Randomized Evaluation of a Remote Management Program to Improve Guideline-directed Medical Therapy: The Diabetes Remote Intervention to Improve Use of Evidence-based Medications (DRIVE) Trial

BACKGROUND

Several sodium-glucose transport protein 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) reduce cardiovascular (CV) events and improve kidney outcomes in patients with type 2 diabetes (T2D); however, utilization remains low despite guideline recommendations.

METHODS

A randomized, remote implementation trial in the Mass General Brigham network enrolled patients with T2D at high CV and /or kidney risk. Patients eligible for, but not prescribed, SGLT2i or GLP-1 RA were randomly assigned to simultaneous virtual patient education with concurrent prescription of SGLT2i or GLP-1 RA ("simultaneous") or two months of virtual education followed by medication prescription ("education-first") delivered by a multi-disciplinary team driven by non-licensed navigators and clinical pharmacists who prescribed SGLT2i or GLP-1 RA using a standardized treatment algorithm. The primary outcome was the proportion of patients with prescriptions for either SGLT2i or GLP-1 RA by 6 months.

RESULTS

Between March 2021 and December 2022, 200 patients were randomized. Mean age was 66.5 years, 36.5% were female, 22.0% were non-White. Overall, 30.0% had cardiovascular CV disease, 5.0% had cerebrovascular disease, and 1.5% had both. Mean estimated glomerular filtration rate (eGFR) 77.9 mL/min/1.73m2 and mean urine/albumin creatinine ratio (UACR) 88.6mg/g. After two months, 69/200 (34.5%) patients received a new prescription for either SGLT2i or GLP-1 RA: 53.4% of patients in the simultaneous arm vs. 8.3% of patients were in the education-first arm (p<0.001). After six months, 128/200 (64.0%) received a new prescription: 69.8 % of patients in the simultaneous arm vs. 56.0% of patients in education-first (p<0.001). Patient self-report of taking SGLT2i or GLP-1 RA within six months of trial entry was similarly higher in the simultaneous versus education-first arm (69 /116; 59.5% vs 37/84; 44.0%; p<0.001) Median time to first prescription was 24 (IQR 13, 50) vs 85 days (IQR 65, 106), respectively (p<0.001).

CONCLUSIONS

In this randomized trial, a remote team-based program that identifies patients with T2D and high CV or kidney risk, provides virtual education, and prescribes SGLT2i or GLP-1 RA improves GDMT. These findings support greater utilization of virtual team-based approaches to optimize chronic disease management.

Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs

OBJECTIVES

The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists.

MATERIALS AND METHODS

A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.

RESULTS

The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.

CLINICAL SIGNIFICANCE

We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.