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

Sheng Feng Sung, Cheng Yang Hsieh, Yea Huei Kao Yang, Huey Juan Lin, Chih Hung Chen, Yu Wei Chen, Ya Han Hu

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


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.

Original languageEnglish
Pages (from-to)1292-1300
Number of pages9
JournalJournal of Clinical Epidemiology
Issue number11
StatePublished - Nov 2015


  • Acute ischemic stroke
  • Administrative data
  • Data mining
  • Disease severity
  • Outcomes research
  • Prediction model


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