Machine Learning Characterization of Readmissions After Chronic Subdural Hematoma Hospitalizations.
Only 22.5% of chronic subdural hematoma readmissions are surgical recurrences.
Machine Learning Characterization of Readmissions After Chronic Subdural Hematoma Hospitalizations.
The full spectrum of readmission events, their clinical impact, and the heterogeneity of the affected patient population remain poorly understood.
This study seeks to characterize the incidence, diversity, and outcomes of 90-day readmissions after cSDH/sSDH hospitalization and to identify patient phenotypes with distinct readmission risk profiles using machine learning-driven clustering.
This was a retrospective cohort study using the Nationwide Readmissions Database (2016-2022).
Of 22,387 patients (mean age 70.8 years; 29.6% female), 6,497 (29.0%) were readmitted within 90 days across diverse causes.
Non-SDH readmissions carried substantial clinical impact: infection readmissions had the highest mortality (9.6%), exceeding surgical SDH (2.9%) more than 3-fold, and the highest rate of new disability (45.5%) among patients initially discharged with routine self-care.
Machine learning-based phenotyping uncovered substantial patient heterogeneity, highlighting the need for new therapeutic strategies, expanded clinical trial outcome targets beyond surgical recurrence, and comprehensive postdischarge care models tailored to distinct patient subgroups.