Recruitment

Recruitment Status
Active, not recruiting
Estimated Enrollment
Same as current

Summary

Conditions
  • Acute Respiratory Failure Following Trauma and Surgery
  • Acute Cardiac Failure
  • Acute Kidney Failure
  • Acute Respiratory Decompensation
  • Multi Organ Failure
  • Shock
  • Respiratory Failure
  • Acute Respiratory Failure
  • Acute Respiratory Failure Post Surgical
  • Shock, Septic
  • Acute Respiratory Failure Post Traumatic
  • Acute Respiratory Failure Postprocedural
  • Acute Respiratory Failure Requiring Reintubation
  • Shock, Cardiogenic
  • Respiratory Distress Syndrome
  • Acute Respiratory Failure With Hypercapnia
  • Acute Respiratory Failure With Hypoxia
  • Respiratory Arrest
Type
Observational
Design
Observational Model: CohortTime Perspective: Retrospective

Participation Requirements

Age
Between 18 years and 90 years
Gender
Both males and females

Description

Currently available studies are not clear about avoidable risk factors as actionable tools to reduce patient deterioration triggered by respiratory complications. The lack of this crucial knowledge leads to errors in further cases, and errors in medical documentation leads to limited learning from e...

Currently available studies are not clear about avoidable risk factors as actionable tools to reduce patient deterioration triggered by respiratory complications. The lack of this crucial knowledge leads to errors in further cases, and errors in medical documentation leads to limited learning from errors and potentially preventable harm to patients. The respiratory measurement is an early indicator of disease, yet many clinicians underestimate its importance and hospitals report a poor level of respiratory rate recordings. As respiratory abnormalities are early markers of patient deterioration, it is hoped that improved and continued data collection and monitoring will have an impact on the nature and timeliness of the response to critical illness. Data concordance plays a major role in documentation quality, especially for data-mining and knowledge extraction analysis, therefore it is essential to address the reliability of 'respiratory abnormalities' labelled data within the Electronic Health Record (EHR) system. It is hypothesized that an exploratory analysis of historical medical records by using an advanced algorithm could reveal novel and improved knowledge about the nature of Respiratory Abnormalities. However, the quality, computability, reliability, accuracy and completeness of the data are questionable. It's also hypothesized that efficacious and preventive intervention can reduce the increased burden of illness followed by respiratory abnormalities, reduce the enormous number of treatable incidences and be cost-effective when delivered in the real-life clinical environment.

Tracking Information

NCT #
NCT04079829
Collaborators
  • Memorial Hermann Hospital
  • CRG Medical, Inc.
Investigators
Principal Investigator: Paul G Loubser, MD Memorial Hermann Principal Investigator: Nadav Lankin Efficacy Care R&D Ltd