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Rationale <p>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.</p> Objectives <p>Overcome the data management, security, and standardization barriers to large-scale critical illness EHR studies.</p> Methods <p>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.</p> Measurements and main results <p>We 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.</p> Conclusions <p>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.</p>

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A common longitudinal intensive care unit data format (CLIF) for critical illness research

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Juan C. Rojas,
  • Patrick G. Lyons,
  • Kaveri Chhikara,
  • Vaishvik Chaudhari,
  • Sivasubramanium V. Bhavani,
  • Muna Nour,
  • Kevin G. Buell,
  • Kevin D. Smith,
  • Catherine A. Gao,
  • Saki Amagai,
  • Chengsheng Mao,
  • Yuan Luo,
  • Anna K. Barker,
  • Mark Nuppnau,
  • Michael Hermsen,
  • Jay L. Koyner,
  • Haley Beck,
  • Rachel Baccile,
  • Zewei Liao,
  • Kyle A. Carey,
  • Brenna Park-Egan,
  • Xuan Han,
  • Alexander C. Ortiz,
  • Benjamin E. Schmid,
  • Gary E. Weissman,
  • Chad H. Hochberg,
  • Nicholas E. Ingraham,
  • William F. Parker

摘要

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], 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.

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.