Cost-effectiveness of a patient-centred approach to managing multimorbidity in primary care:a pragmatic cluster randomised controlled trial

Objective Patients with multiple chronic health conditions are often managed in a disjointed fashion in primary care, with annual review clinic appointments offered separately for each condition. This study aimed to determine the cost-effectiveness of the 3D intervention, which was developed to improve the system of care. Design Economic evaluation conducted alongside a pragmatic cluster-randomised trial. Setting General practices in three centres in England and Scotland. Participants 797 adults with three or more chronic conditions were randomised to the 3D intervention, while 749 participants were randomised to receive usual care. Intervention The 3D approach: comprehensive 6-monthly general practitioner consultations, supported by medication reviews and nurse appointments. Primary and secondary outcome measures The primary economic evaluation assessed the cost per quality-adjusted life year (QALY) gained from the perspective of the National Health Service (NHS) and personal social services (PSS). Costs were related to changes in a range of secondary outcomes (QALYs accrued by both participants and carers, and deaths) in a cost-consequences analysis from the perspectives of the NHS/PSS, patients/carers and productivity losses. Results Very small increases were found in both QALYs (adjusted mean difference 0.007 (-0.009 to 0.023)) and costs (adjusted mean difference 126 pound (-739 pound to 991)) pound in the intervention arm compared with usual care after 15 months. The incremental cost-effectiveness ratio was 18 pound 499, with a 50.8% chance of being cost-effective at a willingness-to-pay threshold of 20 pound 000 per QALY (55.8% at 30 pound 000 per QALY). Conclusions The small differences in costs and outcomes were consistent with chance, and the uncertainty was substantial; therefore, the evidence for the cost-effectiveness of the 3D approach from the NHS/PSS perspective should be considered equivocal

Management of multimorbidity using a patient-centred care model:a pragmatic cluster-randomised trial of the 3D approach

Background: The management of people with multiple chronic conditions challenges health-care systems designed around single conditions. There is international consensus that care for multimorbidity should be patient-centred, focus on quality of life, and promote self-management towards agreed goals. However, there is little evidence about the effectiveness of this approach. Our hypothesis was that the patient-centred, so-called 3D approach (based on dimensions of health, depression, and drugs) for patients with multimorbidity would improve their health-related quality of life, which is the ultimate aim of the 3D intervention. Methods: We did this pragmatic cluster-randomised trial in general practices in England and Scotland. Practices were randomly allocated to continue usual care (17 practices) or to provide 6-monthly comprehensive 3D reviews, incorporating patient-centred strategies that reflected international consensus on best care (16 practices). Randomisation was computer-generated, stratified by area, and minimised by practice deprivation and list size. Adults with three or more chronic conditions were recruited. The primary outcome was quality of life (assessed with EQ-5D-5L) after 15 months' follow-up. Participants were not masked to group assignment, but analysis of outcomes was blinded. We analysed the primary outcome in the intention-to-treat population, with missing data being multiply imputed. This trial is registered as an International Standard Randomised Controlled Trial, number ISRCTN06180958. Findings: Between May 20, 2015, and Dec 31, 2015, we recruited 1546 patients from 33 practices and randomly assigned them to receive the intervention (n=797) or usual care (n=749). In our intention-to-treat analysis, there was no difference between trial groups in the primary outcome of quality of life (adjusted difference in mean EQ-5D-5L 0·00, 95% CI −0·02 to 0·02; p=0·93). 78 patients died, and the deaths were not considered as related to the intervention. Interpretation: To our knowledge, this trial is the largest investigation of the international consensus about optimal management of multimorbidity. The 3D intervention did not improve patients' quality of life. Funding: National Institute for Health Research

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF-SIMS and Self-Organizing maps

Biofilm formation is a major cause of hospital-acquired infections. Research into biofilm-resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high-throughput approach to enable the efficient discovery of novel biofilm-resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a diverse set of polymeric surfaces. The relationships between time-of-flight secondary ion mass spectrometry (ToF-SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self-organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment

Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response

Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic respons