A randomized study on the usefulness of an electronic outpatient hypoglycemia risk calculator for clinicians of patients with diabetes in a safety-net institution

Abstract

Objective. Hypoglycemia (HG) occurs in up to 60% of patients with diabetes mellitus (DM) each year. Our objective was to assess a HG alert tool in an electronic health record system, and determine the tool's effect on clinical practice and outcomes.Methods. The tool used a logistic-regression model to provide patient-specific information about HG risk. We randomized academic outpatient primary-care providers (PCPs) to see or not see the alerts. Adult patients were assigned to study group according to the first PCP seen during four months. We assessed five months' prescriptions, diagnostic testing, and HG. Categorical variables were compared by multinomial model, binary variables by logistic model, and continuous variables by linear model. Results. A total of 3350 patients visited 123 intervention PCPs; 3395 patients visited 220 control PCPs. Intervention PCPs were shown 18,645 alerts. Patients' mean age was 55 years, with 61% female, 49% black, and 49% with Medicaid. Mean baseline A1c (8.7%) and body mass index (35.2 kg/m 2 ) were similar between groups. During follow-up, the number of A1c and glucose tests, and number of new, refilled, changed, or discontinued insulin prescriptions, were highest for patients with highest risk. Per 100 patients, the intervention group had significantly fewer sulfonylurea refills (6 vs. 8; p<0.05) and outpatient encounters (470 vs. 502; p<0.05). Frequency of A1c testing and HG events was unchanged.Conclusions. Informing PCPs about risk of HG led to fewer sulfonylurea refills and visits.Longer-term studies are needed to assess the potential for long-term benefits of the alert.

Prediction models of diabetes complications: a scoping review

“…Most models included sex/gender as candidate predictors (n=61) and three performed sex-specific analyses 27–30. Studies were either restricted to people with type 1 (n=8)30–37 or type 2 diabetes (n=54)26–29 38–87 or combined both (n=7) 88–94. Eight studies did not specify diabetes types95–102 and one mixed people with pre-diabetes and diabetes (n=1) 103.…”

A scoping review of the predictive models of diabetes complications

“…Selected studies were either restricted to people with type 1 (n=8) [39][40][41][42][43][44][45][46] or type 2 diabetes (n=54), [35][36][37][38] or combined both types (n=7). [97][98][99][100][101][102][103] Some studies did not specify diabetes types (n=8) [104][105][106][107][108][109][110][111] and one mixed people with prediabetes and diabetes (n=1). 112 Most selected studies included adults only, either up to 60 years old (n=11) 39,40,42,[44][45][46]64,82,96,98,99 or without an upper age limit (n=65).…”

“…55 Most studies included people of diverse race/ethnicity (n=40). 35,39,47,48,51,52,[57][58][59][62][63][64]67,69,70,73,76,[79][80][81][82][83][84][85][86][87][88]93,[95][96][97][100][101][102][103][104][105][108][109][110] A small number of studies studied only either white people (n=2), 54,56 Black people (n=1) 48 or Asian people (n=9). [36][37][38]68,74,75,[89]…”

“…Concerning We included 8 studies reporting on 14 validated prediction models. 35,52,61,62,101,103,104,107 Some of the selected models focused on severe hypoglycemia (n=4/14), which refers to an episode with typical symptoms (e.g., sweating, dizziness, tremor, visual disturbance) that resolves after treatment (oral carbohydrate, intramuscular glucagon, or intravenous glucose) administered by another person. 115 Models often predicted the risk of hypoglycemia in people with type 2 diabetes 35,52,61,62 or unspecified types.…”

Technical Requirement of Clinical Decision Support system for Diabetic Patients

“…For the treatment of diabetes, achievement of target glucose levels is difficult because physicians have no benchmark to make decisions about whether to start, continue, or adjust insulin doses or injections. Since clinicians should regularly assess the risk of HG in patients with DM, an automated, point-of-care approach to estimating risk may help clinicians to save time and identify strategies to limit risk of HG among their patients [7]. Therefore, an obvious gap exists between actual clinical practice and optimal patient care.…”