Construction of a nomogram model for predicting the risk of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on clinical-imaging-laboratory multimodal data

Objective To construct and validate a nomogram model for predicting the risk of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) based on clinical-imaging-laboratory multimodal data.

Methods A total of 162 patients with aSAH were selected as the study subjects and divided into DCI group and non-DCI group according to the occurrence of DCI. General information, laboratory indicators, and imaging indicators of patients in the two groups within 72 h after admission were collected. Univariate analysis and multivariate Logistic regression analysis were used to screen the independent influencing factors for the occurrence of DCI in patients with aSAH. The screened factors were imported into R software to construct a nomogram model for predicting the risk of DCI after aSAH. Internal validation was performed using the Bootstrap resampling method (repeated sampling 1 000 times), and the corrected C-index was calculated to evaluate the model's discriminative ability. Calibration curves were plotted to assess the consistency between the predicted probability and the actual event incidence rate, and the Hosmer-Lemeshow test was used to evaluate the model's goodness-of-fit. The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the model's discriminatory power. Decision curve analysis (DCA) was usedto evaluate the clinical net benefit of the model.

Results Among the 162 patients, 50 developed DCI, with an incidence rate of 30.86%. Multivariate Logistic regression analysis showed that Hunt-Hess grade, cerebral vasospasm, C-reactive protein (CRP), D-dimer (D-D), neurofilament light chain protein (NfL), interleukin-6 (IL-6), microRNA-21-5p (miR-21-5p), microRNA-126 (miR-126), cerebral blood flow (CBF), mean transit time (MTT), and cerebral blood volume (CBV) were all independent influencing factors for the occurrence of DCI in patients with aSAH (P < 0.05). Based on the above influencing factors, a nomogram model was constructed. After internal validation by the Bootstrap method, the average C-index was 0.814 (95%CI, 0.792 to 0.951), and the optimism bias was 0.034. The ROC curve showed that the AUC of this model for predicting the occurrence of DCI in patients with aSAH was 0.930 (95%CI, 0.873 to 0.978), with a sensitivity of 96.00% and a specificity of 86.80%. The slope of the calibration curve (Shrinkage coefficient) was 0.87, indicating that the predicted probability of the model was basically consistent with the actual probability. The Hosmer-Lemeshow goodness-of-fit test showed χ²=1.315 and P=0.821. DCA showed that the model had a high clinical net benefit.

Conclusion Hunt-Hess grade, cerebral vasospasm, CRP, D-D, NfL, IL-6, miR-21-5p, miR-126, MTT, CBV, and CBF are all independent influencing factors for the occurrence of DCI in patients with aSAH. The risk nomogram model constructed based on these factors has good predictive performance for DCI after aSAH.