The causal relationship between HIV-1 infection and the spectrum of Coronavirus disease 2019: a Mendelian randomisation study (preprint)

There is currently little available evidence about the causal relationship between HIV infection and Coronavirus disease 2019 (COVID-19), and these relationships may differ across populations and socioeconomic contexts. In this study, two-sample Mendelian randomisation analyses were conducted using summary-level data from genome-wide association studies of the European ancestry population. We initially examined the causality from HIV-1 infection to the spectrum of COVID-19 [severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalisation, and very severe respiratory disease]. Then we additionally tested the causality from socioeconomic-position (SEP) indicators (educational attainment and household income) to HIV-1 infection, and from educational attainment and household income to the spectrum of COVID-19. We also tested the causality from HIV-1 infection to body height as a negative control. Null causality from HIV-1 infection to the spectrum of COVID-19 was found. The risk of HIV-1 infection significantly decreased as educational attainment and household income increased. Higher educational attainment was causally associated with lower odds of the spectrum of COVID-19. Higher household income was causally associated with lower odds of COVID-19 hospitalisation and very severe respiratory disease. Negative control shared a similar result with the estimates between HIV-1 infection and the spectrum of COVID-19. We concluded that HIV-1 infection was not causally associated with SARS-CoV-2 infection, COVID-19 hospitalisation, and very severe respiratory disease. Reduction in socioeconomic inequality could potentially ameliorate COVID-19-related and HIV-related health inequities.

ANDC: an early warning score to predict mortality risk for patients with Coronavirus Disease 2019 (preprint)

Background: Patients with severe Coronavirus Disease 2019 (COVID-19) will progress rapidly to acute respiratory failure or death. We aimed to develop a quantitative tool for early predicting mortality risk of patients with COVID-19.Methods: 301 patients with confirmed COVID-19 admitted to Main District and Tumor Center of the Union Hospital of Huazhong University of Science and Technology (Wuhan, China) between January 1, 2020 to February 15, 2020 were enrolled in this retrospective two-centers study. Data on patient demographic characteristics, laboratory findings and clinical outcomes was analyzed. A nomogram was constructed to predict the death probability of COVID-19 patients.Results: Age, neutrophil-to-lymphocyte ratio, D-dimer and C-reactive protein obtained on admission were identified by LASSO regression as predictors of mortality for COVID-19 patients. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.921 and 0.975 for the derivation and validation cohort, respectively. An integrated score (named ANDC) with its corresponding death probability was derived. Using ANDC cut-off values of 59 and 101, COVID-19 patients were classified into three subgroups. The death probability of low risk group (ANDC < 59) was less than 5%, moderate risk group (59 ≤ ANDC ≤ 101) was 5% to 50%, and high risk group (ANDC > 101) was more than 50%, respectively.Conclusion: The prognostic nomogram exhibited good discrimination power in early identification of COVID-19 patients with high mortality risk, and ANDC score may help physicians to optimize patient stratification management.