Diabetes mellitus affects about 7% of the populations of Canada and the United States -some 23 million people -and accounts for direct annual health care costs of about $105 billion.1,2 At least 90% of people with diabetes have type 2 diabetes. In addition to being a major risk factor for cardiovascular disease (whereby the risks of myocardial infarction and stroke are 2-4 times those in the nondiabetic population), diabetes is the primary cause of renal failure, blindness and nontraumatic limb amputation.1,2 International guidelines recommend interventions to prevent these complications, mainly on the basis of evidence from large randomized clinical trials.3-7 These interventions include control of glucose, blood pressure and lipids; vascular protection with acetylsalicylic acid; diet; exercise; renal protection; smoking cessation for smokers; prevention and treatment of retinopathy; and education about foot surveillance. In a recent study, intensive intervention to address multiple risk factors was associated with lower rates of mortality (by 56%), cardiovascular events (by 59%), nephropathy (by 56%) and retinopathy (by 55%) over 13 years relative to conventional therapy. 8 These major changes in the frequency of events occurred despite the small differences (0.3% for glycated hemoglobin, 6 mm Hg for systolic blood pressure and 0.2 mmol/L for low-density lipoprotein [LDL] cholesterol) between groups by the end of the open follow-up period. However, optimal care of patients with diabetes in the community has been difficult to achieve, because it can be difficult to sustain regular monitoring and attention to many risk factors over many years, especially for patients with multiple health care providers. 9,10 Most diabetes care takes place in the community, largely managed in the primary care setting. In this environment, short visits, competing visit objectives, lack of proactive systems for disease surveillance and alerting support, difficulties staying up to date on ever-shifting targets, challenges associated with managing multiple medications and inertia related to chronic disease (on the part of both patient and physician)

The development of a pan-Canadian network of primary care research networks for studying issues in primary care has been the vision of Canadian primary care researchers for many years.

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Background Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body’s inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. Methods Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity – the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. Results The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. Conclusions The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.