections, which comprised 93.9% of the additional HAIs. The ML model considered 447 features for HAI classification. Among them, 148 features (33.1%) were related to infection signs and symptoms; 101 (22.6%) were related to patient severity status, 51 features (11.4%) were related to bacterial laboratory results; 40 features (8.9%) were related to invasive procedures; 34 (7.6%) were related to antibiotic use; and 31 features (6.9%) were related to patient comorbidities. Among these 447 features, 229 (51.2%) were similar to those proposed by NHSN as criteria for HAI classification.

CONCLUSION:

The ML algorithm, which included most NHSN criteria and >200 features, augmented the human capacity for HAI classification. Well-documented algorithm performances may facilitate the incorporation of AI tools in clinical or epidemiological practice and overcome the drawbacks of traditional HAI surveillance.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecção Hospitalar Tipo de estudo: Guideline / Prognostic_studies / Screening_studies Limite: Humans Idioma: En Revista: Infect Control Hosp Epidemiol Assunto da revista: DOENCAS TRANSMISSIVEIS / ENFERMAGEM / EPIDEMIOLOGIA / HOSPITAIS Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecção Hospitalar Tipo de estudo: Guideline / Prognostic_studies / Screening_studies Limite: Humans Idioma: En Revista: Infect Control Hosp Epidemiol Assunto da revista: DOENCAS TRANSMISSIVEIS / ENFERMAGEM / EPIDEMIOLOGIA / HOSPITAIS Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos