The Effect of Real Time Analytics on Adverse Events Among Hospitalized Patients

NCT04674098 | Withdrawn FDA Device

• 1 other identifier: 300854-UT
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

Brief Summary

This study will examine the effect of providing nurses with continuous, remote, real-time monitoring of their patient's vital signs and MEWS scores using the BAS on the occurrence of adverse events, admissions to the ICU, hospital length of stay and activation of the rapid response team among patients on non-intensive care hospital units. A longitudinal study will measure the outcome variables among an estimated 60 patients per month during 6 month intervals when the BAS is not and is available to the nursing staff.

Conditions

Hospital Acquired Condition

Outcome Measures

Primary Outcomes (4)

• Adverse events

  Development of an infection or sepsis, cardiac or respiratory failure, and death

  during hospital admission averaging 7 days

• Length of stay in the hospital

  Duration of days during which a subject was admitted to the hospital

  during hospital admission averaging 7 days

• Transfer to the ICU

  Transfer of the patient to the intensive care unit

  during hospital admission averaging 7 days

• RRT activation

  Activation of the hospital's rapid response team to support the care of the patient

  during hospital admission averaging 7 days

Study Arms (2)

Usual Care

Every patient who is admitted or transferred to the target unit during both the baseline and intervention phases of the study will be approached by a member of the research staff to be a subject in the study. The patient will be informed of the overall study objectives and be requested to provide informed consent to participate. The patient's involvement in the study will include having the research staff access and extract relevant outcome variables collected from their electronic health record (EHR) (AEs, admissions to the ICU, hospital length of stay and activation of the rapid response team) as a result of their hospital stay.

Intervention

If the patient provides consent during the intervention phase, the BAS technology will passively monitor their vital signs generated by the Philips vital sign monitor by relaying their deidentified vital signs data to the CLU, proprietary Cloud server, and subsequently Lumori® on a study-issued cell phone of the RN who is primarily responsible for the patient's care. Patient's admitted or transferred to the targeted unit will NOT be excluded from being approached to participate in the study.

Device: The Beat Analytics System (BAS)

Interventions

The Beat Analytics System (BAS) DEVICE

There are three subsystems to the BAS; data aggregation, data analysis and data presentation.

Intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Subjects for this study will include patients who are admitted to one of the two non-intensive care targeted units within the UTMC hospital.

You may qualify if:

• Admitted to one of two non-intensive care units within the University of Toledo Medical Center (UTMC) hospital.

You may not qualify if:

• unable to provide informed consent

Sponsors & Collaborators

• Bob Topp: lead

Study Sites (1)

The University of Toledo Medical Center

Toledo, Ohio, 43614, United States

Location

Related Publications (15)

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  PMID: 23587448 RESULT

• Van Den Bos J, Rustagi K, Gray T, Halford M, Ziemkiewicz E, Shreve J. The $17.1 billion problem: the annual cost of measurable medical errors. Health Aff (Millwood). 2011 Apr;30(4):596-603. doi: 10.1377/hlthaff.2011.0084.

  PMID: 21471478 RESULT

• Lapointe-Shaw L, Bell CM. Measuring the cost of adverse events in hospital. CMAJ. 2019 Aug 12;191(32):E877-E878. doi: 10.1503/cmaj.190912. No abstract available.

  PMID: 31613792 RESULT

• Islam MM, Nasrin T, Walther BA, Wu CC, Yang HC, Li YC. Prediction of sepsis patients using machine learning approach: A meta-analysis. Comput Methods Programs Biomed. 2019 Mar;170:1-9. doi: 10.1016/j.cmpb.2018.12.027. Epub 2018 Dec 26.

  PMID: 30712598 RESULT

• Kim J, Chae M, Chang HJ, Kim YA, Park E. Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data. J Clin Med. 2019 Aug 29;8(9):1336. doi: 10.3390/jcm8091336.

  PMID: 31470543 RESULT

• Jayasundera R, Neilly M, Smith TO, Myint PK. Are Early Warning Scores Useful Predictors for Mortality and Morbidity in Hospitalised Acutely Unwell Older Patients? A Systematic Review. J Clin Med. 2018 Sep 28;7(10):309. doi: 10.3390/jcm7100309.

  PMID: 30274205 RESULT

• Kause J, Smith G, Prytherch D, Parr M, Flabouris A, Hillman K; Intensive Care Society (UK); Australian and New Zealand Intensive Care Society Clinical Trials Group. A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in Australia and New Zealand, and the United Kingdom--the ACADEMIA study. Resuscitation. 2004 Sep;62(3):275-82. doi: 10.1016/j.resuscitation.2004.05.016.

  PMID: 15325446 RESULT

• Downey CL, Tahir W, Randell R, Brown JM, Jayne DG. Strengths and limitations of early warning scores: A systematic review and narrative synthesis. Int J Nurs Stud. 2017 Nov;76:106-119. doi: 10.1016/j.ijnurstu.2017.09.003. Epub 2017 Sep 13.

  PMID: 28950188 RESULT

• Ludikhuize J, Smorenburg SM, de Rooij SE, de Jonge E. Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the Modified Early Warning Score. J Crit Care. 2012 Aug;27(4):424.e7-13. doi: 10.1016/j.jcrc.2012.01.003. Epub 2012 Feb 14.

  PMID: 22341727 RESULT

• Kim WY, Shin YJ, Lee JM, Huh JW, Koh Y, Lim CM, Hong SB. Modified Early Warning Score Changes Prior to Cardiac Arrest in General Wards. PLoS One. 2015 Jun 22;10(6):e0130523. doi: 10.1371/journal.pone.0130523. eCollection 2015.

  PMID: 26098429 RESULT

• van Galen LS, Dijkstra CC, Ludikhuize J, Kramer MH, Nanayakkara PW. A Protocolised Once a Day Modified Early Warning Score (MEWS) Measurement Is an Appropriate Screening Tool for Major Adverse Events in a General Hospital Population. PLoS One. 2016 Aug 5;11(8):e0160811. doi: 10.1371/journal.pone.0160811. eCollection 2016.

  PMID: 27494719 RESULT

• Wang AY, Fang CC, Chen SC, Tsai SH, Kao WF. Periarrest Modified Early Warning Score (MEWS) predicts the outcome of in-hospital cardiac arrest. J Formos Med Assoc. 2016 Feb;115(2):76-82. doi: 10.1016/j.jfma.2015.10.016. Epub 2015 Dec 24.

  PMID: 26723861 RESULT

• Brekke IJ, Puntervoll LH, Pedersen PB, Kellett J, Brabrand M. The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review. PLoS One. 2019 Jan 15;14(1):e0210875. doi: 10.1371/journal.pone.0210875. eCollection 2019.

  PMID: 30645637 RESULT

• Smith GB, Recio-Saucedo A, Griffiths P. The measurement frequency and completeness of vital signs in general hospital wards: An evidence free zone? Int J Nurs Stud. 2017 Sep;74:A1-A4. doi: 10.1016/j.ijnurstu.2017.07.001. Epub 2017 Jul 4. No abstract available.

  PMID: 28701265 RESULT

• Yoder JC, Yuen TC, Churpek MM, Arora VM, Edelson DP. A prospective study of nighttime vital sign monitoring frequency and risk of clinical deterioration. JAMA Intern Med. 2013 Sep 9;173(16):1554-5. doi: 10.1001/jamainternmed.2013.7791. No abstract available.

  PMID: 23817602 RESULT

MeSH Terms

Conditions

Iatrogenic Disease

Condition Hierarchy (Ancestors)

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

Study Officials

• Robert Topp, PhD

  College of Nursing

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: December 4, 2020
• First Posted: December 19, 2020
• Study Start: April 1, 2021
• Primary Completion: January 30, 2022
• Study Completion: January 30, 2022
• Last Updated: June 7, 2024

Record last verified: 2024-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)