Department of Neurology, Peking University People's Hospital, Beijing, China.
Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing, China.
Front Immunol. 2022 Jul 29;13:930087. doi: 10.3389/fimmu.2022.930087. eCollection 2022.
Ischemic stroke (IS) is the most common and life-threatening arterial manifestation of antiphospholipid syndrome (APS). It is related to high mortality and severe permanent disability in survivors. Thus, it is essential to identify patients with APS at high risk of IS and adopt individual-level preventive measures. This study was conducted to identify risk factors for IS in patients with APS and to develop a nomogram specifically for IS prediction in these patients by combining the adjusted Global Anti-Phospholipid Syndrome Score (aGAPSS) with additional clinical and laboratory data.
A total of 478 consecutive patients with APS were enrolled retrospectively. All patients were randomly assigned to the training and validation cohorts. Univariate and multivariate binary logistic analyses were conducted to identify predictors of IS in the training cohort. Then, a nomogram was developed based on these predictors. The predictive performance of the nomogram for the training and validation cohorts was evaluated by determining areas under the receiver operating characteristic curve (AUROC) and creating calibration plots. A decision curve analysis (DCA) was conducted to compare the potential net benefits of the nomogram with those of the aGAPSS.
During a mean follow-up period of 2.7 years, 26.9% (129/478) of the patients were diagnosed with IS. Binary logistic regression analysis revealed that five risk factors were independent clinical predictors of IS: age ( < 0.001), diabetes ( = 0.030), hyperuricemia ( < 0.001), the platelet count ( = 0.001), and the aGAPSS ( = 0.001). These predictors were incorporated into the nomogram, named the aGAPSS-IS. The nomogram showed satisfactory performance in the training [AUROC = 0.853 (95% CI, 0.802-0.896] and validation [AUROC = 0.793 (95% CI, 0.737-0.843)] cohorts. Calibration curves showed good concordance between observed and nomogram-predicted probability in the training and validation cohorts. The DCA confirmed that the aGAPSS-IS provided more net benefits than the aGAPSS in both cohorts.
Age, diabetes, hyperuricemia, the platelet count, and the aGAPSS were risk factors for IS in patients with APS. The aGAPSS-IS may be a good tool for IS risk stratification for patients with APS based on routinely available data.
缺血性脑卒中（IS）是抗磷脂综合征（APS）最常见且危及生命的动脉表现。它与幸存者中的高死亡率和严重永久性残疾有关。因此，识别有发生 IS 风险的 APS 患者并采取个体化预防措施至关重要。本研究旨在确定 APS 患者发生 IS 的危险因素，并通过将调整后的全球抗磷脂综合征评分（aGAPSS）与其他临床和实验室数据相结合，为这些患者建立专门用于预测 IS 的列线图。
回顾性纳入 478 例连续 APS 患者。所有患者均被随机分配至训练集和验证集。对训练集中的 IS 患者进行单因素和多因素二元逻辑分析，以确定 IS 的预测因素。然后，基于这些预测因素建立列线图。通过确定接收者操作特征曲线下面积（AUROC）和绘制校准图来评估列线图在训练集和验证集的预测性能。通过决策曲线分析（DCA）比较列线图与 aGAPSS 的潜在净获益。
在平均 2.7 年的随访期间，26.9%（129/478）的患者被诊断为 IS。二元逻辑回归分析显示，年龄（<0.001）、糖尿病（=0.030）、高尿酸血症（<0.001）、血小板计数（=0.001）和 aGAPSS（=0.001）是 IS 的 5 个独立临床预测因素。这些预测因素被纳入列线图，命名为 aGAPSS-IS。列线图在训练集（AUROC=0.853（95%CI，0.802-0.896）和验证集（AUROC=0.793（95%CI，0.737-0.843））中均表现出良好的性能。校准曲线显示，训练集和验证集中，观察到的和列线图预测的概率之间具有良好的一致性。DCA 证实，在两个队列中，aGAPSS-IS 比 aGAPSS 提供了更多的净获益。
年龄、糖尿病、高尿酸血症、血小板计数和 aGAPSS 是 APS 患者发生 IS 的危险因素。aGAPSS-IS 可能是一种基于常规可用数据对 APS 患者进行 IS 风险分层的良好工具。
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