Automated Detection of Patient Ventilator Asynchrony Using Pes Signal

NCT06186557 | Recruiting

• 1 other identifier: 2202-061
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

Rationale: Patient-ventilator asynchrony (PVA) in mechanical ventilation is associated with adverse patient outcome such as a prolonged stay in the ICU and even mortality. The prevalence of asynchronies is, however, difficult to quantify. It is common to use only the pressure and flow signal of the ventilator to detect asynchronies. The detection method is often based on definitions. The investigators will use new techniques (esophageal pressure signal and machine learning (ML)) to improve detection and quantification of patient-ventilator asynchronies. The hypothesis is that an algorithm which uses the Pes signal and ML to detect and quantify asynchronies is superior to previous techniques. Objective: 1. To develop an asynchrony detection algorithm based on pressure, flow and Pes signal using ML. 2. To develop a second algorithm with the same ML technique based on pressure an flow signal only. 3. To compare the performance of these models in comparison with an expert team and with each other. Study design: The investigators will collect internal data from the ventilator connected to patients on mechanical ventilation (population described below). First, the investigators will, with a dedicated expert team, identify and annotate the asynchronies based on visual inspection of the pressure, flow and Pes signal. Second, the investigators will develop an ML algorithm which will be trained with the annotated data from the visual inspection. Third, the performance of the AI algorithm will be compared with the performance of the expert panel using newly obtained data. Fourth, the performance of the AI algorithm will be compared with the second algorithm which uses the pressure and flow signal only. Study population: All patients admitted to the adult ICU of the LUMC on mechanical ventilation who are ventilated \> 24 hours and are equipped with an esophageal balloon catheter. Intervention (if applicable): None. Main study parameters/endpoints: The performance of the detection algorithm.

Conditions

Mechanical Ventilation Complication

Keywords

detection algorithm; mechanical ventilation; patient-ventilator interaction; convolutional neural network

Outcome Measures

Primary Outcomes (1)

• Performance of detection algorithm

  Model evaluation: The first part of the dataset will be used to construct/train the model. The second part of the dataset will be used to evaluate the performance of the model. The labels attained by the experts are considered the ground truth. The labeling of the algorithm will be compared with the labels of the experts to assess the performance of the algorithm. The performance of the primary algorithm will be compared with the performance of the second algorithm, which is based only on pressure and flow signals. The performance of the second algorithm will be assessed as described above. The agreement between the experts will be assessed using Fleiss's kappa, which evaluates the agreement between more than two raters.

  8 hours

Interventions

No intervention OTHER

No intervention

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study will recruit as much patients as possible, but at least 50 patients, during the study duration. After every 25 patients the algorithm will be tested for improvement. The study population consists of ICU patients on mechanical ventilation because of acute respiratory failure or with a ventilation duration of at least 24 hours that are equipped with an esophageal balloon catheter. Patients are recruited in the ICU of the LUMC.

You may qualify if:

• admission to the ICU of the LUMC;
• age of 18 years or older;
• intubated and receiving mechanical ventilation because of acute respiratory failure or with a ventilation duration of at least 24 hours; and
• equipped with an esophageal balloon catheter

You may not qualify if:

• after recent pneumectomy or lobectomy;
• no informed consent

Sponsors & Collaborators

• Leiden University Medical Center: lead

Study Sites (1)

Leiden University Medical Centre

Leiden, South Holland, 2333 ZA, Netherlands

RECRUITING

Related Publications (7)

• Esperanza JA, Sarlabous L, de Haro C, Magrans R, Lopez-Aguilar J, Blanch L. Monitoring Asynchrony During Invasive Mechanical Ventilation. Respir Care. 2020 Jun;65(6):847-869. doi: 10.4187/respcare.07404.

  PMID: 32457175 BACKGROUND

• Shi ZH, Jonkman A, de Vries H, Jansen D, Ottenheijm C, Girbes A, Spoelstra-de Man A, Zhou JX, Brochard L, Heunks L. Expiratory muscle dysfunction in critically ill patients: towards improved understanding. Intensive Care Med. 2019 Aug;45(8):1061-1071. doi: 10.1007/s00134-019-05664-4. Epub 2019 Jun 24.

  PMID: 31236639 BACKGROUND

• Doorduin J, Roesthuis LH, Jansen D, van der Hoeven JG, van Hees HWH, Heunks LMA. Respiratory Muscle Effort during Expiration in Successful and Failed Weaning from Mechanical Ventilation. Anesthesiology. 2018 Sep;129(3):490-501. doi: 10.1097/ALN.0000000000002256.

  PMID: 29771711 BACKGROUND

• Gilstrap D, MacIntyre N. Patient-ventilator interactions. Implications for clinical management. Am J Respir Crit Care Med. 2013 Nov 1;188(9):1058-68. doi: 10.1164/rccm.201212-2214CI.

  PMID: 24070493 BACKGROUND

• Blanch L, Villagra A, Sales B, Montanya J, Lucangelo U, Lujan M, Garcia-Esquirol O, Chacon E, Estruga A, Oliva JC, Hernandez-Abadia A, Albaiceta GM, Fernandez-Mondejar E, Fernandez R, Lopez-Aguilar J, Villar J, Murias G, Kacmarek RM. Asynchronies during mechanical ventilation are associated with mortality. Intensive Care Med. 2015 Apr;41(4):633-41. doi: 10.1007/s00134-015-3692-6. Epub 2015 Feb 19.

  PMID: 25693449 BACKGROUND

• Rehm GB, Han J, Kuhn BT, Delplanque JP, Anderson NR, Adams JY, Chuah CN. Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony. Methods Inf Med. 2018 Sep;57(4):208-219. doi: 10.3414/ME17-02-0012. Epub 2018 Sep 24.

  PMID: 30919393 BACKGROUND

• Akoumianaki E, Lyazidi A, Rey N, Matamis D, Perez-Martinez N, Giraud R, Mancebo J, Brochard L, Richard JM. Mechanical ventilation-induced reverse-triggered breaths: a frequently unrecognized form of neuromechanical coupling. Chest. 2013 Apr;143(4):927-938. doi: 10.1378/chest.12-1817.

  PMID: 23187649 BACKGROUND

Study Officials

• Abraham Schoe, MD, PhD

  Leiden University Medical Centre

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Abraham Schoe, MD, PhD

CONTACT

+31-715265018; a.schoe@lumc.nl

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, PhD

Study Record Dates

• First Submitted: March 1, 2023
• First Posted: January 2, 2024
• Study Start: February 1, 2023
• Primary Completion: December 31, 2024
• Study Completion: April 30, 2025
• Last Updated: March 13, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)