NACCulator

NACCulator is a Python 3-based data converter that changes REDCap .csv exported data to NACC’s fixed-width .txt format. It is configured for UDS3 forms, including FTLD and LBD (versions 3.0 and 3.1). It will perform basic data integrity checks during a run: verifying that each field is the correct type and length, verifying that there are no illegal characters in the Char fields, verifying that Num fields are within the acceptable range as defined in NACC's Data Element Dictionary for each form, and checking that no blanking rules have been violated. NACCulator outputs a .txt file that is immediately ready to submit to NACC's database.

Note: NACCulator requires Python 3.

HOW TO Convert from REDCap to NACC

To install NACCulator, run:

$ pip3 install git+https://github.com/ctsit/nacculator.git

Once the project data is exported from REDCap to the CSV file data.csv, run:

$ redcap2nacc <data.csv >data.txt

This command will work only in the simplest case; UDS3 IVP data only. NACCulator will automatically skip PTIDs with errors, so the output data.txt file will be ready to submit to NACC. In order to properly filter the data in the csv, NACCulator is expecting that REDCap visits (denoted by redcap_event_name) contain certain keywords: "initial" for initial visit packets, "follow" for all followups, "milestone" for milestone packets, "neuropath" for neuropathology packets, "telephone" for telephone followup packets, "covid" for covid-related survey packets

NACCulator collects data from the Z1X form first and uses that to determine the presence of other forms in the packet. The Z1X form for that record must be marked "Unverified" or "Complete" for NACCulator to recognize the record, and each optional form must be marked as submitted within the Z1X for NACCulator to find those forms.

Note: output is written to STDOUT; errors are written to STDERR; input is expected to be from STDIN (the command line) unless a file is specified using the -file flag.

Usage

$ redcap2nacc -h
usage: redcap2nacc [-h]
                   [-fvp | -ivp | -tfp | -np | -m | -cv | -csf | -f {cleanPtid,replaceDrugId,fixHeaders,fillDefault,updateField,removePtid,removeDateRecord,getPtid}]
                   [-lbd | -ftld] [-file FILE] [-meta FILTER_META] [-ptid PTID]
                   [-vnum VNUM] [-vtype VTYPE]

Process redcap export data through nacculator.

optional arguments:
  -h, --help            show this help message and exit
  -fvp                  Set this flag to process as FVP data
  -ivp                  Set this flag to process as IVP data
  -tfp                  Set this flag to process as Telephone Followup Packet v3.2 data
  -tfp3                 Set this flag to process as TFP v3.0 (pre-2020) data
  -np                   Set this flag to process as Neuropathology data
  -m                    Set this flag to process as Milestone data
  -cv                   Set this flag to process as COVID data
  -csf                  Set this flag to process as NACC BIDSS CSF data

  -f {cleanPtid,replaceDrugId,fixHeaders,fillDefault,updateField,removePtid,removeDateRecord,getPtid}, --filter {cleanPtid,replaceDrugId,fixHeaders,fillDefault,updateField,removePtid,removeDateRecord,getPtid}
                          Set this flag to process the filter
  -lbd                  Set this flag to process as Lewy Body Dementia data (FORMVER = 3)
  -lbdsv                Set this flag to process as Lewy Body Dementia short version data (FORMVER = 3.1)
  -ftld                 Set this flag to process as Frontotemporal Lobar Degeneration data

  -file FILE            Path of the csv file to be processed.
  -meta FILTER_META     Input file for the filter metadata (in case -filter is used)
  -ptid PTID            Ptid for which you need the records
  -vnum VNUM            Ptid for which you need the records
  -vtype VTYPE          Ptid for which you need the records

Example - Process a Neuropathology form:

$ redcap2nacc -np -file data.csv >data.txt

Example - Processing LBD Follow-up visit packets:

redcap2nacc -lbd -fvp -file data.csv >data.txt

Both LBD / LBDSV and FTLD forms can have IVP or FVP arguments.

Example - Run data through the cleanPtid filter:

$ redcap2nacc -f cleanPtid -meta nacculator_cfg.ini <data.csv >filtered_data.csv

HOW TO Filter Data Using NACCulator

If your data is not clean enough to be processed by NACCulator, there are some built in functions to clean (read: transform) the data.

In order to properly use the filters, the first step is to check and validate that nacculator_cfg.ini has the proper settings for the filter to run. In order to create this file, find the nacculator_cfg.ini.example file and remove the .example portion, and then fill in your center's information. The config file contains sections with in-code filter function name. Each of these sections contains elements necessary for the filter to run. The filters described below will discuss what is required, if anything.

The filters can be run all at once with your REDCap API token using:

$ nacculator_filters nacculator_cfg.ini

You can find more details on nacculator_filters under the section: HOW TO Acquire current-db-subjects.csv for the filters

Or they can be run one at a time on a .csv file with the -f and -meta flags. For example, to run the fixHeaders filter:

$ redcap2nacc -f fixHeaders -meta nacculator_cfg.ini <data_input.csv >filtered_output.csv

If the filter requires the config, it must be passed with the -meta flag like the example above shows.

• cleanPtid

  This filter requires a section in the config called filter_clean_ptid. This section will contain a single key filepath which will point to a csv (usually called current-db-subjects.csv) file of ptids to be removed. All the records whose ptid with same packet and visit num found in the passed meta file will be discarded in the output file. This filter also removes events that lack a visit number in REDCap.

  Example meta file:

  Patient ID,Packet type,Visit Num,Status
  110001,I,1,Current
  110001,M,M1,Current
  110003,I,001,Current
  110003,F,002,Current
  

• replaceDrugId

  This filter replaces the first character of non empty fields of columns drugid_1 to drugid_30 with character "d".

• fixHeaders

  This filter requires a section in the config called filter_fix_headers with as many keys as needed to replace the necessary columns. See example below. This filter fixes the column names of any column found in the filter mapping. This filter does not check for any data. It only replaces the column names if found.

  For example, the configuration would look like this:

  [filter_fix_headers]
  c1s_2a_npsylan: c1s_2_npsycloc
  c1s_2a_npsylanx: c1s_2a_npsylan
  b6s_2a1_npsylanx: c1s_2a1_npsylanx
  fu_otherneur: fu_othneur
  fu_otherneurx: fu_othneurxs
  fu_strokedec: fu_strokdec
  fukid9agd: fu_kid9agd
  fusib17pdx: fu_sib17pdx
  

• fillDefault

  This filter is used to set some predefined fields to their corresponding predefined values. Below are the current defaults :

  nogds    -> 0
  formver  -> 3
  

  If field is blank, it will be updated to default value.

• updateField

  This filter is used to update fields that already had a value in the REDCap export. Currently, only adcid is updated.

• removePtid

  This filter requires a section in the config called filter_remove_ptid with a single key called ptid_format. The value for that key is a regex string to match ptids that are to be kept. 11\d.* keeps all PTIDs that fit the format 11xxxx, such as 110001.

  This filter is used to remove ptids that may have a different set of ids for a different study, or help limit which ids show up in the final result.

  config:
  ptid_format: 11\d.*
  

• removeDateRecord

  This filter is used to remove records who may be missing visit dates. It searches for rows missing the visit day, month, or year. If any of those fields are missing, it removes the row.

• getPtid

  This filter is used to get information about a single PatientID and is not present in the config file. You need to use the -ptid flag to specify the patient ID. You can use the -vnum to get the records with particular visit number and Patient ID or use -vtype to get records with particular visit type and Patient ID.

    $ redcap2nacc -f getPtid -ptid $SOME_PATIENT_ID -vnum $SOME_VISIT_NUM -vtype $SOMEVISIT_TYPE <data.csv >data.txt
  

HOW TO Acquire current-db-subjects.csv for the filters

This file is a csv that determines which of your center's PTIDs are already present in NACC's current database using the patient's PTID, the packet type (ivp or fvp, etc), the visit number, and the status (working or current). In order to get it, you need to use the contents of tools/preprocess/get_subject_list.js. The script is meant to be run on the "Finalize Data" page of the NACC UDS3 upload system.

Navigate to "Finalize Data" and right-click anywhere on the page. Select "Inspect" or "Inspect element" to open the browser's Inspect panel. Click on the "Console" tab and copy/paste the contents of get_subject_list.js into the console. Then, press the "Enter" or "Return" key on your keyboard. This will collect all of the PTIDs in your center's Working and Current databases into a csv called current-db-subjects.csv in your Downloads folder. You may then move it to whatever location you specified in your nacculator_cfg.ini file.

The csv is used by the filter_clean_ptid filter to identify and cull all packets already in NACC's Current database from your input csv. It is used to make NACCulator run faster for very large databases.

Example Workflow

Once you have edited the nacculator_cfg.ini file with your API token and desired filters, you can get a filtered CSV file of the raw REDCap data with:

$ nacculator_filters nacculator_cfg.ini

This will create a run folder labeled with the current date ($run_CURRENT-DATE) (for example, run_01-01-2000) that contains the csv and each iteration of filter, ending with final_update.csv.

Note: The files created by redcap2nacc will not be in the run folder created by run_filters.py. They will be in the base directory. The filepaths in the following commands are modified so that the output is deposited in your $run_CURRENT-DATE folder.

Next, you will need to run the actual redcap2nacc program to produce the fixed width text file for NACC. One type of flag can be used at a time, so the program must be run once for each type of packet.

$ redcap2nacc -ivp < $run_CURRENT-DATE/final_Update.csv > $run_CURRENT-DATE/iv_nacc_complete.txt 2> $run_CURRENT-DATE/ivp_errors.txt
$ redcap2nacc -fvp < $run_CURRENT-DATE/final_Update.csv > $run_CURRENT-DATE/fv_nacc_complete.txt 2> $run_CURRENT-DATE/fvp_errors.txt

This will place the text files (iv_nacc_complete.txt) in the run folder created earlier, as well as a log of the run that contains any found errors (ivp_errors.txt).

Development

Quickstart

$ git clone https://github.com/ctsit/nacculator.git nacculator
$ cd nacculator
$ python3 -mvenv venv
$ source venv/bin/activate
$ pip install -e .

Files

This is not exhaustive, but here is an explanation of some important files.

• nacc/: top-level Python package for all things NACC.

• nacc/redcap2nacc.py: converts a CSV data file exported from REDCap into NACC's UDS3 fixed-width format.

• nacc/uds3/blanks.py: specialized library for "Blanking Rules".

• nacc/uds3/ivp/forms.py: UDS3 IVP forms represented as Python classes.

• tools/generator.py: generates Python objects based on NACC Data Element Dictionaries in CSV. Used by developers to update the existing forms.py files as necessary.

• nacculator_cfg.ini: configuration file for the filters, built from nacculator_cfg.ini.example in the root nacculator/ directory.

• nacc/run_filters.py and tools/preprocess/run_filters.sh: pulls data from REDCap based on the settings found in nacculator_cfg.ini (for .py) and filters_config.cfg (for .sh).

Testing

To run all the tests:

$ python3 -m unittest

To run only the tests in a file:

$ python3 tests/WHICHEVER_test.py

Generating Forms

Warning: the generator is currently broken due to changes in the CSV format.

You only need to generate forms when there are new DEDs from NACC. The NACCulator install includes the current forms automatically.

Before running the generator, read the warnings in ./nacc/uds3/ivp/forms.py first.

$ python3 tools/generator.py tools/uds3/ded/csv/ >nacc/uds3/ivp/forms.py
$ edit nacc/uds3/ivp/forms.py

Note: execute generator.py from the same folder as the corrected folder, which should contain any "corrected" DEDs.

Resources

• UDS3 forms: https://www.alz.washington.edu/NONMEMBER/UDS/DOCS/VER3/UDS3csvded.html
• NACC forms and documentation: https://www.alz.washington.edu/NONMEMBER/NACCFormsAndDoc.html
• UDS submission site: https://www.alz.washington.edu/MEMBER/sitesub.htm