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BackgroundThe impact of delayed discharge on patients, health‐care staff and hospital costs has been incompletely characterized.AimTo systematically review experiences of delay from the perspectives of patients, health professionals and hospitals, and its impact on patients’ outcomes and costs.MethodsFour of the main biomedical databases were searched for the period 2000‐2016 (February). Quantitative, qualitative and health economic studies conducted in OECD countries were included.ResultsThirty‐seven papers reporting data on 35 studies were identified: 10 quantitative, 8 qualitative and 19 exploring costs. Seven of ten quantitative studies were at moderate/low methodological quality; 6 qualitative studies were deemed reliable; and the 19 studies on costs were of moderate quality. Delayed discharge was associated with mortality, infections, depression, reductions in patients’ mobility and their daily activities. The qualitative studies highlighted the pressure to reduce discharge delays on staff stress and interprofessional relationships, with implications for patient care and well‐being. Extra bed‐days could account for up to 30.7% of total costs and cause cancellations of elective operations, treatment delay and repercussions for subsequent services, especially for elderly patients.ConclusionsThe poor quality of the majority of the research means that implications for practice should be cautiously made. However, the results suggest that the adverse effects of delayed discharge are both direct (through increased opportunities for patients to acquire avoidable ill health) and indirect, secondary to the pressures placed on staff. These findings provide impetus to take a more holistic perspective to addressing delayed discharge.

Outcomes of hepatitis C virus (HCV) infection and treatment depend on viral and host genetic factors. We use human genome-wide genotyping arrays and new whole-genome HCV viral sequencing technologies to perform a systematic genome-to-genome study of 542 individuals chronically infected with HCV, predominately genotype 3. We show that both HLA alleles and interferon lambda innate immune system genes drive viral genome polymorphism, and that IFNL4 genotypes determine HCV viral load through a mechanism that is dependent on a specific polymorphism in the HCV polyprotein. We highlight the interplay between innate immune responses and the viral genome in HCV control.

Affordable next-generation sequencing (NGS) technologies for hepatitis C virus (HCV) may potentially identify both viral genotype and resistance genetic motifs in the era of directly acting antiviral (DAA) therapies. This study compared the ability of high-throughput NGS methods to generate full-length, deep, HCV sequence data sets and evaluated their utility for diagnostics and clinical assessment. NGS methods using (i) unselected HCV RNA (metagenomics), (ii) preenrichment of HCV RNA by probe capture, and (iii) HCV preamplification by PCR implemented in four United Kingdom centers were compared. Metrics of sequence coverage and depth, quasispecies diversity, and detection of DAA resistance-associated variants (RAVs), mixed HCV genotypes, and other coinfections were compared using a panel of samples with different viral loads, genotypes, and mixed HCV genotypes/subtypes [geno(sub)types]. Each NGS method generated near-complete genome sequences from more than 90% of samples. Enrichment methods and PCR preamplification generated greater sequence depth and were more effective for samples with low viral loads. All NGS methodologies accurately identified mixed HCV genotype infections. Consensus sequences generated by different NGS methods were generally concordant, and majority RAVs were consistently detected. However, methods differed in their ability to detect minor populations of RAVs. Metagenomic methods identified human pegivirus coinfections. NGS provided a rapid, inexpensive method for generating whole HCV genomes to define infecting genotypes, RAVs, comprehensive viral strain analysis, and quasispecies diversity. Enrichment methods are particularly suited for high-throughput analysis while providing the genotype and information on potential DAA resistance.