Machine Learning in Predicting the Cognitive Improvement of Ventriculoperitoneal Shunt for Chronic Normal Pressure Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage

Abstract

Background

Chronic normal pressure hydrocephalus (CNPH) is a recognized sequela of aneurysmal subarachnoid hemorrhage (ASAH). Ventriculoperitoneal shunt (VPS) is a conventional treatment for hydrocephalus, though its effectiveness for CNPH post-ASAH remains unclear.

Methods

We included ASAH patients with CNPH who underwent VPS surgery. Changes in the modified Rankin Scale (mRS) before and after surgery were analyzed to evaluate VPS benefits. The least absolute shrinkage and selection operator identified relevant variables and predictive models were constructed using 8 supervised machine learning algorithms to assess VPS benefit.

Results

Among 75 patients (39 males and 36 females), 48 (64%) benefited from VPS, while 27 (36%) did not. The beneficial group showed a longer disease course, higher cerebrospinal fluid (CSF) pressure, lower red and white blood cell counts in CSF, and lower modified Fisher (MF) and Hunt–Hess (HH) grades compared to the nonbeneficial group. Univariate logistic regression analysis indicated that disease course, CSF pressure, red blood cell/white blood cell (WBC) counts in CSF, WBC count in blood, MF grade, HH grade, and preoperative mRS were associated with favorable VPS outcomes. The extreme gradient boosting (XGB) model demonstrated the highest area under the curve of 0.946 and lowest residual error. A nomogram was subsequently developed and demonstrated a satisfactory performance.

Conclusions

VPS benefits in CNPH patients after ASAH were associated with disease course, CSF pressure, red blood cell/WBC counts in CSF, WBC count in blood, MF and HH grades, and preoperative mRS. The XGB model demonstrated optimal predictive performance, with an area under the curve of 0.946.