To Construct a Prognosis Prediction Model for ECMO Patients Based on Machine Learning Algorithms

NCT06654388 | Completed DMC

• 1 other identifier: 2022-KY-026-01
• Study Type: observational
• Target: 290
• Locations: 1 country
• Sites: 1

Brief Summary

Extracorporeal membrane oxygenation (ECMO) is a critical life-support technique for patients with severe medical conditions. Various factors affect the mortality rates of patients in intensive care units, presenting a significant clinical challenge in accurately predicting outcomes based on a limited set of indicators.

Conditions

ECMO

Keywords

ecmo; machine learning; predict

Outcome Measures

Primary Outcomes (2)

• survive

  Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months

• death

  Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months

Study Arms (2)

Survive

Death

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

The study involved patients who received ECMO treatment at Zhujiang Hospital of Southern Medical University from 2018 to 2024. Exclusion criteria included patients who received ECMO support at outside hospitals and those under the age of 18. Of the patients, 100 were discharged alive, while 190 died during hospitalization.

You may qualify if:

• All patients who underwent ECMO in our hospital and were registered in the CSECLS registry database (ClinicalTrials.gov Identifier:NCT04158479) from January 1, 2018 to now were retrospectively collected.

You may not qualify if:

• ECMO was discontinued for non-medical reasons.
• Under 18 years of age.

Sponsors & Collaborators

• Zhujiang Hospital: lead

Study Sites (1)

Zhujiang Hospital of Southern Medical University

Guangzhou, Guangdong, 510000, China

Location

Related Publications (2)

• Ayers B, Wood K, Gosev I, Prasad S. Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning. Ann Thorac Surg. 2020 Oct;110(4):1193-1200. doi: 10.1016/j.athoracsur.2020.03.128. Epub 2020 May 23.

  PMID: 32454016 RESULT

• Stephens AF, Seman M, Diehl A, Pilcher D, Barbaro RP, Brodie D, Pellegrino V, Kaye DM, Gregory SD, Hodgson C; Extracorporeal Life Support Organization Member Centres. ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation. Intensive Care Med. 2023 Sep;49(9):1090-1099. doi: 10.1007/s00134-023-07157-x. Epub 2023 Aug 7.

  PMID: 37548758 RESULT

Biospecimen

Retention: SAMPLES WITHOUT DNA

Observational study without biological samples

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 3 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: October 9, 2024
• First Posted: October 23, 2024
• Study Start: January 1, 2018
• Primary Completion: October 1, 2024
• Study Completion: October 1, 2024
• Last Updated: October 23, 2024

Record last verified: 2018-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)