Key result
Predictive analytical modeling based on administrative claims history achieved an average area under the curve of 0.76 (range, 0.70 to 0.82) for predicting adverse events and care utilization.
Why the study?
Can predictive analytical modeling based on administrative claims history accurately predict adverse events and care utilization outcomes for hospitalized patients?
Population
9,085,968 development and 5,336,265 validation Medicare patients with inpatient admissions
Comparison
Logistic regression vs five commonly used machine learning methods
Design
Retrospective model development and prospective validation study
Follow-up
Through 90 days after admission
Authors
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May aid perioperative risk stratification using claims data; hypothesis-generating pending prospective validation.
Observational (n=14,422,233)
Can predictive analytical modeling based on administrative claims history accurately predict adverse events and care utilization outcomes for hospitalized patients?
Effect estimate: average AUC 0.76
Predictive analytical modeling based on administrative claims history can provide individualized risk profiles at hospital admission with good accuracy (average AUC 0.76).
Greenwald et al. (2022) conducted an observational in Hospitalized patients (n=14,422,233). Risk Stratification Index 3.0 (predictive analytical modeling based on administrative claims) vs. Machine learning methods was evaluated on Adverse events and care utilization outcomes (including mortality, readmissions, and complications) (average AUC 0.76). Predictive analytical modeling based on administrative claims history achieved an average area under the curve of 0.76 (range, 0.70 to 0.82) for predicting adverse events and care utilization.
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