About

Monash Neurointervention is a research-intensive clinical service at Monash Health and Monash University focused on improving outcomes for patients with neurovascular disease.

Our work sits at the intersection of:

• Clinical care — stroke, intracranial aneurysms, and cerebrovascular disease
• Research — outcomes, risk modelling, and registry-based studies
• Innovation — endovascular technologies, robotics, and systems of care

We aim to translate high-quality clinical data into practical tools, reproducible methods, and scalable models of care.

Our Impact (2016–2025)

Over the past decade, Monash Neurointervention has developed a research- intensive clinical service focused on improving patient outcomes and translating evidence into real-world care.

Key results across the team include:

• 501 peer-reviewed publications
• 11,373 citations from researchers worldwide
• ~1 in 3 papers published in top 10% journals globally
• ~1 in 6 papers among the most cited globally in our field

These metrics reflect a consistent and long-term focus on:

• Research that matters for patient care
• High-quality and rigorous methods
• Translation into clinical practice and health systems

Our Research Repositories

This GitHub organisation hosts open and reproducible research outputs from the Monash Neurointervention team.

Current repositories include:

DAH30 (Days Alive and at Home at 30 Days)

• Reference implementation of DAH30, a patient-centred outcome measure capturing recovery and time at home after neurointerventional procedures

UIA Competing Risk Analysis

• Methods and code for analysing rupture risk in unruptured intracranial aneurysms, accounting for competing mortality.

Systematic Review & Meta-Analysis (UIA-SR-MA)

• Reproducible workflows and datasets for systematic review and meta-analysis in intracranial aneurysm research.

Reproducibility and Open Science

We are committed to:

• Transparent and reproducible research methods
• Clear and standardised definitions of clinical outcomes
• Open-source code where appropriate
• Use of synthetic or de-identified datasets to enable reuse

Our goal is to move beyond publication alone and provide:

• Reusable code
• Standardised analytical methods
• Reference implementations for other research groups

For Researchers

We welcome collaboration with researchers, clinicians, and data scientists.

This repository structure is designed to:

• Support external validation of our work
• Enable reuse of analytical methods
• Provide templates for registry-based and outcomes research

If you are interested in:

• Applying these methods to your own data
• Collaborating on multicentre studies
• Contributing to ongoing projects We would be very interested to hear from you.

Future Direction

This platform will expand to include:

• Additional research outputs
• Registry-based analytics
• Translational and implementation resources
• Multi-centre collaborative research projects

COLLABORATION