Stroke lifetime: health economics calculations for lifetime after stroke.
Project description
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.
