O C Durojaiye1, E I Kritsotakis2, P Johnston3, T Kenny3, F Ntziora4, K Cartwright4. 1. Department of Infection and Tropical Medicine, Royal Hallamshire Hospital, Sheffield, UK. Electronic address: chris.durojaiye@sth.nhs.uk. 2. Department of Epidemiology and Medical Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK. 3. Sheffield Teaching Hospital NHS Foundation Trust, Sheffield, UK. 4. Department of Infection and Tropical Medicine, Royal Hallamshire Hospital, Sheffield, UK.
Abstract
OBJECTIVES: Outpatient parenteral antimicrobial therapy (OPAT) is increasingly used to treat a wide range of infections. However, there is risk of hospital readmissions. The study aim was to develop a prediction model for the risk of 30-day unplanned hospitalization in patients receiving OPAT. METHODS: Using a retrospective cohort design, we retrieved data on 1073 patients who received OPAT over 2 years (January 2015 to January 2017) at a large teaching hospital in Sheffield, UK. We developed a multivariable logistic regression model for 30-day unplanned hospitalization, assessed its discrimination and calibration abilities, and internally them validated using bootstrap resampling. RESULTS: The 30-day unplanned hospitalization rate was 11% (123/1073). The main indication for hospitalization was worsening or nonresponse of infection (52/123, 42%). The final regression model consisted of age (adjusted odds ratio (aOR), 1.18 per decade; 95% confidence interval (CI), 1.04-1.34), Charlson comorbidity score (aOR, 1.11 per unit increase; 95% CI, 1.00-1.23), prior hospitalizations in past 12 months (aOR, 1.30 per admission; 95% CI, 1.17-1.45), concurrent intravenous antimicrobial therapy (aOR, 1.89; 95% CI, 1.03-3.47) and endovascular infection (aOR, 3.51; 95% CI, 1.49-8.28). Mode of OPAT treatment was retained in the model as a confounder. The model had adequate concordance (c-statistic 0.72; 95% CI 0.67-0.77) and calibration (Hosmer-Lemeshow p 0.546; calibration slope 0.99; 95% CI 0.78-1.21), and low degree of optimism (bootstrap optimism corrected c-statistic, 0.70). CONCLUSIONS: We identified a set of six important predictors of unplanned hospitalization based on readily available data. The prediction model may help improve OPAT outcomes through better identification of high-risk patients and provision of tailored care.
OBJECTIVES:Outpatient parenteral antimicrobial therapy (OPAT) is increasingly used to treat a wide range of infections. However, there is risk of hospital readmissions. The study aim was to develop a prediction model for the risk of 30-day unplanned hospitalization in patients receiving OPAT. METHODS: Using a retrospective cohort design, we retrieved data on 1073 patients who received OPAT over 2 years (January 2015 to January 2017) at a large teaching hospital in Sheffield, UK. We developed a multivariable logistic regression model for 30-day unplanned hospitalization, assessed its discrimination and calibration abilities, and internally them validated using bootstrap resampling. RESULTS: The 30-day unplanned hospitalization rate was 11% (123/1073). The main indication for hospitalization was worsening or nonresponse of infection (52/123, 42%). The final regression model consisted of age (adjusted odds ratio (aOR), 1.18 per decade; 95% confidence interval (CI), 1.04-1.34), Charlson comorbidity score (aOR, 1.11 per unit increase; 95% CI, 1.00-1.23), prior hospitalizations in past 12 months (aOR, 1.30 per admission; 95% CI, 1.17-1.45), concurrent intravenous antimicrobial therapy (aOR, 1.89; 95% CI, 1.03-3.47) and endovascular infection (aOR, 3.51; 95% CI, 1.49-8.28). Mode of OPAT treatment was retained in the model as a confounder. The model had adequate concordance (c-statistic 0.72; 95% CI 0.67-0.77) and calibration (Hosmer-Lemeshow p 0.546; calibration slope 0.99; 95% CI 0.78-1.21), and low degree of optimism (bootstrap optimism corrected c-statistic, 0.70). CONCLUSIONS: We identified a set of six important predictors of unplanned hospitalization based on readily available data. The prediction model may help improve OPAT outcomes through better identification of high-risk patients and provision of tailored care.
Authors: Monica V Mahoney; Lindsey M Childs-Kean; Parisa Khan; Christina G Rivera; Ryan W Stevens; Keenan L Ryan Journal: Curr Infect Dis Rep Date: 2021-11-09 Impact factor: 3.725
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