Ongoing Projects within the CLIF consortium

The CLIF consortium is a network of NIH funded investigators.

Principal Investigators (PIs) in the consortium are supported by a wide range of NIH grants that use big data and advanced ML techniques to answer open scientific questions related to critical illness. Each of these projects would be better with CLIF rather than done in silos.

Improving the efficiency and equity of critical care allocation during a crisis with place-based disadvantage indices

  • CLIF Project Description: Creating a life support allocation triage score for crisis standards of care
  • Grant: R01 LM014263
  • PI: William Parker, University of Chicago

Identifying Areas to Improve ICU Outcomes through Provider Variation

  • CLIF Project Description: Studying hospital variation in low-tidal volume ventilation across hospital systems
  • Grant: K23 HL166783
  • PI: Nicholas Ingraham, University of Minnesota

Identification of Precision Sepsis Subphenotypes Using Vital Sign Trajectories

  • CLIF Project Description: Validating the association of temperature trajectories and ICU mortality across hospital systems
  • Grant: K23 GM144867
  • PI: Sivasubramanium Bhavani, Emory University

Sustainable Implementation of Prone Positioning for the Acute Respiratory Distress Syndrome

  • CLIF Project Description: Studying the association between proning practice patterns and ARDS survival
  • Grant: K23 HL169743
  • PI: Chad Hochberg, John Hopkins University

Early Mobilization: Operationalizing Big Data & Implementation Science to Lead Expansion to ICUs (E-MOBILE-ICU)

  • CLIF Project Description: Identifying critically ill patients who are eligible for mobilization using the electronic healthcare record
  • Grant: K23 HL148387
  • PI: Bhakti Patel, University of Chicago

Machine learning to predict cure in severe pneumonia episodes

  • CLIF Project Description: Prediction of severe pneumonia using machine learning
  • Grant: K23HL169815
  • PI: Catherine Gao, Northwestern University

SPOT-IT: Sepsis Prediction in Oncology Through Implementation Science and Technology

  • CLIF Project Description: Develop an oncology-specific sepsis prediction model using EHR data and human-centered design
  • Grant: K08CA270383
  • PI: Patrick Lyons, Oregon Health and Science University