보건행정학회지 (Health Policy and Management)

  • 제26권4호
  • /
  • Pages.359-372
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  • 2016
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  • 1225-4266(pISSN)
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  • 2289-0149(eISSN)
한국보건행정학회 (Korean Academy of Health Policy and Management)
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병원 성과 비교를 위한 급성기 뇌졸중 사망률 위험보정모형의 타당도 평가

Evaluation of the Validity of Risk-Adjustment Model of Acute Stroke Mortality for Comparing Hospital Performance

  • 최은영 (울산대학교 의과대학 예방의학교실) ;
  • 김선하 (단국대학교 간호대학 간호학과) ;
  • 옥민수 (울산대학교 의과대학 예방의학교실) ;
  • 이현정 (울산대학교 의과대학 예방의학교실) ;
  • 손우승 (울산대학교 의과대학 예방의학교실) ;
  • 조민우 (울산대학교 의과대학 예방의학교실) ;
  • 이상일 (울산대학교 의과대학 예방의학교실)
  • Choi, Eun Young (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Kim, Seon-Ha (Department of Nursing, Dankook University College of Nursing) ;
  • Ock, Minsu (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Lee, Hyeon-Jeong (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Son, Woo-Seung (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Jo, Min-Woo (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Lee, Sang-il (Department of Preventive Medicine, University of Ulsan College of Medicine)
  • 투고 : 2016.10.31
  • 심사 : 2016.11.30
  • 발행 : 2016.12.31
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초록

Background: The purpose of this study was to develop risk-adjustment models for acute stroke mortality that were based on data from Health Insurance Review and Assessment Service (HIRA) dataset and to evaluate the validity of these models for comparing hospital performance. Methods: We identified prognostic factors of acute stroke mortality through literature review. On the basis of the avaliable data, the following factors was included in risk adjustment models: age, sex, stroke subtype, stroke severity, and comorbid conditions. Survey data in 2014 was used for development and 2012 dataset was analysed for validation. Prediction models of acute stroke mortality by stroke type were developed using logistic regression. Model performance was evaluated using C-statistics, $R^2$ values, and Hosmer-Lemeshow goodness-of-fit statistics. Results: We excluded some of the clinical factors such as mental status, vital sign, and lab finding from risk adjustment model because there is no avaliable data. The ischemic stroke model with age, sex, and stroke severity (categorical) showed good performance (C-statistic=0.881, Hosmer-Lemeshow test p=0.371). The hemorrhagic stroke model with age, sex, stroke subtype, and stroke severity (categorical) also showed good performance (C-statistic=0.867, Hosmer-Lemeshow test p=0.850). Conclusion: Among risk adjustment models we recommend the model including age, sex, stroke severity, and stroke subtype for HIRA assessment. However, this model may be inappropriate for comparing hospital performance due to several methodological weaknesses such as lack of clinical information, variations across hospitals in the coding of comorbidities, inability to discriminate between comorbidity and complication, missing of stroke severity, and small case number of hospitals. Therefore, further studies are needed to enhance the validity of the risk adjustment model of acute stroke mortality.

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