Using Machine Learning to Optimise the Danish Drowning Formula (DROWN_DDF2)

Prehospital Center, Region Zealand

Status: Active, not recruiting

Conditions: Drowning and Submersion While in Natural Water; Drowning and Submersion While in Bath-Tub; Drowning, Near; Drowning and Nonfatal Submersion; Drowning; Drowning and Submersion While in Swimming-Pool; Drowning; Asphyxia; Drowning and Submersion Due to Fall Off Ship

Treatments: Other: Drowning incident

Study type: Observational

Funder types: Other

Identifiers

NCT06310525

DROWN_DDF2

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 1,500 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

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

Trial design

1,500 participants in 2 patient groups

Fatal drowning

Description:

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

Treatment:

Other: Drowning incident

Non-fatal drowning

Description:

Drowning incidents where the patient survived to 30 days

Treatment:

Other: Drowning incident