scholarly journals Identification of thresholds in relationships between specific antibiotic use and carbapenem-resistant Acinetobacter baumannii (CRAb) incidence rates in hospitalized patients in Jordan

2020 ◽  
Author(s):  
Wail A Hayajneh ◽  
Sayer Al-Azzam ◽  
Dawood Yusef ◽  
William J Lattyak ◽  
Elizabeth A Lattyak ◽  
...  
Keyword(s):  
Time Series ◽  
Acinetobacter Baumannii ◽  
Linear Time ◽  
Antibiotic Use ◽  
Incidence Rates ◽  
Third Generation ◽  
Carbapenem Resistant ◽  
Tertiary Teaching ◽  
Non Linear ◽  
The Impact

Abstract Background Antibiotic resistance is a major threat to public health worldwide. The relationship between the intensity of antibiotic use and resistance might not be linear, suggesting that there might be a threshold of antibiotic use, beyond which resistance would be triggered. Objectives To identify thresholds in antibiotic use, below which specific antibiotic classes have no significant measurable impact on the incidence of carbapenem-resistant Acinetobacter baumannii (CRAb), but above which their use correlates with an increase in the incidence of CRAb. Methods The study took place at a tertiary teaching hospital in Jordan. The study was ecological in nature and was carried out retrospectively over the period January 2014 to December 2019. The outcome time series for this study was CRAb cases. The primary explanatory variables were monthly use of antibiotics and the use of alcohol-based hand rub (ABHR). Non-linear time-series methods were used to identify thresholds in antibiotic use. Results Non-linear time-series analysis determined a threshold in third-generation cephalosporin and carbapenem use, where the maximum use of third-generation cephalosporins and carbapenems should not exceed 8 DDD/100 occupied bed days (OBD) and 10 DDD/100 OBD, respectively. ABHR had a significant reducing effect on CRAb cases even at lower usage quantities (0.92 L/100 OBD) and had the most significant effect when ABHR exceeded 3.4 L/100 OBD. Conclusions The identification of thresholds, utilizing non-linear time-series methods, can provide a valuable tool to inform hospital antibiotic policies through identifying quantitative targets that balance access to effective therapies with control of resistance. Further studies are needed to validate the identified thresholds, through being prospectively adopted as a target for antimicrobial stewardship programmes, and then to evaluate the impact on reducing CRAb incidence.

scholarly journals Impact of an antimicrobial stewardship programme on reducing broad-spectrum antibiotic use and its effect on carbapenem-resistant Acinetobacter baumannii (CRAb) in hospitals in Jordan

2020 ◽  
Author(s):  
Dawood Yusef ◽  
Wail A Hayajneh ◽  
Ali Bani Issa ◽  
Rami Haddad ◽  
Sayer Al-Azzam ◽  
...  
Keyword(s):  
Acinetobacter Baumannii ◽  
Antimicrobial Stewardship ◽  
Broad Spectrum ◽  
Antibiotic Use ◽  
Broad Spectrum Antibiotic ◽  
Carbapenem Resistant ◽  
Restriction Policy ◽  
Prior Approval ◽  
Tertiary Teaching ◽  
The Impact

Abstract Objectives To evaluate the impact of an antimicrobial stewardship programme (ASP) on reducing broad-spectrum antibiotic use and its effect on carbapenem-resistant Acinetobacter baumannii (CRAb) in hospitalized patients. Methods The study was a retrospective, ecological assessment in a tertiary teaching hospital over 6 years (January 2014 to December 2019). The intervention involved the implementation of an ASP in February 2018, which remains in effect today. This ASP consists of several components, including education, antibiotic guidelines, antibiotic restriction policy with prior approval, audit of compliance to the restriction policy and feedback. Restricted antibiotics were imipenem/cilastatin, ertapenem, meropenem, vancomycin, teicoplanin, tigecycline, colistin, amikacin, piperacillin/tazobactam, levofloxacin and ciprofloxacin. The intervention was evaluated by time-series methods. Results Statistically significant decreases in the level of antibiotic use, after the introduction of the ASP, were observed for the following antibiotics: imipenem/cilastatin (P = 0.0008), all carbapenems (P = 0.0001), vancomycin (P = 0.0006), colistin (P = 0.0016) and third-generation cephalosporins (P = 0.0004). A statistically significant decrease in the slope, after the introduction of the ASP, for ertapenem (P = 0.0044) and ciprofloxacin (P = 0.0117) was observed. For piperacillin/tazobactam, there was a significant increasing trend (P = 0.0208) before the introduction of the ASP. However, this increased trend was halted post-introduction of the ASP (P = 0.4574). The introduction of the ASP was associated with a significant impact on reducing the levels of CRAb (P = 0.0237). Conclusions The introduced antimicrobial stewardship interventions contributed to a reduction in the use of several broad-spectrum antibiotics, reversed the trends of increasing use of other antibiotics and were associated with a significant reduction in CRAb.

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

scholarly journals Hybrid forecasting model for non-linear time series

2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Forecasting Model ◽  
Non Linear ◽  
Hybrid Forecasting

scholarly journals Impact of Removal of Automatic 7-Day Stop Orders for Inpatient Antimicrobials

2020 ◽  
Vol 41 (S1) ◽  
pp. s264-s265
Author(s):  
Afia Adu-Gyamfi ◽  
Keith Hamilton ◽  
Leigh Cressman ◽  
Ebbing Lautenbach ◽  
Lauren Dutcher
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Antibiotic Use ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Treatment Interruptions ◽  
Days Of Therapy ◽  
Quasi Experimental ◽  
The Impact

Background: Automatic discontinuation of antimicrobial orders after a prespecified duration of therapy has been adopted as a strategy for reducing excess days of therapy (DOT) as part of antimicrobial stewardship efforts. Automatic stop orders have been shown to decrease antimicrobial DOT. However, inadvertent treatment interruptions may occur as a result, potentially contributing to adverse patient outcomes. To evaluate the effects of this practice, we examined the impact of the removal of an electronic 7-day ASO program on hospitalized patients. Methods: We performed a quasi-experimental study on inpatients in 3 acute-care academic hospitals. In the preintervention period (automatic stop orders present; January 1, 2016, to February 28, 2017), we had an electronic dashboard to identify and intervene on unintentionally missed doses. In the postintervention period (April 1, 2017, to March 31, 2018), the automatic stop orders were removed. We compared the primary outcome, DOT per 1,000 patient days (PD) per month, for patients in the automatic stop orders present and absent periods. The Wilcoxon rank-sum test was used to compare median monthly DOT/1,000 PD. Interrupted time series analysis (Prais-Winsten model) was used to compared trends in antibiotic DOT/1,000 PD and the immediate impact of the automatic stop order removal. Manual chart review on a subset of 300 patients, equally divided between the 2 periods, was performed to assess for unintentionally missed doses. Results: In the automatic stop order period, a monthly median of 644.5 antibiotic DOT/1,000 PD were administered, compared to 686.2 DOT/1,000 PD in the period without automatic stop orders (P < .001) (Fig. 1). Using interrupted time series analysis, there was a nonsignificant increase by 46.7 DOT/1,000 PD (95% CI, 40.8 to 134.3) in the month immediately following removal of automatic stop orders (P = .28) (Fig. 2). Even though the slope representing monthly change in DOT/1,000 PD increased in the period without automatic stop orders compared to the period with automatic stop orders, it was not statistically significant (P = .41). Manual chart abstraction revealed that in the period with automatic stop orders, 9 of 150 patients had 17 unintentionally missed days of therapy, whereas none (of 150 patients) in the period without automatic stop orders did. Conclusions: Following removal of the automatic stop orders, there was an overall increase in antibiotic use, although the change in monthly trend of antibiotic use was not significantly different. Even with a dashboard to identify missed doses, there was still a risk of unintentionally missed doses in the period with automatic stop orders. Therefore, this risk should be weighed against the modest difference in antibiotic utilization garnered from automatic stop orders.Funding: NoneDisclosures: None

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (4) ◽  
pp. 1103-1109 ◽  
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

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