Parmar Priya, Krishnamurthi Rita, Ikram M Arfan, Hofman Albert, Mirza Saira S, Varakin Yury, Kravchenko Michael, Piradov Michael, Thrift Amanda G, Norrving Bo, Wang Wenzhi, Mandal Dipes Kumar, Barker-Collo Suzanne, Sahathevan Ramesh, Davis Stephen, Saposnik Gustavo, Kivipelto Miia, Sindi Shireen, Bornstein Natan M, Giroud Maurice, Béjot Yannick, Brainin Michael, Poulton Richie, Narayan K M Venkat, Correia Manuel, Freire António, Kokubo Yoshihiro, Wiebers David, Mensah George, BinDhim Nasser F, Barber P Alan, Pandian Jeyaraj Durai, Hankey Graeme J, Mehndiratta Man Mohan, Azhagammal Shobhana, Ibrahim Norlinah Mohd, Abbott Max, Rush Elaine, Hume Patria, Hussein Tasleem, Bhattacharjee Rohit, Purohit Mitali, Feigin Valery L
AUT University, NZ.
Int J Stroke. 2015 Feb;10(2):231-44. doi: 10.1111/ijs.12411. Epub 2014 Dec 10.
The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer(TM) , has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods.
752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer(TM) ) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R(2) statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm.
The Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS = 75.0% (95% CI 72.3%-77.6%), Stroke Riskometer(TM) = 74.0(95% CI 71.3%-76.7%) and females [FSRS = 70.3% (95% CI 67.9%-72.8%, Stroke Riskometer(TM) = 71.5% (95% CI 69.0%-73.9%)], and better than QStroke [males - 59.7% (95% CI 57.3%-62.0%) and comparable to females = 71.1% (95% CI 69.0%-73.1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0.51-0.56, D-statistic ranging from 0.01-0.12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0.006).
The Stroke Riskometer(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.
降低中风负担的最大潜力在于首次中风的一级预防，首次中风占所有中风病例的四分之三。除了针对全体人群的预防策略（“大众”方法）外，“高危”方法旨在识别中风高危个体，并相应地改变其风险因素和风险。目前评估和改变中风风险的方法普通大众难以获取和实施，而未来大多数中风病例将发生在这一群体中。为了帮助减轻中风对个体和人群的负担，一款名为“中风风险评估仪（Stroke Riskometer™）”的新应用程序已开发出来。我们旨在探讨该应用程序与当前最佳方法相比在预测中风风险方面的有效性。
利用来自三个国家（新西兰、俄罗斯和荷兰）9501名个体样本中的752例中风结果，研究一种新型中风风险预测工具算法（中风风险评估仪（Stroke Riskometer™））与两种既定的中风风险评分预测算法（弗雷明汉姆中风风险评分[FSRS]和QStroke）相比的性能。我们计算了受试者工作特征（ROC）曲线和ROC曲线下面积（AUROC）及其95%置信区间、用于衡量区分度的哈雷尔C统计量和D统计量、用于表明各预测算法所解释的变异程度的R²统计量、用于校准的霍斯默-莱梅肖统计量以及各算法的敏感性和特异性。
中风风险评估仪（Stroke Riskometer™）在男性（FSRS = 75.0%（95% CI 72.3%-77.6%），中风风险评估仪（Stroke Riskometer™） = 74.0（95% CI 71.3%-76.7%））和女性（FSRS = 70.3%（95% CI 67.9%-72.8%），中风风险评估仪（Stroke Riskometer™） = 71.5%（95% CI 69.0%-73.9%））中与FSRS的五年AUROC表现相当，且优于QStroke（男性 - 59.7%（95% CI 57.3%-62.0%），女性与之相当 = 71.1%（95% CI 69.0%-73.1%））。所有算法的区分能力都较低（C统计量范围为0.51 - 0.56，D统计量范围为0.01 - 0.12）。霍斯默-莱梅肖检验表明，所有预测风险评分与观察到的事件数据校准不佳（P < 0.006）。
中风风险评估仪（Stroke Riskometer™）在中风预测性能方面与FSRS和QStroke相当。所有三种算法在预测中风事件方面表现同样不佳。中风风险评估仪（Stroke Riskometer™）将持续开发和验证，以满足改进当前中风风险评分系统的需求，从而更准确地预测中风，特别是通过识别可靠的种族/民族群体和特定国家的风险因素。
Zotero 插件
