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Using Machine Learning to Optimise the Danish Drowning Formula

NCT06310525 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2025-08-27

No results posted yet for this study

Summary

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.

Conditions

  • 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

Interventions

OTHER

Drowning incident

Drowning was defined by the WHO in 2002 as "the process of experiencing respiratory impairment from submersion or immersion in liquid".

Sponsors & Collaborators

  • Prehospital Center, Region Zealand

    lead OTHER

Principal Investigators

  • Helle Collatz Christensen, Ass. Prof. · Prehospital Center, Region Zealand

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-01
Primary Completion
2025-12-31
Completion
2025-12-31

Countries

  • Denmark

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06310525 on ClinicalTrials.gov