Post-Acute Care Data for Predicting Readmission After Ischemic Stroke: A Nationwide Cohort Analysis Using the Minimum...

Area of Science:

• Gerontology
• Health Services Research
• Stroke Medicine

Background:

• Reducing hospital readmissions is crucial for improving stroke care quality.
• Current prediction models for stroke readmissions lack accuracy due to limited data from acute hospitalizations only.

Purpose of the Study:

• To investigate if patient-level factors from a nationwide post-acute care database can improve the accuracy of stroke readmission prediction models.

Main Methods:

• Utilized Medicare inpatient claims (2008) to identify ischemic stroke patients over 65.
• Linked patient data with Minimum Data Set (MDS) assessments for post-acute care information.
• Constructed risk-adjusted readmission models using logistic regression with MDS-derived covariates.

Main Results:

• Among 39,178 patients, 21% were readmitted within 30 days.
• The readmission model using MDS data achieved a c-statistic of 0.65 (95% CI 0.64 to 0.66).
• Higher social engagement in nursing homes was associated with lower readmission odds (OR 0.71).

Conclusions:

• Post-acute care data offered only a modest improvement in prediction accuracy compared to previous models.
• Individual-level characteristics from post-acute care settings do not sufficiently explain the risk of acute hospital readmission.