Background: Predictions of primary liver cancer (PLC) incidence rates and case numbers are critical to understand and plan for PLC disease burden.Methods: Data on PLC incidence rates and case numbers from 1990 to 2017 were retrieved from the Global Burden of Disease database. The estimated average percentage change (EAPC) was calculated to quantify the trends of PLC age-standardized incidence rates (ASRs). Bayesian age-period-cohort models were constructed to project PLC incidence rates and case numbers through 2030.Results: Globally, the PLC case number doubled from 472 300 in 1990 to 953 100 in 2017. The case number will further increase to 1 571 200 in 2030, and the ASR will increase from 11.80 per 100 000 in 2018 to 14.08 per 100 000 in 2030. The most pronounced increases are observed in people afflicted by non-alcoholic steatohepatitis (NASH) and in older people. The trends of PLC incidence rates between 1990 and 2030 are heterogeneous among countries and can be summarized as five scenarios: (i) 46 countries that have and will continue to experience a persistent increase (e.g. Australia); (ii) 21 countries that experienced an initial decrease (or remained stable) but are predicted to increase (e.g. China); (iii) 7 countries that experienced an initial increase but are predicted to remain stable (e.g. USA); (iv) 29 countries that experienced an initial increase but are predicted to decrease (e.g. Egypt); and (v) 82 countries that have and will continue to experience a persistent decrease (e.g. Japan).Conclusion: PLC incidence rates and case numbers are anticipated to increase at the global level through 2030. The increases in people afflicted by NASH and among older people suggest a dearth of attention for these populations in current prevention strategies and highlight their priority in future schedules for global control of PLC.

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods: Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results: The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. Conclusions: Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.