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Electronic health record-based readmission risk model performance for patients undergoing outpatient parenteral
Richard Drew1,2, Ethan Brenneman3, Jason Funaro3
1Duke University School of Medicine (Division of Infectious Diseases), Durham, North Carolina, United States of America.
Insights
The EPIC Unplanned Readmission Model 1 failed to accurately predict 30-day hospital readmissions for patients receiving Outpatient Parenteral Antibiotic Therapy (OPAT). Further research is needed to identify better predictors for this vulnerable patient group.
Area of Science:
- Health Services Research
- Clinical Informatics
- Pharmacoeconomics
Background:
- Outpatient Parenteral Antibiotic Therapy (OPAT) offers an alternative to inpatient care but may increase readmission risks due to reduced monitoring.
- Predictive models for hospital readmissions are crucial for patient safety but require validation in specific populations like OPAT.
- Identifying high-risk OPAT patients can guide targeted interventions to prevent readmissions.
Purpose of the Study:
- To evaluate the predictive performance of the EPIC Unplanned Readmission Model 1 for 30-day all-cause hospital readmissions in OPAT patients.
- To assess the impact of incorporating OPAT-specific variables on the model's predictive accuracy.
- To determine the utility of the EPIC model in the Duke University Health System (DUHS) OPAT program.
Main Methods:
- Retrospective cohort study of adult patients receiving DUHS OPAT between July 2019 and February 2020.
- Exclusion criteria included planned OPAT duration < 7 days, LTAC administration, or ongoing renal replacement therapy.
- Multivariable logistic regression analyzed the relationship between EPIC readmission scores and 30-day unplanned readmissions, with performance assessed using Brier score and C-index.
Main Results:
- The EPIC Unplanned Readmission Model 1 demonstrated poor discrimination for predicting 30-day unplanned readmissions (C-index ~0.51).
- Inclusion of OPAT-specific variables did not significantly improve the model's predictive ability (C-index ~0.55-0.56).
- Models for OPAT-related readmissions also showed limited predictive accuracy (C-index ~0.54).
Conclusions:
- EPIC Unplanned Readmission Model 1 scores are not effective for predicting 30-day unplanned readmissions in the DUHS OPAT cohort.
- Current models lack the necessary discrimination to identify high-risk OPAT patients for readmission.
- Further investigation into novel predictors is essential for improving readmission risk stratification in OPAT patients.
Background:
Outpatient Parenteral Antibiotic Therapy (OPAT) provides coordinated services to deliver parenteral antibiotics outside of the acute care setting. However, the reduction in monitoring and supervision may impact the risks of readmission to the hospital. While identifying those at greatest risk of hospital readmission through use of computer decision support systems could aid in its prevention, validation of such tools in this patient population is lacking.
Objective:
The primary aim of this study is to determine the ability of the electronic health record-embedded EPIC Unplanned Readmission Model 1 to predict all-cause 30-day hospital unplanned readmissions in discharged patients receiving OPAT through the Duke University Heath System (DUHS) OPAT program. We then explored the impact of OPAT-specific variables on model performance.
Methods:
This retrospective cohort study included patients ≥ 18 years of age discharged to home or skilled nursing facility between July 1, 2019 -February 1, 2020 with OPAT care initiated inpatient and coordinated by the DUHS OPAT program and with at least one Epic readmission score during the index hospitalization. Those with a planned duration of OPAT < 7 days, receiving OPAT administered in a long-term acute care facility (LTAC), or ongoing renal replacement therapy were excluded. The relationship between the primary outcome (unplanned readmission during 30-day post-index discharge) and Epic readmission scores during the index admission (discharge and maximum) was examined using multivariable logistic regression models adjusted for additional predictors. The performance of the models was assessed with the scaled Brier score for overall model performance, the area under the receiver operating characteristics curve (C-index) for discrimination ability, calibration plot for calibration, and Hosmer-Lemeshow goodness-of-fit test for model fit.
Results:
The models incorporating maximum or discharge Epic readmission scores showed poor discrimination ability (C-index 0.51, 95% CI 0.45 to 0.58 for both models) in predicting 30-day unplanned readmission in the Duke OPAT cohort. Incorporating additional OPAT-specific variables did not improve the discrimination ability (C-index 0.55, 95% CI 0.49 to 0.62 for the max score; 0.56, 95% CI 0.49 to 0.62 for the discharge score). Although models for predicting 30-day unplanned OPAT-related readmission performed slightly better, discrimination ability was still poor (C-index 0.54, 95% CI 0.45 to 0.62 for both models).
Conclusion:
EPIC Unplanned Readmission Model 1 scores were not useful in predicting either all-cause or OPAT-related 30-day unplanned readmission in the DUHS OPAT cohort. Further research is required to assess other predictors that can distinguish patients with higher risks of 30-day unplanned readmission in the DUHS OPAT patients.
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