Published in:
Open Access 01-12-2018 | Original Research
Development of a Risk-Scoring Tool to Determine Appropriate Level of Care in Acute Bacterial Skin and Skin Structure Infections in an Acute Healthcare Setting
Published in: Infectious Diseases and Therapy | Issue 4/2018
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Introduction
Acute bacterial skin and skin structure infections (ABSSSIs) represent a large burden to the US healthcare system. There is little evidence-based guidance regarding the appropriate level of care for ABSSSIs. This study aimed to develop a prediction model and risk-scoring tool to determine appropriate levels of care.
Methods
This was a single-center observational cohort study of adult patients treated for ABSSSIs from 2012 to 2015 at the Detroit Medical Center. The predictive model used to create a novel risk-scoring tool was derived using multinomial regression analysis. The overall accuracy of this tool was compared to the Clinical Resource Efficacy Support Team (CREST) Classification and Standardized Early Warning Score (SEWS) using area-under-the- receiver-operator-curve (AUROC) analysis and Z-statistic.
Results
Final patient disposition was 230 (45.5%) home from the emergency department (ED), 65 (12.8%) observation unit (OU), and 211 (41.7%) initial inpatient. IV antibiotic therapy was used in 358 (70.8%) patients. CREST and SEWS were not accurate in the determination of ED versus OU disposition [AUROC CREST 0.0.682 (95% CI 0.640–0.724), AUROC SEWS 0.686 (95% CI 0.641–0.731)], but performed better in determining ED/OU versus inpatient [AUROC CREST = 0.678 (95% CI 0.630–0.725), AUROC SEWS 0.693 (95% CI 0.645–0.740)]. These scores were also not accurate in determining IV versus PO antibiotic therapy [AUROC CREST = 0.586 (95% CI 0.530–0.624), AUROC SEWS = 0.630 (95% CI 0.576–0.684)]. A risk-scoring tool ranging from 0 to 10 points was derived incorporating WBC, temperature, site of infection, and past medical history of diabetes, liver disease, PVD, AKI, and/or CKD. The AUROC of the new model was 0.675 (95% CI 0.611–0.739) ED versus OU, 0.789 (95% CI 0.748–0.829) ED/OU versus inpatient, and 0.742 (95% CI 0.694–0.789) IV versus oral antibiotics. The new score had a significantly higher AUROC compared to both the CREST and SEWS for determining ED/OU versus inpatient (p < 0.001).
Conclusion
Prediction models based on patient risk may be useful for determining appropriate level of care during for ABSSSIs. While the prediction model demonstrated moderate to high levels of correlation with patient level of care, further validation of a prospective cohort of patients is warranted.