ClinCapture & Laboratory equipment integration for study

General Server Forum (Inactive)
ClinCapture & Laboratory equipment integration for study scott  2018-03-03 08:09
Status: Closed
 
Hello,

In the midst of integrating ClinCapture with Labkey for an upcoming clinical study. Currently we are performing test where we export normalized CRF data from ClinCapture (either in xls or other format) and then import into Labkey. We have split demographic and medical history information into two separate Labkey datasets (clinical demographic fields --> labkey demographic dataset due to the one-to-one primary key limitations of subjectID), however we are not sure about the proper way to handle the normalized medical history information (one-to-many, e.g. subjectID may have multiple entries). Would this be best handled as a clinical dataset or an assay dataset?

Then, then next issue - which is related - is that Clincapture exports are cumulative (all completed CRFs are included for each export), which makes the Labkey importing process tricky. Have others dealt with this before, how is the best way to manage this information.

Finally, we want to consolidate multiple analytical results (Flow, LCMS, ELISA, etc..) for each patient back in Labkey - presumably these are assay datasets, but I am worried that when we import the next batch of patients, we may wipe out the earlier results.

The ultimate goal is to enable a robust data structure for subsequent informatics (via R) across all of these dimensions for analysis.

Hopefully this seeming common scenario has been figured out by the Labkey community and someone can guide me through the study setup / assay dataset configuration process properly.

Thanks!
 
 
Jon (LabKey DevOps) responded:  2018-03-09 13:08
Hi Scott,

Datasets are exclusively a study thing, so there really isn't an "assay dataset" unless you're talking about a dataset that was created by an assay via the Copy To Study function (https://www.labkey.org/Documentation/wiki-page.view?name=publishAssayData).

When it comes to a one-to-many situation with your data, a dataset would be able to handle this since other than the demographics dataset which holds the subject, other datasets can have the individual medical history for those subjects (i.e. the multiple entries). If you take a look at our study demo and set it up locally, you can see this in action where it has a demographic dataset that contains the subjects, and other datasets like LabResults that show the subjects individual results on multiple rows. (https://www.labkey.org/Documentation/wiki-page.view?name=studySetupManual)

With regard to cumulative data where you have all the previous data AND new data to go with it, your only options are to either:

- Strip out the old data that already exists and only import in the new data from your import file
- Delete the data out of the dataset and then import in the entire file

The above is this way via the UI since the system does not reconcile duplicates in datasets. However, if you have direct access to your ClinCapture database, you could potentially build your own ETL module, which would allow you to not have to manually import this information in and it would even handle merging of data on those datasets (https://www.labkey.org/Documentation/wiki-page.view?name=etlSchedule#target)

With multiple analytical results from assays, if you have the same subjectIds and those match up to the ones in the study, that should all aggregate within the individual Participant View for each subject. For example: https://www.labkey.org/home/Demos/Study/demo/study-participant.view?participantId=249318596

Additionally, I would highly recommend discussing your future plans with LabKey with our Sales Team. I understand that Jason has been in contact with you and the deeper you go into using the LabKey platform, it may require some special solutions that would go well beyond our Community Forums and our fantastic docs on LabKey.org. Many of our satisfied clients using the Premium Edition of LabKey have made some wonderful strides in scaling up in their studies, assays, and workflows, even creating special unique modules that allow them to work better and more efficiently in their research! I'll ask Jason to follow up with you!

Regards,

Jon