[Prediction of severe outcomes of patients with COVID-19].

Objective: To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients. Methods: Based on the patients' admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model. Results: In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94%, peripheral white blood cells count >8.0×10(9), change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7%, and the missed diagnosis rate was 38.3%. The prediction sensitivity of the model based on the test set was 75.0%, and the missed diagnosis rate was 25.0%. Conclusions: Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model's prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.

[Thoughts on the reform of preventive medicine education in the context of new medicine].

Talent training is the core and foundation of public health system construction. Shortage of talents in the field of disease prevention and public health exposed by COVID-19 pandemic highlights the importance of developing preventive medical education. This article analyzes the challenges of medical education in the dilemma of "separation of medical treatment and prevention", and the new requirements for preventive medical education in the construction of New Medicine under the Healthy China strategy. Four aspects including stepping up the resource allocation and investment, educating responsible public health professionals, the education of all medical students who implement the core competence of public health, and the establishment of a continuing education system for preventive medicine have been considered. A series of specific suggestions are put forward including the establishment of a full-chain closed-loop research system to support the cultivation of top-notch innovative public health talents, strengthening the assessment of core public health capabilities for clinical medical professional admission, formulating a "medical and preventive integration" training program for primary health personnel, and implementing "combination of peace and war" public health personnel reserve system, with the purpose of providing reference for the reform and development of preventive medical education in China.