Revisiting the Endoscopic Third Ventriculostomy Success Score using machine learning: can we do better?

ABSTRACT:

Objective

The Endoscopic Third Ventriculostomy Success Score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined traditionally as no repeat cerebrospinal fluid diversion surgery needed within 6 months. Nonetheless, the performance of the logistic regression (LR) model in the original 2009 study was modest, with an area under the receiver operating characteristic curve (AUROC) of 0.68. The authors sought to use a larger dataset to develop more accurate machine learning (ML) models to predict endoscopic third ventriculostomy (ETV) success and also to perform the largest validation of the ETVSS to date.

Methods

The authors queried the MarketScan national database for the years 2005-2022 to identify patients < 18 years of a

SUBMITTER: Adil SM 

PROVIDER: S-EPMC12551986 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature