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Prediction of impending central-line-associated bloodstream infections in hospitalized cardiac patients: development and testing of a machine-learning model.
Bonello, K; Emani, S; Sorensen, A; Shaw, L; Godsay, M; Delgado, M; Sperotto, F; Santillana, M; Kheir, J N.
Afiliación
  • Bonello K; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Paediatrics, Harvard Medical School, Boston, MA, USA.
  • Emani S; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Paediatrics, Harvard Medical School, Boston, MA, USA.
  • Sorensen A; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • Shaw L; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • Godsay M; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • Delgado M; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • Sperotto F; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Paediatrics, Harvard Medical School, Boston, MA, USA; Paediatric Cardiac Intensive Care Unit, Department of Women's and Children's Health, University of Padova, Padova, Italy.
  • Santillana M; Harvard Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Kheir JN; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Paediatrics, Harvard Medical School, Boston, MA, USA. Electronic address: john.kheir@childrens.harvard.edu.
J Hosp Infect ; 127: 44-50, 2022 Sep.
Article en En | MEDLINE | ID: mdl-35738317
BACKGROUND: While modelling of central-line-associated blood stream infection (CLABSI) risk factors is common, models that predict an impending CLABSI in real time are lacking. AIM: To build a prediction model which identifies patients who will develop a CLABSI in the ensuing 24 h. METHODS: We collected variables potentially related to infection identification in all patients admitted to the cardiac intensive care unit or cardiac ward at Boston Children's Hospital in whom a central venous catheter (CVC) was in place between January 2010 and August 2020, excluding those with a diagnosis of bacterial endocarditis. We created models predicting whether a patient would develop CLABSI in the ensuing 24 h. We assessed model performance based on area under the curve (AUC), sensitivity and false-positive rate (FPR) of models run on an independent testing set (40%). FINDINGS: A total of 104,035 patient-days and 139,662 line-days corresponding to 7468 unique patients were included in the analysis. There were 399 positive blood cultures (0.38%), most commonly with Staphylococcus aureus (23% of infections). Major predictors included a prior history of infection, elevated maximum heart rate, elevated maximum temperature, elevated C-reactive protein, exposure to parenteral nutrition and use of alteplase for CVC clearance. The model identified 25% of positive cultures with an FPR of 0.11% (AUC = 0.82). CONCLUSIONS: A machine-learning model can be used to predict 25% of patients with impending CLABSI with only 1.1/1000 of these predictions being incorrect. Once prospectively validated, this tool may allow for early treatment or prevention.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cateterismo Venoso Central / Bacteriemia / Infecciones Relacionadas con Catéteres / Catéteres Venosos Centrales Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Humans Idioma: En Revista: J Hosp Infect Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cateterismo Venoso Central / Bacteriemia / Infecciones Relacionadas con Catéteres / Catéteres Venosos Centrales Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Humans Idioma: En Revista: J Hosp Infect Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido