Developing a risk prediction model for 30-day unplanned hospitalization in patients receiving outpatient parenteral antimicrobial therapy

Abstract

ObjectivesOutpatient 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. MethodsUsing 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. ResultsThe 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). ConclusionsWe 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.