A study to evaluate the implications of cost burden on anti-microbial resistance(AMR) using data analytics techniques.
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 1,320
- Locations
- 1
- Primary Endpoint
- Difference in total spending to treat an infection and length of stay between three groups.
Study Overview
Brief Summary
The main objective of the study is to:
To study the impact of AMR on attributable cost and length of stay (LOS) in hospitalized patient. To develop predictive model for the outcomes in patients with Antimicrobial resistant infection. To assess the correlation between the antibiotic’s usage and bacterial resistance pattern in a tertiary care hospital setting.Total sample size would be 1320. Patient will be divided into 3 categories: Susceptible, Resistance, and multi-resistant group and the outcome of interest will be compared using various machine learning algorithms.
Currently recruited 142 patients.
Study Design
- Study Type
- Observational
Eligibility Criteria
- Ages
- 18.00 Year(s) to 90.00 Year(s) (—)
- Sex
- All
Inclusion Criteria
- •Both Community acquired and hospital Acquired Infection Patients, Both Gram positive and Gram Negative Infections, Multiple infections and/or multiple site of infections, Direct cost.
Exclusion Criteria
- •Incomplete susceptibility data, Incomplete financial data, Indirect cost.
Outcomes
Primary Outcomes
Difference in total spending to treat an infection and length of stay between three groups.
Time Frame: Every 6 months the cost expenditure and length of stay will be analysed using data analytics techniques.
Resistant bacterial species which caused the additional cost burden and LOS in patients.
Time Frame: Every 6 months the cost expenditure and length of stay will be analysed using data analytics techniques.
Secondary Outcomes
- Total antibiotic use and resistance pattern will be observed.(Every 6 months the pattern of bacterial resistance will be observed.)