stroke-lifetime

Toolkit for calculating lifetime survival probabilities and resource use for patients after stroke.

• Source code: https://github.com/stroke-optimist/stroke-lifetime
• Full background and methods: (link to paper to be added when available)
• Detailed workthrough of methods: https://lifetime-stroke-outcome.streamlit.app/
• PyPI package: https://pypi.org/project/stroke-lifetime/
• Parent project: stroke-optimist Stroke OPTIMIST Project: OPTimising IMplementation of Ischaemic Stroke Thrombectomy

➡️ Get started

This toolkit works with Python versions 3.8 and up.

Install the package with:

pip install stroke-lifetime

And follow the link to the code demonstration in the "External resources" section below.

🏥 Motivation in brief:

Given values for a patient's age, sex, and Modified Rankin Scale (mRS) score on discharge from hospital following a stroke, we can calculate the following quantities across the remainder of that patient's lifespan:

• The probability of survival of the patient in each year.
• The number of Quality-Adjusted Life Years (QALYs).
• The expected use of resources (e.g. number of admissions to A&E and number of years spent in residential care) and the cost of those resources.
• The discounted total net benefit by change in mRS score.

The model is described in a paper that's not yet published. Link will be added when available.

📦 Package details:

There are three main modules in the package:

• models.py - Basic models.
• fixed_params.py - Constants.
• main_calculations.py - Gathers the basic models and calculates all of the useful outputs.

👑 Example usage: Streamlit app

View a Streamlit app that uses this package and presents the methods in detail:

• Streamlit app: https://lifetime-stroke-outcome.streamlit.app/
• DOI: https://doi.org/10.5281/zenodo.8269389

📚 External resources

The following resources are not included within the package files and are accessible on the GitHub repository.

A conda environment file, environment.yml, is provided in the GitHub repository for use with the demonstration Jupyter notebook.

• - Code demonstration of running the main function and using the results. It also contains reference tables to describe the results categories.