Hospital Acquired ConditionNCT04674098
The Effect of Real Time Analytics on Adverse Events Among Hospitalized Patients
An observational study of Hospital Acquired Condition, run by Bob Topp at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hospital Acquired Condition
- What is being tested?
- The Beat Analytics System (BAS)
- Type of treatment
- Medical device
- How many people
- 0 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Apr 2021to 30 Jan 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Admitted to one of two non-intensive care units within the University of Toledo Medical Center (UTMC) hospital.
Who cannot join
- unable to provide informed consent
Trial locations and contacts
1 site in 1 country.
United States1 site
The University of Toledo Medical Center
Toledo, Ohio, 43614
About this study
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.
Read the detailed description
Adverse events (AEs) among hospitalized patients are defined as "Unintended injuries or complications that result in disability at discharge, death or prolonged hospital stay and are caused by events other than the patient's underlying disease." Annually, approximately 1.1% of all hospital admissions, or 400,000 patients, die due to AEs while costing the US economy roughly 17.1 billion dollars. The Canadian Adverse Events Study reported AEs among 7.5% of hospital admissions, with 37% of these events deemed preventable. Common AEs, including infections and sepsis, cardiac and respiratory failure and deaths have been reported to be proceeded by a change in the patient's vital signs recognizable up to 48 hours prior to the AE being diagnosed. In many cases, subtle changes in a patient's vital signs have been recognized retrospectively as precursors of AEs that can lead to unplanned admissions to ICUs or death.
A number of early warning systems have been developed to assist nursing staff in identifying changes in vital signs as precursors to AEs. The commonly used Modified Early Warning Score (MEWS) attempts to identify acute clinical deterioration based on the patient's vital signs and level of consciousness. The higher the MEWS score, the greater risk of an AE. However, the efficacy of the MEWS score is contingent upon the frequency of both the score being recorded and being assessed by the nursing staff. Although continuous monitoring of vital signs takes place within and outside of ICUs these data are rarely provided to the nurse when they are outside of the patient's room in real time. Further, the vital signs and MEWS scores are commonly recorded in the patient record at scheduled intervals during a 24-hour period (e.g. every 4, 6, 8 or 12 hours). If a patient's physiological condition deteriorates between these scheduled intervals and the nurse is not continually with the patient, the opportunity is lost for early recognition of this deterioration that may lead to an AE. The importance of monitoring vital signs in clinical practice is indisputable, but how to best monitor and interpret them and how frequently they should be measured in order to minimize AEs remains unclear.
In order to address this gap in the literature, the project team has developed an innovative technology. The Beat Analytics System (BAS) provides nurses with both real-time monitoring of the patient's vital signs and continuous calculation of their patient's MEWS scores through an app on their cell phone. This information can be presented both numerically (with boundary conditions for alerts) and graphically, in order to observe change in the MEWS score over time. The purpose of this study is to examine the effect of providing nurses with remote, continuous, real-time monitoring of their patients vital signs and MEWS scores using the BAS on the occurrence of AEs, admissions to the ICU, hospital length of stay, and activation of the rapid response team among patients on non-intensive care hospital units. This purpose will be addressed through a longitudinal sequential study design in which the outcome variables (AEs, admissions to the ICU hospital length of stay and activation of the rapid response team) will be monitored on two 20-bed non-intensive care units monthly for 6 months without the BAS. The 6-month baseline data collection period will be followed by a month of training the nursing staff on the targeted unit to using the BAS. Following this orientation month, the outcome variables will again be measured during a 6-month intervention phase in which the BAS will be available to the nurses caring for patients on the targeted unit where the patient's vital signs will be continually recorded.
- Conditions
- Hospital Acquired Condition
- Being tested
- The Beat Analytics System (BAS)
- Medical subject headings
- Iatrogenic Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The Beat Analytics System (BAS)Device
There are three subsystems to the BAS; data aggregation, data analysis and data presentation.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: The Beat Analytics System (BAS) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Adverse events
Development of an infection or sepsis, cardiac or respiratory failure, and death
Measured: during hospital admission averaging 7 days
Length of stay in the hospital
Duration of days during which a subject was admitted to the hospital
Measured: during hospital admission averaging 7 days
Transfer to the ICU
Transfer of the patient to the intensive care unit
Measured: during hospital admission averaging 7 days
RRT activation
Activation of the hospital's rapid response team to support the care of the patient
Measured: during hospital admission averaging 7 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Dec 2020
- Study start1 Apr 2021
- Primary completion30 Jan 2022
- Study completion30 Jan 2022
Why it stopped: COVID protocols limited access to data. PI then moved to a different institution
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for Hospital Acquired ConditionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bob Topp
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Bob Topp, University of Toledo
- Investigators
- Robert Topp, PhD, Principal investigator, College of Nursing
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04674098
- Sponsor's protocol ID
- 300854-UT
- Status last verified
- Jun 2024
- First submitted
- 4 Dec 2020
- Last updated
- 7 Jun 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: No Plan Has been developed
Publications
Unbeck M, Schildmeijer K, Henriksson P, Jurgensen U, Muren O, Nilsson L, Pukk Harenstam K. Is detection of adverse events affected by record review methodology? an evaluation of the "Harvard Medical Practice Study" method and the "Global Trigger Tool". Patient Saf Surg. 2013 Apr 15;7(1):10. doi: 10.1186/1754-9493-7-10.
PubMed 23587448Van 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.
PubMed 21471478Lapointe-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.
PubMed 31613792Islam 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.
PubMed 30712598Kim 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.
PubMed 31470543Jayasundera 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.
PubMed 30274205Kause 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.
PubMed 15325446Downey 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.
PubMed 28950188Ludikhuize 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.
PubMed 22341727Kim 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.
PubMed 26098429van 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.
PubMed 27494719Wang 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.
PubMed 26723861Brekke 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.
PubMed 30645637Smith 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.
PubMed 28701265Yoder 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.
PubMed 23817602
Study at a glance
From the registry record, last updated by the sponsor on 7 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Effect of Real Time Analytics on Adverse Events Among Hospitalized Patients
- ClinicalTrials.gov ID
- NCT04674098
- Acronym / sponsor ID
- 300854-UT
- Conditions
- Hospital Acquired Condition
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: COVID protocols limited access to data. PI then moved to a different institution
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Bob ToppUniversity, hospital or other
- Study start
- 1 Apr 2021 (actual)
- Primary completion
- 30 Jan 2022 (actual)
- Study completion
- 30 Jan 2022 (actual)
- First posted
- 19 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Adverse events (AEs) among hospitalized patients are defined as "Unintended injuries or complications that result in disability at discharge, death or prolonged hospital stay and are caused by events other than the patient's underlying disease." Annually, approximately 1.1% of all hospital admissions, or 400,000 patients, die due to AEs while costing the US economy roughly 17.1 billion dollars. The Canadian Adverse Events Study reported AEs among 7.5% of hospital admissions, with 37% of these events deemed preventable. Common AEs, including infections and sepsis, cardiac and respiratory failure and deaths have been reported to be proceeded by a change in the patient's vital signs recognizable up to 48 hours prior to the AE being diagnosed. In many cases, subtle changes in a patient's vital signs have been recognized retrospectively as precursors of AEs that can lead to unplanned admissions to ICUs or death.
A number of early warning systems have been developed to assist nursing staff in identifying changes in vital signs as precursors to AEs. The commonly used Modified Early Warning Score (MEWS) attempts to identify acute clinical deterioration based on the patient's vital signs and level of consciousness. The higher the MEWS score, the greater risk of an AE. However, the efficacy of the MEWS score is contingent upon the frequency of both the score being recorded and being assessed by the nursing staff. Although continuous monitoring of vital signs takes place within and outside of ICUs these data are rarely provided to the nurse when they are outside of the patient's room in real time. Further, the vital signs and MEWS scores are commonly recorded in the patient record at scheduled intervals during a 24-hour period (e.g. every 4, 6, 8 or 12 hours). If a patient's physiological condition deteriorates between these scheduled intervals and the nurse is not continually with the patient, the opportunity is lost for early recognition of this deterioration that may lead to an AE. The importance of monitoring vital signs in clinical practice is indisputable, but how to best monitor and interpret them and how frequently they should be measured in order to minimize AEs remains unclear.
In order to address this gap in the literature, the project team has developed an innovative technology. The Beat Analytics System (BAS) provides nurses with both real-time monitoring of the patient's vital signs and continuous calculation of their patient's MEWS scores through an app on their cell phone. This information can be presented both numerically (with boundary conditions for alerts) and graphically, in order to observe change in the MEWS score over time. The purpose of this study is to examine the effect of providing nurses with remote, continuous, real-time monitoring of their patients vital signs and MEWS scores using the BAS on the occurrence of AEs, admissions to the ICU, hospital length of stay, and activation of the rapid response team among patients on non-intensive care hospital units. This purpose will be addressed through a longitudinal sequential study design in which the outcome variables (AEs, admissions to the ICU hospital length of stay and activation of the rapid response team) will be monitored on two 20-bed non-intensive care units monthly for 6 months without the BAS. The 6-month baseline data collection period will be followed by a month of training the nursing staff on the targeted unit to using the BAS. Following this orientation month, the outcome variables will again be measured during a 6-month intervention phase in which the BAS will be available to the nurses caring for patients on the targeted unit where the patient's vital signs will be continually recorded.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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 | – | Device: The Beat Analytics System (BAS)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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Admitted to one of two non-intensive care units within the University of Toledo Medical Center (UTMC) hospital.
Key exclusion criteria (1 listed)
- unable to provide informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| The Beat Analytics System (BAS)There are three subsystems to the BAS; data aggregation, data analysis and data presentation. | Device | Intervention |
Primary outcome
Adverse events
Time frame: during hospital admission averaging 7 days
Development of an infection or sepsis, cardiac or respiratory failure, and death
Length of stay in the hospital
Time frame: during hospital admission averaging 7 days
Duration of days during which a subject was admitted to the hospital
Transfer to the ICU
Time frame: during hospital admission averaging 7 days
Transfer of the patient to the intensive care unit
RRT activation
Time frame: during hospital admission averaging 7 days
Activation of the hospital's rapid response team to support the care of the patient
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jun 2024.
- Results publication: PubMed 23587448Unbeck M, Schildmeijer K, Henriksson P, Jurgensen U, Muren O, Nilsson L, Pukk Harenstam K. Is detection of adverse events affected by record review methodology? an evaluation of the "Harvard Medical Practice Study" method and the "Global Trigger Tool". Patient Saf Surg. 2013 Apr 15;7(1):10. doi: 10.1186/1754-9493-7-10.
- Results publication: PubMed 21471478Van 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.
- Results publication: PubMed 31613792Lapointe-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.
- Results publication: PubMed 30712598Islam 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.
- Results publication: PubMed 31470543Kim 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.
- Results publication: PubMed 30274205Jayasundera 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.
- Results publication: PubMed 15325446Kause 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.
- Results publication: PubMed 28950188Downey 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.
- Results publication: PubMed 22341727Ludikhuize 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.
- Results publication: PubMed 26098429Kim 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.
- Results publication: PubMed 27494719van 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.
- Results publication: PubMed 26723861Wang 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.
- Individual participant dataSharing planned: No. No Plan Has been developed
Related trials for Hospital Acquired Condition
Recruiting
Assisted Ambulation to Improve Health Outcomes for Older Medical Inpatients
Mobility Limitation, Frailty
Not yet recruiting
Evaluation of an AI-Assisted Tool for CLABSI Surveillance at VA Medical Centers
Central Line-Associated Bloodstream Infection (CLABSI), Bacteremia
Active, not recruitingObservational study
Initial and Subsequent Healthcare-Associated Infections in Intensive Care Patients
Cross Infection, Intensive Care Units
Not yet recruiting
Implementation Study on Hospital-to-home Advanced Rehabilitation Technologies (ART)
Brain Injuries
Not yet recruitingObservational study
Traditional Chinese Medicine in Multidrug-Resistant Bacterial Pneumonia
Multi Drug Resistant Infections, Pneumnia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Robert Topp, PhD principal investigator
College of Nursing
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hospital Acquired Condition, run by Bob Topp at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 7 Jun 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Adverse events (time frame: during hospital admission averaging 7 days). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04674098, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jun 2024.