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Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.
Monfredi, Oliver J; Moore, Christopher C; Sullivan, Brynne A; Keim-Malpass, Jessica; Fairchild, Karen D; Loftus, Tyler J; Bihorac, Azra; Krahn, Katherine N; Dubrawski, Artur; Lake, Douglas E; Moorman, J Randall; Clermont, Gilles.
Afiliación
  • Monfredi OJ; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
  • Moore CC; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
  • Sullivan BA; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America.
  • Keim-Malpass J; Center for Advanced Medical Analytics, University of Virginia, United States of America; School of Nursing, University of Virginia, United States of America.
  • Fairchild KD; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America.
  • Loftus TJ; Department of Surgery, University of Florida, United States of America.
  • Bihorac A; Department of Medicine, University of Florida, United States of America.
  • Krahn KN; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
  • Dubrawski A; Robotics Institute, Carnegie Mellon University, United States of America.
  • Lake DE; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
  • Moorman JR; Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America. Electronic address: rm3h@virginia.edu.
  • Clermont G; Department of Critical Care, University of Pittsburgh, United States of America.
J Electrocardiol ; 76: 35-38, 2023.
Article en En | MEDLINE | ID: mdl-36434848
The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Electrocardiografía / Deterioro Clínico Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: J Electrocardiol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Electrocardiografía / Deterioro Clínico Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: J Electrocardiol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos