Prediction Of Serious Adverse Event in Emergency Department With dysKAlemia, Retrospective Study

NCT05788523 | Completed

• 1 other identifier: 2023-01Obs-CHRMT
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Dyskalemia, hypo- and hyperkalemia are common in the emergency department and are associated with increased morbidity and mortality. The potential seriousness of dyskalemia results from the potential alteration of intracardiac conduction and the increase in cardiac rhythm disorders, all associated with a lethal risk. Given the aspecific symptoms of dyskalemia and the complex causal links to be acquired, it is difficult to judge the severity of the disorders in front of an initial clinical presentation of a patient in the emergency room, as the patient may present a serious condition during his stay in the emergency room, related to the dyskalemia and not prejudged at first. The ECG is recommended to judge the severity of the disorder, in association with the level of kalemia, but the electrical changes of its pattern in the context of dyskalemia are sometimes so fine that even the eye of the practitioner is not able to detect them. To date and to our knowledge, there is no tool for predicting the risk of RTA or death in dyskalemia. The existing studies on the subject, including ECGs, seek to detect dyskalemia and not its complications, and the proposed tools only take into account the ECG and not the clinical context of the patient.

Conditions

Emergencies

Keywords

artificial intelligence; kalemia; dyskalemia

Outcome Measures

Primary Outcomes (1)

• Number of participants with Cardiac arrest and/or rhythm disorder

  Number of patients with Cardiac arrest and/or rhythm disorder within 48 hours of the ED visit

  within 48 hours

Secondary Outcomes (2)

• Rate of Mortality

  within 48 hours

• The type of received treatment for dyskalemia

  within 48 hours

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients consulted in the emergency unit of the participating centre, with quality criteria for the EHCs; this cohort will validate the Artificial Intelligence method developed in partnership with the laboratory

You may qualify if:

• Major patient consulting in an emergency department participating in the study
• who received at least a digital ECG AND an ionogram in the emergency department during the study period

You may not qualify if:

• Patient who has expressed opposition to the reuse of their data for the study

Sponsors & Collaborators

• Centre Hospitalier Régional Metz-Thionville: lead
• Georgia Institute of Technology: collaborator

Study Sites (1)

CHR Metz-Thionville/Hopital de Mercy

Metz, 57085, France

Location

MeSH Terms

Conditions

Emergencies

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Laure ABENSUR VUILLAUME, MD, PhD

  CHR Metz Thionville Hopital de Mercy

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 15, 2023
• First Posted: March 29, 2023
• Study Start: January 2, 2023
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: April 27, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)