⚡ Quick Summary
The development of the Common Longitudinal Intensive Care Unit (CLIF) data format aims to enhance critical illness research by harmonizing electronic health record (EHR) data. This initiative has already shown promising results in predicting in-hospital mortality and analyzing temperature trajectories in critically ill patients.
🔍 Key Details
- 📊 Dataset: 111,440 critically ill patient admissions from 2020 to 2021
- 🧩 Features used: Longitudinal EHR data across 9 health systems and 38 hospitals
- ⚙️ Technology: Open-source CLIF database format
- 🏆 Performance: In-hospital mortality prediction model AUCs: 0.73-0.81
🔑 Key Takeaways
- 📊 CLIF format standardizes EHR data for critical illness research.
- 💡 Proof-of-concept studies validate its effectiveness in mortality prediction.
- 👩🔬 Data from a diverse patient population enhances the robustness of findings.
- 🏆 Mortality prediction model shows varying performance across different health systems.
- 🌡️ Temperature trajectories reveal significant associations with patient outcomes.
- 🤖 Future applications include AI models and real-time quality dashboards.
- 🌍 Research conducted across multiple health systems enhances generalizability.
📚 Background
Critical illness is a major health challenge, affecting millions globally each year. The integration of electronic health records (EHR) into research can provide valuable insights into patient care and treatment outcomes. However, barriers such as data management, security, and standardization have hindered large-scale studies. The introduction of the CLIF format seeks to address these challenges, paving the way for more effective research in critical care.
🗒️ Study
The study involved the creation of the Common Longitudinal Intensive Care Unit (CLIF) data format, designed to harmonize EHR data for critical illness research. Researchers conducted proof-of-concept studies, including an external validation of a mortality prediction model and an analysis of temperature trajectories in critically ill patients. The dataset comprised admissions from 111,440 patients across various health systems, providing a comprehensive view of critical illness.
📈 Results
The mortality prediction model demonstrated an AUC range of 0.73 to 0.81, indicating varying performance across different sites. Additionally, the study found that patients with hypothermic and hyperthermic-slow-resolver temperature trajectories had the highest mortality rates. These findings underscore the importance of temperature monitoring in critical care settings.
🌍 Impact and Implications
The CLIF format represents a significant advancement in critical care research, enabling more transparent and reproducible studies across diverse health systems. Its potential applications, including AI-driven models and real-time quality dashboards, could transform how we approach critical illness management and improve patient outcomes. This initiative not only enhances research capabilities but also fosters collaboration among health systems.
🔮 Conclusion
The introduction of the Common Longitudinal Intensive Care Unit (CLIF) data format marks a pivotal step in critical illness research. By harmonizing EHR data, researchers can gain deeper insights into patient care and treatment efficacy. The future of critical care research looks promising, with the potential for innovative applications that could significantly improve patient outcomes. Continued exploration in this field is essential for advancing healthcare practices.
💬 Your comments
What are your thoughts on the CLIF data format and its implications for critical illness research? We would love to hear your insights! 💬 Leave your comments below or connect with us on social media:
A common longitudinal intensive care unit data format (CLIF) for critical illness research.
Abstract
RATIONALE: Critical 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.
OBJECTIVES: Overcome the data management, security, and standardization barriers to large-scale critical illness EHR studies.
METHODS: We 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 RESULTS: We converted longitudinal data from 111,440 critically ill patient admissions from 2020 to 2021 (mean age 60.7 years [standard deviation 17.1], 31% Black, 6% Hispanic, 44% female) across 9 health systems and 38 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.
CONCLUSIONS: CLIF 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.
Author: [‘Rojas JC’, ‘Lyons PG’, ‘Chhikara K’, ‘Chaudhari V’, ‘Bhavani SV’, ‘Nour M’, ‘Buell KG’, ‘Smith KD’, ‘Gao CA’, ‘Amagai S’, ‘Mao C’, ‘Luo Y’, ‘Barker AK’, ‘Nuppnau M’, ‘Hermsen M’, ‘Koyner JL’, ‘Beck H’, ‘Baccile R’, ‘Liao Z’, ‘Carey KA’, ‘Park-Egan B’, ‘Han X’, ‘Ortiz AC’, ‘Schmid BE’, ‘Weissman GE’, ‘Hochberg CH’, ‘Ingraham NE’, ‘Parker WF’]
Journal: Intensive Care Med
Citation: Rojas JC, et al. A common longitudinal intensive care unit data format (CLIF) for critical illness research. A common longitudinal intensive care unit data format (CLIF) for critical illness research. 2025; (unknown volume):(unknown pages). doi: 10.1007/s00134-025-07848-7
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