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 human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.