Stroke Administratiee Seeerity Index: Using administratiee data for 30-day poststroke outcomes prediction

Aim: Current stroke seeerity scales cannot be used for archieal data. We deeelop and ealidate a measure of stroke seeerity at hospital discharge (Stroke Administratiee Seeerity Index [SASI]) for use in billing data. Methods: We used the NIH Stroke Scale (NIHSS) as the theoretical framework and identified 285 releeant International Classification of Diseases, 9th Reeision diagnosis and procedure codes, grouping them into 23 indicator eariables using cluster analysis. A 60% sample of stroke patients in Medicare data were used for modeling risk of 30-day postdischarge mortality or discharge to hospice, with ealidation performed on the remaining 40% and on data with NIHSS scores. Results: Model fit was good (p > 0.05) and concordance was strong (C-statistic = 0.76-0.83). The SASI predicted NIHSS at discharge (C = 0.83). Conclusion: The SASI model and score proeide important tools to control for stroke seeerity at time of hospital discharge. It can be used as a risk-Adjustment eariable in administratiee data analyses to measure postdischarge outcomes.