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Aim: To investigate whether the physician‐encounter interval for patients with type 2 diabetes (T2D) can be optimized from 2–3 to 4–6 months among those with a calculated 10‐year cardiovascular disease (CVD) risk score of less than 20% without compromising their long‐term outcomes. Materials and Methods: Using territory‐wide public electronic medical records in Hong Kong, we emulated a target trial to compare the effectiveness of the physician‐encounter intervals of 4‐6 versus 2‐3 months for T2D patients without prior CVDs and with a predicted risk for CVDs of less than 20% (i.e. those patients not in the high‐risk category). Propensity score matching was used to emulate the randomization of participants at baseline, where 42 154 matched individuals were included for analysis. The marginal structural model was applied to estimate the hazard ratio (HR) for CVD incidence and all‐cause mortality, the incidence rate ratio of secondary and tertiary care utilization, as well as the between‐group differences in HbA1c, blood pressure and cholesterol levels. Results: During a follow‐up period of up to 12 (average: 5.1) years, there was no significantly increased risk of CVD in patients with physician‐encounter intervals of 4‐6 months compared with those patients with physician‐encounter intervals of 2‐3 months (HR [95% confidence interval {CI}]: 1.01 [0.90, 1.14]; standardized 10‐year risk difference [95% CI]: −0.1% [−0.7%, 0.6%]), nor for all‐cause mortality (HR: 1.00 [0.84, 1.20]; standardized 10‐year risk difference: −0.1% [−0.5%, 0.3%]). Additionally, there was no observable difference in the utilization of secondary and tertiary care or key clinical parameters between these two follow‐up frequencies. Conclusions: For T2D patients with a calculated 10‐year CVD risk of less than 20%, the interval of regular physician encounters can be optimized from 2–3 to 4–6 months without compromising patients' long‐term outcomes and saving substantial service resources in primary care. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the association between changes in haemoglobin A1c (HbA1c) and the concurrent incidence of cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM) patients. Method: We conducted a retrospective cohort study among T2DM patients with HbA1c measurement after T2DM diagnosis between August 2009 and September 2010. The patients were classified into six subgroups based on baseline HbA1c (<7%; 7%–7.9%; ≥8%) and age (<65; ≥65 years), and then clustered into classes by HbA1c trajectory and CVD incidence over the 12‐year follow‐up period using joint latent class mixture models. We explored the HbA1c trajectories and CVD incidences in each latent class. Multinomial logistic regression was used to compare the baseline characteristics among different latent classes. Results: A total of 128 843 T2DM patients were included with a median follow‐up period of 11.7 years. Ten latent classes were identified in patients with baseline HbA1c ≥ 8% and age <65 years, while seven classes were identified in the other five groups. Among all the identified latent classes, patients with fluctuating HbA1c trajectories, characterized by alternating periods of increase and decrease, had higher CVD incidences. Male patients, and patients with higher baseline HbA1c and use of antidiabetic drugs were more likely to have a fluctuating HbA1c trajectory. More specifically, patients aged < 65 years with younger age or a smoking habit, and patients aged ≥ 65 years with a longer duration of T2DM were more likely to have a fluctuating HbA1c trajectory. Conclusion: We found that T2DM patients with fluctuating HbA1c trajectories could have a higher CVD risk. Different trajectory‐associated characteristics in age subgroups highlight the need for individualized management of T2DM patients. [ABSTRACT FROM AUTHOR]

Aim: To develop 10‐year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. Methods: In this 10‐year population‐based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end‐stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two‐thirds of the patients were randomly selected to develop sex‐specific CVD risk prediction models. The remaining one‐third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML‐based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. Results: During a median follow‐up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin‐to‐creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U‐shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. Conclusion: Using routinely available predictors and ML‐based algorithms, this study established 10‐year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self‐care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care. [ABSTRACT FROM AUTHOR]