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Introduction: The Middle East and North Africa region accounts for the largest prevalence of type 2 diabetes (T2D). Qatar ranks top 10 for global T2D prevalence, with 17 percent in 2022. Also, 50 to 70 percent of the cardiovascular events occurred in T2D. We sought to estimate the future health and economic burden of cardiovascular disease (CVD) in T2D from 2023 to 2032. Methods: A dynamic multistate model in people with T2D was constructed. The demographic profile of the population was based on Qatari citizens and residents with T2D aged 40 to 90 years in 2022. First CVD events (i.e., myocardial infarction [MI] and stroke) were calculated via the 2013 Pooled Cohort Equation using data from Primary Health Care Corporation. Recurrent CVD events were sourced from the global Reduction of Atherothrombosis for Continued Health (REACH) registry. Outcomes were MI and stroke, years of life lived, quality-adjusted life years (QALYs), total direct and productivity loss costs. Utility and cost model inputs were drawn from published sources. The model adopted a Qatari societal perspective. Results: The model estimates 123,524 non-fatal MIs (95% uncertainty interval [UI]: 116,923, 130,065), 70,466 non-fatal strokes (95% UI: 67,945, 73,476) and 15,410 CVD deaths (95% UI: 15,217, 15,794), respectively. T2D population accrued 4,834,146 (95% UI: 4,781,235, 4,881,695) total years of life lived and 3,817,246 (95% UI: 3,756,963, 3,870,616) total QALYs. Direct costs accounted for 59.52 percent of the total costs, with a projection of QAR43.59 billion (USD11.98 billion) (95% UI: QAR9.14 billion [USD2.5 billion], QAR134.20 billion [USD36.87 billion]), while the total indirect costs were expected to exceed QAR29.65 billion (USD8.14 billion) (95% UI: QAR2.40 billion [USD659.34 million], QAR113 billion [USD31.04 billion]). Conclusions: This study highlights that the considerable rising burden of CVD in T2D in Qatar will impact not only the healthcare system but also the society overall. The findings may be used to prioritize strategies targeting T2D to prevent CVD burden. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes is a key risk factor for cardiovascular disease (CVD), with a two-to-four-times higher risk than that in non-diabetes. The health burden associated with CVD imposes a major economic concern on healthcare systems and on society. We sought to estimate the future burden of CVD in terms of health and economic outcomes in type 2 diabetes (T2D) from 2022 to 2031. Methods: From Australian public healthcare and societal perspectives over the next decade, two dynamic models with annual cycles were designed to predict myocardial infarction (MI) and stroke in patients aged 40 to 90 years. First events at risk of CVD people were estimated using the 2013 pooled cohort equation, while recurrent events in existing CVD were acquired from the global Reduction of Atherothrombosis for Continued Health registry. Costs and utilities were obtained using public sources. Outcomes were fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years (QALYs), total direct and indirect costs. An annual discount rate of 5 percent was used. Results: Over the next 10 years, the model projected a total of 83,618 non-fatal MIs (95% confidence interval [CI]: 83,170, 84,053) and 58,774 non-fatal strokes (95% CI: 58,458, 59,013). In terms of health outcomes, total years of life lived and QALYs were 9,549,487 (95% CI: 9,416,423, 9,654,043) and 6,632,897 (95% CI: 5,065,606, 7,591,679), respectively. In terms of economic outcomes, total direct and indirect were AUD9.59 billion (USD6.38 billion) (95% CI: AUD1.90 billion [USD1.26 billion], AUD30.45 billion [USD20.26 billion]) and AUD9.07 billion (USD6.03 billion) (95% CI: AUD663.53 million [USD441.61 million], AUD33.19 billion [USD21.96 billion]), respectively. The chronic costs of CVD contributed most of the total direct cost, while morbidity contributed most of the total indirect costs. Conclusions: Our study shows that from 2022 to 2031, CVD as a complication from T2D will cause a significant impact on the Australian healthcare system and society. These estimates can be used to explore different strategies to optimize the control of risk factors for the prevention and management of CVD in T2D in Australia. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes has been shown to influence the individual's work productivity in terms of both presenteeism (reduced productivity while at work) and absenteeism (absence from work because of illness). We sought to estimate the potential productivity gains associated with the modification of cardiovascular disease (CVD) risk in type 2 diabetes (T2D) over the next 10 years in Australia, from 2023 to 2032. Methods: Dynamic models were structured to estimate productivity-adjusted life years (PALYs) lived by Australians aged 20 to 69 years. The model simulation was first undertaken assuming currently expected trends in the incidence of myocardial infarction (MI) and stroke in T2D (original cohort), as calculated using the 2013 PCE-ASCVD algorithm. Subsequent models were then re-simulated using hypothetical scenarios that reflected the potential benefits of CVD reduction using published trials. The model was also repeated assuming that the original cohort has no CVD. Differences in PALYs lived by the "original cohort" and the different cohorts with reduced CVD risk reflected the PALYs gained. Sensitivity analyses were conducted. Results: Using data from published studies, the model assumed a reduction of 50 percent in systolic blood pressure (SBP), a reduction of 50 percent in smoking, 50 percent increase in high-density lipoprotein cholesterol (HDL), and a reduction of 35 percent in incidence of T2D. Over the working lifetime, from 2023 to 2032, reducing SBP, smoking, and incidence of T2D led to the gain of 140,105, 333,127, and 998,805 PALYs, respectively. Further, increasing HDL and assuming the original cohort with T2D has no CVD are expected to lead to the gain of 71,623 and 889,455 PALYs, respectively. Sensitivity analyses confirmed the robustness of study findings. Conclusions: The impact of CVD as a complication from T2D on work productivity is significant. Screening and prevention strategies tailored early in life are likely to exert a positive impact on health and work productivity. [ABSTRACT FROM AUTHOR]

Author(s): Shane Nanayakkara 1,2,3,*, Sam Fogarty 4,5, Michael Tremeer 5, Kelvin Ross 4,5, Brent Richards 5,6, Christoph Bergmeir 7, Sheng Xu 7, Dion Stub 1,2,3, Karen Smith 8,9, Mark Tacey [...], Background Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. Methods and findings Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79-0.82] and 0.81 [95% CI 0.8-0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81-0.83], DeLong test, p < 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86-0.88], DeLong test, p < 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient&apos;s survival. Key limitations include the absence of pre-hospital data and absence of external validation. Conclusions ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.