Search

Abstract This study looked at novel data sources for cardiovascular risk prediction including detailed lifestyle questionnaire and continuous blood pressure monitoring, using ensemble machine learning algorithms (MLAs). The reference conventional risk score compared against was the Framingham Risk Score (FRS). The outcome variables were low or high risk based on calcium score 0 or calcium score 100 and above. Ensemble MLAs were built based on naive bayes, random forest and support vector classifier for low risk and generalized linear regression, support vector regressor and stochastic gradient descent regressor for high risk categories. MLAs were trained on 600 Southeast Asians aged 21 to 69 years free of cardiovascular disease. All MLAs outperformed the FRS for low and high-risk categories. MLA based on lifestyle questionnaire only achieved AUC of 0.715 (95% CI 0.681, 0.750) and 0.710 (95% CI 0.653, 0.766) for low and high risk respectively. Combining all groups of risk factors (lifestyle survey questionnaires, clinical blood tests, 24-h ambulatory blood pressure and heart rate monitoring) along with feature selection, prediction of low and high CVD risk groups were further enhanced to 0.791 (95% CI 0.759, 0.822) and 0.790 (95% CI 0.745, 0.836). Besides conventional predictors, self-reported physical activity, average daily heart rate, awake blood pressure variability and percentage time in diastolic hypertension were important contributors to CVD risk classification.

Evidence on the influence of patient characteristics on HbA1c treatment response for add-on medications in patients with type 2 diabetes (T2D) is unclear. This study aims to investigate the predictors of HbA1c treatment response for three add-on medications (sulfonylureas (SU), dipeptidyl peptidase-4 (DPP-4) and sodium–glucose cotransporter-2 (SGLT-2) inhibitor) in metformin monotherapy treated patients with T2D. This retrospective cohort study was conducted using the electronic health record data from six primary care clinics in Singapore. A total of 9748 adult patients with T2D on metformin monotherapy receiving SU, DPP-4 or SGLT-2 add-on were 1:1 propensity score matched to patients receiving other add-on medications. Patient demographics, laboratory results, diabetes related complications, comedications, and treatment response at two endpoints (HbA1c reduction ≥ 1% at 6th month, HbA1c goal attainment < 7% at 12th month) were examined. Multiple logistic regression analyses were used to identify patient characteristics associated with the treatment responses. After matching, there were 1073, 517, and 290 paired cohorts of SU, DPP-4 and SGLT-2 respectively. Besides baseline HbA1c, patients with longer hypertension disease duration and higher cholesterol HDL were associated with better treatment response to SU medication add-on. Lower estimated glomerular filtration rate (eGFR), and angiotensin-II receptor medications were associated with better treatment response to DPP-4 add-on. Lower cholesterol HDL, higher creatinine serum, absence of renal complications and beta-blockers medications were associated with better treatment response to SGLT-2 add-on. The cholesterol HDL, creatinine serum, eGFR, hypertension disease duration, angiotensin-II receptors and beta-blockers class of medications can influence the HbA1c treatment response for SU, DPP-4 and SGLT-2 add-on medications. Knowing the patients' characteristics that influence treatment response can assist in guiding clinical decisions when selecting the appropriate add-on medication, ultimately helping to prevent the development of diabetes-related complications. [ABSTRACT FROM AUTHOR]

Introduction: Cardiovascular disease was the top cause of deaths and disability in Singapore in 2018, contributing extensively to the local healthcare burden. Primary prevention identifies at-risk individuals for the swift implementation of preventive measures. This has been traditionally done using the Singapore-adapted Framingham Risk Score (SG FRS). However, its most recent recalibration was more than a decade ago. Recent changes in patient demographics and risk factors have undermined the accuracy of SG FRS, and the rising popularity of wearable health metrics has led to new data types with the potential to improve risk prediction., Methods: In healthy Singaporeans enrolled in SingHEART study (absence of any clinical outcomes), we investigated improvements in SG FRS to predict myocardial infarction risk based on high/low classification of the Agatston score (surrogate outcome). Logistic regression, receiver operating characteristic and net reclassification index (NRI) analyses were conducted., Results: We demonstrated a significant improvement in the area under curve (AUC) of SG FRS (AUC = 0.641) after recalibration and incorporation of additional variables (fasting blood glucose and wearable-derived activity levels) (AUC = 0.774) ( P < 0.001). SG FRS++ significantly increases accuracy in risk prediction (NRI = 0.219, P = 0.00254)., Conclusion: Existing Singapore cardiovascular disease risk prediction guidelines should be updated to improve risk prediction accuracy. Recalibrating existing risk functions and utilising wearable metrics that provide a large pool of objective health data can improve existing risk prediction tools. Lastly, activity levels and prediabetic state are important factors for coronary heart disease risk stratification, especially in low-risk individuals., (Copyright © 2024 Copyright: © 2024 Singapore Medical Journal.)