Using Machine Learning to Optimise the Danish Drowning Formula

NCT06310525 | Active Not Recruiting

• 1 other identifier: DROWN_DDF2
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Danish Drowning Formula; Epidemiology; Drowning; Machine learning; Text search; Utstein; Prehospital; Register; Database; Denmark

Outcome Measures

Primary Outcomes (4)

• Sensitivity of the machine learning algorithm as a drowning identification tool

  Sensitivity \[TP / (TP+FN)\] will be calculated to show the performance of the machine learning as a drowning identification tool.

  The sensitivity of the trained machine learning will be calculated based on data from 2022 and 2023.

• Specificity of the machine learning algorithm as a drowning identification tool

  Specificity \[TN / (FP+TN)\] will be calculated to show the performance of the machine learning as a drowning identification tool.

  The specificity of the trained machine learning will be calculated based on data from 2022 and 2023.

• PPV of the machine learning algorithm

  PPV \[TP / (TP+FP)\] will be calculated to show the machine learning test result.

  The PPV of the trained machine learning will be calculated based on data from 2022 and 2023.

• NPV of the machine learning algorithm

  NPV \[TN / (FN+TN)\] will be calculated to show the machine learning test result.

  The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.

Study Arms (2)

Fatal drowning

Drowning incidents where the patient died within 30 days after the incident as a consequence of the submersion injury

Other: Drowning incident

Non-fatal drowning

Drowning incidents where the patient survived to 30 days

Other: Drowning incident

Interventions

Drowning incident OTHER

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

Fatal drowning; Non-fatal drowning

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All fatal and non-fatal drowning patients in Denmark treated by the Emergency Medical Services (EMS) between 2016 and 2023.

You may qualify if:

• The patient must have been experiencing respiratory impairment from submersion or immersion in liquid (including persistent coughing, respiratory arrest, and unconsciousness).
• The patient must have been in contact with the Danish prehospital Emergency Medical Services.

You may not qualify if:

• Duplets
• Invalid civil registration number

Sponsors & Collaborators

• Prehospital Center, Region Zealand: lead

Study Sites (1)

Prehospital Center

Næstved, Region Sjælland, 4700, Denmark

Location

Related Publications (2)

• Breindahl N, Wolthers SA, Jensen TW, Holgersen MG, Blomberg SNF, Steinmetz J, Christensen HC; Danish Cardiac Arrest Group. Danish Drowning Formula for identification of out-of-hospital cardiac arrest from drowning. Am J Emerg Med. 2023 Nov;73:55-62. doi: 10.1016/j.ajem.2023.08.024. Epub 2023 Aug 15.

  PMID: 37619443 BACKGROUND

• Breindahl N, Wolthers SA, Moller TP, Blomberg SNF, Steinmetz J, Christensen HC; Danish Drowning Validation Group. Characteristics and critical care interventions in drowning patients treated by the Danish Air Ambulance from 2016 to 2021: a nationwide registry-based study with 30-day follow-up. Scand J Trauma Resusc Emerg Med. 2024 Mar 6;32(1):17. doi: 10.1186/s13049-024-01189-y.

  PMID: 38448994 BACKGROUND

MeSH Terms

Conditions

Drowning; Near Drowning; Asphyxia

Condition Hierarchy (Ancestors)

Death; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Wounds and Injuries

Study Officials

• Helle Collatz Christensen, Ass. Prof.

  Prehospital Center, Region Zealand

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 7, 2024
• First Posted: March 15, 2024
• Study Start: January 1, 2024
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: August 27, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

DK (1)