Paper creation

This document provides more information on the main results in the academic paper about the stroke outcome model.

For each result, there are links to the pages in this online book where the data was created, and information on accessing the data shown in the paper’s figures and tables or referenced in the text.

The paper is “Modelling disability-level and utility outcome in stroke patients depending on time to intravenous thrombolysis and mechanical thrombectomy. Application to England and Wales”

Preview of the first page of the stroke outcome paper.

mRS distribution derivation

Table 1 contains the derived mRS distributions:

import stroke_outcome.outcome_utilities

mrs_dists, mrs_dists_notes = (
    stroke_outcome.outcome_utilities.import_mrs_dists_from_file())

mrs_dists

	mRS<=0	mRS<=1	mRS<=2	mRS<=3	mRS<=4	mRS<=5	mRS<=6
Stroke type
pre_stroke_nlvo	0.583	0.746	0.850	0.951	0.993	1.000	1
pre_stroke_lvo	0.408	0.552	0.672	0.838	0.956	1.000	1
no_treatment_lvo	0.050	0.129	0.265	0.429	0.676	0.811	1
no_treatment_nlvo	0.198	0.460	0.580	0.708	0.856	0.918	1
no_effect_nlvo_ivt_deaths	0.196	0.455	0.574	0.701	0.847	0.908	1
no_effect_lvo_ivt_deaths	0.048	0.124	0.255	0.414	0.653	0.783	1
no_effect_lvo_mt_deaths	0.048	0.124	0.255	0.412	0.649	0.779	1
t0_treatment_nlvo_ivt	0.445	0.642	0.752	0.862	0.941	0.967	1
t0_treatment_lvo_ivt	0.140	0.233	0.361	0.522	0.730	0.838	1
t0_treatment_lvo_mt	0.306	0.429	0.548	0.707	0.851	0.915	1

mrs_dists_notes.split('\n')

['# If these change, please ensure the no-effect times are still correct.',
 '#',
 '# Acronyms:                                      No-effect times:',
 '#   lvo:  large-vessel occlusion                 IVT: 378mins (6.3hr)',
 '#   nlvo: non-large-vessel occlusion             MT:  480mins (8hr)',
 '#   ivt:  intra-veneous thrombolysis',
 '#   mt:   mechanical thrombectomy',
 '#   t0:   time zero, zero minutes after stroke onset.',
 '#',
 '']

Data creation:

• Derivation of mRS distributions from reference data

Data behind the table:

• Stored on GitHub:

  • Location: stroke_outcome/data/

  • File name for mRS distributions: mrs_dist_probs_cumsum.csv

  • File name for utility weight conversions: utility_dists.csv

• Or install the stroke-outcome package and import the data as above.

Probability with time

Figure 1 contains the mRS probability distributions with time.

Data creation:

• Resulting probabilities with time

The data behind the figure is stored on GitHub in the directory outcome_model_methods/output. The mRS values given are cumulative probabilities, e.g. the mRS<=2 column contains the sum of the probabilities of mRS of zero, one and two.

Data behind the figure:

• nLVO with IVT: probs_with_time_nlvo_ivt.csv

• LVO with IVT: probs_with_time_lvo_ivt.csv

• LVO with MT: probs_with_time_lvo_mt.csv

Supplementary data:

• Calculating outcome probabilities with time to treatment - explains how the probabilities with time are calculated.

Geographic modelling (generic)

Figure:

Data creation:

• Distance-based treatment effects for many cohorts

Figure creation:

• Plot distance-based treatment effects for many cohorts

Supplementary info:

• Example patient population

Data behind the figure: Available on Github in the directory general_results/output:

Times:

• generic_geography_grid_time_travel_directly_to_ivt.csv - time grid of travel directly to IVT centre in the middle of the grid.

• generic_geography_grid_time_travel_directly_diff.csv - time grid of difference in travel times to IVT centre in middle and MT centre at bottom of grid.

• generic_geography_dict_travel_grid.csv - dictionary of information needed to plot the time grids to scale.

Outcomes:

• generic_geography_df_lvo_diff.csv - outcomes for the LVO patients

• generic_geography_df_nlvo_ivt_diff.csv - outcomes for the nLVO patients

• generic_geography_df_mixed_diff.csv - outcomes for the mixed treated ischaemic population

An example of how to import this data into Python for plotting is given in the Figure Creation notebook linked above.

Geographic modelling (England and Wales)

Data creation:

• Model outcomes for drip’and’ship vs mothership based on geography

• Combine populations for weighted outcomes

Supplementary info:

• Admission prediction

• Example patient population

Maps

Figure creation:

• Mapping LSOA results

The data behind the figure is stored on GitHub in the directory england_wales/output.

Data behind the figure:

• fig_data_englandwales_maps_added_utility.csv

Supplementary data:

• Outlier regions mentioned in the Discussion: Mapping outliers

mRS distributions

Figure creation:

• National mRS distributions

The data behind the figure is stored on GitHub in the directory england_wales/output.

Data behind the figure:

• Bars: fig_data_mrs_dists.csv

• Errorbars: fig_data_mrs_dists_std.csv

Supplementary data:

• Mean shift in mRS, added utility, proportion with mRS<=2: fig_data_mrs_dists_means.csv CHECK THIS - file not uploaded yet?

Data creation date

The code was last re-run and so the data created on:

import datetime

print(datetime.date.today().strftime('%d %B %Y'))

19 July 2024

All package versions

The full list of packages used is provided in a conda environment.

The packages are:

import os

with open(os.path.join('..', 'environment.yml'), 'r') as f:
    print(f.read())

name: stroke-outcome
channels:
  - defaults
  - conda-forge
dependencies:
  - jupyterlab
  - matplotlib
  - numpy
  - pip
  - pandas
  - python=3.10
  - scikit-learn
  - pip:
    - geopandas
    - contextily
    - mapclassify
    - stroke-outcome
    - stroke_maps
    

Custom package versions

The versions of the custom packages used were:

import stroke_outcome

stroke_outcome.__version__

'0.1.6'

import stroke_maps

stroke_maps.__version__

'0.5.0'

The code in the packages for those version numbers can be seen by:

• Downloading a specific version of the package. For example, instead of the usual:
  pip install stroke-outcome
  You could download:
  pip install stroke-outcome==0.1.5
  And continue to use the package as normal.

• GitHub: on the main repository page, the “Releases” section lists all of the versions. The tag with the version number opens the branch with a copy of the code at that time.

• PyPI: the main project page has a “Release history” section. In it there are links to all previous versions of the package.