Using Machine Learning to Optimise the Danish Drowning Formula

Drowning Clinical Trial

— DROWN_DDF2  

Official title:

Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: Using Machine Learning to Optimise the Danish Drowning Formula

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06310525
• Other study ID #: DROWN_DDF2
• Status: Active, not recruiting
• Start date: January 1, 2024
• Est. completion date: December 31, 2024

Study information

• Verified date: March 2024
• Source: Prehospital Center, Region Zealand
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 1500
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Asphyxia
• Drowning

Intervention

Other:
• Drowning incident
Drowning was defined by the WHO in 2002 as "the process of experiencing respiratory impairment from submersion or immersion in liquid".

Locations

Country	Name	City	State
Denmark	Prehospital Center	Næstved	Region Zealand

Sponsors (1)

Lead Sponsor	Collaborator
Prehospital Center, Region Zealand

Country where clinical trial is conducted

Denmark, 

References & Publications (2)

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. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	The sensitivity of the trained machine learning will be calculated based on data from 2022 and 2023.
Primary	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.	The specificity of the trained machine learning will be calculated based on data from 2022 and 2023.
Primary	PPV of the machine learning algorithm	PPV [TP / (TP+FP)] will be calculated to show the machine learning test result.	The PPV of the trained machine learning will be calculated based on data from 2022 and 2023.
Primary	NPV of the machine learning algorithm	NPV [TN / (FN+TN)] will be calculated to show the machine learning test result.	The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.