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Developing a stroke severity index based on administrative data was feasible using data mining techniques.

Abstract：

OBJECTIVES:Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data. STUDY DESIGN AND SETTING:We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database. RESULTS:We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models. CONCLUSION:The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

Sung SF,Hsieh CY,Kao Yang YH,Lin HJ,Chen CH,Chen YW,Hu YH

doi

10.1016/j.jclinepi.2015.01.009

subject

Has Abstract

pub_date

2015-11-01 00:00:00

pages

1292-300

issue

11

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(15)00017-7

journal_volume

68

pub_type

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