Association between serum urate levels and all-cause mortality, cardiovascular and renal outcomes among gout patients in Singapore.

Objectives: We investigated the longitudinal association between Serum Urate (SU) level and Acute Myocardial Infarction (AMI), Stroke, End Stage Renal Failure (ESRF) and all-cause mortality.

Design: We conducted a retrospective hospital-based cohort study of individuals with gout managed in specialist outpatient clinics. Cox proportional hazards regression was used to estimate HR and 95% CI, with adjustments for potential confounders.

Understanding loneliness after widowhood: The role of social isolation, social support, self-efficacy, and health-related factors.

Background: Widowhood negatively affects trajectories of social isolation and loneliness. Given the inevitability of spousal bereavement for many, further investigation into potential modifiers of bereavement-related loneliness is warranted.

Aim: To examine the moderating effects of social isolation, social support, sociodemographic, self-efficacy, health, and quality of life factors on changes in loneliness before and after widowhood.

Artificial intelligence improves risk prediction in cardiovascular disease.

Cardiovascular disease (CVD) represents a major public health issue, claiming numerous lives. This study aimed to demonstrate the advantages of employing artificial intelligence (AI) models to improve the prediction of CVD risk using a large cohort of relatively healthy adults aged 70 years or more. In this study, deep learning (DL) models provide enhanced predictions (DeepSurv: C-index = 0.

Visit-to-visit HbA1c variability and risk of potentially avoidable hospitalisations in adults with type 2 diabetes receiving outpatient care at a tertiary hospital.

Aims: This study aims to investigate the relationship between long-term visit-to-visit within-person HbA1c variability and hospitalisation outcomes in adults with type 2 diabetes (T2D).

Methods: We conducted a cohort study at a tertiary hospital in Singapore involving people aged 21 to 101 years with T2D who had ≥3 HbA1c tests over 2 years. HbA1c variability was assessed using coefficient of variation (CV), variability independent of the mean (VIM) and HbA1c variability score (HVS).

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction.