External Validation and Updating of Published Models for Predicting 7-day Risk of Symptomatic Intracranial Hemorrhage after Receiving Alteplase for Acute Ischemic Stroke: A Retrospective Cohort Study

Phaweesa Chawalitpongpun, P. Sonthisombat, N. Piriyachananusorn, Natthakarn Manoyana
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Abstract

Prediction scores for symptomatic intracranial hemorrhage (sICH) in acute ischemic stroke patients receiving thrombolytic therapy have been widely developed, but the external validation of these scores, especially in the Thai population, is lacking. This study aims to externally validate existing models and update the selected model to enhance its performance in our specific context. This cohort study retrospectively collected data from medical records between 2013 and 2022. Acute ischemic stroke patients who received thrombolysis were included. All predictors were gathered at admission. External validation was performed on eight published prediction models; in addition, the observed and expected probabilities of sICH were compared. The most effective model for discrimination was then chosen for further updating using multivariable logistic regression and was bootstrapped for internal validation. Finally, a points-based system for clinical practice was developed from the optimism-corrected model. Fifty patients (10% of the 502 included cohort members) experienced sICH after undergoing thrombolysis. The SICH score outperformed the other seven models in terms of discrimination (area under the receiver operating characteristic [AuROC] curve = 0.74 [95% confidence interval {CI} 0.67 to 0.81]), but it still overstated risk (expected-to-observed outcomes [E/O] ratio = 1.7). Once updated, the optimism-corrected revised SICH model showed somewhat better calibration (E/O = 1 and calibration-in-the-large = 0), slightly worse underprediction in the moderate-to-high risk group (calibration slope = 1.152), and marginally better discrimination (AuROC = 0.78). The points-based system also demonstrated substantial agreement (88.1%) with the risk groups predicted by the logistic regression model (kappa statistic = 0.78). Since the SICH score outperformed seven models in terms of discrimination, it was then modified to the Revised-SICH score, which predicted that patients with at least 5.5 points were at high risk of having sICH.
预测急性缺血性卒中患者接受阿替普酶治疗后 7 天症状性颅内出血风险的已发表模型的外部验证和更新:一项回顾性队列研究
接受溶栓治疗的急性缺血性卒中患者出现症状性颅内出血(sICH)的预测评分已被广泛开发,但这些评分缺乏外部验证,尤其是在泰国人群中。本研究旨在对现有模型进行外部验证,并对选定模型进行更新,以提高其在我国特定环境下的性能。 这项队列研究回顾性地收集了 2013 年至 2022 年间的医疗记录数据。研究纳入了接受溶栓治疗的急性缺血性脑卒中患者。所有预测指标均在入院时收集。对已发表的八个预测模型进行了外部验证;此外,还比较了观察到的 sICH 概率和预期的 sICH 概率。然后选择了最有效的判别模型,利用多变量逻辑回归进行进一步更新,并对其进行引导以进行内部验证。最后,根据乐观校正模型制定了一套基于临床实践的积分系统。 50名患者(占502名队列成员的10%)在接受溶栓治疗后发生了sICH。SICH评分的判别能力优于其他七个模型(接收者操作特征曲线下面积 [AuROC] = 0.74 [95% 置信区间 {CI} 0.67 至 0.81]),但仍高估了风险(预期与观察结果 [E/O] 比 = 1.7)。乐观校正修订后的 SICH 模型在更新后显示出更好的校正效果(E/O = 1 和大校正 = 0),在中高风险组的预测不足情况稍差(校正斜率 = 1.152),辨别力稍好(AuROC = 0.78)。积分系统与逻辑回归模型预测的风险组别也有很大的一致性(88.1%)(kappa 统计量 = 0.78)。 由于 SICH 评分在区分度方面优于七个模型,因此将其修改为修订版 SICH 评分,该评分预测至少有 5.5 分的患者具有发生 sICH 的高风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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