DeliriumNCT04046458
De-escalating Vital Sign Checks
A study testing Nighttime Vital Sign EHR Alert and No EHR alert in people with Delirium and Sleep Disturbance, run by University of California, San Francisco at 1 site in United States.
Trial summary
The overall goals for this study are: 1) to develop a predictive model to identify patients who are stable enough to forego vital sign checks overnight, 2) incorporate this predictive model into the hospital electronic health record so physicians can view its output and use it to guide their decision-making around… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Delirium, Sleep Disturbance
- What is being tested?
- Nighttime Vital Sign EHR Alert and No EHR alert
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 1,436 participants
- Where
- 1 siteUnited States
- Study dates
- 11 Mar 2019to 4 Nov 2019
- Phase
- Interventional
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- All physician teams that operate under the UCSF Division of Hospital Medicine
Who cannot join
- N/A
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Using Predictive Analytics to Reduce Vital Sign Checks in Stable Hospitalized Patients
The overall goals for this study are: 1) to develop a predictive model to identify patients who are stable enough to forego vital sign checks overnight, 2) incorporate this predictive model into the hospital electronic health record so physicians can view its output and use it to guide their decision-making around ordering reduced vital sign checks for select patients.
Read the detailed description
Patients in the hospital often report poor sleep. A lack of sleep not only affects a patient's recovery from illness and their overall feeling of wellness, but it is a leading factor in the development of delirium in the hospital. One method for improving sleep in the hospital is to reduce the number of patient care related interruptions that a patient experiences. Vital sign checks at night are one example. In hospitalized patients who are clinically stable, vital sign checks that interrupt sleep are often unnecessary. However, identifying which patients can forego these checks is not a simple task. Currently, the hospital's quality improvement team asks physicians to think about this issue every day and order reduced, or "sleep promotion", vital sign checks on patients they believe could safely tolerate it. The investigators goal is to use a predictive analytics tool to reduce the cognitive burden of this task for busy physicians.
The investigators plan to develop a logistic regression model, trained on data from the electronic health record (EHR), to predict, for a given patient on a given night, whether they could safely tolerate the reduction of overnight vital sign checks. The model will use variables, such as the patient's age, the number of days they have been in the hospital, the vital signs from that day, the lab values from that day, and other clinical variables to make its prediction. The outcome is a binary variable, whether the patient will or will not have abnormal vital signs that night. The training data is retrospective therefore it contains the nighttime vitals that were observed, which the investigators will code as a binary variable and use as the outcome variable for the model to train against.
The investigators will incorporate this algorithm into an EHR alert so physicians can observe its output during their work, and use this information, complemented by their own clinical judgment, to decide about ordering reduced vital sign checks for a given patient.
The investigators will study the effect of this EHR alert on several outcomes: in-hospital delirium (measured by nurse assessment), sleep opportunity (a measurement, based on observational EHR data, of patient care related sleep interruptions), and patient satisfaction (measured by nationally-administered post-hospitalization HCAHPS surveys). Balancing measures, to ensure that reduced vital sign checks do not cause patient harm, will be rapid response calls and code blue calls.
Physician teams will be randomized to either see the EHR alert (intervention arm) or not see the EHR alert.
- Conditions
- Delirium, Sleep Disturbance
- Being tested
- Nighttime Vital Sign EHR Alert, No EHR alert
- Medical subject headings
- Delirium, Parasomnias
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Nighttime Vital Sign EHR AlertBehavioural
A pop-up window in the EHR will notify a physician that their patient has been judged by a predictive algorithm to be safe for reduced overnight vital sign checks.
No EHR alertOther
No change to EHR function; no alert visible to providers
| Study group | Role | What they receive |
|---|---|---|
| EHR Alert | Experimental | Physician teams will observe the EHR alert as they perform their clinical duties in the EHR. Receives: Behavioral: Nighttime Vital Sign EHR Alert |
| No Alert | Placebo comparator | Physician teams will perform their clinical duties in the EHR as usual, with no visible alert. Receives: Other: No EHR alert |
What is being measured
The primary outcome is the main question the study is designed to answer.
delirium
Nursing Delirium Screening Scale (Nu-DESC score) - assessed by the nurse, can range from zero to ten, a score > 2 has good accuracy for delirium
Measured: average will be measured at study completion (6 months from study start date - Sep 11, 2019)
Secondary outcomes (2)
sleep opportunity
a *novel* measurement based on observational EHR data - for every night in the hospital, the investigators can extract from the EHR all event timestamps that could have interrupted the patient's sleep (measured between 11 pm and 6 am). These are blood pressure recordings, fingerstick glucose checks, blood draws for labs, and not-as-needed medication administrations. The maximum time period between such events is considered the patient's sleep opportunity for that night (measured in hours). A higher sleep-opportunity on a given night is better. The investigators can calculate an average sleep-opportunity for a hospital encounter and then an average sleep-opportunity for all encounters in a clinical trial arm.
Measured: average will be calculated at study completion (6 months from study start date - Sep 11, 2019)
patient satisfaction
results from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys administered to patients after discharge from the hospital (scale is a categorical response: never, sometimes, usually, or always)
Measured: average score will be measured at study completion (6 months from study start date - Sep 11, 2019)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Aug 2019
- Study start11 Mar 2019
- Primary completion4 Nov 2019
- Study completion4 Nov 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for DeliriumStudies of the same condition, open and under way.
- More trials run by University of California, San FranciscoThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- The investigators' intervention, which is a notification to the physician that is seen in the EHR, is randomized at the patient level. The patients randomized to the control group do not have a notification shown to their physician while the intervention patients do.
- Masking
- None (open label)
- Enrolment
- 1,436 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of California, San Francisco
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Mark Pletcher, MD, Study director, Director of the UCSF Informatics and Research Innovation Program
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04046458
- Sponsor's protocol ID
- nightvitals
- Status last verified
- Dec 2019
- First submitted
- 9 Mar 2018
- Last updated
- 4 Dec 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Participants are physician teams. The investigators may submit their alert-response data to an online resource.
Publications
Najafi N, Robinson A, Pletcher MJ, Patel S. Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients: A Randomized Clinical Trial. JAMA Intern Med. 2022 Feb 1;182(2):172-177. doi: 10.1001/jamainternmed.2021.7387.
PubMed 34962506
Study at a glance
From the registry record, last updated by the sponsor on 4 Dec 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Predictive Analytics to Reduce Vital Sign Checks in Stable Hospitalized Patients
- ClinicalTrials.gov ID
- NCT04046458
- Acronym / sponsor ID
- nightvitals
- Conditions
- Delirium, Sleep Disturbance
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,436 participants (actual)
- Lead sponsor
- University of California, San FranciscoUniversity, hospital or other
- Study start
- 11 Mar 2019 (actual)
- Primary completion
- 4 Nov 2019 (actual)
- Study completion
- 4 Nov 2019 (actual)
- First posted
- 6 Aug 2019 (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.
The overall goals for this study are: 1) to develop a predictive model to identify patients who are stable enough to forego vital sign checks overnight, 2) incorporate this predictive model into the hospital electronic health record so physicians can view its output and use it to guide their decision-making around ordering reduced vital sign checks for select patients.
Detailed description
Patients in the hospital often report poor sleep. A lack of sleep not only affects a patient's recovery from illness and their overall feeling of wellness, but it is a leading factor in the development of delirium in the hospital. One method for improving sleep in the hospital is to reduce the number of patient care related interruptions that a patient experiences. Vital sign checks at night are one example. In hospitalized patients who are clinically stable, vital sign checks that interrupt sleep are often unnecessary. However, identifying which patients can forego these checks is not a simple task. Currently, the hospital's quality improvement team asks physicians to think about this issue every day and order reduced, or "sleep promotion", vital sign checks on patients they believe could safely tolerate it. The investigators goal is to use a predictive analytics tool to reduce the cognitive burden of this task for busy physicians.
The investigators plan to develop a logistic regression model, trained on data from the electronic health record (EHR), to predict, for a given patient on a given night, whether they could safely tolerate the reduction of overnight vital sign checks. The model will use variables, such as the patient's age, the number of days they have been in the hospital, the vital signs from that day, the lab values from that day, and other clinical variables to make its prediction. The outcome is a binary variable, whether the patient will or will not have abnormal vital signs that night. The training data is retrospective therefore it contains the nighttime vitals that were observed, which the investigators will code as a binary variable and use as the outcome variable for the model to train against.
The investigators will incorporate this algorithm into an EHR alert so physicians can observe its output during their work, and use this information, complemented by their own clinical judgment, to decide about ordering reduced vital sign checks for a given patient.
The investigators will study the effect of this EHR alert on several outcomes: in-hospital delirium (measured by nurse assessment), sleep opportunity (a measurement, based on observational EHR data, of patient care related sleep interruptions), and patient satisfaction (measured by nationally-administered post-hospitalization HCAHPS surveys). Balancing measures, to ensure that reduced vital sign checks do not cause patient harm, will be rapid response calls and code blue calls.
Physician teams will be randomized to either see the EHR alert (intervention arm) or not see the EHR alert.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- The investigators' intervention, which is a notification to the physician that is seen in the EHR, is randomized at the patient level. The patients randomized to the control group do not have a notification shown to their physician while the intervention patients do.
| Arm | Type | Receives |
|---|---|---|
| EHR Alert | Experimental | Behavioral: Nighttime Vital Sign EHR AlertPhysician teams will observe the EHR alert as they perform their clinical duties in the EHR. |
| No Alert | Placebo comparator | Other: No EHR alertPhysician teams will perform their clinical duties in the EHR as usual, with no visible alert. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- All physician teams that operate under the UCSF Division of Hospital Medicine
Key exclusion criteria (1 listed)
- N/A
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 |
|---|---|---|
| Nighttime Vital Sign EHR AlertA pop-up window in the EHR will notify a physician that their patient has been judged by a predictive algorithm to be safe for reduced overnight vital sign checks. | Behavioural | EHR Alert |
| No EHR alertNo change to EHR function; no alert visible to providers | Other | No Alert |
Primary outcome
delirium
Time frame: average will be measured at study completion (6 months from study start date - Sep 11, 2019)
Nursing Delirium Screening Scale (Nu-DESC score) - assessed by the nurse, can range from zero to ten, a score > 2 has good accuracy for delirium
2 secondary outcomes
sleep opportunity
Time frame: average will be calculated at study completion (6 months from study start date - Sep 11, 2019)
patient satisfaction
Time frame: average score will be measured at study completion (6 months from study start date - Sep 11, 2019)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Dec 2019.
- Derived from this study: PubMed 34962506Najafi N, Robinson A, Pletcher MJ, Patel S. Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients: A Randomized Clinical Trial. JAMA Intern Med. 2022 Feb 1;182(2):172-177. doi: 10.1001/jamainternmed.2021.7387.
- Individual participant dataSharing planned: No. Participants are physician teams. The investigators may submit their alert-response data to an online resource.
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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.
Mark Pletcher, MD study director
Director of the UCSF Informatics and Research Innovation Program
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Nighttime Vital Sign EHR Alert and No EHR alert in people with Delirium and Sleep Disturbance, run by University of California, San Francisco 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: finished, as of the sponsor's last update on 4 Dec 2019. 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. Some participants in this study may receive a placebo. 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 is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
delirium (time frame: average will be measured at study completion (6 months from study start date - Sep 11, 2019)). 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 NCT04046458, which also lists 1 linked publication.
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 4 Dec 2019.