Deriving a Passive Surveillance Stroke Severity Indicator From Routinely Collected Administrative Data: The PaSSV Indicator

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.

Using random forests to model 90-day hometime in people with stroke

“… Q1: first quartile; Q3: third quartile; PaSSV: Passive Surveillance Stroke seVerity indicator a A continuous score ranging from 0 to 99 where scores < 5 indicate low risk of frailty, scores from 5 to 15 indicate intermediate risk of frailty, and scores > 15 indicate high risk of frailty [ 18 ] b A continuous score where < 4 indicates severe stroke, 4–8 indicates moderate stroke severity, and > 8 indicates mild stroke severity [ 19 ] …”

“…b A continuous score where < 4 indicates severe stroke, 4–8 indicates moderate stroke severity, and > 8 indicates mild stroke severity [ 19 ]…”

“…Frailty score is a continuous score from 0 to 99 derived from administrative data where scores < 5 indicate low risk of frailty, scores 5–15 indicate moderate risk of frailty and scores > 15 indicate high risk of frailty [ 18 ]. Passive Surveillance Stroke seVerity Indicator (PaSSV) score is a continuous score calculated from administrative data where scores < 4 indicate severe stroke, scores 4–8 indicate moderate stroke severity, and scores > 8 indicates mild stroke severity [ 19 ]

Fig. 4 Two-way partial dependence plots depicting the relationship between frailty, stroke severity, and predicted 90-day hometime.

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“…Frailty scores range from 0 to 99 with higher scores indicating greater risk of frailty (scores < 5 indicate low risk of frailty, scores 5–15 indicate moderate risk of frailty and scores > 15 indicate high risk of frailty) [ 18 ]. Stroke severity is measured using the Passive Surveillance Stroke seVerity Indicator (PaSSV) where scores < 4 indicate severe stroke, scores 4–8 indicate moderate stroke severity, and scores > 8 indicates mild stroke severity [ 19 ] …”

“…Covariates of interest included age, sex, arrival by ambulance, stroke type, treatment with thrombolysis, stroke unit care, frailty (measured using the Hospital Frailty Risk Score, a continuous score from 0 to 99 derived from administrative data where scores < 5 indicate low risk of frailty, scores 5–15 indicate moderate risk of frailty and scores > 15 indicate high risk of frailty) [ 18 ], stroke severity (measured using the Passive Surveillance Stroke seVerity Indicator (PaSSV) where scores < 4 indicate severe stroke, scores 4–8 indicate moderate stroke severity, and scores > 8 indicates mild stroke severity) [ 19 ], rural vs. urban home location, quintile of median neighbourhood income, and the following comorbidities: atrial fibrillation, diabetes, hypertension, myocardial infarction. A 5-year lookback window was used for all comorbidities.…”