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Active, Not Recruiting

NCT Number: NCT06310525

Using Machine Learning to Optimise the Danish Drowning Formula

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Drowning Asphyxia Death Drowning and Nonfatal Submersion Drowning and Submersion Due to Fall Off Ship Drowning and Submersion While in Bath-Tub Drowning and Submersion While in Natural Water Drowning and Submersion While in Swimming-Pool Drowning, Near Drowning; Asphyxia Near Drowning Pathologic Processes Pathological Conditions, Signs and Symptoms Wounds and Injuries

Sex eligibility

All sexes

Study type

Observational

Primary location

Prehospital Center

Næstved, Region Sjælland, 4700, Denmark

About this study

The DDF was published in 2023. It is a text-search algorithm designed to search the unstructured text fields in databases containing electronic medical records to identify all potential water-related incidents. The DDF consists of numerous trigger words related to submersion injury (e.g., "drukn"/ drown, "vand"/water, "hav"/ocean, and "båd"/ boat).

An ongoing study showed impressive performance metrics of the DDF as a drowning identification tool when applied to the Danish PEMR on unrestricted terms. However, the PPV was low for detecting drowning incidents specifically. This study aims to augment the DDF's positive predictive value and reduce the temporal demands associated with manual validation.

Data are extracted from the Danish nationwide Prehospital Electronic Medical Record using the DDF and manually validated before entered into the Danish Prehospital Drowning Data (DPDD).

Data from the DPDD from 2016-2021 will be split into 80% (training data) and 20% (test data) and used to train the machine learning.

Data from the DPDD from 2022-2023 will be used as validation data to calculate the performance metrics for the machine learning.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • Duplets
  • Invalid civil registration number

Treatment and study plan

Drowning incident

Other

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

Primary outcomes

  1. Sensitivity of the machine learning algorithm as a drowning identification tool

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

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

  2. Specificity of the machine learning algorithm as a drowning identification tool

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

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

  3. PPV of the machine learning algorithm

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

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

  4. NPV of the machine learning algorithm

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

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

Sponsors and collaborators

Lead sponsor

Prehospital Center, Region Zealand

Other

Registry information

Official study title

Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: Using Machine Learning to Optimise the Danish Drowning Formula

Acronym: DROWN_DDF2

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Mar 15, 2024
Registry last updated
Aug 27, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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