RKP Monitoring Software for Ventilator-Associated Events

Recruiting in Palo Alto (17 mi)

Age: Any Age

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: The University of Texas Medical Branch, Galveston

Disqualifiers: Non-RKP ICU, High frequency ventilation, ECMO

No Placebo Group

Trial Summary

What is the purpose of this trial?

Ventilator associated events (VAE) is a quality metric defined by 48 hours of stability followed by 48 hours of escalation of ventilator settings within the ICU. VAE have been associated with poor outcomes and increases the cost of care, yet is not easy to avoid. Operationalizing all the standards of care known to improve outcomes of those requiring mechanical ventilation in the critical care environment requires a comprehensive approach. ICU teams are encouraged to follow best practice protocols to help liberate and prevent VAEs. Yet, compliance with protocols in most ICUs is suboptimal for multiple reasons. With the advent of computerized mechanical ventilators capable of streaming data from breath to breath and biomedical integration systems (BMDI) such as Capsule (UTMB's BMDI system), software systems have been developed to help identify variances in the standard of care. Automation in near real-time ventilator data feedback has been shown to reduce the incidences of VAEs. This quality improvement project will leverage Vyaire's Respiratory Knowledge Portal (RKP) to collect and store meaningful data regarding ventilator-associated events (VAE), alarm policy compliance, ventilator weaning, and lung protective analytics. Goals: 1. To collect quality metrics utilizing RKP from patients requiring mechanical ventilation over a 3-4-month period for a retrospective baseline analysis. 2. Provide the RKP tool to the ICU team to determine if the use of RKP's webportal and Messenger Zebra phone app improves quality of mechanical ventilation and outcomes. 3. To determine a return on investment (ROI) for a software system like RKP.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment RKP Monitoring Software for Ventilator-Associated Events?

The research suggests that computer-assisted systems, like the RKP Monitoring Software, can improve respiratory care by providing real-time data and alerts, which may reduce complications and improve patient outcomes in hospitals. ^{1 2 3 4 5}

How does the RKP Monitoring Software for Ventilator-Associated Events differ from other treatments for this condition?

The RKP Monitoring Software is unique because it automates the detection of ventilator-associated events, reducing the manual labor required by hospital staff and potentially improving patient outcomes by providing timely alerts. Unlike traditional methods that rely heavily on manual data collection and interpretation, this software uses electronic surveillance to streamline the process. ^{2 6 7 8 9}

Research Team

Eligibility Criteria

This trial is for patients in the ICU who require mechanical ventilation due to various respiratory failures and conditions. It aims to improve their care by reducing complications associated with ventilators.

Inclusion Criteria

I need a machine to help me breathe.

Subjects in one of the monitored Galveston campus ICUs

I am between the ages of 0 and 100.

Exclusion Criteria

Is in an ICU not monitored by RKP

I am an adult using a high-frequency ventilator.

Any patient requiring ECMO

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Analysis

Collect quality metrics utilizing RKP from patients requiring mechanical ventilation for a retrospective baseline analysis

3-4 months

Intervention

Provide the RKP tool to the ICU team to determine if the use of RKP's webportal and Messenger Zebra phone app improves quality of mechanical ventilation and outcomes

3-4 months

Follow-up

Participants are monitored for safety and effectiveness after intervention

4 weeks

Treatment Details

Interventions

• RKP Monitoring Software (Behavioural Intervention)

Trial Overview The Respiratory Knowledge Portal (RKP) Monitoring Software is being tested. This software collects data on ventilator use, aiming to enhance patient outcomes by adhering to best practice protocols more closely.

Participant Groups

Group I: Control Active Control 1 Intervention

We will monitor without clinician use of the software.

Group II: Use of RKP Active Control 1 Intervention

Use of RKP alerts to the standard of care.

Who Is Running the Clinical Trial?

The University of Texas Medical Branch, Galveston

Lead Sponsor

Trials

263

Recruited

55,400+

Dr. Vicente Resto

The University of Texas Medical Branch, Galveston

Chief Medical Officer since 2024

MD from Harvard Medical School

Dr. Jochen Reiser

The University of Texas Medical Branch, Galveston

Chief Executive Officer since 2023

MD, PhD from Ruprecht Karls University of Heidelberg, Germany

Vyaire Medical

Industry Sponsor

Trials

11

Recruited

1,800+

Findings from Research

A new patient categorization and scoring system was developed to objectively measure compliance with mechanical ventilation standards in a pediatric ICU, analyzing data from 222 subjects over 1,578 days of ventilation.

The system showed a median acceptable ventilation score of 84.6% and an acceptable oxygenation score of 70.1%, indicating that while the risk of barotrauma was minimal (100% barotrauma-free state), there was a notable risk of volutrauma (56.1% volutrauma-free state).

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Categorization in Mechanically Ventilated Pediatric Subjects: A Proposed Method to Improve Quality. Walsh, BK., Smallwood, CD., Rettig, JS., et al.[2017]

The computer-assisted quality assurance system developed at LDS Hospital allows for efficient monitoring of respiratory care services without the need for time-consuming chart reviews, potentially improving patient outcomes.

By implementing a daily surveillance program, such as the medical director's alert, hospitals can proactively address issues related to inadequate oxygen therapy and airway management, which are significant contributors to preventable deaths, especially among less severely ill patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quality assurance for respiratory care services: a computer-assisted program. Elliott, CG.[2000]

Ventilator weaning protocols have been shown to significantly reduce the duration of mechanical ventilation, the incidence of ventilator-associated pneumonia, and the rate of re-intubation compared to physician-directed weaning.

This project aims to enhance adherence to these protocols by continuously monitoring patients' weaning status and providing real-time feedback, potentially leading to better patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Promoting adherence to ventilator management and ventilator weaning protocols. Berutti, TW., Starmer, J., Johnson, KB., et al.[2019]

The KUSIVAR system is a knowledge-based decision support tool designed to assist medical staff in optimizing respirator treatment by providing real-time advice based on patient data from monitoring equipment.

The system has been developed collaboratively between hospitals, universities, and industry, and is currently undergoing validation and evaluation through animal experiments and clinical studies to ensure its effectiveness and safety in critical care settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Integrating knowledge-based technology into computer aided ventilation systems. Gill, H., Ludwigs, U., Matell, G., et al.[2019]

The study developed and validated predictive models for ICU length of stay and mortality using machine learning algorithms, achieving approximately 89% accuracy for mortality prediction and 65% for length of stay prediction, based on data from the MIMIC database involving adult ICU patients.

The innovative aspect of the research was the use of a quantiles approach to enhance the predictive power of the models by engineering vital sign features, allowing for effective predictions without relying on external data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation. Alghatani, K., Ammar, N., Rezgui, A., et al.[2021]

The implementation of an electronic tool for identifying Ventilator-Associated Events (VAE) at Barnes Jewish Hospital significantly reduced the labor required for manual surveillance by hospital infection prevention staff.

This electronic surveillance system utilizes an internal clinical data repository, enhancing the efficiency of monitoring and potentially improving patient safety by allowing for timely identification of complications related to mechanical ventilation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development, Implementation and Use of Electronic Surveillance for Ventilator-Associated Events (VAE) in Adults. Resetar, E., McMullen, KM., Russo, AJ., et al.[2021]

The 'SERVO WINDOW' system integrates advanced real-time respiratory monitoring techniques, providing clinicians with continuous access to critical respiratory data such as airway pressure, flow, and CO2 levels, enhancing patient care in anesthesiology.

This system allows for detailed analysis of lung mechanics and blood gas data, with trends updated every minute, which can improve decision-making during surgeries and critical care situations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-time respiratory monitoring workstation--software and hardware engineering aspects. Govindarajan, N., Meiyappan, S., Prakash, O.[2019]

A new computer program was developed to enhance quality control and risk management for patients on ventilators, ensuring that the ventilator is safe to use before connecting it to a patient by checking for hardware status and alarm settings.

The software allows for continuous monitoring during ventilation, automatically recording patient data and alarm conditions, which helps clinicians make informed decisions and provides a comprehensive report after the session.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Ventilator risk management using a programmed monitor. Silvern, DA., Gupte, PM.[2019]

The introduction of a computer-based monitoring system in intensive care has significantly improved the collection, storage, and analysis of vital patient data, enhancing the quality of care since its inception in 1973.

The system now allows for real-time monitoring of critical parameters like heart rate and blood pressure, as well as automated calculations for fluid balance and renal function, which helps reduce nursing workload and improve patient management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer assisted monitoring in intensive medicine. Lauwers, P., Ferdinande, P., Van de Walle, J., et al.[2011]

References

1. United States pubmed.ncbi.nlm.nih.gov

Categorization in Mechanically Ventilated Pediatric Subjects: A Proposed Method to Improve Quality. [2017]

2. United States pubmed.ncbi.nlm.nih.gov

Quality assurance for respiratory care services: a computer-assisted program. [2000]

3. United States pubmed.ncbi.nlm.nih.gov

Promoting adherence to ventilator management and ventilator weaning protocols. [2019]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Integrating knowledge-based technology into computer aided ventilation systems. [2019]

5. Canada pubmed.ncbi.nlm.nih.gov

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation. [2021]

6. United States pubmed.ncbi.nlm.nih.gov

Development, Implementation and Use of Electronic Surveillance for Ventilator-Associated Events (VAE) in Adults. [2021]

7. Netherlands pubmed.ncbi.nlm.nih.gov

Real-time respiratory monitoring workstation--software and hardware engineering aspects. [2019]

8. United States pubmed.ncbi.nlm.nih.gov

Ventilator risk management using a programmed monitor. [2019]

9. Belgium pubmed.ncbi.nlm.nih.gov

Computer assisted monitoring in intensive medicine. [2011]