Suppr超能文献

颅内动脉瘤性蛛网膜下腔出血结局预测模型的建立和验证:SAHIT 多中心队列研究。

Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study.

机构信息

Division of Neurosurgery, St Michael's Hospital, Toronto, ON, Canada.

Neuroscience Research Program of the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada.

出版信息

BMJ. 2018 Jan 18;360:j5745. doi: 10.1136/bmj.j5745.

Abstract

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.

摘要

目的

开发并验证一套实用的预测工具,以可靠地预测颅内破裂动脉瘤性蛛网膜下腔出血(SAH)的结果。

设计

队列研究,逻辑回归分析结合预测因素和治疗方式。

设置

蛛网膜下腔出血国际试验员(SAHIT)数据存储库,包括随机临床试验、前瞻性观察性研究和医院登记处。

参与者

研究人员合作汇集了来自多个地理区域的前瞻性观察性研究、医院登记处和随机临床试验的数据集,以开发和验证临床预测模型。

主要观察指标

根据格拉斯哥结局量表评分预测三个月时的死亡率或功能结局的风险。

结果

利用 10936 名患者的个体患者数据开发了临床预测模型,并在模型开发后利用来自 3355 名患者的数据进行了验证。在验证队列中,一个包含患者年龄、发病前高血压和入院时神经功能分级的核心模型,预测功能结局风险的能力具有良好的区分度,受试者工作特征曲线下面积(AUC)为 0.80(95%置信区间 0.78-0.82)。当将核心模型扩展为包含血栓体积、动脉瘤大小和位置的“神经影像学模型”时,AUC 提高到 0.81(0.79-0.84)。通过包含治疗方式进一步扩展神经影像学模型的全模型 AUC 为 0.81(0.79-0.83)。用于预测死亡率风险的类似模型的区分度较低(全模型 AUC 为 0.76,0.69-0.82)。所有模型在验证队列中均表现出令人满意的校准度。

结论

这些预测模型可靠地估计了在各种环境中接受治疗的颅内破裂动脉瘤性蛛网膜下腔出血患者的结局。预测因素项目可在入院时获得。基于网络的 SAHIT 预后计算器(http://sahitscore.com)和相关应用程序可以作为辅助工具,支持患者的治疗。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验