Glycaemic control of Asian patients with type-2 diabetes mellitus on tiered up-titration of metformin monotherapy: A one-year real-world retrospective longitudinal study in primary care.

Aims: To determine the glycaemic control and associated factors among patients with type-2 diabetes mellitus on tiered metformin monotherapy over one-year.

Methods: Adult Asian patients on metformin monotherapy with tiered dosage up-titration (low < 500 mg/day; medium 500-<1000 mg/day and high ≥ 1000 mg/day) are divided into four sub-cohorts based on their baseline HbA1c < 7%(C); 7%-<8%(C); 8%-<9%(C) and ≥ 9%(C). The HbA1c absolute reduction, time to reach glycaemic control (HbA1c < 7%), and time from glycaemic control to failure (HbA1c ≥ 7%) after the dosage up-titration were the outcomes.

The effect of oral diabetes medications on glycated haemoglobin (HbA1c) in Asians in primary care: a retrospective cohort real-world data study.

Background: Clinical trials have demonstrated that initiating oral anti-diabetic drugs (OADs) significantly reduce glycated hemoglobin (HbA1c) levels. However, variability in lifestyle modifications and OAD adherence impact on their actual effect on glycemic control. Furthermore, evidence on dose adjustments and discontinuation of OAD on HbA1c is lacking.

Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics.

Patient similarity analytics has emerged as an essential tool to identify cohorts of patients who have similar clinical characteristics to some specific patient of interest. In this study, we propose a patient similarity measure called D3K that incorporates domain knowledge and data-driven insights. Using the electronic health records (EHRs) of 169,434 patients with either diabetes, hypertension or dyslipidaemia (DHL), we construct patient feature vectors containing demographics, vital signs, laboratory test results, and prescribed medications.

Patient similarity analytics for explainable clinical risk prediction.

Background: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility.

Commercially Successful Blockchain Healthcare Projects: A Scoping Review.

Background: The healthcare industry is the new frontier for blockchain technology. Given its properties of immutability and decentralization, blockchain represents an opportunity for unprecedented level of privacy and security for all stakeholders by ensuring data integrity while giving patients control over their own health data. On a backdrop of rising interest in blockchain in general and blockchain healthcare applications in particular, there has been a proliferation of blockchain healthcare projects over the past few years.

Blockchain Personal Health Records: Systematic Review.

Background: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users.

LDL-cholesterol change and goal attainment following statin intensity titration among Asians in primary care: a retrospective cohort study.

Background: Clinical trials have demonstrated that either initiating or up-titrating a statin dose substantially reduce Low-Density Lipoprotein-Cholesterol (LDL-C) levels. However, statin adherence in actual practice tends to be suboptimal, leading to diminished effectiveness. This study aims to use real-world data to determine the effect on LDL-C levels and LDL-C goal attainment rates, when selected statins are titrated in Asian patients.