Infusion Devices
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Author(s):  
Andjar Pudji Pudji ◽  
Anita Miftahul Maghfiroh ◽  
Nuntachai Thongpance

Infusion devices are the basis for primary health care, that is to provide medicine, nutrition, and hydration to patients. One of the infusion devices is a syringe pump and an infusion pump. This device is very important to assist the volume and flow that enters the patient's body, especially in situations related to neonatology or cancer treatment. Therefore, a comparison tool is needed to see whether the equipment is used or not. The purpose of this research is to make an infusion device analyzer (IDA) design with a flow rate parameter. The contribution of this research is that the tool can calculate the correct value of the flow rate that comes out of the infusion pump and syringe pump. The water released by the infusion pump or syringe pump will be converted into droplets which are then detected by the sensor. This tool uses an infrared sensor and a photodiode. The results obtained by the sensor will come by Arduino nano and code it to the 16x2 Character Liquid Crystal Display (LCD) and can be stored on an SD Card so that it can be analyzed further. In setting the flow rate for the syringe pump of 100 mL / hour, the error value is 3.9, 50 ml / hour 0.02, 20 mL / hour 0.378, 10 mL / hour 0.048, and 5 mL / hour 0.01. The results show that the average error of the syringe pump performance read by the module is 0.87. The results obtained from this study can be implemented for the calibration of the infusion pump and the syringe pump so that it can be determined whether the device is suitable or not


2021 ◽  
Vol 18 (14) ◽  
pp. 3106-3111
Author(s):  
Sachiko Omotani ◽  
Yasutoshi Hatsuda ◽  
Yasuhiro Katsui ◽  
Ayumi Asao ◽  
Hiroyuki Toujou ◽  
...  

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S108-S108
Author(s):  
Cynthia Yamaga ◽  
David L Bostick ◽  
Ying P Tabak ◽  
Ann Liu-Ferrara ◽  
Didier Morel ◽  
...  

Abstract Background Automated infusion devices captures actual infused medication administration data in real-time. Vancomycin use is now recommended to be driven by AUC (area under the curve) dosing. We evaluated automated infusion device data to depict vancomycin administration practices in acute care hospitals. Figure 1. Distribution of vancomycin infusion dosing Figure 2. Distribution of time intervals between each vancomycin infusion session (mostly around 8 or 12 hours) Methods We analyzed archived vancomycin infusion data from 2,417 patients captured by automated infusion systems from 3 acute care hospitals. The infusion device informatics software recorded a variety of events during infusion – starting and stopping times, alarms and alerts, vancomycin dose, and other forms of timestamped usage information. We evaluated infusion session duration and dosing, using data-driven clustering algorithms. Results A total of 13,339 vancomycin infusion sessions from 2,417 unique adult patients were analyzed. Approximately 26.1% of patients had just one infusion of vancomycin. For the rest of the patients, the median number of infusion sessions per patient was 4; the interquartile range was 3 and 8. The most common dose was 1.0 gram (53.7%) or 1.5 gram (24.6%) (see Figure 1). The distribution of infusion session duration (hours) was 4.2% (≤1.0 hh); 40.1% (1.01–1.5 hh); 29.1% (1.51–2.0 hh); and 26.6% (>2.0 hh). The dosing frequency was 39.5% (q8 hh), 42.9% (q12 hh), 11.1% (q24 hh), and 6.5% (>q24 hh) (Figure 2), demonstrating clinical interpretability. Conclusion A considerable number of patients received just one vancomycin infusion during their hospital stay, suggesting a potential overuse of empiric vancomycin. The majority of infusion doses were between 1 to 1.5 grams and most infusion sessions were administered every 8 or 12 hours. The actual infusion duration for each dose often exceeds the prescribed 1- or 2-hour infusion orders, which may be due to known instances of infusion interruptions due to patient movement, procedures or IV access compromise. The data generated by infusion devices can augment insights on actual antimicrobial administration practices and duration. As vancomycin AUC dosing becomes more prevalent, real world infusion data may aid timely data-driven antimicrobial stewardship and patient safety interventions for vancomycin and other AUC dosed drugs. Disclosures Cynthia Yamaga, PharmD, BD (Employee) David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding)


2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S358-S358
Author(s):  
David L Bostick ◽  
Kalvin Yu ◽  
Cynthia Yamaga ◽  
Ann Liu-Ferrara ◽  
Didier Morel ◽  
...  

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)


2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yogini H. Jani ◽  
Gillian M. Chumbley ◽  
Dominic Furniss ◽  
Ann Blandford ◽  
Bryony Franklin

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