scholarly journals 210. Vancomycin Infusion Frequency and Intensity: Analysis of Real-World Data Generated from Automated Infusion Devices

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 ◽  
Author(s):  
Teng Zhang ◽  
Kelly McFarlane ◽  
Jacqueline Vallon ◽  
Linying Yang ◽  
Jin Xie ◽  
...  

Abstract Background:We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is positive forcoronavirus disease 2019 (COVID-19) and the rate at which COVID-19 is spreading in the population. Our goal was to facilitate the implementation of data-driven recommendations for capacity management with a transparent mathematical simulation designed to answer the specific, local questions hospital leadership considered critical.Methods:The model facilitates hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool with modifiable parameters.Results:The use of the model is illustrated by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. The model was used to inform COVID-19 planning and decision-making, including Intensive Care Unit (ICU) staffing and ventilator procurement.Conclusion:In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.Contributions to the literature:· Generation and implementation of data-driven recommendations for hospital capacity management early in the COVID-19 pandemic· The conceptualization, development, and deployment of an interactive simulation model in two weeks· Data-driven capacity management in the presence of significant uncertainty about the expected volume of patients, their clinical needs, and the availability of the workforceTrial Registration: Not applicable


2011 ◽  
Vol 140 (1) ◽  
pp. 126-136 ◽  
Author(s):  
F. VRIJENS ◽  
F. HULSTAERT ◽  
S. DEVRIESE ◽  
S. VAN DE SANDE

SUMMARYAssessing the overall burden of disease which can be attributed to hospital-acquired infections (HAIs) remains a challenge. A matched cohort study was performed to estimate excess mortality, length of stay and costs attributable to HAIs in Belgian acute-care hospitals, using six matching factors (hospital, diagnosis-related group, age, ward, Charlson score, estimated length of stay prior to infection). Information was combined from different sources on the epidemiology and burden of HAIs to estimate the impact at national level. The total number of patients affected by a HAI each year was 125 000 (per 10·9 million inhabitants). The excess mortality was 2·8% and excess length of stay was 7·3 days, corresponding to a public healthcare cost of €290 million. A large burden was observed outside the intensive-care unit setting (87% of patients infected and extra costs, 73% of excess deaths).


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)


PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260446
Author(s):  
Takuya Okuno ◽  
Hisashi Itoshima ◽  
Jung-ho Shin ◽  
Tetsuji Morishita ◽  
Susumu Kunisawa ◽  
...  

Introduction The coronavirus disease (COVID-19) pandemic has caused unprecedented challenges for the medical staff worldwide, especially for those in hospitals where COVID-19-positive patients are hospitalized. The announcement of COVID-19 hospital restrictions by the Japanese government has led to several limitations in hospital care, including an increased use of physical restraints, which could affect the care of elderly dementia patients. However, few studies have empirically validated the impact of physical restraint use during the COVID-19 pandemic. We aimed to evaluate the impact of regulatory changes, consequent to the pandemic, on physical restraint use among elderly dementia patients in acute care hospitals. Methods In this retrospective study, we extracted the data of elderly patients (aged > 64 years) who received dementia care in acute care hospitals between January 6, 2019, and July 4, 2020. We divided patients into two groups depending on whether they were admitted to hospitals that received COVID-19-positive patients. We calculated descriptive statistics to compare the trend in 2-week intervals and conducted an interrupted time-series analysis to validate the changes in the use of physical restraint. Results In hospitals that received COVID-19-positive patients, the number of patients who were physically restrained per 1,000 hospital admissions increased after the government’s announcement, with a maximum incidence of 501.4 per 1,000 hospital admissions between the 73rd and 74th week after the announcement. Additionally, a significant increase in the use of physical restraints for elderly dementia patients was noted (p = 0.004) in hospitals that received COVID-19-positive patients. Elderly dementia patients who required personal care experienced a significant increase in the use of physical restraints during the COVID-19 pandemic. Conclusion Understanding the causes and mechanisms underlying an increased use of physical restraints for dementia patients can help design more effective care protocols for similar future situations.


2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S301-S302
Author(s):  
William Trick ◽  
Michael Y Lin ◽  
Sharon F Welbel ◽  
Onfofre T Donceras ◽  
Huiyuan Zhang ◽  
...  

Abstract Background Laboratory identification (Lab-ID) of late-onset SARS-CoV-2 positive tests during a hospital stay is a potential public health surveillance approach for hospital-acquired COVID-19. However, prolonged RNA fragment shedding and intermittent detection of SARS-CoV-2 virus via PCR testing among infected patients may hamper interpretation of laboratory-identified events. We aimed to describe the epidemiology of late-onset SARS-CoV-2 laboratory events using clinical criteria, to evaluate the feasibility of a Lab-ID approach to detection of nosocomial SARS-COV-2 infection. Methods We evaluated all SARS-CoV-2 RT-PCR positive results recovered from patients at two acute-care hospitals in Chicago, IL, during March 1 — November 30, 2020. Each hospital maintained stringent infection control policies through-out the study period. Through chart review (WT & CS), we categorized all initial SARS-CoV-2 positive tests collected > Hospital Day 5 (defined as ‘late-onset’ based on the 5-day mean incubation period for COVID-19) into the following clinical categories: Community Acquired; Unlikely Hospital Acquired; Possible Hospital Acquired; and Probable Hospital Acquired. Categorizations were made using hospital day, symptoms, alternative diagnoses, and clinical notes (Figure 1). Results Of 2,671 SARS-CoV-2-positive patients, most positive tests (n=2,551; 96%) were recovered pre-admit or by Hospital Day 2; first positive tests were uncommon during Hospital Days 6 to 14 (n=40; 1.5%); and rare after Hospital Day 14 (n=15; 0.6%). By chart review, of the 55 late-onset records reviewed, categorizations in descending order were: Prior positive at outside facility (n=23); Possible Hospital Acquired (n=16); Community Acquired (n=12); Probable Hospital Acquired (n=4). Less than half of the late-onset cases were categorized as a possible or probable hospital acquisition (Figure 2). Conclusion Hospital-acquired SARS-CoV-2 infection was uncommon. Most late-onset episodes of SARS-CoV-2 were explained by detection at an outside healthcare facility or by delayed diagnosis of patients with symptoms at time of presentation. A Lab-ID approach to nosocomial COVID-19 surveillance would potentially misclassify a substantial number of patients. Disclosures All Authors: No reported disclosures


2020 ◽  
Author(s):  
Takuya Okuno ◽  
Jung-ho Shin ◽  
Daisuke Takada ◽  
Tetsuji Morishita ◽  
Susumu Kunisawa ◽  
...  

Abstract Background: The coronavirus disease (COVID-19) pandemic has caused unprecedented challenges for medical staff worldwide, especially for those working in hospitals in which COVID-19-positive or -suspected patients are being treated. The announcement of COVID-19 hospital restrictions by the Japanese government has led to several limitations in hospital care, including an increased use of physical restraints, that could affect the care of elderly dementia patients. However, few studies have empirically validated the impact of physical restraint use during the COVID-19 pandemic. We aimed to evaluate the impact of regulatory changes, consequent to the pandemic, on physical restraint use among elderly dementia patients in acute care hospitals.Methods: In this retrospective cohort study, we extracted the data of elderly patients (age >65 years) who received dementia care in acute care hospitals to which COVID-19-positive or -suspected patients were admitted between July 1, 2018 and June 30, 2020. We calculated descriptive statistics to compare the year-on-year trend in 2-week intervals and conducted an interrupted time-series analysis to validate the changes in the use of physical restraint.Results: The year-on-year trend in the number of patients who were physically restrained per 1,000 hospital admissions increased after the government’s announcement of COVID-19 restrictions, with a maximum incidence of 111.4% between the 47th and 48th week after the announcement. Additionally, a significant increase in the use of physical restraints in elderly dementia patients was noted (p=.002).Conclusions: Elderly dementia patients who required personal care experienced an obvious and significant increase in the use of physical restraints during the COVID-19 pandemic. Understanding the causes and mechanisms underlying an increased use of physical restraints in dementia patients can help design more effective care protocols for similar future situations.


2017 ◽  
Vol 30 (7) ◽  
pp. 991-1000 ◽  
Author(s):  
Miharu Nakanishi ◽  
Yasuyuki Okumura ◽  
Asao Ogawa

ABSTRACTBackground:In April 2016, the Japanese government introduced an additional benefit for dementia care in acute care hospitals (dementia care benefit) into the universal benefit schedule of public healthcare insurance program. The benefit includes a financial disincentive to use physical restraint. The present study investigated the association between the dementia care benefit and the use of physical restraint among inpatients with dementia in general acute care settings.Methods:A national cross-sectional study design was used. Eight types of care units from acute care hospitals under the public healthcare insurance program were invited to participate in this study. A total of 23,539 inpatients with dementia from 2,355 care units in 937 hospitals were included for the analysis. Dementia diagnosis or symptoms included any signs of cognitive impairment. The primary outcome measure was “use of physical restraint.”Results:Among patients, the point prevalence of physical restraint was 44.5% (n= 10,480). Controlling for patient, unit, and hospital characteristics, patients in units with dementia care benefit had significantly lower percentage of physical restraint than those in any other units (42.0% vs. 47.1%; adjusted odds ratio, 0.76; 95% confident interval [0.63, 0.92]).Conclusions:The financial incentive may have reduced the risk of physical restraint among patients with dementia in acute care hospitals. However, use of physical restraint was still common among patients with dementia in units with the dementia care benefit. An educational package to guide dementia care approach including the avoidance of physical restraint by healthcare professionals in acute care hospitals is recommended.


Author(s):  
Margot Egger ◽  
Christian Bundschuh ◽  
Kurt Wiesinger ◽  
Elisabeth Bräutigam ◽  
Thomas Berger ◽  
...  

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