Hypoglycemia Prediction Model
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|ClinicalTrials.gov Identifier: NCT03006510|
Recruitment Status : Unknown
Verified December 2016 by Robert Rushakoff, University of California, San Francisco.
Recruitment status was: Not yet recruiting
First Posted : December 30, 2016
Last Update Posted : December 30, 2016
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.
We 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||Early Phase 1|
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||6500 participants|
|Intervention Model:||Parallel Assignment|
|Masking:||Single (Care Provider)|
|Official Title:||Leveraging the Power of the EMR: Using a Real Time Prediction Model to Decrease Inpatient Hypoglycemic Events|
|Study Start Date :||January 2017|
|Estimated Primary Completion Date :||January 2018|
|Estimated Study Completion Date :||July 2018|
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.
- The proportion of patients (in each group) who ultimately have a hypoglycemic event [ Time Frame: 72 hours ]
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03006510
|Contact: Robert J Rushakoff, MDfirstname.lastname@example.org|