Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

• Autor(a) principal: Scherer,Juliane de Souza
• Data de Publicação: 2022
• Outros Autores: Pereira,Jéssica Silveira, Debastiani,Mariana Severo, Bica,Claudia Giuliano
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Enfermagem (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-71672022000600157
• topic: Artificial Intelligence; Machine Learning; Sepsis; Clinical Decision Support; Innovation

ABSTRACT Objective: To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex. Methods: An observational retrospective cohort study. The Machine Learning (ML) tool, Robot Laura®, scores changes in vital parameters and lab tests, classifying them by severity. Inpatients and patients over 18 years of age were included. Results: A total of 122,703 alarms were extracted from the platform, classified as 2 to 9. The pre-selection of critical alarms (6 to 9) indicated 263 urgent alerts (0.2%), from which, after filtering exclusion criteria, 254 alerts were delimited for 61 inpatients. Patient mortality from sepsis was 75%, of which 52% was due to sepsis related to the new coronavirus. After the alarms were answered, 82% of the patients remained in the sectors. Conclusions: Far beyond technology, ML models can speed up assertive clinical decisions by nurses, optimizing time and specialized human resources.