PURPOSE To develop and validate a model for identifying the risk factors of poor recovery in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS A prediction model was developed using training data obtained from 1577 aSAH patients from multiple centers. The patients were followed for 6 months on average and assessed using the modified Rankin Scale; patient information was collected with a prospective case report form. The least absolute shrinkage and selection operator regression were applied to optimize factor selection for the poor recovery risk model. Multivariable logistic regression, incorporating the factors selected in the previous step, was used for model predictions. Predictive ability and clinical effectiveness of the model were evaluated using C-index, receiver operating characteristic curve, and decision curve analysis. Internal validation was performed using the C-index, taking advantage of bootstrapping validation. RESULTS The predictors included household income per capita, hypertension, smoking, migraine within a week before onset, Glasgow Coma Scale at admission, average blood pressure at admission, modified Fisher score at admission, treatment method, and complications. Our newly developed model made satisfactory predictions; it had a C-index of 0.796 and an area under the receiver operating characteristic curve of 0.784. The decision curve analysis showed that the poor recovery nomogram was of clinical benefit when an intervention was decided at a poor recovery threshold between 2% and 50%. Internal validation revealed a C-index of 0.760. CONCLUSION Our findings indicate that the novel poor recovery nomogram may be conveniently used for risk prediction in aSAH patients. For patients with intracranial aneurysms, migraine needs to be vigilant. Quitting smoking and blood pressure management are also beneficial.

中文翻译：

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预测动脉瘤性蛛网膜下腔出血的不良恢复风险：基于新预测列线图的临床评估和管理

目的开发和验证一个模型，用于识别动脉瘤性蛛网膜下腔出血 (aSAH) 患者恢复不良的危险因素。方法 使用从多个中心的 1577 名 aSAH 患者获得的训练数据开发预测模型。患者平均随访6个月，采用改良Rankin量表进行评估；使用前瞻性病例报告表收集患者信息。应用最小绝对收缩和选择算子回归来优化恢复不良风险模型的因子选择。多变量逻辑回归，结合上一步中选择的因素，用于模型预测。使用C指数、受试者工作特征曲线和决策曲线分析评估模型的预测能力和临床有效性。内部验证是使用 C-index 进行的，利用引导验证。结果 预测因素包括家庭人均收入、高血压、吸烟、发病前一周内偏头痛、入院时格拉斯哥昏迷量表、入院时平均血压、入院时修正Fisher评分、治疗方法和并发症。我们新开发的模型做出了令人满意的预测；它的 C 指数为 0.796，受试者工作特征曲线下面积为 0.784。决策曲线分析表明，当在 2% 和 50% 之间的不良恢复阈值下决定干预时，不良恢复列线图具有临床益处。内部验证显示 C 指数为 0.760。结论 我们的研究结果表明，新的恢复不良列线图可方便地用于 aSAH 患者的风险预测。对于颅内动脉瘤、偏头痛患者需要警惕。戒烟和血压管理也是有益的。