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Using Machine Learning to Optimise the Danish Drowning Formula

Study acronym: DROWN_DDF2
StatusActive, Not Recruiting
PhaseNot specified
Started
View on ClinicalTrials.gov ↗
Record status
This record was last updated August 27, 2025 (before its estimated December 31, 2025 completion). Its status may not reflect the trial's current state.

Record History

  1. Primary and study completion dates: Dec 31, 2024 to Dec 31, 2025v4
  2. 2 versions Re-verified since Mar 21, 2024, no change to tracked fieldsv2-v3
  3. Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
The Danish Drowning Formula (DDF) was designed to search the unstructured text fields in the Danish nationwide Prehospital Electronic Medical Record on unrestricted terms with comprehensive search criteria to identify all potential water-related incidents and achieve a high sensitivity. This was important as drowning is a rare occurrence, but it resulted in a low Positive Predictive Value for detecting drowning incidents specifically. This study aims to augment the positive predictive value of the DDF and reduce the temporal demands associated with manual validation.
Trial Details
NCT Number NCT06310525
Lead Sponsor Prehospital Center, Region Zealand
Conditions
(from sponsor)		 Drowning, Drowning and Submersion While in Bath-Tub, Drowning and Submersion While in Natural Water, Drowning and Submersion While in Swimming-Pool, Drowning and Submersion Due to Fall Off Ship, Drowning and Nonfatal Submersion, Drowning, Near, Drowning; Asphyxia
Related terms Asphyxia, Drowning, Near Drowning
Enrollment 1,500 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov