Abstract WP109: Framework for Evaluating Sequential Patterns in Post-Acute Transitions of Care Among Ischemic and Hemorrhagic Stroke Survivors: Analysis of Medicare Beneficiaries in the State of Texas

Abstract

Background: Utilizing medical claims derived information, we evaluated temporal trends in post-acute care utilization pathways among patients with acute ischemic stroke (AIS) or intracerebral hemorrhage (ICH). Methods: Data were retrieved from a CMS Qualified Entity housing healthcare utilization data for ≥80% of the Texas state population (100% of Medicare Fee-for-Service). Analytical sample included all Medicare enrollees with a primary discharge diagnosis (AIS or ICH) from 2016 to 2020. Episodes of care within 1 year of discharge were collated and categorized “Discharged”, “Inpatient Rehab”, “Skilled Nursing”, “Hospice”, “Readmission”, or “Death”. Sequential pattern mining was used to calculate conditional probabilities (likelihoods) of post-discharge transitions of care. Prediction rules were generated by converting care sequences into antecedent-consequent pairs. Rule based performance was evaluated by extracting 90 day sequences (antecedent) and assigning predictions of 1 year settings (consequent) based on highest likelihoods (Panel A). Results: A total of 82,829 AIS and 12,761 ICH hospitalizations were analyzed (described in Panel B). An example rulebook of care sequences and associated likelihoods of subsequent transitions for ICH is depicted in Panel C. Whereas ICH survivors entering inpatient rehab had respective likelihoods of 11.2% and 16.2% for subsequently being admitted to hospice care or death, those entering skilled nursing facilities had respective likelihoods of 21.9% and 30.5%. Using 90 day transition of care sequences, predictive accuracy of exact 1 year setting for AIS was 65.3% (1 attempt) and 84.0% (2 attempts); for ICH, the respective accuracy was 50.9% and 82.3% (Panel D). Conclusion: Post stroke transitions of care patterns may yield insights as to the long-term trajectory of outcomes. Predictive capability is expected to strengthen with planned multi-modal integration of EMR-derived clinical and imaging features.