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Fulltext Health utilities in Chinese patients with coronary heart disease and impaired glucose tolerance (ACE): A longitudinal analysis of a randomized, double-blind, placebo-controlled trial

Leal J, Becker F, Lim LL, Holman RR, Gray AM

J Diabetes, 2022 Jul;14(7):455-464.
PMID: 35876124 DOI: 10.1111/1753-0407.13294

BACKGROUND: We estimate health-related quality of life and the impact of four cardiovascular events (myocardial infarction [MI], stroke, congestive heart failure, angina) and gastrointestinal events in 6522 Chinese patients with coronary heart disease (CHD) and impaired glucose tolerance (IGT) participating in the Acarbose Cardiovascular Evaluation (ACE) trial.
METHODS: Health-related quality of life was captured using the EuroQol-5 Dimension-3 Level (EQ-5D-3L), with data collected at baseline and throughout the trial. Multilevel mixed-effects linear regression with random effects estimated health-related quality of life over time, capturing variation between hospital sites and individuals, and a fixed-effects linear model estimated the impact of cardiovascular and gastrointestinal events.
RESULTS: Patients were followed for a median of 5 years (interquartile range 3.4-6.0). The average baseline EQ-5D score of 0.930 (SD 0.104) remained relatively unchanged over the trial period with no evidence of statistically significant differences in EQ-5D score between randomized treatment groups. The largest decrement in the year of an event was estimated for stroke (-0.107, P
Fulltext Estimating Risk Factor Time Paths Among People with Type 2 Diabetes and QALY Gains from Risk Factor Management

Gao N, Dakin HA, Holman RR, Lim LL, Leal J, Clarke P

Pharmacoeconomics, 2024 Sep;42(9):1017-1028.
PMID: 38922488 DOI: 10.1007/s40273-024-01398-4

OBJECTIVES: Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates.
AIMS: (1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations.
METHODS: Using pooled contemporary data from two randomised trials EXSCEL and TECOS (n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA1c, haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations.
RESULTS: All new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations.
DISCUSSION: Incorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients' health, expenditure and quality of life.
TRIAL REGISTRATION: ClinicalTrials.gov NCT01144338 and NCT00790205.
Fulltext Aspects of Multicomponent Integrated Care Promote Sustained Improvement in Surrogate Clinical Outcomes: A Systematic Review and Meta-analysis

Lim LL, Lau ESH, Kong APS, Davies MJ, Levitt NS, Eliasson B, et al.

Diabetes Care, 2018 06;41(6):1312-1320.
PMID: 29784698 DOI: 10.2337/dc17-2010

OBJECTIVE: The implementation of the Chronic Care Model (CCM) improves health care quality. We examined the sustained effectiveness of multicomponent integrated care in type 2 diabetes.

RESEARCH DESIGN AND METHODS: We searched PubMed and Ovid MEDLINE (January 2000-August 2016) and identified randomized controlled trials comprising two or more quality improvement strategies from two or more domains (health system, health care providers, or patients) lasting ≥12 months with one or more clinical outcomes. Two reviewers extracted data and appraised the reporting quality.

RESULTS: In a meta-analysis of 181 trials (N = 135,112), random-effects modeling revealed pooled mean differences in HbA1c of -0.28% (95% CI -0.35 to -0.21) (-3.1 mmol/mol [-3.9 to -2.3]), in systolic blood pressure (SBP) of -2.3 mmHg (-3.1 to -1.4), in diastolic blood pressure (DBP) of -1.1 mmHg (-1.5 to -0.6), and in LDL cholesterol (LDL-C) of -0.14 mmol/L (-0.21 to -0.07), with greater effects in patients with LDL-C ≥3.4 mmol/L (-0.31 vs. -0.10 mmol/L for <3.4 mmol/L; Pdifference = 0.013), studies from Asia (HbA1c -0.51% vs. -0.23% for North America [-5.5 vs. -2.5 mmol/mol]; Pdifference = 0.046), and studies lasting >12 months (SBP -3.4 vs. -1.4 mmHg, Pdifference = 0.034; DBP -1.7 vs. -0.7 mmHg, Pdifference = 0.047; LDL-C -0.21 vs. -0.07 mmol/L for 12-month studies, Pdifference = 0.049). Patients with median age <60 years had greater HbA1c reduction (-0.35% vs. -0.18% for ≥60 years [-3.8 vs. -2.0 mmol/mol]; Pdifference = 0.029). Team change, patient education/self-management, and improved patient-provider communication had the largest effect sizes (0.28-0.36% [3.0-3.9 mmol/mol]).

CONCLUSIONS: Despite the small effect size of multicomponent integrated care (in part attenuated by good background care), team-based care with better information flow may improve patient-provider communication and self-management in patients who are young, with suboptimal control, and in low-resource settings.
The Lancet Commission on diabetes: using data to transform diabetes care and patient lives

Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, et al.

Lancet, 2021 Dec 19;396(10267):2019-2082.
PMID: 33189186 DOI: 10.1016/S0140-6736(20)32374-6