An observational study in Drowning, Drowning and Submersion While in Bath-Tub and Drowning and Submersion While in Natural Water, sponsored by Prehospital Center, Region Zealand. Active, not recruiting at 1 site in Denmark. Per ClinicalTrials.gov, last updated 2025-08-27.
Sponsored by Prehospital Center, Region Zealand · Observational
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.
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.
21 studies on the registry are indexed under Drowning; 6 are open to participants now.
This study's planned enrollment of 1,500 is above the median of 1,099 across 14 observational studies indexed under Drowning.
Browse Drowning studies →Prehospital Center, Region Zealand is the lead sponsor of 16 studies on the registry; 4 are open to participants now.
Counted across the registry records on this site, refreshed daily.
All fatal and non-fatal drowning patients in Denmark treated by the Emergency Medical Services (EMS) between 2016 and 2023.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Drowning incidents where the patient died within 30 days after the incident as a consequence of the submersion injury
Other: Drowning incident
Drowning incidents where the patient survived to 30 days
Other: Drowning incident
Drowning was defined by the WHO in 2002 as "the process of experiencing respiratory impairment from submersion or immersion in liquid".
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.
Plan to share: 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.
Started Jan 2024 (actual). Primary completion is estimated for Dec 2025, 23 months after the start.
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This study is active, not recruiting, as verified in Aug 2025. You cannot join it, but the record below documents what was studied.
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Prehospital Center, Region Zealand