HypoglycemiaNCT03006510
Hypoglycemia Prediction Model
A study testing Hypoglycemia prediction alert in people with Hypoglycemia, run by University of California, San Francisco.
Trial summary
Our goal for this Learning Healthcare System Demonstration Project is to reduce the rate of inpatient hypoglycemia. Hypoglycemia can result in longer lengths of stay and increased morbidity and mortality (ie falls and cardiovascular or cerebral events). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hypoglycemia
- What is being tested?
- Hypoglycemia prediction alert
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 498 participants
- Where
- No sites listed yet
- Study dates
- Jan 2017to 1 Jun 2018
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All adult inpatients having glucoses measured (point of care)
Who cannot join
- adults admitted to obstetrics
About this study
Official title: Leveraging the Power of the EMR: Using a Real Time Prediction Model to Decrease Inpatient Hypoglycemic Events
Our goal for this Learning Healthcare System Demonstration Project is to reduce the rate of inpatient hypoglycemia. Hypoglycemia can result in longer lengths of stay and increased morbidity and mortality (ie falls and cardiovascular or cerebral events).
The group at Washington University (WSL) developed a predictive hypoglycemia risk score. Using current glucose, body weight, creatinine clearance, insulin type and dosing, and oral diabetic therapy, they identified patients at high risk for hypoglycemia and then provided in-person education to the providers of these patients. This resulted in a 68% reduction in severe hypoglycemia (blood glucose < 40 mg/dL). This approach required significant personnel hours and is difficult to replicate in other systems.
Read more
The investigators will implement an EHR-based intervention at UCSF to predict which patients are at high risk of inpatient hypoglycemia and take action to prevent the hypoglycemic event. In real time, all adult (non OB) patients with a glucose < 90, and a high risk of future hypoglycemia (based on the WSL formula) will be identified. Patients will be randomly assigned to intervention or no intervention (current standard care). The intervention will consist of an automated provider alert with recommendations on what adjustments could be made to avoid a potentially serious hypoglycemic event.
The outcomes that will be measured include: 1) reductions in serious hypoglycemic events, 2) monitor the changes made by providers as a result of alerts in order to study provider behavior and identify future areas of intervention, and 3) provider satisfaction with the alert system.
- Conditions
- Hypoglycemia
- Being tested
- Hypoglycemia prediction alert
- Keywords
- inpatient diabetes, hypoglycemia
- Medical subject headings
- Hypoglycemia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Hypoglycemia prediction alertOther
In real time, for a patient with a glucose <90 mg/d, using a hypoglycemia prediction model that takes into account patient weight, renal function, eating and insulin dosing a risk score is produced.
If the Risk score is >35, then the patient is determined to be at risk for hypoglycemia in the next 72 hours.
If a patient is determined to be at risk for hypoglycemia, the following will occur:
Alert will be generated and sent via "careweb" a pager alert system that sends the alert specifically to the current oncall provider The "alert" also points the provider to the EMR order section where a formal more detailed alert gives recommendationsd for changes in insulin dosing to potentially prevent hypoglycemia.
| Study group | Role | What they receive |
|---|---|---|
| Alert | Active comparator | If glucose <90 mg/dl and hypoglycemia prediction score >35, then alert with suggestion for intervention sent to treating team Receives: Other: Hypoglycemia prediction alert |
| No alert | No intervention | Routine standard care. If glucose <90 mg/dl and hypoglycemia prediction score >35, then report for investigators will be collected, but no active alert will be sent to teams. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The proportion of patients (in each group) who ultimately have a hypoglycemic event
Measured: 72 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2016 (estimated)
- Study startJan 2017
- Primary completion1 Jun 2018
- Study completion1 Jun 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for HypoglycemiaStudies 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
- Masking
- Single blind
- Who is masked
- Care provider
- Enrolment
- 498 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of California, San Francisco
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Robert J Rushakoff, MD, Principal investigator, University of California, San Francisco
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03006510
- Sponsor's protocol ID
- 16-20565
- Status last verified
- Oct 2021
- First submitted
- 28 Dec 2016
- Last updated
- 8 Oct 2021
- Results posted
- No
- Data monitoring committee
- No
Publications
Turchin A, Matheny ME, Shubina M, Scanlon JV, Greenwood B, Pendergrass ML. Hypoglycemia and clinical outcomes in patients with diabetes hospitalized in the general ward. Diabetes Care. 2009 Jul;32(7):1153-7. doi: 10.2337/dc08-2127.
PubMed 19564471Nirantharakumar K, Marshall T, Kennedy A, Narendran P, Hemming K, Coleman JJ. Hypoglycaemia is associated with increased length of stay and mortality in people with diabetes who are hospitalized. Diabet Med. 2012 Dec;29(12):e445-8. doi: 10.1111/dme.12002.
PubMed 22937877Kagansky N, Levy S, Rimon E, Cojocaru L, Fridman A, Ozer Z, Knobler H. Hypoglycemia as a predictor of mortality in hospitalized elderly patients. Arch Intern Med. 2003 Aug 11-25;163(15):1825-9. doi: 10.1001/archinte.163.15.1825.
PubMed 12912719Carey M, Boucai L, Zonszein J. Impact of hypoglycemia in hospitalized patients. Curr Diab Rep. 2013 Feb;13(1):107-13. doi: 10.1007/s11892-012-0336-x.
PubMed 23065370Garg R, Hurwitz S, Turchin A, Trivedi A. Hypoglycemia, with or without insulin therapy, is associated with increased mortality among hospitalized patients. Diabetes Care. 2013 May;36(5):1107-10. doi: 10.2337/dc12-1296. Epub 2012 Dec 17.
PubMed 23248192Desouza C, Salazar H, Cheong B, Murgo J, Fonseca V. Association of hypoglycemia and cardiac ischemia: a study based on continuous monitoring. Diabetes Care. 2003 May;26(5):1485-9. doi: 10.2337/diacare.26.5.1485.
PubMed 12716809Schwartz AV, Vittinghoff E, Sellmeyer DE, Feingold KR, de Rekeneire N, Strotmeyer ES, Shorr RI, Vinik AI, Odden MC, Park SW, Faulkner KA, Harris TB; Health, Aging, and Body Composition Study. Diabetes-related complications, glycemic control, and falls in older adults. Diabetes Care. 2008 Mar;31(3):391-6. doi: 10.2337/dc07-1152. Epub 2007 Dec 4.
PubMed 18056893Dendy JA, Chockalingam V, Tirumalasetty NN, Dornelles A, Blonde L, Bolton PM, Meadows RY, Andrews SS. Identifying risk factors for severe hypoglycemia in hospitalized patients with diabetes. Endocr Pract. 2014 Oct;20(10):1051-6. doi: 10.4158/EP13467.OR.
PubMed 24936545Ulmer BJ, Kara A, Mariash CN. Temporal occurrences and recurrence patterns of hypoglycemia during hospitalization. Endocr Pract. 2015 May;21(5):501-7. doi: 10.4158/EP14355.OR. Epub 2015 Feb 9.
PubMed 25667368Elliott MB, Schafers SJ, McGill JB, Tobin GS. Prediction and prevention of treatment-related inpatient hypoglycemia. J Diabetes Sci Technol. 2012 Mar 1;6(2):302-9. doi: 10.1177/193229681200600213.
PubMed 22538139Kilpatrick CR, Elliott MB, Pratt E, Schafers SJ, Blackburn MC, Heard K, McGill JB, Thoelke M, Tobin GS. Prevention of inpatient hypoglycemia with a real-time informatics alert. J Hosp Med. 2014 Oct;9(10):621-6. doi: 10.1002/jhm.2221. Epub 2014 Jun 5.
PubMed 24898687
Study at a glance
From the registry record, last updated by the sponsor on 8 Oct 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Leveraging the Power of the EMR: Using a Real Time Prediction Model to Decrease Inpatient Hypoglycemic Events
- ClinicalTrials.gov ID
- NCT03006510
- Acronym / sponsor ID
- 16-20565
- Conditions
- Hypoglycemia
- Keywords
- inpatient diabetes, hypoglycemia
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 498 participants (actual)
- Lead sponsor
- University of California, San FranciscoUniversity, hospital or other
- Study start
- Jan 2017
- Primary completion
- 1 Jun 2018 (actual)
- Study completion
- 1 Jun 2018 (actual)
- First posted
- 30 Dec 2016 (estimated)
- 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.
Our goal for this Learning Healthcare System Demonstration Project is to reduce the rate of inpatient hypoglycemia. Hypoglycemia can result in longer lengths of stay and increased morbidity and mortality (ie falls and cardiovascular or cerebral events).
The group at Washington University (WSL) developed a predictive hypoglycemia risk score. Using current glucose, body weight, creatinine clearance, insulin type and dosing, and oral diabetic therapy, they identified patients at high risk for hypoglycemia and then provided in-person education to the providers of these patients. This resulted in a 68% reduction in severe hypoglycemia (blood glucose < 40 mg/dL). This approach required significant personnel hours and is difficult to replicate in other systems.
The investigators will implement an EHR-based intervention at UCSF to predict which patients are at high risk of inpatient hypoglycemia and take action to prevent the hypoglycemic event. In real time, all adult (non OB) patients with a glucose < 90, and a high risk of future hypoglycemia (based on the WSL formula) will be identified. Patients will be randomly assigned to intervention or no intervention (current standard care). The intervention will consist of an automated provider alert with recommendations on what adjustments could be made to avoid a potentially serious hypoglycemic event.
The outcomes that will be measured include: 1) reductions in serious hypoglycemic events, 2) monitor the changes made by providers as a result of alerts in order to study provider behavior and identify future areas of intervention, and 3) provider satisfaction with the alert system.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (care provider)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Alert | Active comparator | Other: Hypoglycemia prediction alertIf glucose <90 mg/dl and hypoglycemia prediction score >35, then alert with suggestion for intervention sent to treating team |
| No alert | No intervention | –Routine standard care. If glucose <90 mg/dl and hypoglycemia prediction score >35, then report for investigators will be collected, but no active alert will be sent to teams. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All adult inpatients having glucoses measured (point of care)
Key exclusion criteria (1 listed)
- adults admitted to obstetrics
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 |
|---|---|---|
| Hypoglycemia prediction alertIn real time, for a patient with a glucose <90 mg/d, using a hypoglycemia prediction model that takes into account patient weight, renal function, eating and insulin dosing a risk score is produced. If the Risk score is >35, then the patient is determined to be at risk for hypoglycemia in the next 72 hours. If a patient is determined to be at risk for hypoglycemia, the following will occur: Alert will be generated and sent via "careweb" a pager alert system that sends the alert specifically to the current oncall provider The "alert" also points the provider to the EMR order section where a formal more detailed alert gives recommendationsd for changes in insulin dosing to potentially prevent hypoglycemia. | Other | Alert |
Primary outcome
The proportion of patients (in each group) who ultimately have a hypoglycemic event
Time frame: 72 hours
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 8 Oct 2021.
- Background: PubMed 19564471Turchin A, Matheny ME, Shubina M, Scanlon JV, Greenwood B, Pendergrass ML. Hypoglycemia and clinical outcomes in patients with diabetes hospitalized in the general ward. Diabetes Care. 2009 Jul;32(7):1153-7. doi: 10.2337/dc08-2127.
- Background: PubMed 22937877Nirantharakumar K, Marshall T, Kennedy A, Narendran P, Hemming K, Coleman JJ. Hypoglycaemia is associated with increased length of stay and mortality in people with diabetes who are hospitalized. Diabet Med. 2012 Dec;29(12):e445-8. doi: 10.1111/dme.12002.
- Background: PubMed 12912719Kagansky N, Levy S, Rimon E, Cojocaru L, Fridman A, Ozer Z, Knobler H. Hypoglycemia as a predictor of mortality in hospitalized elderly patients. Arch Intern Med. 2003 Aug 11-25;163(15):1825-9. doi: 10.1001/archinte.163.15.1825.
- Background: PubMed 23065370Carey M, Boucai L, Zonszein J. Impact of hypoglycemia in hospitalized patients. Curr Diab Rep. 2013 Feb;13(1):107-13. doi: 10.1007/s11892-012-0336-x.
- Background: PubMed 23248192Garg R, Hurwitz S, Turchin A, Trivedi A. Hypoglycemia, with or without insulin therapy, is associated with increased mortality among hospitalized patients. Diabetes Care. 2013 May;36(5):1107-10. doi: 10.2337/dc12-1296. Epub 2012 Dec 17.
- Background: PubMed 12716809Desouza C, Salazar H, Cheong B, Murgo J, Fonseca V. Association of hypoglycemia and cardiac ischemia: a study based on continuous monitoring. Diabetes Care. 2003 May;26(5):1485-9. doi: 10.2337/diacare.26.5.1485.
- Background: PubMed 18056893Schwartz AV, Vittinghoff E, Sellmeyer DE, Feingold KR, de Rekeneire N, Strotmeyer ES, Shorr RI, Vinik AI, Odden MC, Park SW, Faulkner KA, Harris TB; Health, Aging, and Body Composition Study. Diabetes-related complications, glycemic control, and falls in older adults. Diabetes Care. 2008 Mar;31(3):391-6. doi: 10.2337/dc07-1152. Epub 2007 Dec 4.
- Background: PubMed 24936545Dendy JA, Chockalingam V, Tirumalasetty NN, Dornelles A, Blonde L, Bolton PM, Meadows RY, Andrews SS. Identifying risk factors for severe hypoglycemia in hospitalized patients with diabetes. Endocr Pract. 2014 Oct;20(10):1051-6. doi: 10.4158/EP13467.OR.
- Background: PubMed 25667368Ulmer BJ, Kara A, Mariash CN. Temporal occurrences and recurrence patterns of hypoglycemia during hospitalization. Endocr Pract. 2015 May;21(5):501-7. doi: 10.4158/EP14355.OR. Epub 2015 Feb 9.
- Background: PubMed 22538139Elliott MB, Schafers SJ, McGill JB, Tobin GS. Prediction and prevention of treatment-related inpatient hypoglycemia. J Diabetes Sci Technol. 2012 Mar 1;6(2):302-9. doi: 10.1177/193229681200600213.
- Background: PubMed 24898687Kilpatrick CR, Elliott MB, Pratt E, Schafers SJ, Blackburn MC, Heard K, McGill JB, Thoelke M, Tobin GS. Prevention of inpatient hypoglycemia with a real-time informatics alert. J Hosp Med. 2014 Oct;9(10):621-6. doi: 10.1002/jhm.2221. Epub 2014 Jun 5.
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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.
Robert J Rushakoff, MD principal investigator
University of California, San Francisco
Questions about this trial
What is this clinical trial studying?
A study testing Hypoglycemia prediction alert in people with Hypoglycemia, run by University of California, San Francisco.
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 8 Oct 2021. Status can change between updates, so confirm with the study contact before making plans.
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 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?
The proportion of patients (in each group) who ultimately have a hypoglycemic event (time frame: 72 hours). 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 NCT03006510, which also lists 11 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 8 Oct 2021.