Automatic Prediction of Platelet Utilization by Time Series Analysis in a Large Tertiary Care Hospital

Background: Dengue fever is a major public health problem, the concern is high as the disease is closely related to climate change.Methods: This was a retrospective study, conducted for 1 year in a tertiary care hospital in the city of Mumbai. Data of Dengue cases and climate for the city of Mumbai between 2011 and 2015 were obtained. Data was analysed using SPSS- time series analysis and forecasting model.Results: 33% cases belonged to the 21-30 years, proportion of men affected were more than women. A seasonal distribution of cases was observed. A strong correlation was noted between the total number of cases reported and (a) mean monthly rainfall and (b) number of days of rainfall. ARIMA model was used for forecasting.Conclusions: The trend analysis along with forecasting model helps in being prepared for the year ahead.

Download Full-text

Impact of restricting procalcitonin measurements in a Swiss tertiary-care hospital on antibiotic use, clinical outcomes, and costs: An interrupted time-series analysis

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.1314 ◽

2020 ◽

pp. 1-3

Author(s):

Mohamed Abbas ◽

Nathalie Vernaz ◽

Elodie von Dach ◽

Nicolas Vuilleumier ◽

Stephan J. Harbarth ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Tertiary Care Hospital ◽

Tertiary Care ◽

Interrupted Time Series ◽

Antibiotic Use ◽

Care Hospital ◽

Interrupted Time Series Analysis ◽

Series Analysis ◽

The Impact

Abstract We evaluated the impact of a restriction of procalcitonin measurements on antibiotic use, length of stay, mortality, and cost in a Swiss tertiary-care hospital using interrupted time-series analysis. There was no significant change in level or slope for rates of antibiotic consumption, and costs decreased considerably, by ~54,488 CHF (US$55,714) per month.

Download Full-text

Assessment of preauthorization and 24-hour expert consultation as a restrictive antimicrobial stewardship bundle in a Brazilian tertiary-care hospital: an interrupted time series analysis

Infection Prevention in Practice ◽

10.1016/j.infpip.2022.100201 ◽

2022 ◽

pp. 100201

Author(s):

Andréa Beltrami Doltrario ◽

Gilberto Gambero Gaspar ◽

Andrea Queiroz Ungari ◽

Roberto Martinez ◽

Antonio Pazin Filho ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Antimicrobial Stewardship ◽

Tertiary Care Hospital ◽

Tertiary Care ◽

Interrupted Time Series ◽

Care Hospital ◽

Expert Consultation ◽

Interrupted Time Series Analysis ◽

Series Analysis

Download Full-text

Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽

10.3390/pathogens10040480 ◽

2021 ◽

Vol 10 (4) ◽

pp. 480

Author(s):

Rania Kousovista ◽

Christos Athanasiou ◽

Konstantinos Liaskonis ◽

Olga Ivopoulou ◽

George Ismailos ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Acinetobacter Baumannii ◽

Regression Models ◽

Tertiary Care ◽

Antimicrobial Use ◽

Care Hospital ◽

Arima Models ◽

Series Analysis ◽

Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

Download Full-text