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590. Vancomycin Infusion: Algorithmic Analysis of Unstructured Real-World Data Captured from Automated Infusion Devices

Bostick, David L ; Yu, Kalvin ; Yamaga, Cynthia ; Liu-Ferrara, Ann ; Morel, Didier ; Tabak, Ying P

Open forum infectious diseases, 2020-12, Vol.7 (Supplement_1), p.S358-S358 [Periódico revisado por pares]

US: Oxford University Press

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  • Título:
    590. Vancomycin Infusion: Algorithmic Analysis of Unstructured Real-World Data Captured from Automated Infusion Devices
  • Autor: Bostick, David L ; Yu, Kalvin ; Yamaga, Cynthia ; Liu-Ferrara, Ann ; Morel, Didier ; Tabak, Ying P
  • Assuntos: Poster Abstracts
  • É parte de: Open forum infectious diseases, 2020-12, Vol.7 (Supplement_1), p.S358-S358
  • Descrição: Abstract Background Large scale research on antimicrobial usage in real-world populations traditionally does not consist of infusion data. With automation, detailed infusion events are captured in device systems, providing opportunities to harness them for patient safety studies. However, due to the unstructured nature of infusion data, the scale-up of data ingestion, cleansing, and processing is challenging. Figure 1. Illustration of dosing complexity Methods We applied algorithmic techniques to quantitate and visualize vancomycin administration data captured in real-time by automated infusion devices from 3 acute care hospitals. The device data included timestamped infusion events – infusion started, paused, restarted, alarmed, and stopped. We used time density-based segmentation algorithms to depict infusion sessions as bursts of event activity. We examined clinical interpretability of the cluster-defined sessions in defining infusion events, dosing intensity, and duration. Results The algorithms identified 13,339 vancomycin infusion sessions from 2,417 unique patients (mean = 5.5 sessions per patient). Clustering captured vancomycin infusion sessions consistently with correct event labels in >98% of cases. It disentangled ambiguity associated with unexpected events (e.g. multiple stopped/started events within a single infusion session). Segmentation of vancomycin infusion events on an example patient timeline is illustrated in Figure 1. The median duration of infusion sessions was 1.55 (1st, 3rd quartiles: 1.14, 2.02) hours, demonstrating clinical plausibility. Conclusion Passively captured vancomycin administration data from automated infusion device systems provide ramifications for real-time bed-side patient care practice. With large volume of data, temporal event segmentation can be an efficient approach to generate clinically interpretable insights. This method scales up accuracy and consistency in handling longitudinal dosing data. It can enable real-time population surveillance and patient-specific clinical decision support for large patient populations. Better understanding of infusion data may also have implications for vancomycin pharmacokinetic dosing. Disclosures David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding) Cynthia Yamaga, PharmD, BD (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee)
  • Editor: US: Oxford University Press
  • Idioma: Inglês
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