clif-consortium / CLIF

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Common Longitudinal ICU Format (CLIF)

Official Website to the CLIF Consortium - CLIF Consortium

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CLIF Project Workflow Documentation

Introduction

Multicenter critical care research often relies on sharing sensitive patient data across sites, requiring complex data use agreements (DUAs) and yielding redundant work to account for diverse data infrastructures. Common data models (CDMs) like the Observational Medical Outcomes Partnership can allow for standardized analysis scripts, mitigating these challenges but requiring advanced data engineering skills and risking a loss of crucial granular clinical information. To overcome these barriers, we present the Common Longitudinal ICU Format (CLIF), designed specifically for observational studies of critically ill patients across multiple centres. Through CLIF, we aim to streamline data organization into a longitudinal format and establish standard vocabularies to facilitate standardized analysis scripts and improve data readability for researchers and clinicians.

The CLIF consortium, comprising critical care researchers from eight US academic health systems, collaboratively developed CLIF's schema, clinical vocabularies, and "proof of concept" datasets. CLIF’s tables emphasize care processes, clinical outcomes, and granular clinical physiology measures.

This README file contains detailed instructions on how to set up your heathcare system's EHR data into the Relational CLIF format. The repository also provides a detailed data dictionary for each table in the R-CLIF database, along with the required limited vocabulary defined by clinical experts in the consortium.

Relational CLIF

In an iterative and ongoing fashion, we developed CLIF's schema, contents, and limited clinical vocabularies through collective discussion to consensus. The consortium's broad use case of clinical research on critically ill patients focused the decision-making on care processes, outcomes, and granular measures of clinical physiology. The primary development outcomes were (1) an initial schema, (2) a limited vocabulary set for important clinical variables, and (3)"proof of concept" datasets to demonstrate usability and interoperability.

To develop a structured relational database, we initiated a comprehensive data collection and cleaning effort at the eight health systems. We designed CLIF as an encounter-centric relational database with a clinically determined limited vocabulary for vitals, laboratory names, medications, patient locations, and respiratory device names. By consensus, we determined that CLIF would prioritize (1) completeness of routine clinical data, (2) temporal granularity, and (3) consistently measured clinical outcomes. The entity-relationship diagram from relational CLIF is presented below as a human-readable and clinically meaningful flow of information. Tables are organized into clinically relevant column categories (demographics, objective measures, respiratory support, orders and inputs-outputs)

Updated ERD and data dictionary

Each CLIF table has an assigned clinician who serves as the primary point of contact for any inquiries related to the schema or common data elements specific to that table.

Tables Point of Contact Email GitHub Username
patient Pat Lyons, MD lyonspa@ohsu.edu plyons
hospitalization Nicholas Ingraham, MD ingra107@umn.edu ingra107
admission_diagnosis J.C. Rojas, MD, MS juan_rojas@rush.edu sajor2000
provider Nicholas Ingraham, MD ingra107@umn.edu ingra107
adt Nicholas Ingraham, MD ingra107@umn.edu ingra107
vitals Catherine Gao, MD catherine.gao@northwestern.edu cloverbunny
scores Snigdha Jain, MD snigdha.jain@yale.edu snigdhajainyale
dialysis Jay Koyner, MD jkoyner@uchicago.edu
intake_output
procedures J.C. Rojas, MD juan_rojas@rush.edu sajor2000
therapy_session William Parker, MD, PhD & Bhakti Patel, MD wparker@uchicago.edu; bpatel@bsd.uchicago.edu 08wparker
therapy_details William Parker, MD, PhD & Bhakti Patel, MD wparker@uchicago.edu; bpatel@bsd.uchicago.edu 08wparker
respiratory_support Nicholas Ingraham, MD ingra107@umn.edu ingra107
position Chad Hochberg, MD chochbe1@jh.edu chochbe1
ecmo_mcs Nicholas Ingraham, MD ingra107@umn.edu ingra107
labs Catherine Gao, MD catherine.gao@northwestern.edu cloverbunny
microbiology_culture Kevin Buell, MBBS kevin.buell@uchicagomedicine.org
microbiology_nonculture Kevin Buell, MBBS kevin.buell@uchicagomedicine.org
sensitivity Kevin Buell, MBBS kevin.buell@uchicagomedicine.org
medication_orders Anna Barker, MD, PhD baanna@med.umich.edu baanna23
medication_admin_intermittent Anna Barker, MD, PhD baanna@med.umich.edu baanna23
medication_admin_continuous Chad Hochberg, MD chochbe1@jh.edu chochbe1

Data Architecture

One of CLIF's key contributions is an open-source web application that enables users to convert a relational database into a longitudinal dataset with custom time intervals, select study-specific variables, and choose a preferred programming language. This facilitates straightforward data processing and enables effortless cross-center comparisons and integrations, bypassing the need for DUAs when analytic queries do not need pooled patient-level data. CLIF's deployment across four of the planned eight health systems has successfully compiled a robust ICU encounter-centric relational database, documenting 87,120 ICU admissions and capturing data from 71,190 unique patients.

Diagram_CLIF_ATS_v3.jpg

Current team members

Clinicians

Data Scientists

Collaborators