Risk factors for SARS-CoV-2 re-positivity in COVID-19 patients after discharge.

OBJECTIVE: Re-positivity of SARS-CoV-2 in discharged COVID-19 patients have been reported; however, early risk factors for SARS-CoV-2 re-positivity evaluation are limited. METHODS: This is a prospective study, a total of 145 COVID-19 patients were treated and all discharged according to the guideline criteria by Mar 11th 2020. After discharge, clinical visits and viral RT-PCR tests by the second and fourth week follow-up were carried-out. Patient demographic and clinical characteristics and laboratory data on admission and discharge were retrieved, and predictive factors for SARS-CoV-2 re-positivity were analyzed. RESULTS: 13 out of 145 (9.0%) COVID-19 patients were confirmed re-positivity of SARS-CoV-2 by RT-PCR test. The median interval between disease onset to recurrence was 38 days. SARS-CoV-2 re-positive cases were of significantly longer virus shedding duration, notably higher body temperature, heart rate and lower TNF-α and IgG levels on admission. Covariate logistic regression analysis revealed virus shedding duration and IgG levels are independent risk factors for SARS-CoV-2 return positive after discharge. CONCLUSION: Longer viral shedding duration and lower IgG levels are risk factors for re-positivity of SARS-CoV-2 for discharged COVID-19 patients.

Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

Mask wearing in pre-symptomatic patients prevents SARS-CoV-2 transmission: An epidemiological analysis.

OBJECTIVES: Pandemic COVID-19 has become a seriously public health priority worldwide. Comprehensive strategies including travel restrictions and mask-wearing have been implemented to mitigate the virus circulation. However, detail information on community transmission is unavailable yet. METHODS: From January 23 to March 1, 2020, 127 patients (median age: 46 years; range: 11-80) with 71 male and 56 female, were confirmed to be infected with the SARS-CoV-2 in Taizhou, Zhejiang, China. Epidemiological trajectory and clinical features of these COVID-19 cases were retrospectively retrieved from electronic medical records and valid individual questionnaire. RESULTS: The disease onset was between January 9 to February 14, 2020. Among them, 64 patients are local residents, and 63 patients were back home from Wuhan from January 10 to 24, 2020 before travel restriction. 197 local residents had definite close-contact with 41 pre-symptomatic patients back from Wuhan. 123 and 74 of them contact with mask-wearing or with no mask-wearing pre-symptomatic patients back from Wuhan, respectively. Data showed that incidence of COVID-19 was significantly higher for local residents close-contact with no mask-wearing Wuhan returned pre-symptomatic patients (19.0% vs. 8.1%, p < 0.001). Among 57 close-contact individuals, 21 sequential local COVID-19 patients originated from a pre-symptomatic Wuhan returned couple, indicated dense gathering in congested spaces is a high risk for SARS-CoV-2 transmission. CONCLUSIONS: Our findings provided valuable details of pre-symptomatic patient mask-wearing and restriction of mass gathering in congested spaces particularly, are important interventions to mitigate the SARS-CoV-2 transmission.