Amy Y X Yu1,2, Peter C Austin2, Mohammed Rashid2, Jiming Fang2, Joan Porter2, Michael D Hill3, Moira K Kapral2,4. 1. Department of Medicine (Neurology), University of Toronto, Sunnybrook Health Sciences Centre, ON, Canada (A.Y.X.Y.). 2. ICES, Toronto, ON, Canada (A.Y.X.Y., P.C.A., M.R., J.F., J.P., M.K.K.). 3. Department of Clinical Neurosciences, Community Health Sciences, and Hotchkiss Brain Institute, University of Calgary, AB, Canada (M.D.H.). 4. Department of Medicine (General Internal Medicine), University of Toronto-University Health Network, ON, Canada (M.K.K.).
Abstract
BACKGROUND: Adjusting for stroke severity is crucial for stroke outcomes research. However, this information is not available in administrative healthcare data. We aimed to derive an indicator of baseline stroke severity using these data. METHODS AND RESULTS: We identified patients with stroke enrolled in a population-based registry in Ontario, Canada, and used the Canadian Neurological Scale (CNS), documented in the registry, as a measure of stroke severity. We derived an estimated CNS from a linear regression model in which we regressed the observed CNS on predictor variables: age, sex, arrival by ambulance, interhospital transfer, mechanical ventilation, and an emergency department triage score. The effect of stroke severity on the estimated hazard ratios for 30-day mortality was determined in 3 Cox-proportional hazards models with (1) no CNS, (2) observed CNS, and (3) estimated CNS, all adjusted for age, sex, Charlson index, and stroke type. We assessed model discrimination using C statistics. To assess for construct validity, we repeated these analyses in a subset of patients with documented National Institute of Health Stroke Scale and in a cohort of patients with stroke external to the registry. We derived the estimated stroke severity in 41 481 patients (48.7% female, median age of 75 years [interquartile range, 64- 83]). The magnitude of the association between stroke severity and mortality was similar for the observed and estimated CNS. The discriminative ability of the Cox-proportional hazards models to predict mortality was highest when the observed CNS was included (C statistic, 0.82 [95% CI, 0.81-0.82]), moderate with estimated CNS (0.76 [0.75-0.76]), and lowest without CNS (0.69 [0.69-0.70]. Our findings were replicated with the National Institute of Health Stroke Scale and in the external cohort. CONCLUSIONS: We derived an estimated measure of stroke severity using administrative data. This can be applied for risk adjustment in population-based stroke outcomes research and in assessments of health system performance.
BACKGROUND: Adjusting for stroke severity is crucial for stroke outcomes research. However, this information is not available in administrative healthcare data. We aimed to derive an indicator of baseline stroke severity using these data. METHODS AND RESULTS: We identified patients with stroke enrolled in a population-based registry in Ontario, Canada, and used the Canadian Neurological Scale (CNS), documented in the registry, as a measure of stroke severity. We derived an estimated CNS from a linear regression model in which we regressed the observed CNS on predictor variables: age, sex, arrival by ambulance, interhospital transfer, mechanical ventilation, and an emergency department triage score. The effect of stroke severity on the estimated hazard ratios for 30-day mortality was determined in 3 Cox-proportional hazards models with (1) no CNS, (2) observed CNS, and (3) estimated CNS, all adjusted for age, sex, Charlson index, and stroke type. We assessed model discrimination using C statistics. To assess for construct validity, we repeated these analyses in a subset of patients with documented National Institute of Health Stroke Scale and in a cohort of patients with stroke external to the registry. We derived the estimated stroke severity in 41 481 patients (48.7% female, median age of 75 years [interquartile range, 64- 83]). The magnitude of the association between stroke severity and mortality was similar for the observed and estimated CNS. The discriminative ability of the Cox-proportional hazards models to predict mortality was highest when the observed CNS was included (C statistic, 0.82 [95% CI, 0.81-0.82]), moderate with estimated CNS (0.76 [0.75-0.76]), and lowest without CNS (0.69 [0.69-0.70]. Our findings were replicated with the National Institute of Health Stroke Scale and in the external cohort. CONCLUSIONS: We derived an estimated measure of stroke severity using administrative data. This can be applied for risk adjustment in population-based stroke outcomes research and in assessments of health system performance.
Authors: Amy Y X Yu; Eric E Smith; Murray Krahn; Peter C Austin; Mohammed Rashid; Jiming Fang; Joan Porter; Manav V Vyas; Susan E Bronskill; Richard H Swartz; Moira K Kapral Journal: Neurology Date: 2021-08-18 Impact factor: 11.800
Authors: Hooman Kamel; Neal S Parikh; Abhinaba Chatterjee; Luke K Kim; Jeffrey L Saver; Lee H Schwamm; Kori S Zachrison; Raul G Nogueira; Opeolu Adeoye; Iván Díaz; Andrew M Ryan; Ankur Pandya; Babak B Navi Journal: Stroke Date: 2021-05-13 Impact factor: 10.170
Authors: Raed A Joundi; Eric E Smith; Amy Y X Yu; Mohammed Rashid; Jiming Fang; Moira K Kapral Journal: J Am Heart Assoc Date: 2021-09-13 Impact factor: 5.501
Authors: María Carmen Lea-Pereira; Laura Amaya-Pascasio; Patricia Martínez-Sánchez; María Del Mar Rodríguez Salvador; José Galván-Espinosa; Luis Téllez-Ramírez; Fernando Reche-Lorite; María-José Sánchez; Juan Manuel García-Torrecillas Journal: Int J Environ Res Public Health Date: 2022-03-08 Impact factor: 3.390