ArticleIntensive care medicine2025
A common longitudinal intensive care unit data format (CLIF) for critical illness research.
Article in Intensive care medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- PAUSE-Agents: A Clinician-in-the-Loop Multi-Agent AI Pipeline for ICU-to-Ward Handoff Briefs.medRxiv : the preprint server for health sciences · 2026Article
- Prone Positioning in a North American Cohort of Hypoxemic Patients on Mechanical Ventilation.Critical care medicine · 2026Observational
- Reproducible clinical archetypes in acute respiratory failure: a multi-cohort trajectory analysis.Intensive care medicine · 2026Article
- The future of heart transplant data science requires common data models.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2026Article
- Longitudinal dynamics of gut microbiota and mycobiota in pneumonia-derived sepsis: evidence of taxonomic stability and trans-kingdom network reorganization.Gut pathogens · 2026Article
- It Is Time to Move Beyond Task-Specific Critical Care Prediction Models and Prioritize a Foundation Model Built on Continuous Physiological Data.Critical care medicine · 2026Article
- Federation, not centralization: a new paradigm for electronic health record-based critical care research.Annals of the American Thoracic Society · 2026Article
- An Electronic Health Record-Based Classifier for Moderate-to-Severe Acute Respiratory Distress Syndrome With Persistent Hypoxemia.Critical care medicine · 2026Observational
- Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2).Critical care medicine · 2026Article
- Data-driven spatial-temporal framework for exploring the heterogeneity and temporality of sepsis.Chinese medical journal · 2026Review
- Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Characteristics and Outcomes of Patients With Hematologic Malignancies Hospitalized With Respiratory Viral Infections.Open forum infectious diseases · 2026Article
- Modernizing the national transplant database.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Review
- Deep Learning-Based 30-Day Mortality Prediction in Critically Ill Bone and Bone Marrow Metastasis Patients: A Multicenter Retrospective Cohort Study.Current oncology (Toronto, Ont.) · 2025Article
- Prone Positioning in a North American Cohort of Hypoxemic Patients on Mechanical Ventilation.medRxiv : the preprint server for health sciences · 2025Article
- Correspondence on "A common longitudinal intensive care unit data format (CLIF) for critical illness research". Author's reply.Intensive care medicine · 2025Article
- Article
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Predictions With a Purpose: Elevating Standards for Clinical Modeling Research.Critical care explorations · 2025Article
Corrections and comments
- Erratum issued
- Update of
Authors and funding
28 authors.
Funding
Abstract
rationaleCritical illness threatens millions of lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness.
objectivesOvercome the data management, security, and standardization barriers to large-scale critical illness EHR studies.
methodsWe developed a Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format to harmonize EHR data necessary to study critical illness. We conducted proof-of-concept studies with a federated research architecture: (1) an external validation of an in-hospital mortality prediction model for critically ill patients and (2) an assessment of 72-h temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models. MEASUREMENTS AND MAIN
resultsWe converted longitudinal data from 111,440 critically ill patient admissions from 2020 to 2021 (mean age 60.7 years [standard deviation 17.1], 28% Black, 7% Hispanic, 44% female) across 9 health systems and 39 hospitals into CLIF databases. The in-hospital mortality prediction model had varying performance across CLIF consortium sites (AUCs: 0.73-0.81, Brier scores: 0.06-0.10) with degradation in performance relative to the derivation site. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality.
conclusionsCLIF enables transparent, efficient, and reproducible critical care research across diverse health systems. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational artificial intelligence (AI) models of critical illness, and real-time critical care quality dashboards.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.