Health Policy and Management (보건행정학회지)

Korean Academy of Health Policy and Management (한국보건행정학회)
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Evaluation of the Validity of Risk-Adjustment Model of Acute Stroke Mortality for Comparing Hospital Performance

병원 성과 비교를 위한 급성기 뇌졸중 사망률 위험보정모형의 타당도 평가

  • 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)
  • 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)
  • Received : 2016.10.31
  • Accepted : 2016.11.30
  • Published : 2016.12.31
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Abstract

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.

Keywords

References

  1. Statistics Korea. Statistics for cause of death 2014 [Internet]. Daejeon: Statistics Korea; 2014 [cited 2016 Aug 4]. Avaliable from: http://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1012.
  2. Health Insurance Review and Assessment Service. 2014 Acute stroke assessment report [Internet]. Wonjul: Health Insurance Review and Assessment Service; 2014 [cited 2016 Aug 4]. Avaliable from: http://www.hira.or.kr/re/diag/getDiagEvlList.do?pgmid=HIRAA030004000000.
  3. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart disease and stroke statistics: 2015 update: a report from the American Heart Association. Circulation 2015;131(4):e29-e322. DOI: https://doi.org/10.1161/CIR.0000000000000152.
  4. Mainz J. Defining and classifying clinical indicators for quality improvement. Int J Qual Health Care 2003;15(6):523-530. DOI: https://doi.org/10.1093/intqhc/mzg081.
  5. Mant J. Process versus outcome indicators in the assessment of quality of health care. Int J Qual Health Care 2001;13(6):475-480. DOI: https://doi.org/10.1093/intqhc/13.6.475.
  6. Pitches DW, Mohammed MA, Lilford RJ. What is the empirical evidence that hospitals with higher-risk adjusted mortality rates provide poorer quality care?: a systematic review of the literature. BMC Health Serv Res 2007;7:91. DOI: https://doi.org/10.1186/1472-6963-7-91.
  7. Smith EE, Shobha N, Dai D, Olson DM, Reeves MJ, Saver JL, et al. Risk score for in-hospital ischemic stroke mortality derived and validated within the Get With the Guidelines-Stroke Program. Circulation 2010;122(15):1496-1504. DOI: https://doi.org/10.1161/CIRCULATIONAHA.109.932822.
  8. Adams HP Jr, Davis PH, Leira EC, Chang KC, Bendixen BH, Clarke WR, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology 1999;53(1):126-131. DOI: https://doi.org/10.1212/wnl.53.1.126.
  9. Counsell C, Dennis M, McDowall M, Warlow C. Predicting outcome after acute and subacute stroke: development and validation of new prognostic models. Stroke 2002;33(4):1041-1047. DOI: https://doi.org/10.1161/hs0402.105909.
  10. Katzan IL, Spertus J, Bettger JP, Bravata DM, Reeves MJ, Smith EE, et al. Risk adjustment of ischemic stroke outcomes for comparing hospital performance: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014;45(3):918-944. DOI: https://doi.org/10.1161/01.str.0000441948.35804.77.
  11. Smith EE, Shobha N, Dai D, Olson DM, Reeves MJ, Saver JL, et al. A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke. J Am Heart Assoc 2013;2(1):e005207. DOI: https://doi.org/10.1161/JAHA.112.005207.
  12. Tabak YP, Johannes RS, Silber JH. Using automated clinical data for risk adjustment: development and validation of six disease-specific mortality predictive models for pay-for-performance. Med Care 2007;45(8):789-805. DOI: https://doi.org/10.1097/mlr.0b013e31803d3b41.
  13. Kai S, Jiaoyan Q, Weihua S, Yinhui Z, Channa Z, Li S, et al. Low estimated glomerular filtration rate is associated with high recurrence rate and poor prognosis of hemorrhage stroke. Curr Neurovasc Res 2015;12(2):109-119. DOI: https://doi.org/10.2174/1567202612666150305110709.
  14. Iezzoni LI, Risk adjustment for measuring health care outcomes. 3rd ed. Chicago (IL): Health Adminstration Press; 2003.
  15. Lane-Fall MB, Neuman MD. Outcomes measures and risk adjustment. Int Anesthesiol Clin 2013;51(4):10-21. DOI: https://doi.org/10.1097/AIA.0b013e3182a70a52.
  16. Iezzoni LI, Shwartz M, Ash AS, Mackiernan YD. Predicting in-hospital mortality for stroke patients: results differ across severity-measurement methods. Med Decis Making 1996;16(4):348-356. DOI: https://doi.org/10.1177/0272989x9601600405.
  17. Centers for Medicare and Medicaid Services. Outcome measures [Internet]. Baltimore (MD): Centers for Medicare and Medicaid Services; 2015 [cited 2016 Oct 11]. Avaliable from: https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/hospitalqualityinits/outcomemeasures.html.
  18. Canadian Institute for Health Information. HSMR: a new approach for measuring hospital mortality trends in Canada [Internet]. Ottawa (ON): Canadian Institute for Health Information; 2007 [cited 2016 Oct 11]. Available from: https://secure.cihi.ca/free_products/HSMR_hospital_mortality_trends_in_canada.pdf.
  19. National Health Performance Authority. Towards public reporting of standardised hospital mortality in Australia [Internet]. Sydney (NSW): National Health Performance Authority; 2016 [cited 2016 Oct 11]. Available from: http://www.nhpa.gov.au/internet/nhpa/publishing.nsf/Content/0D85279DAD3E50E8CA257F56001F92FA/$File/Mortality_update_FINAL%20RELEASE_27Jan16.pdf.
  20. Saposnik G, Baibergenova A, O'Donnell M, Hill MD, Kapral MK, Hachinski V, et al. Hospital volume and stroke outcome: does it matter? Neurology 2007;69(11):1142-1151. DOI: https://doi.org/10.1212/01.wnl.0000268485.93349.58.
  21. Reeves MJ, Smith E, Fonarow G, Hernandez A, Pan W, Schwamm LH, et al. Off-hour admission and in-hospital stroke case fatality in the get with the guidelines-stroke program. Stroke 2009;40(2):569-576. DOI: https://doi.org/10.1161/STROKEAHA.108.519355.
  22. Tung YC, Chang GM, Chen YH. Associations of physician volume and weekend admissions with ischemic stroke outcome in Taiwan: a nationwide population-based study. Med Care 2009;47(9):1018-1025. DOI: https://doi.org/10.1097/MLR.0b013e3181a81144.
  23. Stroke Unit Trialists' Collaboration. Organised inpatient (stroke unit) care for stroke. Cochrane Database Syst Rev 2007;(4):CD000197. DOI: https://doi.org/10.1002/14651858.cd000197.pub2.
  24. Xian Y, Holloway RG, Chan PS, Noyes K, Shah MN, Ting HH, et al. Association between stroke center hospitalization for acute ischemic stroke and mortality. JAMA 2011;305(4):373-380. DOI: https://doi.org/10.1001/jama.2011.22.
  25. Sandercock PA, Counsell C, Gubitz GJ, Tseng MC. Antiplatelet therapy for acute ischaemic stroke. Cochrane Database Syst Rev 2008;(3):CD000029. DOI: https://doi.org/10.1002/14651858.CD000029.pub2.
  26. Bravata DM, Wells CK, Lo AC, Nadeau SE, Melillo J, Chodkowski D, et al. Processes of care associated with acute stroke outcomes. Arch Intern Med 2010;170(9):804-810. DOI: https://doi.org/10.1001/archinternmed.2010.92.
  27. Hacke W, Kaste M, Bluhmki E, Brozman M, Davalos A, Guidetti D, et al. Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. N Engl J Med 2008;359(13):1317-1329. DOI: https://doi.org/10.1056/NEJMoa0804656.
  28. Higginson IJ, Sen-Gupta GJ. Place of care in advanced cancer: a qualitative systematic literature review of patient preferences. J Palliat Med 2000;3(3):287-300. DOI: https://doi.org/10.1089/jpm.2000.3.287.
  29. Bae SO, Kang GW. A comparative study of the disease codes between Korean National Health Insurance claims and Korean national hospital discharge in-depth injury survey. Health Policy Manag 2014;24(4):322-329. DOI: https://doi.org/10.4332/KJHPA.2014.24.4.322.
  30. Mohammed MA, Deeks JJ, Girling A, Rudge G, Carmalt M, Stevens AJ, et al. Evidence of methodological bias in hospital standardised mortality ratios: retrospective database study of English hospitals. BMJ 2009;338: b780. DOI: https://doi.org/10.1136/bmj.b780.
  31. Nicholl J. Case-mix adjustment in non-randomised observational evaluations: the constant risk fallacy. J Epidemiol Community Health 2007; 61(11):1010-1013. DOI: https://doi.org/10.1136/jech.2007.061747.