Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?
Friedbert Kohler A B E , Roger Renton B C , Hugh G. Dickson B C , John Estell D and Carol E. Connolly AA Braeside Hospital, Locked Bag 82, Wetherill Park, NSW 2164, Australia. Email: carol.connolly@sswahs.nsw.gov.au
B School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia.
C Liverpool Hospital, Locked Bag 7103, Liverpool BC, NSW 1871, Australia. Email: roger.renton@sswahs.nsw.gov.au; hugh.dickson@sswahs.nsw.gov.au
D St George Hospital, 50 Gray Street, Kogarah, NSW 2217, Australia. Email: john.estell@sesiahs.health.nsw.gov.au
E Corresponding author. Email: f.kohler@unsw.edu.au
Australian Health Review 35(1) 1-8 https://doi.org/10.1071/AH09806
Submitted: 6 July 2009 Accepted: 28 March 2010 Published: 25 February 2011
Abstract
Objective. We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2.
Method. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement.
Results. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination.
Conclusion. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.
What is known about the topic? AN-SNAP v2, a major classification tool for inpatient rehabilitation units has been described and used in a small number of published studies. The ability to predict variance by AN-SNAP v2 has not been previously described.
What does this paper add? This paper indicates that AN-SNAP v2 is not a good predictor of outcomes in patients in medical rehabilitation units, challenging its utility as a classification tool.
What are the implications for practitioners? Practitioners will have a broader understanding of the strengths and weaknesses of the AN-SNAP v2 classification.
References
[1] Thomson W. Lord Kelvin (Sir William Thomson). Quotations. 1883. Available at http://zapatopi.net/kelvin/quotes/ [verified 14 December 2008].[2] Hornbrook M. Hospital case mix: its definition, measurement and use: part 1. The conceptual framework. Med Care Rev 1982; 39 1–43.
| Hospital case mix: its definition, measurement and use: part 1. The conceptual framework.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL387kslagsQ%3D%3D&md5=4fb6622e57daffd118a6fe74b6ee7c97CAS | 10309583PubMed |
[3] Gibberd R. Performance measurement: is it now more scientific? Int J Qual Health Care 2005; 17 185–6.
| Performance measurement: is it now more scientific?Crossref | GoogleScholarGoogle Scholar | 15908699PubMed |
[4] Vladeck BC, Kramer PS. Case mix measures: DRGs and alternatives. Annu Rev Public Health 1988; 9 333–59.
| Case mix measures: DRGs and alternatives.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL1c3ktVKmug%3D%3D&md5=4ff49fe804620e4fc361f7ed736c0e32CAS | 3132182PubMed |
[5] National Centre for Heath Statistics. International Classification of Diseases, eighth revision. Publication no. 1963. Washington, DC: US Government Printing Office; 1968.
[6] Fetter RB, Youngsoo S, Freeman JL, Averill RF, Thompson JD. Case mix definition by diagnosis-related groups. Med Care 1980; 18 i-53
[7] Wilke M, Höcherl EFJ, Scherer J, Janke L. Introducing the new DRG-based payment system in German hospitals: a difficult operation? Eur J Health Econ 2001; 2 79–85.
| Introducing the new DRG-based payment system in German hospitals: a difficult operation?Crossref | GoogleScholarGoogle Scholar |
[8] Stineman MG, Escarce JJ, Goin JE, Hamilton BB, Granger CV, Williams SV. A casemix classification for medical rehabilitation. Med Care 1994; 32 366–79.
| A casemix classification for medical rehabilitation.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK2c7osleqsQ%3D%3D&md5=bf1ac8f8d92255ef87fddc51bd87f1ddCAS | 8139301PubMed |
[9] Eagar K, Gordon R, Hodkinson A, Green J, Eagar L, Erven J, Eckstein G, Spooner G, Kennedy C, Owen A, Cromwell D, Leotta T, Rigley A. The Australian National Sub-Acute and Non-Acute Patient Classification (AN-SNAP): report of the National Sub-Acute and Non-Acute Casemix Classification Study. Centre for Health Service Development, University of Wollongong; 1997.
[10] Green J, Gordon R, Poulos C, Broadbent A. Report on the Development of Version 2 of the AN-SNAP Classification. Centre for Health Service Development, University of Wollongong; 2006.
[11] Lowthian P, Disler P, Ma S, Eagar K, Green J, de Graaff S. The Australian National Sub-acute and Non-acute Patient Casemix Classification (AN-SNAP): its application and value in a stroke rehabilitation programme. Clin Rehabil 2000; 14 532–7.
| The Australian National Sub-acute and Non-acute Patient Casemix Classification (AN-SNAP): its application and value in a stroke rehabilitation programme.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD3cvptFCgsg%3D%3D&md5=d84b3669d0f46077dccfa1e18d0f2d68CAS | 11043880PubMed |
[12] McKenna K, Tooth L, Strong J, Ottenbacher K, Connell J, Cleary M. Predicting discharge outcomes for stroke patients in Australia. Am J Phys Med Rehabil 2002; 81 47–56.
| Predicting discharge outcomes for stroke patients in Australia.Crossref | GoogleScholarGoogle Scholar | 11807333PubMed |
[13] Tooth L, McKenna K, Goh K, Varghese P. Length of stay, discharge destination and functional improvement: utility of the Australian national subacute and nonacute patient casemix classification. Stroke 2005; 36 1519–25.
| Length of stay, discharge destination and functional improvement: utility of the Australian national subacute and nonacute patient casemix classification.Crossref | GoogleScholarGoogle Scholar | 15920028PubMed |
[14] Saitto C, Marino C, Fusco D, Arca M, Perucci CA. Toward a new payment system for inpatient rehabilitation. Part 1: predicting resource consumption. Med Care 2005; 43 844–55.
| Toward a new payment system for inpatient rehabilitation. Part 1: predicting resource consumption.Crossref | GoogleScholarGoogle Scholar | 16116349PubMed |
[15] Duckett S, Gray L, Howe A. Designing a funding system for rehabilitation services. Part 1: rationale and recent developments. Aust Health Rev 1995; 18 30–44.
| 1:STN:280:DyaK28%2FjslChsg%3D%3D&md5=98c88d7b45db54b3105e78fdfc1d2ce9CAS | 10152273PubMed |
[16] Duckett S, Gray L, Howe A. Designing a funding system for rehabilitation services Part 2: policy objectives and options for achieving efficiency and quality of care. Aust Health Rev 1995; 18 62–77.
| Designing a funding system for rehabilitation services Part 2: policy objectives and options for achieving efficiency and quality of care.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK287jsFOguw%3D%3D&md5=5a07d990e084b5a6c1767a86d9b2f1d4CAS | 10154018PubMed |
[17] Fuhs PA, Martin JB, Hancock WM. The use of length of stay distributions to predict hospital discharges. Med Care 1979; 17 355–68.
| The use of length of stay distributions to predict hospital discharges.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaE1M7lsFersA%3D%3D&md5=8984e2d010f9aed26050ea16901ade8eCAS | 431147PubMed |
[18] Evers S, Voss G, Nieman F, Ament A, Groot T, Lodder J, et al Predicting the cost of hospital stay for stroke patients: the use of diagnosis related groups. Health Policy 2002; 61 21–42.
| Predicting the cost of hospital stay for stroke patients: the use of diagnosis related groups.Crossref | GoogleScholarGoogle Scholar | 12173495PubMed |
[19] Dickson HG, Hodgkinson A, Kohler F. Inpatient quality assurance by local analysis of UDS data. J Qual Clin Pract 1994; 14 145–8.
| 1:STN:280:DyaK2M%2Fnt1ymuw%3D%3D&md5=fae6efb06aa3348f800ad35328922cb7CAS | 7981934PubMed |
[20] Linting M. Nonparametric Inference in Nonlinear Principal Components Analysis: Exploration and Beyond. Leiden: Leiden University; 2007.
[21] Feigin V, Carter K, Hackett M, Barber P, McNaughton H, Dyall L, Chen M, Anderson C. Ethnic disparities in incidence of stroke subtypes: Auckland Regional Community Stroke Study, 2002–2003. Lancet Neurol 2006; 5 130–9.
| Ethnic disparities in incidence of stroke subtypes: Auckland Regional Community Stroke Study, 2002–2003.Crossref | GoogleScholarGoogle Scholar | 16426989PubMed |
