Key result
An EHR-derived prediction model utilizing baseline clinical factors and risk assessments from the first 24 hours of admission demonstrated excellent discrimination for predicting discharge to a post-acute care facility, with an AUC of 0.875.
Why the study?
Timely discharge to post-acute care settings requires early identification of eligible patients.
Does an EHR-derived prediction model using data from the first 24 hours of hospitalization accurately predict discharge to a post-acute care facility in adult inpatients?
Cohort (n=23,566)
No
Does an EHR-derived prediction model using data from the first 24 hours of hospitalization accurately predict discharge to a post-acute care facility in adult inpatients?
Effect estimate: AUC 0.875
An EHR-derived prediction model using clinical data from the first 24 hours of admission can accurately identify adult inpatients who will require discharge to a post-acute care facility.
BACKGROUND: Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patient's likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. METHODS: This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. RESULTS: Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. CONCLUSIONS: A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.
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Bhatia et al. (2023) conducted a cohort in Adult inpatient admissions (n=23,566). EHR-derived prediction model (PAC-Predict) was evaluated on Model discrimination for predicting discharge to a post-acute care facility (AUC 0.875). An EHR-derived prediction model utilizing baseline clinical factors and risk assessments from the first 24 hours of admission demonstrated excellent discrimination for predicting discharge to a post-acute care facility, with an AUC of 0.875.
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