Predicting the Poor Recovery Risk of Aneurysmal Subarachnoid Hemorrhage: Clinical Evaluation and Management Based on a New Predictive Nomogram

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.