Hypoglycemia Prediction Model

Sponsor

University of California, San Francisco (Other)

Overall Status

Completed

CT.gov ID

NCT03006510

Collaborator

(none)

498

Enrollment

Arms

Duration (Months)

Study Details

Study Description

Brief 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).

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.

Condition or Disease	Intervention/Treatment	Phase
Hypoglycemia	Other: Hypoglycemia prediction alert	N/A

Study Design

Study Type:

Interventional

Actual Enrollment :

498 participants

Allocation:

Randomized

Intervention Model:

Parallel Assignment

Masking:

Single (Care Provider)

Primary Purpose:

Prevention

Official Title:

Leveraging the Power of the EMR: Using a Real Time Prediction Model to Decrease Inpatient Hypoglycemic Events

Study Start Date :

Jan 1, 2017

Actual Primary Completion Date :

Jun 1, 2018

Actual Study Completion Date :

Jun 1, 2018

Arms and Interventions

Arm	Intervention/Treatment
Active Comparator: Alert If glucose <90 mg/dl and hypoglycemia prediction score >35, then alert with suggestion for intervention sent to treating team	Other: Hypoglycemia prediction alert 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.
No Intervention: No alert 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.

Arm

Intervention/Treatment

Active Comparator: Alert

If glucose <90 mg/dl and hypoglycemia prediction score >35, then alert with suggestion for intervention sent to treating team

Other: Hypoglycemia prediction alert

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.

No Intervention: No alert

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.

Outcome Measures

Primary Outcome Measures

The proportion of patients (in each group) who ultimately have a hypoglycemic event [72 hours]

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

All adult inpatients having glucoses measured (point of care)

Exclusion Criteria:

adults admitted to obstetrics

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

University of California, San Francisco

Investigators

Principal Investigator: Robert J Rushakoff, MD, University of California, San Francisco

Study Documents (Full-Text)

None provided.

More Information

Publications

Responsible Party:

University of California, San Francisco

ClinicalTrials.gov Identifier:

NCT03006510

Other Study ID Numbers:

16-20565

First Posted:

Dec 30, 2016

Last Update Posted:

Oct 8, 2021

Last Verified:

Oct 1, 2021

Keywords provided by University of California, San Francisco

inpatient diabetes

hypoglycemia

Additional relevant MeSH terms:

Hypoglycemia

Study Results

No Results Posted as of Oct 8, 2021