DrowningDROWN_DDF2NCT06310525
Using Machine Learning to Optimise the Danish Drowning Formula
An observational study of Drowning and Drowning and Submersion While in Bath-Tub, run by Prehospital Center, Region Zealand at 1 site in Denmark.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Drowning, Drowning and Submersion While in Bath-Tub, Drowning and Submersion While in Natural Water
- What is being tested?
- Drowning incident
- How many people
- 1,500 participants
- Where
- 1 siteDenmark
- Study dates
- 1 Jan 2024to 31 Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All fatal and non-fatal drowning patients in Denmark treated by the Emergency Medical Services (EMS) between 2016 and 2023.
Who may be able to join
- 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.
Who cannot join
- Duplets
- Invalid civil registration number
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: 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.
Read the 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.
- 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
- Being tested
- Drowning incident
- Keywords
- Danish Drowning Formula, Epidemiology, Drowning, Machine learning, Text search, Utstein, Prehospital, Register, Database, Denmark
- Medical subject headings
- Drowning, Near Drowning, Asphyxia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Drowning incidentOther
Drowning was defined by the WHO in 2002 as "the process of experiencing respiratory impairment from submersion or immersion in liquid".
| Study group | Role | What they receive |
|---|---|---|
| Fatal drowning | Drowning incidents where the patient died within 30 days after the incident as a consequence of the submersion injury Receives: Other: Drowning incident | |
| Non-fatal drowning | Drowning incidents where the patient survived to 30 days Receives: Other: Drowning incident |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Mar 2024
- Study start1 Jan 2024
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for DrowningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Prehospital Center, Region Zealand
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Niklas Breindahl, Region Zealand
- Investigators
- Helle Collatz Christensen, Ass. Prof., Study director, Prehospital Center, Region Zealand
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06310525
- Sponsor's protocol ID
- DROWN_DDF2
- Acronym
- DROWN_DDF2
- Status last verified
- Aug 2025
- First submitted
- 7 Mar 2024
- Last updated
- 27 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The data are intended for use nationally and internationally by researchers to reduce the incidence, mortality, and morbidity of drowning. The data are available upon reasonable request from researchers after application to the corresponding author, provided the necessary approvals are obtained from the relevant authorities.
Publications
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.
PubMed 37619443Breindahl 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.
PubMed 38448994Breindahl N, Bitzer K, Sorensen OB, Wildenschild A, Wolthers SA, Lindskou T, Steinmetz J, Blomberg SNF, Christensen HC; Danish Drowning Validation Group. The Danish Drowning Cohort: Utstein-style data from fatal and non-fatal drowning incidents in Denmark. BMC Med Res Methodol. 2025 Jan 30;25(1):28. doi: 10.1186/s12874-025-02483-8.
PubMed 39885431
Study at a glance
From the registry record, last updated by the sponsor on 27 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: Using Machine Learning to Optimise the Danish Drowning Formula
- ClinicalTrials.gov ID
- NCT06310525
- Acronym / sponsor ID
- DROWN_DDF2 / DROWN_DDF2
- 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
- Keywords
- Danish Drowning Formula, Epidemiology, Drowning, Machine learning, Text search, Utstein, Prehospital, Register
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Prehospital Center, Region ZealandUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 15 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Fatal drowning | – | Other: Drowning incidentDrowning incidents where the patient died within 30 days after the incident as a consequence of the submersion injury |
| Non-fatal drowning | – | Other: Drowning incidentDrowning incidents where the patient survived to 30 days |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All fatal and non-fatal drowning patients in Denmark treated by the Emergency Medical Services (EMS) between 2016 and 2023.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (2 listed)
- Duplets
- Invalid civil registration number
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Drowning incidentDrowning was defined by the WHO in 2002 as "the process of experiencing respiratory impairment from submersion or immersion in liquid". | Other | Fatal drowning, Non-fatal drowning |
Primary outcome
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.
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.
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Aug 2025.
- Background: PubMed 37619443Breindahl 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.
- Background: PubMed 38448994Breindahl 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.
- Derived from this study: PubMed 39885431Breindahl N, Bitzer K, Sorensen OB, Wildenschild A, Wolthers SA, Lindskou T, Steinmetz J, Blomberg SNF, Christensen HC; Danish Drowning Validation Group. The Danish Drowning Cohort: Utstein-style data from fatal and non-fatal drowning incidents in Denmark. BMC Med Res Methodol. 2025 Jan 30;25(1):28. doi: 10.1186/s12874-025-02483-8.
- Individual participant dataSharing planned: No. The data are intended for use nationally and internationally by researchers to reduce the incidence, mortality, and morbidity of drowning. The data are available upon reasonable request from researchers after application to the corresponding author, provided the necessary approvals are obtained from the relevant authorities.
Related trials for Drowning
Recruiting
Water Competency Intervention in Autism
Autism Spectrum Disorders
Recruiting
Drowning-related Acute Respiratory Failure
Drowning
Active, not recruitingObservational study
Primary or Secondary Drowning
Drowning, Drowning; Anoxia
RecruitingObservational study
The Emergency Call on Drowning
Drowning, Drowning; Anoxia
Not yet recruiting
The Effect of Digital and Classical Education Based on Planned Behavior Theory Given to Mothers in the Risk Group on Their Self-Efficacy in Identifying Safety Measures and First Aid for Home Accidents
Pediatric Nursing
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Helle Collatz Christensen, Ass. Prof. study director
Prehospital Center, Region Zealand
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Drowning and Drowning and Submersion While in Bath-Tub, run by Prehospital Center, Region Zealand at 1 site in Denmark.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 27 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Denmark. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06310525, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Aug 2025.