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ObjectivesEarly identification of SARS-CoV-2 infection is important to guide quarantine and reduce transmission. This study evaluates the diagnostic performance of lung ultrasound (LUS), an affordable, consumable-free point-of-care tool, for COVID-19 screening.Design, setting and participantsThis prospective observational cohort included adults presenting with cough and/or dyspnoea at a SARS-CoV-2 screening centre of Lausanne University Hospital between 31 March and 8 May 2020.InterventionsInvestigators recorded standardised LUS images and videos in 10 lung zones per patient. Two blinded independent experts reviewed LUS recording and classified abnormal findings according to prespecified criteria to investigate their predictive value to diagnose SARS-CoV-2 infection according to PCR on nasopharyngeal swabs (COVID-19 positive vs COVID-19 negative).Primary and secondary outcome measuresWe finally combined LUS and clinical findings to derive a multivariate logistic regression diagnostic score.ResultsOf 134 included patients, 23% (n=30/134) were COVID-19 positive and 77% (n=103/134) were COVID-19 negative; 85%, (n=114/134) cases were previously healthy healthcare workers presenting within 2–5 days of symptom onset (IQR). Abnormal LUS findings were significantly more frequent in COVID-19 positive compared with COVID-19 negative (45% vs 26%, p=0.045) and mostly consisted of focal pathologic B lines. Combining clinical findings in a multivariate logistic regression score had an area under the receiver operating curve of 80.3% to detect COVID-19, and slightly improved to 84.5% with the addition of LUS features.ConclusionsCOVID-19-positive patients are significantly more likely to have lung pathology by LUS. However, LUS has an insufficient sensitivity and is not an appropriate screening tool in outpatients. LUS only adds little value to clinical features alone.

Background Lower respiratory tract infections (LRTIs) are among the most frequent infections and a significant contributor to inappropriate antibiotic prescription. Currently, no single diagnostic tool can reliably identify bacterial pneumonia. We thus evaluate a multimodal approach based on a clinical score, lung ultrasound (LUS), and the inflammatory biomarker, procalcitonin (PCT) to guide prescription of antibiotics. LUS outperforms chest X-ray in the identification of pneumonia, while PCT is known to be elevated in bacterial and/or severe infections. We propose a trial to test their synergistic potential in reducing antibiotic prescription while preserving patient safety in emergency departments (ED). Methods The PLUS-IS-LESS study is a pragmatic, stepped-wedge cluster-randomized, clinical trial conducted in 10 Swiss EDs. It assesses the PLUS algorithm, which combines a clinical prediction score, LUS, PCT, and a clinical severity score to guide antibiotics among adults with LRTIs, compared with usual care. The co-primary endpoints are the proportion of patients prescribed antibiotics and the proportion of patients with clinical failure by day 28. Secondary endpoints include measurement of change in quality of life, length of hospital stay, antibiotic-related side effects, barriers and facilitators to the implementation of the algorithm, cost-effectiveness of the intervention, and identification of patterns of pneumonia in LUS using machine learning. Discussion The PLUS algorithm aims to optimize prescription of antibiotics through improved diagnostic performance and maximization of physician adherence, while ensuring safety. It is based on previously validated tests and does therefore not expose participants to unforeseeable risks. Cluster randomization prevents cross-contamination between study groups, as physicians are not exposed to the intervention during or before the control period. The stepped-wedge implementation of the intervention allows effect calculation from both between- and within-cluster comparisons, which enhances statistical power and allows smaller sample size than a parallel cluster design. Moreover, it enables the training of all centers for the intervention, simplifying implementation if the results prove successful. The PLUS algorithm has the potential to improve the identification of LRTIs that would benefit from antibiotics. When scaled, the expected reduction in the proportion of antibiotics prescribed has the potential to not only decrease side effects and costs but also mitigate antibiotic resistance. Trial registration This study was registered on July 19, 2022, on the ClinicalTrials.gov registry using reference number: NCT05463406. Trial status Recruitment started on December 5, 2022, and will be completed on November 3, 2024. Current protocol version is version 3.0, dated April 3, 2023.

To investigate the predictive value of both mental status, assessed with the AVPUC (Alert, responds to Voice, responds to Pain, Unresponsive, and new Confusion) scale, and mobility assessments, and their interrater reliability (IRR) between triage clinicians and a research team. Prospective study of consecutive patients who presented to an ED. Mental status and mobility were assessed by triage clinicians and by a dedicated research team. 4,191 patients were included. After adjustment for age and sex, patients with altered mental status have an odds ratio of 6.55 [4.09–10.24] to be admitted in the ICU and an odds ratio of 21.16 [12.06–37.01] to die within 30 days; patients with impaired mobility have an odds ratio of 7.08 [4.60–11.12] to be admitted in the ICU and an odds ratio of 12.87 [5.93–32.30] to die within 30 days. The kappa coefficient between triage clinicians and the research team for mental status assessment was 0.75, and 0.80 for mobility. Assessment of mental status by the AVPUC scale, and mobility by a simple dichotomous scale are suitable for ED triage. Both altered mental status and impaired mobility are associated with adverse outcomes. Mental status and mobility assessment have good interrater reliability.

Background: There is a high incidence of nonspecific Low Back Pain (LBP) in patients visiting Emergency Departments (EDs), but there is a lack of knowledge regarding emergency physiotherapy for LBP. The effect of on-site physiotherapy in these patients was therefore never demonstrated. We assessed short-term outcomes, feasibility and patient satisfaction with physiotherapy in ED patients presenting with nonspecific LBP. Methods: A block-randomized, controlled, open-label trial with a follow-up of 42 days. Patients aged 18 years or older presenting to an ED with nonspecific LBP were prospectively enrolled. Both groups received the same booklet with written information on LBP management and exercises. Patients in the intervention group were given additional instructions by a certified physiotherapist. Results: We included 86 patients in the primary analysis. The median age was 40, and 40.7% were female. At day 7, the median Oswestry Disability Index (ODI) was 2 points lower in the intervention group compared to the control group, which was not statistically significant. There was no between-group difference in pain at day 7. Patients who received physiotherapy felt significantly more confident with the exercises they were taught (p = 0.004, effect size = 0.3 [95% CI 0.1 to 0.5]). Conclusions: On-site physiotherapy in ED patients presenting with nonspecific low back pain is associated with higher patient satisfaction, compared to standard of care. The effect of physiotherapy was small, with only minimal improvement in disability, but without a reduction in pain. Despite the very small effect size, physiotherapeutic interventions should be investigated in larger cohorts with an extended intervention including patient education, exercises, and other physiotherapeutic modalities.

Osteosarcoma is the most common primary cancer of bone with a peak incidence in children and young adults. Despite progress, the genomic aberrations underpinning osteosarcoma evolution remain poorly understood. Using multi-region whole-genome sequencing, we find that chromothripsis is an ongoing mutational process, occurring subclonally in 74% of tumours. Chromothripsis drives the acquisition of oncogenic mutations and generates highly unstable derivative chromosomes, the evolution of which drives clonal diversification and intra-tumour heterogeneity. In addition, we report a novel mechanism, loss-translocation-amplification (LTA) chromothripsis, which mediates rapid malignant transformation and punctuated evolution in about half of paediatric and adult high-grade osteosarcomas. Specifically, a single double-strand break triggers concomitant TP53 inactivation and segmental amplifications, often amplifying oncogenes to high copy numbers in extrachromosomal circular DNA elements through breakage-fusion-bridge cycles involving multiple chromosomes. LTA chromothripsis is detected at low frequency in soft-tissue sarcomas, but not in epithelial cancers, including those driven by TP53 mutation. Finally, we identify genome-wide loss of heterozygosity as a strong prognostic indicator for high-grade osteosarcoma.Competing Interest StatementThe authors have declared no competing interest.

Accurate detection of somatic structural variants (SVs) and copy number aberrations (SCNAs) is critical to inform the diagnosis and treatment of human cancers. Here, we describe SAVANA, a computationally efficient algorithm designed for the joint analysis of somatic SVs, SCNAs, tumour purity and ploidy using long-read sequencing data. SAVANA relies on machine learning to distinguish true somatic SVs from artefacts and provide prediction errors for individual SVs. Using high-depth Illumina and nanopore whole-genome sequencing data for 99 human tumours and matched normal samples, we establish best practices for benchmarking SV detection algorithms across the entire genome in an unbiased and data-driven manner using simulated and sequencing replicates of tumour and matched normal samples. SAVANA shows significantly higher sensitivity, and 9- and 59-times higher specificity than the second and third-best performing algorithms, yielding orders of magnitude fewer false positives in comparison to existing long-read sequencing tools across various clonality levels, genomic regions, SV types and SV sizes. In addition, SAVANA harnesses long-range phasing information to detect somatic SVs and SCNAs at single-haplotype resolution. SVs reported by SAVANA are highly consistent with those detected using short-read sequencing, including complex events causing oncogene amplification and tumour suppressor gene inactivation. In summary, SAVANA enables the application of long-read sequencing to detect SVs and SCNAs reliably in clinical samples.