Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH).
Design Cohort study with logistic regression analysis to combine predictors and treatment modality.
Setting Subarachnoid Haemorrhage International Trialists’ (SAHIT) data repository, including randomised clinical trials, prospective observational studies, and hospital registries.
Participants Researchers collaborated to pool datasets of prospective observational studies, hospital registries, and randomised clinical trials of SAH from multiple geographical regions to develop and validate clinical prediction models.
Main outcome measure Predicted risk of mortality or functional outcome at three months according to score on the Glasgow outcome scale.
Results Clinical prediction models were developed with individual patient data from 10 936 patients and validated with data from 3355 patients after development of the model. In the validation cohort, a core model including patient age, premorbid hypertension, and neurological grade on admission to predict risk of functional outcome had good discrimination, with an area under the receiver operator characteristics curve (AUC) of 0.80 (95% confidence interval 0.78 to 0.82). When the core model was extended to a “neuroimaging model,” with inclusion of clot volume, aneurysm size, and location, the AUC improved to 0.81 (0.79 to 0.84). A full model that extended the neuroimaging model by including treatment modality had AUC of 0.81 (0.79 to 0.83). Discrimination was lower for a similar set of models to predict risk of mortality (AUC for full model 0.76, 0.69 to 0.82). All models showed satisfactory calibration in the validation cohort.
Conclusion The prediction models reliably estimate the outcome of patients who were managed in various settings for ruptured intracranial aneurysms that caused subarachnoid haemorrhage. The predictor items are readily derived at hospital admission. The web based SAHIT prognostic calculator (http://sahitscore.com) and the related app could be adjunctive tools to support management of patients.

中文翻译：

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动脉瘤性蛛网膜下腔出血结果预测模型的开发和验证：SAHIT 多国队列研究

目的开发和验证一套实用的预测工具，能够可靠地估计颅内动脉瘤（SAH）破裂引起的蛛网膜下腔出血的预后。
使用逻辑回归分析设计队列研究，以结合预测因子和治疗方式。
设置蛛网膜下腔出血国际试验者 (SAHIT) 数据库，包括随机临床试验、前瞻性观察研究和医院登记。
参与者研究人员合作汇集了来自多个地理区域的 SAH 的前瞻性观察研究、医院登记和随机临床试验的数据集，以开发和验证临床预测模型。
主要结果测量根据格拉斯哥结果量表评分预测三个月内的死亡或功能结果风险。
结果使用来自 10 936 名患者的个体患者数据开发临床预测模型，并在开发模型后使用来自 3355 名患者的数据进行验证。在验证队列中，包括患者年龄、病前高血压和入院时的神经系统分级来预测功能结果风险的核心模型具有良好的区分度，受试者操作特征曲线下面积 (AUC) 为 0.80（95% 置信区间 0.78至 0.82）。当核心模型扩展到“神经影像模型”时，包括凝块体积、动脉瘤大小和位置，AUC 提高到 0.81（0.79 到 0.84）。通过包含治疗方式扩展神经影像模型的完整模型的 AUC 为 0.81（0.79 至 0.83）。一组相似模型预测死亡风险的区分度较低（完整模型的 AUC 为 0.76, 0. 69 至 0.82）。所有模型在验证队列中都显示出令人满意的校准。
结论预测模型可靠地估计了在各种环境中因颅内动脉瘤破裂导致蛛网膜下腔出血而接受治疗的患者的结果。预测项目在入院时很容易得出。基于网络的 SAHIT 预后计算器 (http://sahitscore.com) 和相关应用程序可能是支持患者管理的辅助工具。