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From Clinical Laboratory Results to REDCap: An Automated Workflow for Longitudinal CAR-T Research Data.

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Summary

Researchers developed a workflow to automatically process Excel lab results for clinical research. This system integrates with REDCap, transforming raw data into analysis-ready longitudinal datasets efficiently and audibly.

Keywords:
Data integrationREDCapdata automationdata sharinglab data

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Area of Science:

  • Biomedical Informatics
  • Clinical Data Management
  • Translational Research

Background:

  • Clinical research often relies on REDCap for cohort management.
  • Laboratory results are frequently received as large Excel files, hindering analysis.
  • Manual processing of lab data is time-consuming and error-prone.

Purpose of the Study:

  • To develop an automated workflow for processing laboratory results from Excel exports into REDCap.
  • To enable efficient and auditable access to longitudinal laboratory data for clinical research.

Main Methods:

  • A Django-based drag-and-drop upload system was created.
  • An R-based mapper was developed to link Excel files to REDCap records.
  • The REDCap API was utilized to extract and populate selected laboratory parameters into repeating instruments.

Main Results:

  • A prototype system was successfully built and validated at LMU Klinikum.
  • The workflow processed data for 242 CAR-T patients, extracting 65 laboratory parameters.
  • The system demonstrated rapid and auditable access to routine laboratory data.

Conclusions:

  • The developed end-to-end ingest workflow transforms impractical Excel exports into analysis-ready longitudinal datasets.
  • This solution enhances the utility of REDCap in clinical research by streamlining laboratory data integration.
  • The system facilitates efficient, auditable, and rapid access to routine laboratory results for research purposes.