Construction and verification of risk predicting models to evaluate the possibility of hydrocephalus following aneurysmal subarachnoid hemorrhage.

ABSTRACT

BACKGROUND: Hydrocephalus following a ruptured aneurysm portends a poor prognosis. The authors aimed to establish a nomogram to predict the risk of hydrocephalus after aneurysmal subarachnoid hemorrhage (aSAH). METHODS: A total of 421 patients with aSAH who were diagnosed by digital subtraction angiography in The General Hospital of Northern Theater Command center from January 2020 to June 2021 were screened to establish the training cohort. An additional 135 patients who enrolled between July 2021 and May 2022 were used for the validation cohort. Variate difference analysis and stepwise logistic regression (model A) and univariate and multivariate logistic regressions (model B) were respectively used to construct two models. Then, the net reclassification improvement (NRI), integrated discrimination improvement (IDI), and receiver operating characteristic (ROC) curve were used to compare the predictive abilities of the two models. Finally, two nomograms were constructed and externally validated. RESULTS: After screening, 556 patients were included. The area under the ROC curve of models A and B in the training cohort were respectively 0.884 (95 % confidence interval [CI] 0.847-0.921) and 0.834 (95 % CI 0.787-0.881). The prediction ability of the model A was superior to model B (NRI > 0, IDI > 0, p < 0.05). The C-index of models A and B was 0.8835 and 0.8392, respectively. Regarding clinical usefulness, the two models offered a net benefit with a threshold probability of between 0.12 and 1 in the decision curve analysis, suggesting that the two models can accurately predict hydrocephalus events. CONCLUSIONS: Both models have good prediction accuracy. Compared with model B, model A has better discrimination and calibration. Further, the easy-to-use nomogram can help neurosurgeons to make rapid clinical decisions and apply early treatment measures in high-risk groups, which ultimately benefits patients.