Canadian Primary Sentinel Surveillance Network Research Articles

Underuse of cardiorenal protective agents in high-risk diabetes patients in primary care: a cross-sectional study

BackgroundSodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) have shown benefits in patients with diabetes and cardiovascular disease (CVD), heart failure (HF), and chronic kidney disease (CKD).ObjectiveWe assessed benchmark outcomes (Hemoglobin A1c, LDL-C, and blood pressure), identified the prevalence of cardiorenal indications for SGLT2i and GLP-1RA, and compared prescribing rates of GLP1-RA and SGLT2i in those with and without cardiorenal indications.MethodsWe analyzed data from January 2018–June 2019 for 7168 patients with diabetes using electronic medical records from the Northern Alberta Primary Care Research Network, a regional network of the Canadian Primary Sentinel Surveillance Network (CPCSSN). Patients with and without cardiorenal comorbidities were compared using descriptive statistics and two proportion Z tests.ResultsHemoglobin A1c ≤ 7.0% was met by 56.8%, blood pressure < 130/80 mmHg by 62.1%, LDL-C ≤ 2.0 mmol/L by 45.3% of patients. There were 4377 patients on glucose lowering medications; metformin was most common (77.7%), followed by insulin (24.6%), insulin secretagogues (23.6%), SGLT2i (19.7%), dipeptidyl peptidase-4 inhibitor (19.3%), and GLP-1RA (9.4%). A quarter of patients had cardiorenal indications for SGLT2i or GLP-1RA. Use of SGLT2i in these patients was lower than in patients without cardiorenal comorbidities (14.9% vs 21.2%, p < 0.05). GLP-1RA use in these patients was 4.6% compared with 11% in those without cardiorenal comorbidities (p < 0.05).DiscussionContrary to current evidence and recommendations, SGLT2i and GLP1-RA were less likely to be prescribed to patients with pre-existing CVD, HF, and/or CKD, revealing opportunities to improve prescribing for patients with diabetes at high-risk for worsening cardiorenal complications.

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology

BackgroundWe developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction. MethodsWe used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived. ResultsThe resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology. ConclusionsIf the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.