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Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree

Benjamin W Y Lo¹, Hitoshi Fukuda², Mark Angle¹

¹Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Surgical Neurology International

|August 12, 2016

Summary

This summary is machine-generated.

This study developed a clinical prediction tool for aneurysmal subarachnoid hemorrhage (SAH) using classification and regression tree analysis. The tool identifies seven prognostic subgroups to aid in patient prognostication and treatment decisions.

Keywords:

Aneurysmal subarachnoid hemorrhage brain–body interactions classification and regression tree analysis prognostic decision making

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Area of Science:

Neurology
Medical Informatics
Biostatistics

Background:

Classification and regression tree (CART) analysis is a method for creating decision trees through recursive data partitioning.
Limited research exists on applying CART to develop clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH).

Purpose of the Study:

To develop a clinical prediction tool for aneurysmal subarachnoid hemorrhage (SAH) using CART analysis.
To identify prognostic subgroups and understand risk factor interactions in SAH patients.

Main Methods:

CART analysis was performed on the multicenter Tirilazad database (3551 patients).
Neurologic, systemic, and demographic factors were analyzed to elucidate prognostic subgroups.
The dichotomized Glasgow Outcome Score at 3 months served as the dependent variable.

Main Results:

Seven distinct prognostic subgroups were identified using CART analysis.
Key predictors included neurological grade, post-admission stroke, post-admission fever, and age.
Split sample validation showed 79% accuracy on training and 77% on testing datasets.
Fever within one week post-SAH increased stroke odds (OR: 1.83, 95% CI: 1.56-2.45).

Conclusions:

A clinically valuable classification tree was generated as a prediction tool for SAH.
The tool can guide bedside prognostication and clinical treatment decisions.
The algorithm highlights complex risk factor interactions influencing SAH outcomes.