Drowning, 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; Asphyxia, Drowning, Near
Conditions
Keywords
Danish Drowning Formula, Epidemiology, Drowning, Machine learning, Text search, Utstein, Prehospital, Register, Database, Denmark
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.
Interventions
OTHERDrowning incident
Drowning was defined by the WHO in 2002 as the process of experiencing respiratory impairment from submersion or immersion in liquid.
Sponsors
Prehospital Center, Region Zealand
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of the machine learning algorithm as a drowning identification tool | 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 | 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 | 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 | 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. |
Countries
Denmark
Outcome results
None listed