Clinical trial NCT06310525: Using Machine Learning to Optimise the Danish Drowning Formula
Official title: Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: Using Machine Learning to Optimise the Danish Drowning Formula
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
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
Lead sponsor: Prehospital Center, Region Zealand
Sponsor class: OTHER
First posted: 2024-03-15
Enrollment: 1500
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Sensitivity of the machine learning algorithm as a drowning identification tool
timeFrame: The sensitivity of the trained machine learning will be calculated based on data from 2022 and 2023.
measure: Specificity of the machine learning algorithm as a drowning identification tool
timeFrame: The specificity of the trained machine learning will be calculated based on data from 2022 and 2023.
measure: PPV of the machine learning algorithm
timeFrame: The PPV of the trained machine learning will be calculated based on data from 2022 and 2023.
measure: NPV of the machine learning algorithm
timeFrame: The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.
Secondary outcomes:
Other outcomes:
Locations:
city: Næstved
country: Denmark
facility: Prehospital Center
state: Region Zealand
Study officials:
affiliation: Prehospital Center, Region Zealand
name: Helle Collatz Christensen, Ass. Prof.
role: STUDY_DIRECTOR
References:
citation: 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.
citation: 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.