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S-gene target failure (SGTF) is neither specific nor accurate for identification of Omicron lineage of SARS-CoV-2. We observed N-gene target failure (NGTF) in 402 out of 412 SARS-CoV2 positive cases from December to mid-January 2022 using a commercially available assay. This phenomenon was not observed with more than 15,000 cases tested previously. We sequenced the genome of five samples with NGTF and compared these results with six cases where NGTF was not seen. We confirm that cases with NGTF were the Omicron lineage while cases with preserved N-gene amplification belonged to Delta lineage. We discovered that the ERS31-33 deletion (nucleotide 28362-28370del) overlaps with N gene probe used, explaining NGTF. As the stealth Omicron variant also harbors ERS31-33 deletion, this approach will work for the detection of stealth Omicron variant as well. We suggest that NGTF can be used as a low cost, rapid screening strategy for detection of Omicron.
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ObjectivesDiagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. MethodsUK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FindingsUK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. InterpretationWe confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings. HighlightsO_LIWidely recommended symptoms identified only [~]70% COVID-19 cases C_LIO_LIAdditional symptoms increased case finding to > 90% but tests needed doubled C_LIO_LIOptimal symptom combinations maximise case capture considering available resources C_LIO_LIImplications for COVID-19 vaccine efficacy trials and wider public health C_LI
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ObjectiveDiabetes is a known risk factor for mortality in Coronavirus disease 2019 (COVID-19) patients. Our objective was to identify prevalence of hyperglycemia in COVID-19 patients with and without diabetes and quantify its association with COVID-19 disease course. Research Design and MethodsIn this observational cohort study, all consecutive COVID-19 patients admitted to John H Stroger Jr. Hospital, Chicago, IL from March 15, 2020 to May 15, 2020 were included. The primary outcome was hospital mortality and the main predictor was hyperglycemia (any blood glucose [≥]7.78 mmol/L during hospitalization). ResultsOf 403 COVID-19 patients studied, 228 (57%) developed hyperglycemia. Of these, 83 (21%) had hyperglycemia without diabetes. A total of 51 (12.7%) patients died. Compared to the reference group no-diabetes/no-hyperglycemia patients the no-diabetes/hyperglycemia patients showed higher mortality (1.8% versus 20.5%, adjusted odds ratio 21.94 (95% confidence interval 4.04-119.0), p < 0.001); improved prediction of death (p=0.0162) and faster progression to death (p=0.0051). Hyperglycemia within the first 24 and 48 hours was also significantly associated with mortality (odds ratio 2.15 and 3.31, respectively). Further, compared to the same reference group, no-diabetes/hyperglycemia patients had higher risk of ICU admission (p<0.001), mechanical ventilation (p<0.001) and acute respiratory distress syndrome (p<0.001) and a longer hospital stay in survivors (p<0.001). ConclusionsHyperglycemia in the absence of diabetes was common (21% of hospitalized COVID-19 patients) and was associated with an increased risk of and faster progression to death. Development of hyperglycemia in COVID-19 patients who do not have diabetes is an early indicator of poor prognosis.
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PurposeEarly identification of a potentially deteriorating clinical course in hospitalized COVID-19 patients is critical since there exists a resource-demand gap for the ventilators. MaterialsWe aimed to develop and validate a deep learning-based approach to predict the need for mechanical ventilation as early as at the time of initial radiographic evaluation. We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images derived from 528 hospitalized COVID-19 patients. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for purpose of validation. ResultsWe found that our deep learning model predicted the need for ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately three days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24-13.25%. ConclusionOur deep learning model accurately predicted the need for mechanical ventilation early during hospitalization of COVID-19 patients. Until effective preventive or treatment measures become widely available for COVID-19 patients, prognostic stratification as provided by our model is likely to be highly valuable.
