BACKGROUND In aneurysmal subarachnoid hemorrhage (SAH) and multiple intracranial aneurysms (MIAs) identification of the bleeding source cannot always be assessed according to the hemorrhage pattern. Therefore, we developed a statistical model for the prediction of the ruptured aneurysm in patients with SAH and multiple potential bleeding sources at the time of ictus. METHODS Between 2012 and 2015, 252 patients harboring 619 aneurysms were admitted to the authors' institution. Patients were followed prospectively. Aneurysm and patient characteristics, as well as radiological findings were entered into a computerized database. Gradient boosting techniques were used to derive the statistical model for the prediction of the ruptured aneurysm. Based on the statistical prediction model, a scoring system was produced for the use in the clinical setting. The aneurysm with the highest score poses the highest possibility of being the bleeding source. The prediction score was then prospectively applied to 34 patients suffering from SAH and harboring MIAs. RESULTS According to the statistical prediction model the main factors affecting the rupture in patients harboring multiple aneurysms were: 1) aneurysm size, 2) aneurysm location and 3) aneurysm shape. The prediction score identified correctly the ruptured aneurysm in all the patients that were used in the prospective validation. Even in the five most debatable and challenging cases assessed in the period of prospective validation, for which the score was designed for, the ruptured aneurysm was predicted correctly. CONCLUSIONS This new and simple prediction score might provide additional support for neurovascular teams for treatment decision in SAH patients harboring multiple aneurysms. In a small prospective sample, the prediction score performed with high accuracy but larger cohorts for external validation are warranted.

中文翻译：

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蛛网膜下腔出血的多发性动​​脉瘤-当出血方式不明确时可识别破裂的动脉瘤-制定新的预测评分。

背景技术在动脉瘤性蛛网膜下腔出血（SAH）和多发性颅内动脉瘤（MIA）中，无法始终根据出血方式评估出血来源的鉴定。因此，我们开发了一种统计模型，用于预测SAH发作时有多个潜在出血源的SAH患者的动脉瘤破裂。方法在2012年至2015年期间，有619例动脉瘤的252例患者被纳入作者所在的机构。对患者进行前瞻性随访。动脉瘤和患者特征以及放射学检查结果已输入计算机数据库。使用梯度增强技术来推导用于预测动脉瘤破裂的统计模型。根据统计预测模型，制作了一个评分系统，供临床使用。得分最高的动脉瘤成为出血源的可能性最高。然后将预测分数前瞻性地应用于34例SAH合并MIA的患者。结果根据统计预测模型，影响具有多个动脉瘤的患者破裂的主要因素为：1）动脉瘤大小； 2）动脉瘤位置； 3）动脉瘤形状。预测评分正确地识别了所有用于前瞻性验证的患者的动脉瘤破裂。即使在前瞻性验证期间评估的最有争议和最具挑战性的五个案例中（针对该评分而设计），也可以正确预测破裂的动脉瘤。结论这一新的，简单的预测评分可能为神经血管小组在患有多发性动脉瘤的SAH患者的治疗决策中提供额外的支持。在少量的前瞻性样本中，预测分数的准确性很高，但需要更大的队列来进行外部验证。