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Validation of the Passive Surveillance Stroke Severity Score in Three Canadian Provinces
Published online by Cambridge University Press: 06 March 2024
Abstract:
Background:
Stroke outcomes research requires risk-adjustment for stroke severity, but this measure is often unavailable. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based stroke severity measure that was developed in Ontario, Canada. We assessed the geographical and temporal external validity of PaSSV in British Columbia (BC), Nova Scotia (NS) and Ontario, Canada.
Methods:
We used linked administrative data in each province to identify adult patients with ischemic stroke or intracerebral hemorrhage between 2014-2019 and calculated their PaSSV score. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models’ discriminative values using Uno’s c-statistic, comparing models with versus without PaSSV.
Results:
We included 86,142 patients (n = 18,387 in BC, n = 65,082 in Ontario, n = 2,673 in NS). The mean and median PaSSV were similar across provinces. A higher PaSSV score, representing lower stroke severity, was associated with a lower hazard of death (hazard ratio and 95% confidence intervals 0.70 [0.68, 0.71] in BC, 0.69 [0.68, 0.69] in Ontario, 0.72 [0.68, 0.75] in NS) and admission to long-term care (0.77 [0.76, 0.79] in BC, 0.84 [0.83, 0.85] in Ontario, 0.86 [0.79, 0.93] in NS). Including PaSSV in the multivariable models increased the c-statistics compared to models without this variable.
Conclusion:
PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.
Résumé :
Validation du score de gravité de l’accident vasculaire cérébral de surveillance passive dans trois provinces au Canada.
La recherche sur les résultats des accidents vasculaires cérébraux (AVC) nécessite un rajustement du risque du degré de gravité, mais cette mesure souvent n’existe pas. Le score de gravité de l’AVC de surveillance passive (Passive Surveillance Stroke SeVerity ([PaSSV]) est une mesure du degré de gravité des AVC reposant sur des données administratives, qui a été élaborée en Ontario, au Canada. L’étude ici décrite visait donc à évaluer la validité externe du score PaSSV dans le temps et dans l’espace en Colombie-Britannique (C.B.), en Nouvelle-Écosse (N.É.) et en Ontario.
Pour ce faire, l’équipe de recherche a utilisé des données administratives liées de chacune des provinces participantes afin de repérer les adultes qui avaient subi un AVC ischémique ou une hémorragie cérébrale, entre 2014 et 2019, et a calculé leur score PaSSV. Les chercheurs et les chercheuses se sont appuyés sur des modèles des risques proportionnels de Cox pour évaluer l’association du score PaSSV avec le risque de mort sur une période de 30 jours et le risque d’admission dans un établissement de soins prolongés par cause, sur une période de 365 jours. Enfin, les valeurs discriminatives des modèles ont été évaluées à l’aide des valeurs statistiques de concordance d’Uno, par comparaison des modèles avec ou sans score PaSSV.
Au total, 86 142 dossiers de patient ont été retenus dans l’étude (n = 18 387 en C.B.; n = 65 082 en Ontario; n = 2 673 en N.É.). Les scores PaSSV moyen et médian étaient comparables dans toutes provinces. Un score PaSSV élevé, correspondant à un faible degré de gravité, a été associé à un risque moindre de mort (rapport de risques instantanés [RRI] et intervalles de confiance à 95 % : 0,70 [0,68-0,71] en C.B.; 0,69 [0,68-0,69] en Ontario; 0,72 [0,68-0,75] en N.É.) et d’admission dans un établissement de soins prolongés (0,77 [0,76-0,79] en C.B.; 0,84 [0,83-0,85] en Ontario; 0,86 [0,79-0,93] en N.É.). Le fait d’inclure le score PaSSV dans les modèles plurifactoriels a eu pour effet d’accroître les valeurs statistiques de concordance d’Uno par rapport à celles obtenues dans les modèles sans l’intégration de cette variable.
L’étude a permis de démontrer la validité externe du score PaSSV dans le temps et dans l’espace, ce qui en fait un instrument utile de rajustement du risque dans les recherches sur les résultats des AVC, y compris dans les analyses touchant différents territoires de compétence.
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- © The Author(s), 2024. Published by Cambridge University Press on behalf of Canadian Neurological Sciences Federation
References
Feigin, VL, Forouzanfar, MH, Krishnamurthi, R, et al. Global and regional burden of stroke during 1990-2010: findings from the global Burden of disease study 2010. Lancet. 2014;383:245–54.CrossRefGoogle ScholarPubMed
Kapral, MK, Laupacis, A, Phillips, SJ, et al. Stroke care delivery in institutions participating in the registry of the Canadian stroke network. Stroke. 2004;35:1756–62.CrossRefGoogle ScholarPubMed
Yu, AY, Holodinsky, JK, Zerna, C, et al. Use and utility of administrative health data for stroke research and surveillance. Stroke. 2016;47:1946–52.CrossRefGoogle ScholarPubMed
Fonarow, GC, Pan, W, Saver, JL, et al. Comparison of 30-day mortality models for profiling hospital performance in acute ischemic stroke with vs without adjustment for stroke severity. JAMA. 2012;308:257–64.CrossRefGoogle ScholarPubMed
Rost, NS, Bottle, A, Lee, JM, et al. Stroke severity is a crucial predictor of outcome: an international prospective validation study. J Am Heart Assoc. 2016;5:e002433.CrossRefGoogle ScholarPubMed
Katzan, IL, Spertus, J, Bettger, JP, 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;45:918–44.CrossRefGoogle ScholarPubMed
Thompson, MP, Luo, Z, Gardiner, J, Burke, JF, Nickles, A, Reeves, MJ. Impact of missing stroke severity data on the accuracy of hospital ischemic stroke mortality profiling. Circ Cardiovasc Qual Outcomes. 2018;11:e004951.CrossRefGoogle ScholarPubMed
Smith, EE, Shobha, N, Dai, D, et al. Risk score for in-hospital ischemic stroke mortality derived and validated within the get with the guidelines-stroke program. Circulation. 2010;122:1496–504.CrossRefGoogle ScholarPubMed
Yu, AYX, Austin, PC, Rashid, M, et al. Deriving a passive surveillance stroke severity indicator from routinely collected administrative data: the paSSV indicator. Circ Cardiovasc Qual Outcomes. 2020;13:e006269.CrossRefGoogle ScholarPubMed
Goel V, Group. EA. Pan-Canadian Health Data Strategy: Toward a world-class health data system. 2022 [cited 2023 2023-09-05]. Available from: https://www.canada.ca/content/dam/phac-aspc/documents/corporate/mandate/about-agency/external-advisory-bodies/list/pan-canadian-health-data-strategy-reports-summaries/expert-advisory-group-report-03-toward-world-class-health-data-system/expert-advisory-group-report-03-toward-world-class-health-data-system.pdf. Accessed September 5, 2023.Google Scholar
Canadian Institute for Health Information. Discharge Abstract Database (DAD) Metadata. [cited 2023 November 18]. Available from: https://www.cihi.ca/en/types-of-care/hospital-care/acute-care/dad-metadata. Accessed November 18, 2023.Google Scholar
Porter, J, Mondor, L, Kapral, MK, Fang, J, Hall, RE. How reliable are administrative data for capturing stroke patients and their care? Cerebrovasc Dis Extra. 2016;6:96–106.CrossRefGoogle ScholarPubMed
Canadian Institute for Health Information. National Ambulatory Care Reporting System (NACRS) Metadata. [cited 2023 November 18]. Available from: https://www.cihi.ca/en/types-of-care/hospital-care/emergency-and-ambulatory-care/nacrs-metadata. Accessed November 18, 2023.Google Scholar
Canadian Institute for Health Information. Data Quality Documentation, National Ambulatory Care Reporting System Current-Year Information, 2019-2020. 2020 [cited 2023 Nov 22]. Available from: https://www.cihi.ca/sites/default/files/document/nacrs-data-quality-current-year-information-2019-2020-en.pdf. Accessed November 22, 2023.Google Scholar
Cote, R, Battista, RN, Wolfson, C, Boucher, J, Adam, J, Hachinski, V. The Canadian neurological scale: validation and reliability assessment. Neurology. 1989;39:638–43.CrossRefGoogle ScholarPubMed
Yu, AYX, Kapral, MK, Park, AL, et al. Change in hospital risk-standardized stroke mortality performance with and without the passive surveillance stroke severity score. Med Care. 2023. https://doi.org/10.1097/mlr.0000000000001944.CrossRefGoogle ScholarPubMed
British Columbia Ministory of Health [creator]. Vital Events Deaths. V2. Population Data BC [publisher] Data Extract. MOH (2022). 2023 [cited 2023 November 18]. Available from: https://www.popdata.bc.ca/data. Accessed November 18, 2023.Google Scholar
Quan, H, Sundararajan, V, Halfon, P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9.CrossRefGoogle ScholarPubMed
Uno, H, Cai, T, Pencina, MJ, D’Agostino, RB, Wei, LJ. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30:1105–17.CrossRefGoogle ScholarPubMed
Kamel, H, Parikh, NS, Chatterjee, A, et al. Access to mechanical thrombectomy for ischemic stroke in the United States. Stroke. 2021;52:2554–61.CrossRefGoogle ScholarPubMed
Joundi, RA, King, JA, Stang, J, et al. External validation of the passive surveillance stroke severity indicator. Can J Neuro Sci. 2022;50:1–6.Google ScholarPubMed
Kapral, MK, Hall, R, Gozdyra, P, et al. Geographic access to stroke care services in rural communities in Ontario, Canada. Can J Neuro Sci. 2020;47:301–8.CrossRefGoogle ScholarPubMed
Yu et al. supplementary material
Yu et al. supplementary material
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