Respiratory mechanics and patient–ventilator interaction dataset from the ICU

Research output: Contribution to journal › Research article › Contributed › peer-review

Contributors

  • Verónica Santos-Pulpón - , Autonomous University of Barcelona (Author)
  • Francesc Suñol - , Autonomous University of Barcelona, UPC Polytechnic University of Catalonia (Barcelona Tech) (Author)
  • Josefina López-Aguilar - , Autonomous University of Barcelona, Instituto Nacional de Enfermedades Respiratorias (Author)
  • Candelaria de Haro - , Autonomous University of Barcelona, Instituto Nacional de Enfermedades Respiratorias (Author)
  • Sol Fernández-Gonzalo - , Autonomous University of Barcelona, CIBER - Mental Health (Author)
  • Gemma Gomà - , Autonomous University of Barcelona, Instituto Nacional de Enfermedades Respiratorias (Author)
  • Emilio Diaz Santos - , Autonomous University of Barcelona (Author)
  • Robert Huhle - , Department of Anesthesiology and Intensive Care Medicine, Clinical Sensoring and Monitoring, University Hospital Carl Gustav Carus Dresden (Author)
  • Jakob Wittenstein - , Department of Anesthesiology and Intensive Care Medicine, Clinical Sensoring and Monitoring, University Hospital Carl Gustav Carus Dresden (Author)
  • Lluís Blanch - , Autonomous University of Barcelona, Instituto Nacional de Enfermedades Respiratorias (Author)
  • Leonardo Sarlabous - , Autonomous University of Barcelona, Instituto Nacional de Enfermedades Respiratorias (Author)

Abstract

The use of high-resolution data in the intensive care unit (ICU) environment allows for more detailed research into invasive mechanical ventilation (IMV) management and patient–ventilator interactions and their relationship with clinical outcome. The Real-time Agnostic Monitoring for Intensive Care (RAMIC–I) database is a single-center, de-identified dataset containing clinical and physiological data collected from 155 critically ill adults admitted to the ICU of Hospital Universitari Parc Taulí (Sabadell, Spain), between 2015 and 2019. RAMIC–I includes 13,433 hours of mechanical ventilation recorded during the first eight days of IMV, enriched with demographic information, bedside clinical variables (including clinical management variables, administered treatments, and clinical scores), vital signs, and mortality outcomes. Derived variables from respiratory waveforms, including patient-ventilator asynchronies, were aggregated into 10-minute resolution intervals to enable a comprehensive exploration of respiratory physiology during IMV. The dataset is structured according to FAIR data principles and is accessible through a federated repository based on the open-source infrastructure. This detailed resource supports research into predictive modeling, IMV optimization, and the study of patient–ventilator interactions in critical care settings.

Details

Original languageEnglish
Article number1297
JournalScientific data
Volume13
Issue number1
Publication statusPublished - 10 Jun 2026
Peer-reviewedYes

External IDs

PubMed 42270634
ORCID /0000-0003-2185-1819/work/227513976
ORCID /0000-0003-4397-1467/work/227514105