Evaluation of a community pharmacy-based intervention for improving patient adherence to antihypertensives: a randomised controlled trial

BackgroundThe majority of patients using antihypertensive medications fail to achieve their recommended target blood pressure. Poor daily adherence with medication regimens and a lack of persistence with medication use are two of the major reasons for failure to reach target blood pressure. There is no single intervention to improve adherence with antihypertensives that is consistently effective. Community pharmacists are in an ideal position to promote adherence to chronic medications. This study aims to test a specific intervention package that could be integrated into the community pharmacy workflow to enable pharmacists to improve patient adherence and/or persistence with antihypertensive medications - Hypertension Adherence Program in Pharmacy (HAPPY).Methods/DesignThe HAPPY trial is a multi-centre prospective randomised controlled trial. Fifty-six pharmacies have been recruited from three Australian states. To identify potential patients, a software application (MedeMine CVD) extracted data from a community pharmacy dispensing software system (FRED Dispense&reg;). The pharmacies have been randomised to either \u27Pharmacist Care Group\u27 (PCG) or \u27Usual Care Group\u27 (UCG). To check for \u27Hawthorne effect\u27 in the UCG, a third group of patients \u27Hidden Control Group\u27 (HCG) will be identified in the UCG pharmacies, which will be made known to the pharmacists at the end of six months. Each study group requires 182 patients. Data will be collected at baseline, three and six months in the PCG and at baseline and six months in the UCG. Changes in patient adherence and persistence at the end of six months will be measured using the self-reported Morisky score, the Tool for Adherence Behaviour Screening and medication refill data.DiscussionTo our knowledge, this is the first research testing a comprehensive package of evidence-based interventions that could be integrated into the community pharmacy workflow to enable pharmacists to improve patient adherence and/or persistence with antihypertensive medications. The unique features of the HAPPY trial include the use of MedeMine CVD to identify patients who could potentially benefit from the service, control for the \u27Hawthorne effect\u27 in the UCG and the offer of the intervention package at the end of six months to patients in the UCG, a strategy that is expected to improve retention.Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12609000705280<br /

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting