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.
CONCLUSIONS: PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.
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.
CONCLUSIONS: PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.
摘要：
背景：卒中结局研究需要针对卒中严重程度进行风险调整，但这一措施往往是不可用的。被动监测卒中严重程度（PaSSV）评分是在安大略省开发的基于管理数据的卒中严重程度指标。加拿大。我们评估了不列颠哥伦比亚省（BC）PaSSV的地理和时间外部有效性，新斯科舍省（NS）和安大略省，加拿大。
方法：我们使用每个省的关联管理数据来识别2014-2019年间的缺血性卒中或脑出血成年患者，并计算其PaSSV评分。我们使用Cox比例风险模型来评估PaSSV评分与30天以上死亡风险之间的关联以及365天以上长期护理的原因特异性风险。我们使用Uno的c统计量评估了模型的判别值，比较有和没有PaSSV的模型。
结果：我们纳入了86,142例患者（公元前18,387例，n=65,082在安大略省，n=2,673在NS中)。各省的平均和中位数PaSSV相似。更高的PaSSV分数,代表较低的中风严重程度，与较低的死亡风险相关（在BC中，风险比和95％置信区间为0.70[0.68，0.71]，0.69[0.68,0.69]在安大略省，NS为0.72[0.68，0.75]）和长期护理（BC为0.77[0.76，0.79]，0.84[0.83，0.85]在安大略省，0.86[0.79,0.93]单位为NS)。与没有此变量的模型相比，在多变量模型中包括PaSSV增加了c统计量。
结论：PaSSV具有地理和时间有效性，使其对中风结局研究的风险调整有用，包括多司法管辖区分析。
方法：我们使用每个省的关联管理数据来识别2014-2019年间的缺血性卒中或脑出血成年患者，并计算其PaSSV评分。我们使用Cox比例风险模型来评估PaSSV评分与30天以上死亡风险之间的关联以及365天以上长期护理的原因特异性风险。我们使用Uno的c统计量评估了模型的判别值，比较有和没有PaSSV的模型。
结果：我们纳入了86,142例患者（公元前18,387例，n=65,082在安大略省，n=2,673在NS中)。各省的平均和中位数PaSSV相似。更高的PaSSV分数,代表较低的中风严重程度，与较低的死亡风险相关（在BC中，风险比和95％置信区间为0.70[0.68，0.71]，0.69[0.68,0.69]在安大略省，NS为0.72[0.68，0.75]）和长期护理（BC为0.77[0.76，0.79]，0.84[0.83，0.85]在安大略省，0.86[0.79,0.93]单位为NS)。与没有此变量的模型相比，在多变量模型中包括PaSSV增加了c统计量。
结论：PaSSV具有地理和时间有效性，使其对中风结局研究的风险调整有用，包括多司法管辖区分析。
