Stroke Administrative Severity Index: using administrative data for 30-day poststroke outcomes prediction
Year: 2017
Type: article
Abstract: AIM: Current stroke severity scales cannot be used for archival data. We develop and validate a measure of stroke severity at hospital discharge (Stroke Administrative Severity Index [SASI]) for use in billing data. METHODS: We used the NIH Stroke Scale (NIHSS) as the theoretical framework and identified 285 relevant International Classification of Diseases, 9th Revision diagnosis and procedure codes, grouping them into 23 indicator variables 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 validation 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 provide important tools to control for stroke severity at time of hospital discharge. It can be used as a risk-adjustment variable in administrative data analyses to measure postdischarge outcomes.
Authors: Annie N Simpson, Janina Wilmskoetter, Ickpyo Hong, Chih-Ying Li, Edward C Jauch, Heather S Bonilha, Kelly Anderson, Jillian Harvey, Kit N Simpson
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W2766099024