A visualized MAC nomogram online predicts the risk of three-month mortality in Chinese elderly aneurysmal subarachnoid hemorrhage patients undergoing endovascular coiling

OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is an aggressive disease with higher mortality rate in the elderly population. Unfortunately, the previous models for predicting clinical prognosis are still not accurate enough. Therefore, we aimed to construct and validate a visualized nomogram model to predict online the 3-month mortality in elderly aSAH patients undergoing endovascular coiling. METHOD: We conducted a retrospective analysis of 209 elderly aSAH patients at People’s Hospital of Hunan Province, China. A nomogram was developed based on multivariate logistic regression and forward stepwise regression analysis, then validated using the bootstrap validation method (n = 1000). In addition, the performance of the nomogram was evaluated by various indicators to prove its clinical value. RESULT: Morbid pupillary reflex, age, and using a breathing machine were independent predictors of 3-month mortality. The AUC of the nomogram was 0.901 (95% CI: 0.853–0.950), and the Hosmer–Lemeshow goodness-of-fit test showed good calibration of the nomogram (p = 0.4328). Besides, the bootstrap validation method internally validated the nomogram with an area under the curve of the receiver operator characteristic (AUROC) of 0.896 (95% CI: 0.846–0.945). Decision curve analysis (DCA) and clinical impact curve (CIC) indicated the nomogram’s excellent clinical utility and applicability. CONCLUSION: An easily applied visualized nomogram model named MAC (morbid pupillary reflex-age-breathing machine) based on three accessible factors has been successfully developed. The MAC nomogram is an accurate and complementary tool to support individualized decision-making and emphasizes that patients with higher risk of mortality may require closer monitoring. Furthermore, a web-based online version of the risk calculator would greatly contribute to the spread of the model in this field.