IMPORTANCEEarly identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources.OBJECTIVE To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. DESIGN, SETTING, AND PARTICIPANTSCollaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020.MAIN OUTCOMES AND MEASURES Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. RESULTSThe development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

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Limited evidence exists concerning the impact of particulate pollution on acute respiratory distress syndrome (ARDS). We examined the effects of particulate pollution on emergency ambulance dispatches (EAD) for ARDS in Guangzhou, China. Daily air pollution concentrations for PM, PM, and PM, as well as PM chemical compositions, were available from a central air monitoring station. The association between incident ARDS and air pollution on the concurrent and previous 5 days was estimated by an over-dispersed Poisson generalized additive model controlling for meteorological factors, temporal trends, public holidays and day of the week. We identified a total of 17,002 EADs for ARDS during the study period. There were significant associations between concentrations of PM, PM, PM and ARDS; corresponding excess risk (ER) for an interquartile range IQR increase in 1-day lagged concentration was 5.45% [95% confidence interval (CI): 1.70%, 9.33%] for PM (45.4 μg/m), 4.71% (95% CI: 1.09%, 8.46%) for PM (31.5 μg/m), and 4.45% (95% CI: 0.81%, 8.23%) for PM (28.8 μg/m), respectively. For PM chemical compositions, we found that OC, EC, sulfate and ammonium were significantly associated with ARDS. The observed effects remained even after adjusting for potentially confounding factors. This study suggests that PM, PM and PM, as well as chemical constituents from combustion and secondary aerosols might be important triggers of ARDS in Guangzhou.

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Background : Chinese herbal medicine (CHM) has been used for severe illness caused by coronavirus disease 2019 (COVID-19), but its treatment effects and safety are unclear. Purpose : This study reviews the effect and safety of CHM granules in the treatment of patients with severe COVID-19. Methods : We conducted a single-center, retrospective study on patients with severe COVID-19 in a designated hospital in Wuhan from January 15, 2020 to March 30, 2020. The propensity score matching (PSM) was used to assess the effect and safety of the treatment using CHM granules. The ratio of patients who received treatment with CHM granules combined with usual care and those who received usual care alone was 1:1. The primary outcome was the time to clinical improvement within 28 days, defined as the time taken for the patients’ health to show improvement by decline of two categories (from the baseline) on a modified six-category ordinal scale, or to be discharged from the hospital before Day 28. Results : Using PSM, 43 patients (45% male) aged 65.6 (57–70) years from each group were exactly matched. No significant difference was observed in clinical improvement of patients treated with CHM granules compared with those who received usual ( P = 0.851). However, the use of CHM granules reduced the 28-day mortality ( P = 0.049) and shortened the duration of fever (4 days vs. 7 days, P = 0.002). The differences in the duration of cough and dyspnea and the difference in lung lesion ratio on computerized tomography scans were not significant. Commonly, patients in the CHM group had an increased D -dimer level ( P = 0.036). Conclusion : For patients with severe COVID-19, CHM granules, combined with usual care, showed no improvement beyond usual care alone. However, the use of CHM granules reduced the 28-day mortality rate and the time to fever alleviation. Nevertheless, CHM granules may be associated with high risk of fibrinolysis.

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BackgroundRoad-traffic injury (RTI) is a major public-health concern worldwide. However, the effectiveness of laws criminalizing drunk driving on the improvement of road safety in China is not known.MethodsWe collected daily aggregate data on RTIs from the Guangzhou First-Aid Service Command Center from 2009 to 2012. We performed an interrupted time-series analysis to evaluate the change in daily RTIs before (January 1, 2009, to April 30, 2011) and after (May 1, 2011, to December 31, 2012) the criminalization of drunk driving. We evaluated the impact of the intervention on RTIs using the overdispersed generalized additive model after adjusting for temporal trends, seasonality, day of the week, and holidays. Daytime/Nighttime RTIs, alcoholism, and non-traffic injuries were analyzed as comparison groups using the same model.ResultsFrom January 1, 2009, to December 31, 2012, we identified a total of 54 887 RTIs. The standardized daily number of RTIs was almost stable in the pre-intervention period but decreased gradually in the post-intervention period. After the intervention, the standardized daily RTIs decreased 9.6% (95% confidence interval [CI], 6.5%–12.8%). There were similar decreases for the daily daytime and nighttime RTIs. In contrast, the standardized daily cases of alcoholism increased 38.8% (95% CI, 35.1%–42.4%), and daily non-traffic injuries increased 3.6% (95% CI, 1.4%–5.8%).ConclusionsThis time-series study provides scientific evidence suggesting that the criminalization of drunk driving from May 1, 2011, may have led to moderate reductions in RTIs in Guangzhou, China.