[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.

The cutaneous adverse drug reactions: risk factors, prognosis and economic impacts.

AIMS: The aim of this study was to explore the factors associated with the occurrence, subsequent prognoses and need for additional medications following cutaneous adverse drug reactions (ADRs) among inpatients. METHODS AND MEASURES: This is a case-control study, nested in a large cohort study of 473,446 inpatients hospitalised from 2005 to 2008, examined cutaneous ADRs. A 1 : 5 strategy of individually matching age and principal diagnosis was applied to the data of cases (n = 700) and corresponding controls (n = 3365).The severity of ADRs was evaluated using Naranjo algorithms by senior pharmacists in the medical centre. Medical chart reviews and claim data analyses were analysed to explore risk factors associated with the occurrence and impact of cutaneous ADRs. Economic impacts in terms of length of stay and medical expenses were also analysed. RESULTS: The number of drug prescriptions and secondary diagnoses, and the department to which the patient was admitted, significantly contributed to the risk of cutaneous ADRs and subsequent prognosis. In addition to physician's seniority, the Naranjo score was also positively associated with patients' prognosis. Medical expenses associated with cutaneous ADRs patients ($US 916) were more than 2.5-fold higher than those patients who were not afflicted ($US 318). CONCLUSION: The study identified risk factors for cutaneous ADRs in terms of both patient characteristics and drug complexity. The present analyses indicate characteristics and mechanisms of cutaneous ADRs among inpatients, which provide clues for future intervention strategies and management issues in healthcare settings.