pymsprog 1.0.3

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📖 Documentation and TUTORIALS

pymsprog is a Python package providing tools for exhaustive and reproducible analysis of disability course in multiple sclerosis (MS) from longitudinal data. An R version of the library is available as well.

Its core function, MSprog(), detects and characterises the evolution of an outcome measure (Expanded Disability Status Scale, EDSS; Nine-Hole Peg Test, NHPT; Timed 25-Foot Walk, T25FW; Symbol Digit Modalities Test, SDMT; or any custom outcome measure) for one or more subjects, based on repeated assessments through time and on the dates of acute episodes (if any).

The package also provides a small toy dataset for testing and demonstration purposes. The dataset contains artificially generated Extended Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT) longidutinal scores, visit dates, and relapse onset dates in a small cohort of example patients.

If you use this package in your work, please cite as follows:
Montobbio N, Carmisciano L, Signori A, et al. Creating an automated tool for a consistent and repeatable evaluation of disability progression in clinical studies for multiple sclerosis. Mult Scler. 2024;30(9):1185-1192. doi:10.1177/13524585241243157

For any questions, requests for new features, or bug reporting, please contact: [e-mail]. Any feedback is highly appreciated!

Installation

You can install the latest release of pymsprog with:

pip install pymsprog

Alternatively, the development version can be downloaded from GitHub.

Quickstart

The MSprog() function detects disability events sequentially by scanning the outcome values in chronological order.

Let's start by importing toy data and applying MSprog() to analyse EDSS course with the default settings.

from pymsprog import MSprog, load_toy_data

# Load toy data
toydata_visits, toydata_relapses = load_toy_data()

toydata_visits.head()
'''
   id       date  EDSS  SDMT
0   1 2021-09-23   4.5    50
1   1 2021-11-03   4.5    50
2   1 2022-01-19   4.5    51
3   1 2022-04-27   4.5    57
4   1 2022-07-12   5.5    55
'''

toydata_relapses.head()
'''
   id       date
0   2 2021-06-12
1   2 2022-10-25
2   3 2022-12-01
3   6  2022-12-18
'''

# Detect events
summary, results = MSprog(toydata_visits,                          # insert data on visits
                 relapse=toydata_relapses,                         # insert data on relapses
                 subj_col='id', value_col='EDSS', date_col='date', # specify column names 
                 outcome='edss')                                   # specify outcome type
'''
---
Outcome: EDSS
Confirmation over: 84 (-7, +730.5) days
Baseline: fixed
Relapse influence (baseline): [30, 0] days
Relapse influence (event): [0, 0] days
Relapse influence (confirmation): [30, 0] days
Events detected: firstCDW
        
---
Total subjects: 6
---
Subjects with disability worsening: 3 (PIRA: 2; RAW: 1)
'''

Several qualitative and quantitative options for event detection are given as arguments that can be set by the user and reported as a complement to the results to ensure reproducibility. For example, instead of only detecting the first confirmed disability worsening (CDW) for each subject, we can detect all disability events sequentially by moving the baseline after each event (event='multiple', baseline='roving')`:

summary, results = MSprog(toydata_visits,                          # insert data on visits
                 relapse=toydata_relapses,                         # insert data on relapses
                 subj_col='id', value_col='EDSS', date_col='date', # specify column names 
                 outcome='edss',                                   # specify outcome type
                 event='multiple', baseline='roving')              # modify default settings
'''
---
Outcome: EDSS
Confirmation over: 84 (-7, +730.5) days
Baseline: roving
Relapse influence (baseline): [30, 0] days
Relapse influence (event): [0, 0] days
Relapse influence (confirmation): [30, 0] days
Events detected: multiple
        
---
Total subjects: 6
---
Subjects with CDW: 4 (PIRA: 4; RAW: 1)
Subjects with CDI: 2
---
CDW events: 5 (PIRA: 4; RAW: 1)
CDI events: 2
'''

The function prints out a concise report of the results, as well as the specific set of options used to obtain them. Complete results are stored in two pandas.DataFrame objects generated by the function call:

1. A summary table providing the event count for each subject and event type:

print(summary)
'''
  event_sequence  CDI  CDW  RAW  PIRA  undef_CDW
1           PIRA    0    1    0     1          0
2      RAW, PIRA    0    2    1     1          0
3                   0    0    0     0          0
4      CDI, PIRA    1    1    0     1          0
5           PIRA    0    1    0     1          0
6            CDI    1    0    0     0          0
'''

where: event_sequence specifies the order of the events; the other columns count the events of each type.

2. Extended info on each event for all subjects:

print(results)
'''
   id  nevent event_type total_fu  time2event bl2event conf84 PIRA_conf84 sust_days sust_last
0   1       1       PIRA      534         292    292.0      1           1       242         1
1   2       1        RAW      730         198    198.0      1        None        84         0
2   2       2       PIRA      730         539    257.0      1           1       191         1
3   3       0       None      491         491     None   None        None      None      None
4   4       1       impr      586          77     77.0      1        None        98         0
5   4       2       PIRA      586         304    129.0      1           1       282         1
6   5       1       PIRA      637         140    140.0      1           1       497         1
7   6       1       impr      491         120    120.0      1        None       232         0
'''

where: nevent is the cumulative event count for each subject; event_type characterises the event; total_fu is the total follow-up period of the subject in days; time2event is the number of days from the beginning of the follow-up to the event (coincides with length of follow-up if no event is detected); bl2event is the number of days from the current baseline to the event; conf84 reports whether the event was confirmed over 84 days (12 weeks); sust_days is the number of days for which the event was sustained; sust_last reports whether the event was sustained until the last visit.