A Multivariable Prediction Model for 30-day Readmissions for Patients Discharged with Outpatient Intravenous Antibiotic Therapy.
Allison, Geneve.
2013
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Abstract: Thirty-day
unplanned hospital readmissions contribute to patient morbidity and healthcare costs,
and are increasingly scrutinized as a quality measure. Outpatient intravenous
antibiotics are used by 250,000 patients per year in the U.S. Awareness of the patient
and healthcare associated factors at the time of hospital discharge associated with
30-day readmissions could facilitate ... read moretargeted approaches to reduce readmissions and
improve care. However, factors associated with readmission for patients prescribed
intravenous antibiotics at hospital discharge have not been definitively identified to
our knowledge. Studies of readmissions for other patient groups have shown conflicting
results and predictive models of readmissions have been fair to moderate in their
ability to discriminate readmission risk. In this thesis, we describe a new predictive
model for patients discharged with outpatient intravenous antibiotic therapy. We
conducted a retrospective cohort analysis of 784 patients treated in an outpatient
intravenous antibiotic program at a single academic center. We used clinical judgment
and statistical criteria to develop a multivariable model of patient characteristics
associated with 30-day readmissions. Overall readmission rate was 26%. Our final model
included: age by decades (odds ratio 1.09, 95% confidence interval 0.99, 1.21),
aminoglycoside use (OR 2.33, 95% CI 1.17, 4.57), presence of resistant organisms (OR
1.57, 95% CI 1.03, 2.36), and number of prior admissions in the past 12 months (OR 1.2,
95% CI 1.09-1.32). Model discrimination was fair (c-statistic 0.61), likely reflecting
heterogeneity of the underlying population and post-discharge events. Further studies of
outpatient intravenous antibiotics should focus on post-discharge factors that
contribute to readmission and are potentially
modifiable.
Thesis (M.S.)--Tufts University, 2013.
Submitted to the Dept. of Clinical & Translational Science.
Advisors: David Kent, and David Snydman.
Committee: Robin Ruthazer, and Jessica Paulus.
Keyword: Health sciences.read less - ID:
- nz806b71g
- Component ID:
- tufts:20209
- To Cite:
- TARC Citation Guide EndNote
