title: Developing a stroke severity index based on administrative data was feasible using data mining techniques.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data.
authors.affiliation: Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, No.539, Zhongxiao Rd., East Dist., Chiayi City 60002, Taiwan.
authors.forename: Sheng-Feng
authors.initials: SF
authors.lastname: Sung
authors.affiliation: Department of Neurology, Tainan Sin Lau Hospital, No.57, Sec.1, Dongmen Rd., East Dist., Tainan City 70142, Taiwan; Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.
authors.forename: Cheng-Yang
authors.initials: CY
authors.lastname: Hsieh
authors.affiliation: Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.
authors.forename: Yea-Huei
authors.initials: YH
authors.lastname: Kao Yang
authors.affiliation: Department of Neurology, Chi Mei Medical Center, No.901, Zhonghua Rd., Yongkang Dist., Tainan City 710, Taiwan.
authors.forename: Huey-Juan
authors.initials: HJ
authors.lastname: Lin
authors.affiliation: Department of Neurology, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.
authors.forename: Chih-Hung
authors.initials: CH
authors.lastname: Chen
authors.affiliation: Department of Neurology, Landseed Hospital, No.77, Kwang-Tai Rd., Ping-Jen City, Tao-Yuan County 32449, Taiwan; Department of Neurology, National Taiwan University Hospital, No.1, Changde St., Zhongzheng Dist., Taipei City 10048, Taiwan.
authors.forename: Yu-Wei
authors.initials: YW
authors.lastname: Chen
authors.affiliation: Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, No.168 University Road, Min-Hsiung, Chiayi County 62102, Taiwan. Electronic address: yahan.hu@mis.ccu.edu.tw.
authors.forename: Ya-Han
authors.initials: YH
authors.lastname: Hu
mesh_terms: D000067575:Administrative Claims, Healthcare; D000368:Aged; D057225:Data Mining; D016208:Databases, Factual; D005240:Feasibility Studies; D005260:Female; D006761:Hospitals; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012720:Severity of Illness Index; D020521:Stroke / Q000175:diagnosis*
keywords: Acute ischemic stroke; Administrative data; Data mining; Disease severity; Outcomes research; Prediction model