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J Comp Eff Res. 2018 Apr;7(4):293-304. doi: 10.2217/cer-2017-0058. Epub 2017 Oct 23.

Stroke Administrative Severity Index: using administrative data for 30-day poststroke outcomes prediction.

Author information

1
Department of Healthcare Leadership & Management, College of Health Professions, Medical University of South Carolina, 151B Rutledge Ave, MSC 962, Charleston, SC 29425, USA.
2
Department of Otolaryngology - Head & Neck Surgery, Medical University of South Carolina, 135 Rutledge Ave, MSC 550, Charleston, SC 29425, USA.
3
Department of Health Sciences & Research, College of Health Professions, Medical University of South Carolina, 77 President St, MSC 700, Charleston, SC 29425, USA.
4
Division of Rehabilitation Sciences, School of Health Professions, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555, USA.
5
Division of Emergency Medicine, Department of Medicine, College of Medicine, Medical University of South Carolina, 169 Ashley Avenue, MSC 300, Charleston, SC 29425, USA.

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.

KEYWORDS:

health services research; risk adjustment; stroke

PMID:
29057660
DOI:
10.2217/cer-2017-0058
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