In this study, a simple novel risk score was developed and validated, and shown to have reasonable accuracy in predicting MACE events (nonfatal MI, stroke, or CV death) among patients with T2DM and ASCVD. Several important findings were observed. First, the developed risk score incorporated commonly used clinical and laboratory variables, and could identify individuals with 4-year risk of MACE ranging from 6 to 25%. Second, while the more complex model of 19 variables resulted in better discrimination, a simplified risk score of only 9 patient characteristics retained much of the operating characteristics of the larger, more complex model. Finally, the score performed well in an external cohort of patients with T2DM and ASCVD. In all, the findings suggest a novel method to identify the highest risk patients and inform targeting of ASCVD secondary prevention strategies in patients with T2DM and prevalent ASCVD.
Management of patients with T2DM has historically been focused on the achievement of glycemic targets. However, newer classes of medications—such as sodium-glucose cotransporter 2 inhibitors (SGLT2i) and GLP-1 receptor agonists—have been shown to reduce the risk of CV events in patients with T2DM and established ASCVD [19,20,21]. The mechanisms by which these drugs achieve CV benefit and whether these benefits exists across the entire spectrum of ASCVD risk, though, are unclear. Additionally, these newer agents are associated with considerable healthcare costs—especially among low-income countries [22, 23]. Thus, tools to stratify patients with T2DM and ASCVD are needed to most efficiently use these medications, especially in resource-limited settings, and guide further therapy [24]. Similarly, tools to identify individuals most at risk for MACE may help healthcare providers meet quality care metrics and allocate resources to the highest risk patients.
Current American College of Cardiology Foundation/American Heart Association guidelines recommend ASCVD risk stratification in patients with T2DM using available global risk calculators [25]. Unfortunately, existing risk calculators have not been evaluated in individuals with T2DM and above-average ASCVD risk, or specifically with prevalent ASCVD as in the present analyses. Thus, risk stratification models to identify which patients may benefit from pharmacologic interventions, such as GLP-1 receptor agonists or SGLT-2 inhibitor therapy, could be beneficial to allocate resources and guide therapeutic decision making, especially relevant for proprietary therapies for which access and cost effectiveness are key considerations. Similarly, prediction models can help inform patients about their individual risk and prognosis [26]. Results from such risk scores can be used to educate and motivate patients, promoting physical activity and lifestyle modifications. The 9-variable risk prediction tool was developed to address this knowledge gap and predict MACE among individuals with prevalent ASCVD and T2DM.
The present findings also build on previous MACE risk prediction tools from individual cohorts, including the Framingham Heart Study, Swedish National Diabetes Register (NDR), and U.K. Prospective Diabetes Study (UKPDS) cohorts [27,28,29,30]. While the Framingham risk score includes participants with prevalent ASCVD, the risk score was not designed for individuals with T2DM and has been shown to have decreased accuracy in this population [31]. Conversely, the NDR risk score is designed for individuals free of baseline ASCVD and is not generalizable to individuals with a history of ASCVD. Finally, the UKPDS Risk Engine was developed using data collected between 1977 and 1991 from individuals with newly diagnosed T2DM aged 25–65, and may not reflect the current landscape of medical practices and burden of disease seen in individuals with long-standing T2DM, especially those with established ASCVD [5]. For example, the UKPDS score does not include current medications, such as insulin use, which is incorporated in our risk score and reflects current standard-of-care treatment regimens for those with advanced T2DM [32]. Similarly, the UKPDS Risk Engine was designed to predict long-term MACE risk (up to 20+ years). By contrast, our risk score derivation included a diverse cohort of Black and White men and women with above-average short-term ASCVD risk from a recent, clinical trial-based cohort to allow for robust and personalized 4-year estimates of MACE in patients with T2DM and prevalent ASCVD.
Study strengths and limitations
The present study has several strengths, including derivation of the models in a large cohort of participants from the TECOS trial, analyses of data from patients with contemporary clinical care, a large number of events to analyze, use of advanced statistical techniques to identify and analyze variables, and validation of the models in an external cohort of patients with T2DM and ASCVD.
This study also has notable limitations. The TECOS trial was conducted between 2008 and 2012, and certain biomarkers associated with worsening CV outcomes, such as high-sensitivity troponin, natriuretic peptide, C-reactive peptide, and coronary calcium levels, were not available to assess their potential contributions to the models. However, these data are not routinely collected in individuals with stable T2DM and ASCVD. Similarly, the TECOS trial excluded individuals with severe kidney dysfunction—a known risk factor of worsening CV events. Time since prior ASCVD at baseline was not collected but could be potentially informative; however this could be variable in relation to the baseline examination of the trial. Although both populations had T2DM and ASCVD, the participant baseline comorbidities in TECOS and ACCORD occurred with different frequencies. Despite these differences, the model performed well in the validation cohort. Although defined similarly to other studies, in hindsight, the prespecified MACE endpoint for both TECOS and ACCORD studies would have been better if it did not include hemorrhagic stroke, since hemorrhagic stroke is thought to have a different risk profile than other components of the composite. However, if hemorrhagic stroke were removed from the endpoint, only 14 events (< 1%) would be lost and all predictors in the models would remain significant. Details for the models of CV death, nonfatal MI, or nonfatal ischemic stroke (without hemorrhagic stroke) are presented in Additional file 1: Tables S2 and S3. No measures of socioeconomic status (SES) were available to be considered for the model. Lower SES has been shown to be associated with poor prognosis among those with ASCVD, and a clinical trial population is likely under-representative of these higher risk, lower SES patients. Future studies are needed to test the simplified and extended risk scores in a more diverse cohort beyond clinical trial settings. Finally, validation of the extended model in the ACCORD trial was not possible as data on carotid artery stenosis, micro- versus macroalbuminuria, and COPD were not included in the trial report.