DOI: 10.5061/dryad.rjdfn2zcm
title: Generalizable EHR-R-REDCap pipeline for a national multi-institutional rare tumor patient registry

description: Objective: To develop a clinical informatics pipeline designed to capture
 large-scale structured EHR data for a national patient registry. Materials
 and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical
 software to remap and import structured EHR data into the REDCap-based
 multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an
 adaptable data dictionary. Results: Clinical laboratory data were
 extracted from EPIC Clarity across several participating institutions.
 Labs were transformed, remapped and imported into the MCC registry using
 the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests
 encompassing 482,450 results were imported into the registry for 1,109
 enrolled MCC patients. Data-quality assessment revealed highly accurate,
 valid labs. Univariate modeling was performed for labs at baseline on
 overall survival (N=176) using this clinical informatics pipeline.
 Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR
 data is successfully transformed, and bulk-loaded/imported into a
 REDCap-based national registry to execute real-world data analysis and
 interoperability.
description: <b>eLAB Development and Source Code (R statistical
 software):</b> eLAB is written in R (version
 4.0.3), and utilizes the following packages for processing: DescTools,
 REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and
 tidyverse. Source code for eLAB can be downloaded directly
 (https://github.com/TheMillerLab/eLAB). eLAB reformats
 EHR data abstracted for an identified population of patients (e.g. medical
 record numbers (MRN)/name list) under an Institutional Review Board
 (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB
 converts these to MCCPR assigned record identification numbers (record_id)
 before import for de-identification. Functions were
 written to remap EHR bulk lab data pulls/queries from several sources
 including Clarity/Crystal reports or institutional EDW including Research
 Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of
 labs for user-defined patients, may vary. Thus, users may need to adapt
 the initial data wrangling script based on the data input format. However,
 the downstream transformation, code-lab lookup tables, outcomes analysis,
 and LOINC remapping are standard for use with the provided REDCap Data
 Dictionary, DataDictionary_eLAB.csv.  The available R-markdown
 ((https://github.com/TheMillerLab/eLAB) provides suggestions and
 instructions on where or when upfront script modifications may be
 necessary to accommodate input variability. The eLAB
 pipeline takes several inputs. For example, the input for use with the
 ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R
 object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3)
 Collection Time, and 4) Lab Results wherein several lab panels are in one
 data frame cell. A mock dataset in this ‘untidy-format’ is provided for
 demonstration purposes (https://github.com/TheMillerLab/eLAB).
 Bulk lab data pulls often result in subtypes of the same lab. For
 example, potassium labs are reported as “Potassium,” “Potassium-External,”
 “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,”
 “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a
 key-value lookup table with ~300 lab subtypes for remapping labs to the
 Data Dictionary (DD) code. eLAB reformats/accepts only those lab units
 pre-defined by the registry DD. The lab lookup table is provided for
 direct use or may be re-configured/updated to meet end-user
 specifications. eLAB is designed to remap, transform, and filter/adjust
 value units of semi-structured/structured bulk laboratory values data
 pulls from the EHR to align with the pre-defined code of the DD.
 <b>Data Dictionary (DD)</b> EHR
 clinical laboratory data is captured in REDCap using the ‘Labs’ repeating
 instrument (Supplemental Figures 1-2). The DD is provided for use by
 researchers at REDCap-participating institutions and is optimized to
 accommodate the same lab-type captured more than once on the same day for
 the same patient. The instrument captures 35 clinical lab types. The DD
 serves several major purposes in the eLAB pipeline. First, it defines
 every lab type of interest and associated lab unit of interest with a set
 field/variable name. It also restricts/defines the type of data allowed
 for entry for each data field, such as a string or numerics. The DD is
 uploaded into REDCap by every participating site/collaborator and ensures
 each site collects and codes the data the same way. Automation pipelines,
 such as eLAB, are designed to remap/clean and reformat data/units
 utilizing key-value look-up tables that filter and select only the
 labs/units of interest. eLAB ensures the data pulled from the EHR contains
 the correct unit and format pre-configured by the DD. The use of the same
 DD at every participating site ensures that the data field code, format,
 and relationships in the database are uniform across each site to allow
 for the simple aggregation of the multi-site data. For example, since
 every site in the MCCPR uses the same DD, aggregation is efficient and
 different site csv files are simply combined<b>.</b>
 <b>Study Cohort </b> This study
 was approved by the MGB IRB. Search of the EHR was performed to identify
 patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in
 the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019
 (N= 176) were included in the test cohort for exploratory studies of lab
 result associations with overall survival (OS) using eLAB.
 <b>Statistical Analysis</b> OS is
 defined as the time from date of MCC diagnosis to date of death. Data was
 censored at the date of the last follow-up visit if no death event
 occurred. Univariable Cox proportional hazard modeling was performed among
 all lab predictors. Due to the hypothesis-generating nature of the work,
 p-values were exploratory and Bonferroni corrections were not
 applied.