Rising trends in the predicted 10-year risk of cardiovascular diseases among Royal Thai Army personnel from 2017 to 2021

Abstract: Deaths from cardiovascular diseases (CVD) are becoming a growing threat to global health, including in Thailand. The aim of the present study was to identify the recent trends in the predicted 10-year risk of CVD among Royal Thai Army (RTA) personnel from 2017 to 2021. The predicted 10-year risk for CVD was calculated through the use of the 2008 updated version of the risk algorithm derived from the Framingham Heart Study data. The current study included 346,355 active-duty RTA personnel aged 30–60 years. The age- and sex-adjusted mean of the predicted 10-year risk for CVD significantly increased from 10.8% (95% CI: 10.8–10.9%) in 2017 to 11.7% (95% CI: 11.6–11.7%) in 2021 (p for trend < 0.001). The overall age- and sex-adjusted prevalence of intermediate-to-high predicted 10-year risk for CVD remarkably surged from 24.9% (95% CI: 24.4–25.4%) in 2017 to 29.5% (95% CI: 29.0–30.0%) in 2021 (p for trend < 0.001). The modifiable risk factors for CVD, including high systolic blood pressure, high body mass index, and current smoking in this population, should be alleviated to mitigate the risk for CVD in the future.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.