High-throughput DNA sequencing technology has transformed genetic research and is starting to make an impact on clinical practice. However, analyzing high-throughput sequencing data remains challenging, particularly in clinical settings where accuracy and turnaround times are critical. We present a new approach to this problem, implemented in a software package called Platypus. Platypus achieves high sensitivity and specificity for SNPs, indels and complex polymorphisms by using local de novo assembly to generate candidate variants, followed by local realignment and probabilistic haplotype estimation. It is an order of magnitude faster than existing tools and generates calls from raw aligned read data without preprocessing. We demonstrate the performance of Platypus in clinically relevant experimental designs by comparing with SAMtools and GATK on whole-genome and exome-capture data, by identifying de novo variation in 15 parent-offspring trios with high sensitivity and specificity, and by estimating human leukocyte antigen genotypes directly from variant calls.

Background The management of the COVID-19 pandemic is hampered by long delays associated with centralised laboratory PCR testing. In hospitals, these delays lead to poor patient flow and nosocomial transmission. Rapid, accurate tests are therefore urgently needed in preparation for the next wave of the pandemic. MethodsWe did a prospective, interventional, non-randomised, controlled study of molecular point-of-care testing in patients aged 18 years or older presenting with suspected COVID-19 to the emergency department or other acute areas of Southampton General Hospital during the first wave of the pandemic in the UK. Nose and throat swab samples taken at admission from patients in the point-of-care testing group were tested with the QIAstat-Dx Respiratory SARS-CoV-2 Panel. Samples taken from patients in a contemporaneous control group were tested by laboratory PCR. The primary outcome was time to results in the full cohort. This study is registered with ISRCTN (ISRCTN14966673) and is completed. Findings Between March 20 and April 29, 2020, 517 patients were assessed for eligibility, of whom 499 were recruited to the point-of-care testing group and tested by the QIAstat-Dx Respiratory SARS-CoV-2 Panel. 555 contemporaneously identified patients were included in the control group and tested by laboratory PCR. The two groups were similar with regard to the distribution of sex, age, and ethnicity. 197 (39%) patients in the point-of-care testing group and 155 (28%) in the control group tested positive for COVID-19 (difference 11•5% [95% CI 5•8-17•2], p=0•0001). Median time to results was 1•7 h (IQR 1•6-1•9) in the point-of-care testing group and 21•3 h (16•0-27•9) in the control group (difference 19•6 h [19•0-20•3], p<0•0001). A Cox proportional hazards regression model controlling for age, sex, time of presentation, and severity of illness also showed that time to results was significantly shorter in the point-of-care testing group than in the control group (hazard ratio 4023 [95% CI 545-29 696], p<0•0001). Interpretation Point-of-care testing is associated with large reductions in time to results and could lead to improvements in infection control measures and patient flow compared with centralised laboratory PCR testing. Funding University Hospitals Southampton NHS Foundation Trust.

Background The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. Methods Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. Results A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. Conclusions NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.